(upbeat techno music) >> Narrator: From New York, it's theCUBE, Covering Escape/19. >> Okay, welcome back to theCUBE coverage here in New York City for the first inaugural Multi-Cloud Conference called Escape/2019 as in gathering of industry thought leaders, experts, entrepreneurs, engineers, really having substantive conversations around what multi-cloud is, what it's going to look like, what are some of the thing, technical and business opportunities around that, really small intimate conference. Again first inaugural conference. I'm here with my next guest to talk about that Chris Smith, Vice President of Engineering, on Data Science at Ticketmaster. Chris, thanks for coming on. >> Thank you very much Don. >> Appreciate taking the time. >> Glad to talk to you. >> Practitioner out there, you know, we all go scar tissue. >> Yes we do. >> If you don't have scar tissue, if you're not breaking things and then the learning from it then you're not advancing. But sometimes you don't want to step too far forward right? >> Yep, yep. >> Can you get back it's like you know. So you guys have a great experience. Legacy business, I remember buying tickets when I was going to conference back in the day when I was in, you know, in college. >> Yep. >> Buy it at Ticketmaster. >> That's right, that was Ticketmaster then, Ticketmaster now. >> Now it's lot of online provisioning of all direct to consumer. So you guys are a journey, tell the story. >> Well certainly, the company Ticketmaster, has had an incredibly long journey, starting back our first concert was Electric Light Orchestra which kind of like puts that in in context. >> (laughs) I was in eighth grade, '79. >> Yeah, yeah that was back at ASU. And even then we were a very innovative technology company we were making ticketing platforms that performed better, got more capacity out of the hardware than anybody else could do, anything close to that. We were really pioneered that idea of the what was at the time called the electronic ticket. Which was the idea that, you know, you could go to any store that was selling tickets for an event and the same inventory would be available at each store instead of the old model of a bunch of tickets getting sent out to each place >> That was bad-ass back in the day. >> That was really cutting edge and we've been evolving ever since then for 40 years. We were also very early onto the web scene. We were selling tickets online before anybody else was and before most people were selling anything online really to a degree. So we've been pioneers in a lot of areas, we see ourselves as the technology partner for the live events business. That's really what we are. And as a consequence, we're always sitting on that edge right? Trying to innovate and move to new opportunities but at the same time trying to provide that quality of experience at scale. >> Yeah. >> That is so critical to the business. >> And there's a big business so it's not like it's your nimble start up but you got to be agile. What are the learnings? Take us through the cloud learnings as you guys pioneered and started to go into that pioneering mode which was okay, you don't have to be a rocket scientist to figure out what a cloud's going to do. So you guys probably said hey, we got to go look at this, let's go pioneer our impact, take us through that what happened? >> Yeah absolutely, and I think there's two interesting contexts that started that conversation right? One was we're one of the few online businesses that launches a denial of services attack against itself on a regular basis, basically every day, right? And so we have traffic patterns that are unusual even for a typical e-commerce site where we might see loads that are a hundred x, you know beginning of a Taylor Swift on sale. There's going to be traffic like no one's business. And then when all her tickets are sold, there's not going to be nearly as much traffic right? And so that is the nature of our business and cloud is very attractive for its elastic capacity. When we were running on prim, we have to provide all that capacity all the time, just to have it for that one peak moment that might literally be the highest traffic level we see all year, right? So that drew a lot of the interest in looking at the cloud in the first place. And then the other aspect was we'd been working on, you know we'd been running on prim for nearly 40 years at the time and there is a lot of technical debt that had accumulated in the system at that point. And so, there was an interest in maybe potentially being able to leverage cloud vendors' infrastructure, and migrate systems onto that and then sort of declare bankruptcy on some of that technical debt rather than trying to pay it off. And so that, those were the two thoughts that were driving that conversation. I think we got really excited by the possibility and we committed really heavily to the idea of a strategy of just moving aggressively into the cloud as fast as we possibly could. And we knew that in the process, that we would be breaking some things, we'd be you know discovering some challenges et cetera, and that's definitely what happened, right? >> (laughs) What was the big learning? >> I think the biggest learning was that, you know, we had been developing systems for decades literally, with our on prim environment and so the systems were actually very well tuned for that on prim environment and that on prim environment was very well tuned for them. >> Yeah, yeah exactly. >> And it clouds use-- >> On all levels, hardware, software. >> Yeah, all the way through 'cause it's a fully integrated, vertically integrated solution. We build a lot of this stuff custom ourselves. >> John: Yeah, and we would decompose all that. >> And so it was very difficult to migrate some parts of that to the cloud and more importantly we're pretty smart guys, we can figure out how to move stuff into the cloud. But then to do it in a cost effective manner. Required in a lot of cases, really dramatically changing the design and architecture even of the software at a pretty fundamental level that you just can't do overnight. And so ironically, you know, the technical debt that we had in our infrastructure didn't seem quite so huge once you start thinking about the technical debt of the entire stack, right? And so then we realized that we could be much more strategic about how we went after our cloud strategy and that's kind of where we are now. Where we are being smart about, there's a lot of new products that are being developed, that, you know, we can build from the get go with the idea of them being designed for the cloud. >> Cloud native. >> Exactly, so we have a lot of stuff like that, that's just being built, in fact, the bulk of our website now when you go to visit it as a consumer, the bulk of that is running in the cloud right now. But, there are some really critical systems that are core to that experience, that are still running on prim. >> So you guys had to essentially re-architect the operating environment to take into account hybrid operating. >> Yes. >> Decoupling the critical systems that can't be tampered with, maybe put some containers of Kubernetes move some services around. But for the most part treat Cloud Native as Cloud Native, Greenfield apps and nurture-- >> Yeah but there's also refactoring opportunities. So there's a lot of opportunities where you need to go in and change the product anyway and that can be an opportunity to make things a lot more cloud friendly and better take advantage of the capabilities that the cloud has, so it's actually a mix of both. >> Give an example of a good opportunity to refactory, 'cause this comes up a lot in my CUBE interviews. Like okay, 'cause it's all opportunity, opportunistic, but what are the characteristics for a great refactoring opportunity the tune up? >> So a lot of times when you want to refactor really what you want to do is take a set of capabilities that you may have in a much larger system and pull 'em out and manipulate them and play around with them and do things differently. So, our ticket purchasing process we're constantly looking at tweaking the process. Now the core pieces of it remain the same right? But we might want to change the experience and provide something more innovative that's different from what people used to do. And so one of the areas we're working on for this as an example is reserve-less checkout. Where you just buy the ticket without ever actually reserving the seat. That's a very small minor change in the flow, but to make that really work you have to pull out the pieces of the system anyway right? And grab, say I want these four pieces to rearrange differently, so that's a great refactoring opportunity. You can make all those pieces, what we actually did is we've made those pieces into lambdas that are sitting in AWS, they're basically not running most of the time which is great. >> Yeah. (laughs) >> Really cheap when it's not running right? >> Yeah, exactly. >> Very efficient. But then when we need them they run very efficiently and more importantly we can now manipulate the order of operations for this stuff. So breaking things out into those composable parts whenever you know you need to do that anyway, it's a great opportunity to change it. >> So great for work flow refactoring there. >> Absolutely. >> Final question for you, I know we got to break for lunch, but, then really appreciate you coming and sharing your insight. >> Absolutely. >> As a pioneer in data science and data you got machine learning certainly is the engine of AI. AI gets math and cognition are kind of coming into it. Learning machines, deep learning, bla bla bla, what's your, in your opinion, what are some pioneering areas that are ripe pioneering grounds to dig into in data science and data? When you think about CloudScale, Hybrid and just, in general what are the ripe opportunities for people to pioneer in daily. What's the next frontier in your mind? >> So I think the trend right now that's maybe not the frontier, but it's now where the main shift is, is to moving into what I would call real time learning, right? Where you're doing refactor, reinforcement learning, or online learning of some form. Where you're literally, the data's arriving in real time, transforming your model in real time, learning in real time, that's key to our strategy and it's very very common. But I think in terms of where the frontiers are it's actually kind of everywhere, in the sense that the name of the game is the cost of doing that work is getting lower and lower. You know, data's getting cheaper, computes' getting cheaper, and also the products for doing it are getting more productized, so you need less expertise and you can deploy them more quickly. So what you want to look at is businesses that are traditionally been too low margin right? To apply machine learning to but have large scale, right? Which is like the commodity, everything in that's commoditized, right? Now there's an opportunity to, there's the cost have gone so low-- >> To squeeze insight out of those areas. >> That you can now optimize that small margin and get value from it with you know, otherwise like 10 years ago it would have been so costly to build a machine learning infrastructure for it. You would've lost more money than you would've gained. >> So you could, what your saying is, these areas that were not attractive because of cost in the past, that have large scale, there's penetration opportunities to create value and insight that could-- >> Absolutely. >> Bring in new franchises and new capabilities. >> And that's why I think you know the Andreessen's software's eating the world thing, that's what that's really about is as those costs get lower, as the ability to deploy gets easier, suddenly businesses that before didn't make any sense to invest in this way, they totally make sense and in fact there's huge opportunities to completely transform the landscape by getting in. >> Chris you're a man of our world, we love you, thank you for coming on theCUBE. >> Thank you so much. >> That's great insight. >> Look at this we're getting insider on the future of data, which I believe everything that he just said is totally relevant. You're an entrepreneur out there, you can attack big markets and get in there with a position with great IP, great intellectual property, again this is the modern world of computer science. >> It is. >> Don't ya think? >> It absolutely is. >> This is the benefit of scale and cloud. >> Absolutely. >> I wish I was 20 something years old again. (laughs) We've been through the ringer. >> Yes. >> Chris, thanks for coming on. Keep coverage here in New York for the first inaugural conference, Escape/2019, I'm John Furrier here, thanks for watching. (upbeat techno music)

>> Hello friends and welcome back to Fabulous Las Vegas, Nevada. We are here at AWS re:Invent in the heat of day three. Very exciting time. My name is Savannah Peterson, joined with John Furrier here on theCUBE. John, what's your, what's your big hot take from the day? Just from today. >> So right now the velocity of content is continuing to flow on theCUBE. Thank you, everyone, for watching. The security conversations. Also, the cost tuning of the cloud kind of vibe is going on. You're hearing that with the looming recession, but if you look at the show it's the bulk of the keynote time spent talking is on data and security together. So Security, Security Lake, Amazon, they continue to talk about security. This next segment's going to be awesome. We have a multi-, eight-time CUBE alumni coming back and great conversation about security. I'm looking forward to this. >> Alumni VIP, I know, it's so great. Actually, both of these guests have been on theCUBE before so please welcome Dan and Haiyan. Thank you both for being here from F5. How's the show going? You're both smiling and we're midway through day three. Good? >> It's so exciting to be here with you all and it's a great show. >> Awesome. Dan, you having a good time too? >> It's wearing me out. I'm having a great time. (laughter) >> It's okay to be honest. It's okay to be honest. It's wearing out our vocal cords for sure up here, but it is definitely a great time. Haiyan, can you tell me a little bit about F5 just in case the audience isn't familiar? >> Sure, so F5 we specialize in application delivery and security. So our mission is to deliver secure and optimize any applications, any APIs, anywhere. >> I can imagine you have a few customers in the house. >> Absolutely. >> Yeah, that's awesome. So in terms of a problem that, well an annoyance that we've all had, bots. We all want the anti-bots. You have a unique solution to this. How are you helping AWS customers with bots? Let's send it to you. >> Well we, we collect client side signals from all devices. We might study how it does floating point math or how it renders emojis. We analyze those signals and we can make a real time determination if the traffic is from a bot or not. And if it's from a bot, we could take mitigating action. And if it's not, we just forward it on to origin. So client side signals are really important. And then the second aspect of bot protection I think is understanding that bot's retool. They become more sophisticated. >> Savannah: They learn. >> They learn. >> They unfortunately learn as well. >> Exactly, yeah. So you have to have a second stage what we call retrospective analysis where you're looking over all the historical transactions, looking for anything that may have been missed by a realtime defense and then updating that stage one that real time defense to deal with the newly discovered threat. >> Let's take a step back for a second. I want to just set the table in the context for the bot conversation. Bots, automation, that's, people know like spam bots but Amazon has seen the bot networks develop. Can you scope the magnitude and the size of the problem of bots? What is the problem? And give a size of what this magnitude of this is. >> Sure, one thing that's important to realize is not all bots are bad. Okay? Some bots are good and you want to identify the automation from those bots and allow listed so you don't interfere with what they're doing. >> I can imagine that's actually tricky. >> It is, it is. Absolutely. Yeah. >> Savannah: Nuanced. >> Yeah, but the bad bots, these are the ones that are attempting credential stuffing attacks, right? They're trying username password pairs against login forms. And because of consumer habits to reuse usernames and passwords, they end up taking over a lot of accounts. But those are the bookends. There are all sorts of types of bots in between those two bookends. Some are just nuisance, like limited time offer bots. You saw some of this in the news recently with Ticketmaster. >> That's a spicy story. >> Yeah, it really is. And it's the bots that is causing that problem. They use automation to buy all these concert tickets or sneakers or you know, any limited time offer project. And then they resell those on the secondary market. And we've done analysis on some of these groups and they're making millions of dollars. It isn't something they're making like 1200 bucks on. >> I know Amazon doesn't like to talk about this but the cloud for its double edged sword that it is for all the greatness of the agility spinning up resources bots have been taking advantage of that same capability to hide, change, morph. You've seen the matrix when the bots attacked the ship. They come out of nowhere. But Amazon actually has seen the bot problem for a long time, has been working on it. Talk about that kind of evolution of how this problem's being solved. What's Amazon doing about, how do you guys help out? >> Yeah, well we have this CloudFront connector that allows all Amazon CloudFront customers to be able to leverage this technology very, very quickly. So what historically was available only to like, you know the Fortune 500 at most of the global 2000 is now available to all AWS customers who are using CloudFront just by really you can explain how do they turn it on in CloudFront? >> Yeah. So I mean CloudFront technologies like that is so essential to delivering the digital experience. So what we do is we do a integration natively. And so if your CloudFront customers and you can just use our bot defense solution by turning on, you know, that traffic. So go through our API inspection, go through our bot inspection and you can benefit from all the other efficiencies that we acquired through serving the highest and the top institutions in the world. >> So just to get this clarification, this is a super important point. You said it's native to the service. I don't have to bolt it on? Is it part of the customer experience? >> Yeah, we basically built the integration. So if you're already a CloudFront customer and you have the ability to turn on our bot solutions without having to do the integration yourself. >> Flick a switch and it's on. >> Haiyan: Totally. >> Pretty much. >> Haiyan: Yeah. >> That's how I want to get rid of all the spam in my life. We've talked a lot about the easy button. I would also like the anti-spam button if we're >> Haiyan: 100% >> Well we were talking before you came on camera that there's a potentially a solution you can sit charge. There are techniques. >> Yeah. Yeah. We were talking about the spam emails and I thought they just charge, you know 10th of a penny for every sent email. It wouldn't affect me very much. >> What's the, are people on that? You guys are on this but I mean this is never going to stop. We're going to see the underbelly of the web, the dark web continue to do it. People are harvesting past with the dark web using bots that go in test challenge credentials. I mean, it's just happening. It's never going to stop. What's, is it going to be that cat and mouse game? Are we going to see solutions? What's the, when are we going to get some >> Well it's certainly not a cat and mouse game for F5 customers because we win that battle every time. But for enterprises who are still battling the bots as a DIY project, then yes, it's just going to be a cat and mouse. They're continuing to block by IP, you know, by rate limiting. >> Right, which is so early 2000's. >> Exactly. >> If we're being honest. >> Exactly. And the attackers, by the way, the attackers are now coming from hundreds of thousands or even millions of IP addresses and some IPs are using one time. >> Yeah, I mean it seems like such an easy problem to circumnavigate. And still be able to get in. >> What are I, I, let's stick here for a second. What are some of the other trends that you're seeing in how people are defending if they're not using you or just in general? >> Yeah, maybe I'll add to to that. You know, when we think about the bot problem we also sort of zoom out and say, Hey, bot is only one part of the problem when you think about the entire digital experience the customer experiencing, right? So at F5 we actually took a more holistic sort of way to say, well it's about protecting the apps and applications and the APIs that's powering all of those. And we're thinking not only the applications APIs we're thinking the infrastructure that those API workloads are running. So one of the things we're sharing since we acquired Threat Stack, we have been busy doing integrations with our distributed cloud services and we're excited. In a couple weeks you will hear announcement of the integrated solution for our application infrastructure protection. So that's just another thing. >> On that Threat Stack, does that help with that data story too? Because it's a compliance aspect as well. >> Yeah, it helps with the telemetries, collecting more telemetries, the data story but is also think about applications and APIs. You can only be as secure as the infrastructure you're running on it, right? So the infrastructure protection is a key part of application security. And the other dimension is not only we can help with the credentials, staffing and, and things but it's actually thinking about the customer's top line. Because at the end of the day when all this inventory are being siphoned out the customer won't be happy. So how do we make sure their loyal customers have the right experience so that can improve their top line and not just sort of preventing the bots. So there's a lot of mission that we're on. >> Yeah, that surprise and delight in addition to that protection. >> 100% >> If I could talk about the evolution of an engagement with F5. We first go online, deploy the client side signals I described and take care of all the bad bots. Okay. Mitigate them. Allow list all the good bots, now you're just left with human traffic. We have other client side signals that'll identify the bad humans among the good humans and you could deal with them. And then we have additional client side signals that allow us to do silent continuous authentication of your good customers extending their sessions so they don't have to endure the friction of logging in over and over and over. >> Explain that last one again because I think that was, that's, I didn't catch that. >> Yeah. So right now we require a customer to enter in their username and password before we believe it's them. But we had a customer who a lot of their customers were struggling to log in. So we did analysis and we realized that our client side signals, you know of all those that are struggling to log in, we're confident like 40% of 'em are known good customers based on some of these signals. Like they're doing floating point math the way they always have. They're rendering emojis the way they always have all these clients that signals are the same. So why force that customer to log in again? >> Oh yeah. And that's such a frustrating user experience. >> So true. >> I actually had that thought earlier today. How many time, how much of my life am I going to spend typing my email address? Just that in itself. Then I could crawl back under the covers but >> With the biometric Mac, I forget my passwords. >> Or how about solving CAPTCHA's? How fun is that? >> How many pictures have a bus? >> I got one wrong the other day because I had to pick all the street signs. I got it wrong and I called a Russian human click farm and figured out why was I getting it wrong? And they said >> I love that you went down this rabbit hole deeply. >> You know why that's not a street sign. That's a road sign, they told me. >> That's the secret backdoor. >> Oh well yeah. >> Talk about your background because you have fascinating background coming from law enforcement and you're in this kind of role. >> He could probably tell us about our background. >> They expunge those records. I'm only kidding. >> 25, 30 years in working in local, state and federal law enforcement and intelligence among those an FBI agent and a CIA cyber operations officer. And most people are drawn to that because it's interesting >> Three letter agencies can get an eyebrow raise. >> But I'll be honest, my early, early in my career I was a beat cop and that changed my life. That really did, that taught me the importance of an education, taught me the criminal mindset. So yeah, people are drawn to the FBI and CIA background, but I really value the >> So you had a good observation eye for kind of what, how this all builds out. >> It all kind of adds up, you know, constantly fighting the bad guys, whether they're humans, bots, a security threat from a foreign nation. >> Well learning their mindset and learning what motivates them, what their objectives are. It is really important. >> Reading the signals >> You don't mind slipping into the mind of a criminal. It's a union rule. >> Right? It actually is. >> You got to put your foot and your hands in and walk through their shoes as they say. >> That's right. >> The bot networks though, I want to get into, is not it sounds like it's off the cup but they're highly organized networks. >> Dan: They are. >> Talk about the aspect of the franchises or these bots behind them, how they're financed, how they use the money that they make or ransomware, how they collect, what's the enterprise look like? >> Unfortunately, a lot of the nodes on a botnet are now just innocent victim computers using their home computers. They can subscribe to a service and agree to let their their CPU be used while they're not using it in exchange for a free VPN service, say. So now bad actors not, aren't just coming from you know, you know, rogue cloud providers who accept Bitcoin as payment, they're actually coming from residential IPs, which is making it even more difficult for the security teams to identify. It's one thing when it's coming from- >> It's spooky. I'm just sitting here kind of creeped out too. It's these unknown hosts, right? It's like being a carrier. >> You have good traffic coming from it during the day. >> Right, it appears normal. >> And then malicious traffic coming from it. >> Nefarious. >> My last question is your relationship with Amazon. I'll see security center piece of this re:Invent. It's always been day zero as they say but really it's the security data lake. A lot of gaps are being filled in the products. You kind of see that kind of filling out. Talk about the relationship with F5 and AWS. How you guys are working together, what's the status? >> We've been long-term partners and the latest release the connector for CloudFront is just one of the joint work that we did together and try to, I think, to Dan's point, how do we make those technology that was built for the very sophisticated big institutions to be available for all the CloudFront customers? So that's really what's exciting. And we also leverage a lot of the technology. You talked about the data and our entire solution are very data driven, as you know, is automation. If you don't use data, you don't use analytics, you don't use AI, it's hard to really sort of win that war. So a lot of our stuff, it's very data driven >> And the benefit to customers is what? Access? >> The customer's access, the customer's top line. We talked about, you know, like how we're really bringing better experiences at the end of the day. F5's mission is try to bring a better digital world to life. >> And it's also collaborative. We've had a lot of different stories here on on the set about companies collaborating. You're obviously collaborating and I also love that we're increasing access, not just narrowing this focus for the larger companies at scale already, but making sure that these companies starting out, a lot of the founders probably milling around on the floor right now can prevent this and ensure that user experience for their customers. throughout the course of their product development. I think it's awesome. So we have a new tradition here on theCUBE at re:Invent, and since you're alumni, I feel like you're maybe going to be a little bit better at this than some of the rookies. Not that rookies can't be great, but you're veterans. So I feel strong about this. We are looking for your 30-second Instagram reel hot take. Think of it like your sizzle of thought leadership from the show this year. So eventually eight more visits from now we can compile them into a great little highlight reel of all of your sound bites over the evolution of time. Who wants to give us their hot take first? >> Dan? >> Yeah, sure. >> Savannah: You've been elected, I mean you are an agent. A former special agent >> I guess I want everybody to know the bot problem is much worse than they think it is. We go in line and we see 98, 99% of all login traffic is from malicious bots. And so it is not a DIY project. >> 98 to 99%? That means only 1% of traffic is actually legitimate? >> That's right. >> Holy moly. >> I just want to make sure that everybody heard you say that. >> That's right. And it's very common. Didn't happen once or twice. It's happened a lot of times. And when it's not 99 it's 60 or it's 58, it's high. >> And that's costing a lot too. >> Yes, it is. And it's not just in fraud, but think about charges that >> Savannah: I think of cloud service providers >> Cost associated with transactions, you know, fraud tools >> Savannah: All of it. >> Yes. Sims, all those things. There's a lot of costs associated with that much automation. So the client side signals and multi-stage defense is what you need to deal with it. It's not a DIY project. >> Bots are not DIY. How would you like to add to that? >> It's so hard to add to that but I would say cybersecurity is a team sport and is a very data driven solution and we really need to sort of team up together and share intelligence, share, you know, all the things we know so we can be better at this. It's not a DIY project. We need to work together. >> Fantastic, Dan, Haiyan, so great to have you both back on theCUBE. We look forward to seeing you again for our next segment and I hope that the two of you have really beautiful rest of your show. Thank you all for tuning into a fantastic afternoon of coverage here from AWS re:Invent. We are live from Las Vegas, Nevada and don't worry we have more programming coming up for you later today with John Furrier. I'm Savannah Peterson. This is theCUBE, the leader in high tech coverage.

>>from >>around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Welcome to the Cube virtual and our coverage of AWS reinvent 2020. I'm your host, Lisa Martin, and with me today is Amiram Shachar, the V P and GM of Spot by Netapp program. It's great to have you on the program. >>Thank you, Lisa. It's great to be here. >>So here we are in this virtual world that we're all living in great that we can still connected you. But I wanted to understand you're the founder and CEO of Spot, which was acquired by net up earlier this year. Talk to me a little bit about spot about the technology and what's going on since the Net acquisition. >>Absolutely so Spot is the company that was founded in late 2015 on was centered and concentrated about helping companies thio optimize their cloud infrastructure costs through automation off software of how customers air provisioning their compute so we could possibly help customers to choose their best price off server infrastructure in the price and the best size off server infrastructure in the cloud on, You know, since we launched the company. So we help over 1500 customers worldwide. Thio use our technology scale that revenues to tens of millions of dollars of revenue raised money from top VCs, including Intel Capital, Vertex and Highland on just recently, four months ago, we got acquired by buying it up. >>Excellent. So that's a pretty fast from launch to acquisition, You know, less than five years. Must have a neck brace collar on from the whiplash. That and covert, right with flash. So talk to me about acquisition a few months ago. What's going on with the technologies, aunt? How is the netapp, um, customer base helping to expand your market penetration? >>Yeah. So it was clear, uh, during the rationality. You know, when we did the rationale of the acquisition, So it was clear that spot is going to remain a an entity with the netapp. So, for example, we preserved our brand, so it's spot by net up. It's not gonna be just integrated, and that's it. So we're not gonna see spot disappearing. It's actually the opposite. So we're gonna leveraged a market credibility. That net up is 27 years. You know storage leading storage provider has in the market, and we're gonna use spot as a brand to lean forward and lead with cloud native applications on Do. What we're gonna do is we're gonna help to netapp transform, like, you know, net up existing customers. They're moving to the cloud so not only they can use netapp storage in the cloud. They can also use thesaurus fair and automation and optimization layers that sport provides. Actually. >>So talk to me about what's going on in the market today. We've been talking for months now about this acceleration of digital transformation, acceleration of cloud adoption given businesses now are working so differently. Talk to me about what you are seeing, what your customers just seeing how you're helping them to manage and not just keep the lights on right now. But be able to be successful in going positions Well, in the future. >>Mhm. So I'm seeing like to two main trends. The first trend is like more cloud usage, but that's very general, very vague. But what I do see in in addition to that is actually like priorities have been changed. And when I talk about priority like priority is not only moving to the cloud but doing it efficiently. So customers who already using cloud we're moving to the cloud they really need Thio, you know, planned this in the most efficient way. So I can tell you, for example, a lot of customers that actually we were talking to them to use us at the beginning of 2020. So they intended to use us in, like, you know, third quarter, fourth quarter of the year, like it all got accelerated and they started to use our platform because they put their priorities have changed and they wanna have, like, optimization of cost, right? Right now, >>yeah, That's been something that a lot of folks have been wanting and needing even to just keep the lights on, get their eyes on visibility where we spending costs. Are we using cloud efficiently? If not, how can we work with technology vendors to help us get that visibility and optimize our costs and spend so tell me about from a conversation perspective, uh, post, you know, during this interesting year, the acquisition occurred. Are your conversations with customers changing? Are we seeing now? Is cloud rising even up that the C suite stacked to the board in terms of the conversations that you're having, >>you know, and I'm seeing this like for five years. Like how our conversations are changing the year over year and year over year, we're seeing like improvement in our type of conversations because people living in, like, you know, to the Cloud Mawr people thinking about about optimization is becoming a priority. As I mentioned, like, you know, four years ago like it's not about optimizing cloud. It's about moving to the cloud. And right now I have so many things in the cloud. I just need to run it well, I need to understand why them thio bring in the cloud. So our conversation has gotten a lot better on, especially in 2020 on. Do you think about cloud expenditure like this is probably the second biggest line item off every company's expenses. So it goes directly to the cause, which is the cost of good salt of every company. So it goes directly go off the margin off cos so Cloud is definitely becoming a board discussion thing. >>So talk to me about some of the new products and capabilities that you guys have now that you're part of that. >>So first of all, is the company we really believe in, like listening to customers, seeing what they need and innovating on their behalf. I think this is like our mission, and I'm always like saying that like customers always want, like cheaper cloud and more simple cloud. This is like a strategy to build a long term business like, I don't know if customers will not need, like, cheaper cloud in 10 years from now. And I don't know if customers would want more complicated cloud in 10 years from cloud in 10 years from now. Um so in order to keep that momentum So we're doubling down like our existing technology, which helping customers to optimize their pricing purchasing. As you know, we're helping companies to purchase across the three pricing models in the cloud and the first pricing model being on demand, which is you pay by the hour the second pricing model being reserved instances or savings plans, which is you basically reserve capacity for longer time, and then you get a discount. And the third pricing model, called Spot and Spot, basically is like either the resource is idle. Compute resource is that cloud providers have and they're willing to sell you that in a low rate, but they can take it away from you at any moment. So what our technology does it actually balancing in the most economical way across these three pricing models that we basically push the savings to the maximum while we also keep the S. L. A and the SLOC over the customers. So what we're releasing now doubling down on the technology is we're introducing something called predictive re balancing, which is basically customers who are launching spot instances and they want to migrate between spot instances, two different spot instances or two reserved instances so we could do it much more proactively than ever before. We've been investing a lot of machine learning engineers on that problem. We've put a lot of brain power toe work on this, and we're gladly happy to release a new, updated version of that. It can help customers to get warnings off almost an hour before an interruption might happen. >>Predictive re balancing, You said it's called one of the things I was thinking when you were talking about the three pricing tiers and what you guys help businesses do. It sounds very dynamic and iterative in the moment. So based on what's it based on usage data volumes, how do you help customers make that? How does the technology actually help customers with that dynamic, that predictive re balancing? >>So it's a great question, because the way that it works, it really matches the technology stack of the customer. So if we understand that this is like a Web service running behind an elastic load balancer, so the predictive rebalancing will have Behavior X. And if we realize that this is a big data workload that requires, um, some other type of compute provisioning. So the predictive rebalancing will behave like why. And there is also the new wave of APS, which are cloud native containers and micro services so actually predictor of balancing notes to identify that. So, basically, if you think about that, what happens behind the scenes is that we migrate between different pricing models and we just move containers around. The customers don't really know what happened behind the scenes, but they that the output of everything is the most optimized and high available capacity they could possibly get from the cloud. >>So what sort of cost savings are we talking about? Can you share an examples of some what some of your customers has have achieved? >>Mhm. So, yeah, we're talking about like, I think the benchmark is anywhere between 70 to 90%. We have a lot of public use cases with our customers and some of our really valuable customers like check, which is an education tech technology company which are running with us. The entire fleet off containers reported like over 65% off cost reduction of the under compute also, ah, gum gum, which is computer vision company that analyzes, um ads in real time. They also reported over 70% off cost reduction of their off their cloud infrastructure a swell as the well known Ticketmaster, which is also a very large customer virus and using us and all of their production communities clusters and also saved, um, over 60 to 70% off their containers infrastructure. >>So big savings. I think the lowest number heard you say, with 60% so big impact that the technology is able to make talk to me about the existing customers that and you've got some big brand. You mentioned Ticketmaster? Um, how How have things changed for them or not changed for them with the net acquisition, you talked about maintaining the spot brand. But talk to me about kind of that transition for your existing customers. >>So, you know, it's actually a very, um, you know, e felt lucky, like going through this process of an acquisition, you know, is the founder of a company. And it's, uh it's very, uh, very pleasing. Calls to have with customers is when you call customers and you tell them about the acquisition. And I was lucky enough to tell them like, Hey, guys, nothing is gonna change Like our road map is going to continue in the same way our name is going to remain the same way. The only thing we're gonna bother you with, quote unquote as we're going to add storage capabilities into our computer capabilities, >>that must have been music to their ears. But I gotta ask you, what's it like doing an acquisition in the middle of a global pandemic? And we're completely remote, right? >>You know, this is an experience I will never forget. Uh, you know it, Z, you're just You're at home. You know, I was also I was really lucky to become a father for the first time. And I know earlier this year s Oh, it's like you're with your baby wife, family and just all day calls on going and you know it, Z it's It's a really amazing experience that I think I will never forget. >>Yeah, I think I'm with you on that unique experiences. Well, congratulations for being a new father, but that also makes it challenging, right? You're you're CEO of a startup. It's about to get acquired. And you've got a newborn as, ah coworker. So all of a sudden, all these challenges that just add, um ADM or challenges to the mix. So I'm sure it was great to be able to have the conversation with your existing customers about what little is changing, but also for them. What's the opportunity for those existing customers to really start taking advantage of all of net tax net ops capabilities? >>So that's exactly the intersection between spot and ETA, which I feel like super excited about, because if you think about that like we've built a technology of computer optimization over the last five years. That up is a big, brand, credible company. Going for 27 years, more than 27 years. The last seven years get up has has been doing like a massive shift to the cloud supporting their customers were moving from on Prem to the cloud. So net up what never actually did. And I was very amazed by that, which is the imported >>all of their own >>premise t technology and put it in the cloud in the platform that they call cloud volumes, which is you. Basically, you can get >>all the >>features that Netapp provides, like advanced snap shooting and back up and fast restored and compression D duplication, which I remember managing data centers myself from my military days. I was using a lot of netapp stuff >>on. They took all of these great things and put it in the ground. And now what we can >>actually do for our customers is that we can actually attach like the computer they're buying from us and optimizing with us with the storage, the great storage that offers in the cloud in the cloud volume platform. >>It's a lot of opportunities there. In fact, Netapp has We've been talking about this on the Cube for a while. For years, Netapp has gone, undergone a big transformation, a big evolution, and it sounds like what they're doing with spot and also the opportunities that it's providing. So not just your existing customers, but you're prospective customers. And your new customers is just kind of keeping that door wide open. Which right now in this interesting time is is essential is businesses are continuing to pivot as this time unfold, and we know something's going to remain permanent, but that got to be able to pivot quickly. Otherwise, the competition is probably in the rear view mirror, maybe smaller, more agile and ready to take over. >>That's so true and like, you know, generally speaking about, like transformation, like modernization of application. So, like huge trend that we're seeing. And this is also a huge intersection between netapp and spot. Um, is everything kubernetes and everything containers eso we're seeing organizations moving to the cloud, but they're they're not, like, no, not anymore looking at, like just lift and ship their like, lift modernized ship like people want to modernize our application for various reasons. And we see containers. Technology is just becoming like the de facto technology for shipping applications in the cloud, and we're seeing kubernetes on the rise and one of our products. That spot, called Ocean Eyes, is a product that manages compute for kubernetes. So our our tagline is basically serverless containers, which is customers deploy their containers and then we manage the infrastructure underneath. They don't need to define their infrastructure, think about their infrastructure, just like it's a really a near Vanna for for develops, people responsible for communities. And then when we actually, you know, got into Nana and we saw all of, like, the cloud volumes technology that I was telling you about, we said it like, Hey, what if we can take this service, compute off containers and we can actually make it for storage as well? And we basically you can take all the good things that you get from service, which is, you know, no infrastructure management building Mike utility building, you know, scale to zero scale to like, you know, infinite. If you need >>Andi with no infrastructure management and this is actually possible with the netapp cloud >>volumes technology. So, actually, right now we're launching with net up, something will recall storage less volumes, which is basically for containers were going to allow that directly from our ocean product that you will be able to get storage list volumes that are going to be completely Hence we management of storage. And just to set the tone like, what is storage is like, Is there no storage like, what is surveillance are their storage? Are their service there? Of course there are, but you don't manage them. So what storage is? Is there a storage underneath? Of course there is, but you don't need to manage it. And >>is that something that your customers can take advantage of? Now, is that coming in the next quarter or so? What's the timing on that? >>But this is, um this is right now already in preview. So we already opened that to some of our, you know, advanced customers that are using, like all of our latest and greatest you know, features. So it's already with customers validation working with them. They're actually actually love it and love the fact that they don't need to manage storage, because when you move to the cloud. You actually don't really care about storage anymore because it's becoming just oil for your applications. Eso and it's gonna be generally available. Hopefully, in the first half of 2021. >>Exciting. Something positive to look forward to. You'll have to come back and share with us. Some of the results. Mm. It's been great to have you on the Cube. Thanks for spending some time with me >>today. Likewise. Lisa, thank you very much. >>I'm Lisa Martin. You're watching the Cube.

>> Live from Barcelona, Spain. It's theCUBE! Covering KubeCon, CloudNativeCon, Europe 2019. Brought to you by Red Hat, the Cloud Native Computing Foundation, and Ecosystem Partners. >> Hi, and welcome back. This is KubeCon, CloudNativeCon 2019 here in Barcelona. 7700 in attendance according to the CNCF foundation. I'm Stu Miniman and my co-host for this week is Corey Quinn. And happy to welcome back to the program, a cube-i-lom Rob Szumski, who's the Product Manager for Red Hat OpenShift. Rob, thanks so much for joining us >> Happy to be here. >> All right, so a couple of weeks ago, we had theCUBE in Boston. You know, short drive for me, didn't have to take a flight as opposed to... I'm doing okay with the jet lag here, but Red Hat Summit was there. And it was a big crowd there, and the topic we're going to talk about with you is operators. And it was something we talked about a lot, something about the ecosystem. But let's start there. For our audience that doesn't know, What is an operator? How does it fit into this whole cloud-native space in this ecosystem? >> (Corey) And where can you hire one? >> (laughs) So there's software programs first of all. And the idea of an operator is everything it takes to orchestrate one of these complex distributor applications, databases, messaging queues, machine learning services. They all are distinct components that all need to be life-cycled. And so there's operational expertise around that, and this is something that might have been in a bash script before, you have a Wiki page. It's just in your head, and so it's putting that into software so that you can stamp out mini copies of that. So the operational expertise from the experts, so you want to go to the folks that make MongoDB for Mongo, for Reddits, for CouchBase, for TensorFlow, whatever it is. Those organizations can embed that expertise, and then take your user configuration and turn that into Kubernetes. >> Okay, and is there automation in that? When I hear the description, it reminds me a little bit of robotic process automation, or RPA, which you talk about, How can I harem them? RPA is, well there's certain jobs that are rather repetitive and we can allow software to do that, so maybe that's not where it is. But help me to put it into the >> No, I think it is. >> Okay, awesome. >> When you think about it, there's a certain amount of toil involved in operating anything and then there's just mistakes that are made by humans when you're doing this. And so you would rather just automate away that toil so you can spend you human capitol on higher level tasks. So that's what operator's all about. >> (Stu) All right. Great. >> Do you find that operator's are a decent approach to taking things that historically would not have been well-suited for autoscaling, for example, because there's manual work that has to happen whenever a no-joinser leaves a swarm. Is that something operators tend to address more effectively? Or am I thinking about this slightly in the wrong direction? >> Yeah, so you can do kind of any Kubernetes event you can hook into, so if your application cares about nodes coming and leaving, for example, this is helpful for operators that are operating the infrastructure itself, which OpenShift has under the hood. But you might care about when new name spaces are created or this pod goes away or whatever it is. You can kind of hook into everything there. >> So, effectively it becomes a story around running stateful things in what was originally designed for stateless containers. >> Yeah, that can help you because you care about nodes going away because your storage was on it, for example. Or, now I need to re-balance that. Whatever that type of thing is it's really critical for running stateful workloads. >> Okay, maybe give us a little bit of context as to the scope of operators and any customer examples you have that could help us add a little bit of concreteness to it. >> Yeah, they're designed to run almost anything. Every common workload that you can think about on an OpenShift cluster, you've got your messaging queues. We have a product that uses an operator, AMQ Streams. It's Kafka. And we've got folks that heavily use a Prometheus operator. I think there's a quote that's been shared around about one of our customer's Ticketmaster. Everybody needed some container native monitoring and everybody could figure out Prometheus on their own. Or they could use operator. So, they were running, I think 300-some instances of Prometheus and dev and staging and this team, that team, this person just screwing around with something over here. So, instead of being experts in Prometheus, they just use the operator then they can scale out very quickly. >> That's great because one of the challenges in this ecosystem, there's so many pieces of it. We always ask, how many companies need to be expert on not just Kubernetes, but any of these pieces. How does this tie into the CNCF, all the various projects that are available? >> I think you nailed it. You have to integrate all this stuff all together and that's where the value of something like OpenShift comes at the infrastructure layer. You got to pick all your networking and storage and your DNS that you're going to use and wire all that together and upgrade that. Lifecycle it. The same thing happens at a higher level, too. You've got all these components, getting your Fluentd pods down to operating things like Istio on Service Mesh's, serviceless workloads. All this stuff needs to be configured and it's all pretty complex. It's moving so fast, nobody can be an expert. The operator's actually the expert, embedded from those teams which is really awesome. >> You said something before we got started. A little bit about a certification program for operators. What is that about? >> We think of it as the super set of our community operators. We've got the TensorFlow community, for example, curates an operator. But, for companies that want to go to market jointly with Red Hat, we have a certification program that takes any of their community content, or some of their enterprise distributions and makes sure that it's well-tested on OpenShift and can be jointly supported by OpenShift in that partner. If you come to Red Hat with a problem with a MongoDB operator, for example, we can jointly solve that problem with MongoDB and ultimately keep your workload up and keep it running. We've got that times a bunch of databases and all kinds of servers like that. You can access those directly from OpenShift which is really exciting. One-click install of a production-ready Mongo cluster. You don't need to dig through a bunch of documentation for how that works. >> All right, so Rob, are all of these specific only to OpenShift, or will they work with flavors of Kubernetes? >> Most of the operators work just against the generic Kubernetes cluster. Some of them also do hook into OpenShift to use some of our specialized security primitives and things like that. That's where you get a little bit more value on OpenShift, but you're just targeting Kubernetes at the end of the day. >> What do you seeing customers doing with this specifically? I guess, what user stories are you seeing that is validating that this is the right direction to go in? >> It's a number of different buckets. The first one is seeing folks running services internally. You traditionally have a DBA team that maybe runs the shared database tier and folks are bringing that the container native world from their VM's that they're used to. Using operators to help with that and so now it's self-service. You have a dedicated cluster infrastructure team that runs clusters and gives out quota. Then, you're just eating into that quota to run whatever workloads that you want in an operator format. That's kind of one bucket of it. Then, you see folks that are building operators for internal operation. They've got deep expertise on one team, but if you're running any enterprise today especially like a large scale Ecommerce shop, there's a number of different services. You've got caching tier, and load balancing tiers. You've got front-ends, you've got back-ends, you've got queues. You can build operators around each one of those, so that those teams even when they're sharing internally, you know, hey where's the latest version of your stack? Here's the operator, go to town. Run it in staging QA, all that type of stuff. Then, lastly, you see these open source communities building operators which is really cool. Something like TensorFlow, that community curates an operator to get you one consistent install, so everyone's not innovating on 30 different ways to install it and you're actually using it. You're using high level stuff with TensorFlow. >> It's interesting to lay it out. Some of these okay, well, a company is doing that because it's behind something. Others you're saying it's a community. Remind me, just Red Hat's long history of helping to give if you will, adult supervision for all of these changes that are happening in the world out there. >> It's a fast moving landscape and some tools that we have are our operator SDK are helping to tame some of that. So, you can get quickly up and running, building an operator whether you are one of those communities, you are a commercial vendor, you're one of our partners, you're one of our customers. We've got tools for everybody. >> Anything specific in the database world that's something we're seeing, that Cambrian explosion in the database world? >> Yeah, I think that folks are finally wrapping their heads around that Kubernetes is for all workloads. And, to make people feel really good about that, you need something like an operator that's got this extremely well-tested code path for what happens when these databases do fail, how do I fail it over? It wasn't just some person that went in and made this. It's the expert, the folks that are committing to MongoDB, to CouchBase, to MySQL, to Postgres. That's the really exciting thing. You're getting that expertise kind of as extension of your operations team. >> For people here at the show, are there sessions about operators? What's the general discussion here at the show for your team? >> There's a ton. Even too many to mention. There's from a bunch of different partners and communities that are curating operators, talking about best practices for managing upgrades of them. Users, all that kind of stuff. I'm going to be giving a keynote, kind of an update about some of stuff we've been talking about here later on this evening. It's all over the place. >> What do you think right now in the ecosystem is being most misunderstood about operators, if anything? >> I think that nothing is quite misunderstood, it's just wrapping your head around what it means to operate applications in this manner. Just like Kubernetes components, there's this desired state loop that's in there and you need to wrap your head around exactly what needs to be in that. You're declarative state is just the Kubernetes API, so you can look at desired and actual and make that happen, just like all the Kub components. So, just looking at a different way of thinking. We had a panel yesterday at the OpenShift Commons about operators and one of the questions that had some really interesting answers was, What did you understand about your software by building an operator? Cause sometimes you need to tease apart some of these things. Oh, I had hard coded configuration here, one group shared that their leader election was not actually working correctly in every single incidences and their operator forced them to dig into that and figure out why. So, I think it's a give and take that's pretty interesting when you're building one of these things. >> Do you find that customers are starting to rely on operators to effectively run their own? For example, MongoDB inside of their Kubernetes clusters, rather than depending upon a managed service offering provided by their public cloud vendor, for example. Are you starting to see people effectively reducing public cloud to baseline primitives at a place to run containers, rather than the higher level services that are starting to move up the stack? >> A number of different reasons for that too. You see this for services if you find a bug in that service, for example, you're just out of luck. You can't go introspect the versions, you can't see how those components are interacting. With an operator you have an open source stack, it's running on your cluster in your infrastructure. You can go introspect exactly what's going on. The operator has that expertise built in, so it's not like you can screw around with everything. But, you have much more insight into what's going on. Another thing you can't get with a cloud service is you can't run it locally. So, if you've got developers that are doing development on an airplane, or just want to have something local so it's running fast, you can put your whole operator stack right on your laptop. Not something you can do with a hosted service which is really cool. Most of these are opens source too, so you can go see exactly how the operator's built. It's very transparent, especially if you're going to trust this for a core part of the infrastructure. You really want to know what's going on under the hood. >> Just to double check, all this can run on OpenShift? It is agnostic to where it lives, whether public cloud or data center? >> Exactly. These are truly hybrid services, so if you're migrating your database to here, for example, over now you have a truly hybrid just targeting Kubernetes environment. You can move that in any infrastructure that you like. This is one of the things that we see OpenShift customers do. Some of them want to be cloud-to-cloud, cloud-to-on-prem, different environments on prem only, because you've got database workloads that might not be leaving or a mainframe you need to tie into, a lot of our FSI customers. Operators can help you there where you can't move some of those workloads. >> Cloud-on-prem makes a fair bit of sense to me. One thing I'm not seeing as much of in the ecosystem is cloud-to-cloud. What are you seeing that's driving that? >> I think everybody has their own cloud that they prefer for whatever reasons. I think it's typically not even cost. It's tooling and cultural change. And, so you kind of invest in one of those. I think people are investing in technologies that might allow them to leave in the future, and operators and Kubernetes being one of those important things. But, that doesn't meant that they're not perfectly happy running on one cloud versus the other, running Kubernetes on top of that. >> Rob, really appreciate all the updates on operators. Thanks so much for joining us again. >> Absolutely. It's been fun. >> Good luck on the keynote. >> Thank you. >> For Corey Quinn, I'm Stu Miniman, back with more coverage two days live from wall to wall here at KubeCon CloudNativeCon 2019 in Barcelona, Spain. Thanks for watching.

>> Live from San Francisco, it's The Cube, Covering, Google Cloud Next 2018, brought to you by, Google Cloud and it's ecosystem partners. >> Everyone, welcome back, this is The Cube, live in San Francisco for Google Cloud, big event here, called Google Next 2018, #GoogleNext18, I'm John Furrier, Dave Vellante, bringing down all the top stories, all the top technology news, all the stuff that they're announcing on stage, some of the executives, the product managers, customers, analysts, you name it we want to get that signal and extract it and share that with you. Our next guest is Dan here and he's the product manager for Cloud AI at Google, and dialogue flow with a hot product here under his preview. Thanks for joining us! Good to see you! >> Ah, yeah, excited to be here! >> We were bantering off camera because we love video, we love speech to text, we love all kinds of automation that can add value to someone's products rather than having to do a lot of grunt work, or not having any capabilities, so super excited about what your working on, the variety of things, this one's the biggest, dialogue flow, talk about the product. >> Sure, yeah, yeah. >> What is it? Yeah, so Dialogue Flow it's a platform for building conversational applications, conversation interfaces, so could be chatbox, it could be voicebox, and it started from the acquisition of APIAI, that we did a year and a half ago, and its been gaining a lot of momentum since then so last year at Google Cloud Next, we announced that we just crossed 150,000 developers in the Dialog Flow community, yesterday we just announced that we now crossed 600,000 and yeah its uh-- >> Hold on, back up, slow down. I think I just missed that. You had what and then turned in to what? Say it again. >> So it was a 150,000 last year or over a 150,000 and now its now its over 600,000. >> Congratulations, that's massive. >> So yeah, I-- >> That's traction! >> It's very, very exciting. >> Four X. (laughs) >> And yeah, we you know, were still seeing like a lot of strong growth and you know with the new announcements we made yesterday, we think it's going to take a much larger role, especially in larger enterprises and especially in sort of powering enterprise contact centers. >> You know, natural language processing, also know as NLP for the folks that you know, know the jargon, or don't know the jargon, its been around for a long time, there's been a series of open sores, academias done it, just, it just, ontologys been around, its like, it just never cracked the code. Nothing has actually blown me away over the years, until cloud came. So with cloud, you're seeing a rebirth of NLP because now you have scale, you've got compute power, more access to data, this is a real big deal, can you just talk about the importance of why Cloud and NLP and other things that were, I won't say stunted or hit a glass ceiling and the capability, why is cloud so important because you're seeing a surge in new services. >> Yeah, sure, so there's two big things, one is cloud, the other is machine learning and the AI, and they kind of advanced speech recognition, natural language understanding, speech symphysis, all of the big technologies that we're working on, so with Cloud, there's now sort of a lot more processing that's done centrally and there's more availability of data, that he could use to trains models and that feeds well into machine learning and so you know with machine learning we can do stuff that was much harder to do before machine learning existed. And with some of these new tools, like what makes Dialog Flow special is you could use it to build stuff very, very easily, so I showed last year at Google Cloud Next how you build a bot for an imaginary Google Hardware store, we built the whole thing in 15 minutes, and deployed it on a messaging platform and it was done and its so quick and easy anyone can do it now. >> So Dave we could an ask the cube bot, take our transcripts and just have canned answers maybe down the road you automate it away. >> Yeah, yeah, yeah! >> You'd kill our job! (laughs) >> No its pretty awesome. What's interesting is its shifting the focus from kind of developers and IT more to the business users, so what we're seeing is a lot of our customers, one of the people that went on stage yesterday in the Dialog Flow section, they were saying that now 90% of the work is actually done by the business users that are programming the tool. >> Really? Because a low code type of environment? >> Yeah, you can build simple things without coding, now you know, if you were a large enterprise you're probably going to need to have a fulfillment layer, that has code, but it's somewhat abstracted from the analoopies, and so you can do a lot of things directly on the UY without any code. >> So I get started as a business user, develop some function, get used to it and then learn over time and add more value and then bring in my real hardcore devs when I really want some new functions. >> Right. So what it handles is understanding what the user wants. So if you're building a cube bot, and what Dialog Flow will do is help you understand what the user is saying to the cube bot and then what you need to bring in a developer for is to then fulfill it so if you want that, for example, every time they ask for cube merchandise, you want to send them a shirt or a toy or something, you want your developer to connect it to your warehouse or wherever. >> Give us the best bot chain content you have? >> Right. >> There it is. >> So how would we go about that? We have all this corpus of data that we ingest and and we would just, what would we do with that? Take us through an example. >> So you would want to identify what are the really important use cases, that you want to fulfill, you don't want to do everything, you're going to spread yourself thin and it won't be high quality, you want to pick what are the 20% of things that drive 80% of of the traffic, and then fulfill those, and then for the rest, you probably want to just transition to a human and have it handled by a human. >> So, lets say for us we want it to be topical, right, so would we somehow go through and auto categorize the data and pick the top topics and say okay now we want to chat bot to be able to ask questions about the most relevant content in these five areas, ten areas, or whatever, would that be a reasonable use case that you could actually tackle? >> Yeah, definitely. You know there's a lot of tools, some Google offer, some that other offer that can do that kind of of categorization but you would want to kind of figure out what the important use cases that you want to fulfill and then sort of build paths around them. >> Okay and then you've got ML behind this and this is a function I can, this fits into your servalist strategy, your announced GA today, >> We announced GA a few months ago, but what we announced yesterday was five new features that help transform Dialog Flow into sort or from a tool-- >> What are those features take a minute to explain. >> Sure, yeah, yeah, so first is our Dialog Flow phone gateway, what is does is it can turn any bot into a an IVR that can respond within, it take 30 seconds to set up. You basically just choose a phone number and it attaches a phone number and it cost zero dollars per month, zero, nothing, you juts pay for usage if it actually goes above a certain limit, and then it does all of the speech recognition, speech symphysis, natural language understanding orchestration, it does it all for you. So setting up and IVR, a few years ago used to be something that you needed millions of dollars to set up. >> A science project! Yeah absolutely! >> Now you can do it in a few minutes. >> Wow! >> Second is our knowledge connectors. What it does it lets you incorporate enterprise knowledge into your chat bot, it could either be FAQs or articles, and so now if you have some sort of FAQ, again in like less than a minute, you can build it into Dialog Flow without having to intense for it. Then there are a few other smaller ones that we introduced also are speech symphysis, automatic spell correction, which is really important for a chat box because people always have typos, I'm guilty just as much as everyone. Last but not least sentiment analysis, so when it helps you understand when you want to transition to a human, for example, if you have someone sort of that's not super happy-- >> Agent! >> Yeah exactly! >> And some of these capabilities were available separately so for example you could have built a phone gateway and connected it to Dialog Flow before, but it used to be a big project that took a lot of work so, we had a guest speaker yesterday, in the session for Dialog Flow and they've been running POC with a few vendors right now, its been going on for a few months, and they told us that with Dialog Flow, phone gateway and knowledge connectors, they were able to build something in a few hours that took a few months to do with other vendors because they have to stitch together multiple services, configure them, set them up, do all of that. >> So the use case for this, just to kind of, first of all to, chat box have been hot for a while, super great, but now you have an integrated complex system behind it powering an elegant front end, I could see this as a great bolt on to products, whether it's websites or apps, how-tos, instrumentation, education, lot of different apps, that seems to be the use case. How does someone learn more about how they get involved? Do they go to the website, download some code? Just take us through. I want to jump in tomorrow or now, what do I do? >> There's a free edition I can have right? >> Exactly, yeah, so the good news is you could go to either cloud@google.com/dialogflow or dialogflow.com, there's, if you go to dialogflow.com you can sign up for the standard edition which is 100% free, its for text interactions, its unlimited up to small amount of traffic, and you can even play around with the phone gateway and knowledge connectors with a limited amount, without even giving a credit card. If you want cloud terms of service and enterprise grade reliability, we also offer Dialog Flow enterprise edition, which is available on cloud or google.com, and you can sign up there. >> That comes with an SLA that-- >> Exactly, an SLA and like cloud data terms of service, and everything that's kind of attached with that. I'd also encourage people to check out the YouTube clip for the session that was yesterday that was where we demoed all of these new features. >> What was the name of the session? >> Automating you contact center with a virtual agents. >> Okay check that out on YouTube, good session. Okay so take us through the road map, your on the products, so you're product manager so this is, you got to decide priorities, maybe cut some things, make things work better, what's on the roadmap, what's the guiding principles, what's the north star for this product? >> Yeah, so, for us it's all about the quality of the end user experience, so the reality is there's many thousands of bots out there in the world, and most of them are not great. >> I'll say, most of them really suck. (laughs) >> If you Google for why chat bots, why chat bots fail is the first result, and so that's kind of our north star, we want to solve that, we want to help different developers, whether they're start ups, experience they're enterprises, we want to help them build a high quality bots, and so a lot of the features we announced yesterday, are kind of part of that journey, for example, send integrated sentiment experience that as you transition to humans, cause we know we can't solve everything so helps you understand, or knowledge connectors-- >> Automation helps to a certain point but humans are really important, that crossover point. Trying to understand that's important. >> Exactly, and we'd rather help people build bots that are focused on specific use cases, but do them really, really well, versus do a lot, but leave users with a feeling that they were talking to a bot that doesn't understand them and have a bad experience. >> We could take all the questions we've done on the cube, Dave, and turn them into a chat bot. What's the future of bots? >> Yeah. >> Go ahead, answer the question. (laughs) >> So I think, so we're kind of in the last year or two, we've been at an inflection point, where speech recognition has advanced dramatically, and it's now good enough it can understand really complex questions, so you can see with, sort of Google Assistant and Google Home and bunch of other things that people can now converse with bots and get sort of reasonably good answers back. >> And that just feed ML in a big way. >> Right, exactly, so now, you know, Dialog Flow introduced speech recognition in recognition, which just introduced speech recognition yesterday, and so we're now looking to empower all of our developers to build these amazing voice voice based experiences with Dialog-- >> Give an anecdote or an experience that the customers had where you guys are like wow, that blow me away! That is so cool, or that is just so technically amazing, or that was unique and we've never seen that coming, give us, share some color commentary around some of the implementations of the bot, bot world and the Dialog Flow's impact to someones business or life. >> Sure, so I think yesterday the ticketmaster team was showing how they look at their current idea of that's based in the old world, where you have to give very short response like yes or no or like San Francisco California, and because it's built on these short responses, it kind of a guided IVR, it takes 11 steps-- >> What's an IVR again? >> Integrated Voice Response or Interactive Voice Response, it's a system that answers the phone. >> Just want to get the jargon right. >> So now that with something like Dialog Flow they can go and build something like that instead of 11 steps, takes 3 steps. So because someone can just say, I'd like to buy tickets for so and so and complete the sentence. And the cool thing is sort of the example that they gave a recording that I made with them about a year, plus ago, and the example was, I'd like to book tickets for Chainsmokers and then they were showing it yesterday in the conference, they were like oh we know why you chose it, its because the Chainsmokers are preforming at Google Cloud Next! Its probably just a funny coincidence but... >> So they've deployed this now or they're in the processes of deploying it? >> They're in the process of deploying it, first for customer service, and at a later stage its going to be for sales as well. >> Yeah, because of the IVR for Ticketmaster today, I know it well, I'm a customer, I love Ticketmaster, but you're right, it tells you what you just asked them pretty well, but it really doesn't quite solve your problem well so. >> I mean the recognize the sales one was built a long time ago, but they're kind of overhauling all of that. >> I'm excited to see it because its a good point of comparison, you know good reference point that you understand, it's , the takeaway that I'm getting, Dan, is the advice you're giving is, nail the use case, narrow it down, and then start there, don't try to do too wide of a scope. >> Exactly, exactly. Handle the most important thing is delivering great end user experiences because you want people to really enjoy talking to the bot, so in surveys people say, 60% of consumers say that the thing they want to improve most in customer service is getting more self serve tools. They're not looking to talk to humans, but they're forced to because the self services, yeah they're terrible. >> If can get it quickly self served, I'd love that every time, I'd serve myself gas and a variety of other things, airport kiosks have gotten so much better, I don't mind those anymore. Okay one quick follow up on Dave's point about making a focus, I totally agree, that's a great point. Is there a recommendation on how the data should be structured on the ingest side? What's the training data, si there a certain best practice you recommend on having certain kinds of data, is it Q and A, is it just text, speaks this way, is it just a blob of data that gets parsed by the engine? Take us through on the data piece. >> So that really changes a lot, depending on the specific use case, the specific companies, the specific customers, so someone asked in the adience yesterday, asked the guest speaker has many intense they felt in Dialog Flow and each one of them had very different answer, so it depends a lot. But I would say the goal is to kind of focus on the top use cases that really matter, built high quality conversations, and then built a lot of intents and text examples in those, and when I say a lot, it doesn't, we don't need a lot because Dialog Flow is built on machine learning, sometimes a few dozen is enough, or maybe a couple hundred if you need to, but like we see people trying tens of thousands, we don't need that much data. And then for the other stuff that's not in your core use cases, that's where you can use things like knowledge connectors, or other ways to respond to people rather than to manually build them in, or just divert them to human associates that can fill those. >> Great job Dan! So you're the lead product manager? >> I'm the lead product manager on Dialog Flow Enterprise Edition, and there's a large team kind of working with me. >> How big is the team? Roughly. >> We don't talk about that actually. >> What other products do you own? >> I'm also product manager for cloud speech to text and cloud text to speech. >> Well awesome. Glad to have you on, thanks for sharing. Super exciting, love the focus. I think its a great strategy of having something that's not a one trick pony bot kind model, having something that is more comprehensive, see that's why bots fail. But I think there's a real need for great self service, its the Google way, search yourself, get out quick. Get your results, I mean its the Google ethos. (laughs) Get in, get your answer. >> Yeah, we're all about democratizing AI so now with cloud speech to text and cloud text to speech, put the power of Google speech recognition, speech synthesis into the hands of any developer, now with Dialog Flow we are taking that a step further, anyone can build their voice bots with ease, what used to cost like millions of dollars, you don't need special expertise. >> Alright, Dan Harron is the product manager for the Dialog Flow Enterprise Edition and doing Cloud AI for Google to bring you all the best dialog here in the cube, doing our part, soon we'll have a cube bot, you can ask us any question, we'll have a canned answer from one of the cube interviews. Dave Vellante is here with me, I'm John Furrier, thanks for watching! Stay with us we'll be right back! (music)

>> Narrator: Live from San Jose, it's theCUBE. Presenting: Big Data Silicon Valley. Brought to you by SiliconANGLE Media, and its ecosystem partners. >> Welcome back, everyone, we're here live at San Jose for Big Data SV, Big Data Silicon Valley. I'm John Furrier, cohost of theCUBE. We're here with two great guests, Jaspreet Singh, founder and CEO of Druva, and Jake Burns, VP of Cloud Services of Live Nation Entertainment. Welcome to theCUBE, so what's going on with Cloud? Apps are out there, backup, recovery, what's going on? >> So, we went all in with AWS, and late 2015 and through 2016 we moved all of our corporate infrastructure into AWS, and I think we're a little bit unique in that situation, so in terms of our posture, we're 100% Cloud. >> John: Jaspreet, what's going on with you guys in the Cloud, because we've talked about this before, with a lot of the apps in the cloud, backup is really important. What's the key thing that you guys are doing together with Live Nation? >> Sure, so I think the notion of data is now pretty much everywhere. The data is captured, controlled in data center, now it's getting decentralized into getting into apps and ecosystems, and softwares and services deployed either at the edge or in the Cloud. As the data gets more and more decentralized, the notion of data management, bead backup, BD discovery. Anything has to get more and more centralized. And we strongly believe the epicenter of this whole data management has to move to Cloud. So, Druva is a size based provider for data management. And we work with Live Nation to predict the apps not just in the data center. But, also at the edge and also the Cloud data center. The applications deployed in the Cloud, be it Live Nation or Ticketmaster. >> And what are some of the workloads you guys are backing up? That's with Druva. >> Yeah so, it's pretty much all corporate, IT applications. You know, typical things you'd find in any IT shop really. So, you know, we have our financial systems and we have some of our smaller ticketing systems and you know, corporate websites. Things of that nature. So, it's like we have 120 applications that are running and it's just really kind of one of everything. >> We were talking before we came on camera about the history of computing and the Cloud has obviously changed the game. How would you compare the Cloud as a trend relative to operationalizing the role of data and obviously GDPR, Ransomware. These are things that now with the perimeter gone. There's worries. So now, how do you guys look at the Cloud? So Jake, I will start with you. If you can compare and contrast, where we have come from and where we are going. Role of the Cloud. Significant primary, expanding. How would you compare that? And how would you talk to someone who says Hey I'm still in the data center world? What's going on with Cloud? >> Well, yeah, it's significant and it's expanding, both. And you know, it's really transforming the way we do business. So you know just from a high level, things like shortening the time to market for applications, going from three to six months just to get a proof of concept started to today, you know, in the Cloud. Being able to innovate really by trying things trying to... we try 20 different things, decide what works, what doesn't work. And at very low cost. So, it allows us to really do things that just weren't possible before. So, also, we we move more quickly because, you know, we're not afraid of making mistakes. If we provision infrastructure and we don't get it right the first time, we just change it. You know, that's something that we would just never be able to do previously in the data center. So to answer your question, everything is different. >> And as a service model's been kind of key. Is the consumption on your end different like I mean radically different? Like give an example of like how much time would be saved or taken to use other the traditional approaches. >> Oh for sure. You know, in the role of IT has completely changed because you know, instead of worrying about nuts and bolts and servers and storage arrays and data centers. You know, we could really focus on the things that are important to the business. You know, those things delivering results for the business. So, bringing value, bringing applications online and trying things that are going to help you know, us do business rather than focusing on all the minutiae. All that stuff's now been outsourced to Cloud providers. So, really, we kind of have a similar head count and staff. But, we are focused on things that bring value rather than things that are just kind of frivolous. >> Jaspreet, you guys have been very successful startup growing rapidly. The Cloud been a good friend that trend is your friend with the Cloud. >> What's different operationally that you guys are tapping into? What's that tail wind for Druva that's making you guys successful? And is it the ease of use? Is it the ease of consumption? Is it the tech? What's the secret to success with Druva? >> Sure, so, we believe cloud is a very big business transformation trend more than a technology trend. It's how you consumer service with a fixed SLA, with a fixed service agreement across the globe. So, it's ease of consumption. It's simplicity of views. It's orchestration. It's cost control. All those things. So, our promise to our customers is the complexity of data management, backups, archives, data production, which is a risk mitigation project. You know, can be completely abstracted by a simple service. For example, you know, Live Nation consumers, consumer drove a service through Amazon Marketplace. So, think about consuming a critical service like data management through simplicity of marketplace, pay as you go, as you consume the service. Across the globe. In the US, in Australia, and Europe. And also, helps the vendors like us to innovate better. Because we have a control environment to understand how different customers are using the service and be able to orchestrate better security pusher, better threat prevention, better cost control. DevOps. So, it improves the pusher of the service being offered and helps the customer consumer. >> You both are industry veterans by today's standards unless you're like 24 doing some of the cryptocurrency stuff that, you know, doesn't know the old IT baggage. How would you guys view the multi-Cloud conversation? Because we hear that all the time. Multi-Cloud has come up so many times. What does it mean? Jake, what does multi-Cloud actually mean? Is it the same workload across multiple Clouds? Is it the fact that there is multiple Clouds? Certainly, there will be multiple Clouds? But, so, help us digest what that even means these days. >> Yeah, that's a great question and it's a really interesting topic. Multi-Cloud is one of those things where, you know, there's so many benefits to using more than one Cloud provider. But, there are also a lot of pitfalls. So, people really underestimate the difference in the technology and the complexity of managing the technology when you change Cloud providers. I'm talking primarily about infrastructure service providers like Amazon web services. So, you know, I think there's a lot of good reasons to be multi-Cloud to get the best features out of different providers, to not have, you know, the risk of having all your data in one place with one vendor. But, you know, it needs to be done in such a way where you don't take that hit in overhead and complexity and you know, I think that's kind of a prohibitive barrier for most enterprises. >> And what are the big pitfalls that you see? Is it mainly underestimating the stack complexity between them or is it more of just operational questions? I mean what is the pitfalls that you've observed? >> Yeah, so, moving from like a typical IT data center environment to public Cloud provider like AWS. You're essentially asking all your technical staff to start speaking in a new language. Now if you were to introduce a second Cloud provider to that environment, now you're asking them to learn a third language as well. And that's a lot to ask. So, you really have two scenarios where you can make that work today without using a third party. And that's ask all of your staff to know both and that's just not feasible. Or have two tech teams. One for each Cloud platform. That's really not something businesses want to do. So, I think the real answer is to rely on a third party that can come in and abstract one of those Cloud complexities Well, one of those Cloud providers out. So, you don't have to directly manage it. And in that way, you can get the benefit of being multi-Cloud, that data protection of being multi-Cloud. But, not have to introduce that complexity to your environment. >> To provide some abstraction layer. Some sort of software approach. >> Yeah, like for example, if you have your primary systems in AWS, and you use a software like Druva Phoenix to backup your data and you put that data into a second Cloud provider. You don't have to an account with that second Cloud provider. You don't have to have the risk of associating without a complexity associated without that is I think is a very >> And that's where you're looking for differentiation. We look at venues, say hey don't make me work harder. >> Right. >> And add new staff. Solve the problem. >> Yeah, it's all about solving problems right? And that's why we're doing this. >> So, Druva talk about this thing. Because we talked about it earlier about To me we could be oh we're on Azure. Well, they have Office 365 of course they're going to have Microsoft. A lot of people have a lot going on and AWS. So, maybe we're not there at the world where you can actually use provision across Clouds, the same workload, It would be nice to have that someday if it was seamless. But, I think that's might be the nirvana. But at the end of the day, an enterprise might have Office 365 and some Azure. But, I got some mostly Amazon over here I'm doing a lot of development on and doing a DevOps, and I'm on-prim. How do you talk to that? Because that's like you got to backup Office 365, you got to do the on-prim thing, you got to do the Amazon thing. How do you guys solve that problem? What's the conversation? >> Absolutely. I think over time we believe best of breed will win. So, people will deploy different type of cloud for different workloads. Pete's has hosted IaaS or platform like PaaS. When they do that, when they host multiple services, softwares to deploy services. I think its hard to control where the data will go. What we can orchestrate or anybody can orchestrate is the centralizing the data management part of it. So, Druva has the best pusher, has the best coverage across multiple heterogeneous Cloud breed. You know. Services like Office 365, Box, or Saleforce or B platforms like S3 or Dynono DB through our product called Apollo or hosted platforms like what Live Nation is using through our Phoenix product line. So getting the breadth of coverage, consistency of policies on a single platform is what will make enterprises adopt what's best out there without worrying about how you build abstraction for data management. >> Jake, what's the biggest thing you see people who are moving to the Cloud for the first time? What are they struggling with? Is it the idea that there's no perimeter? Is it staff training? I mean what are some of the as people move from Test Dev and or start to put in production the Cloud? What are some of the critical things they should think about? >> Yeah, there are so many of them. But first, really, its just getting buy in, you know, from your technical staff because, you know, in an enterprise environment you bring in a Cloud provider it's very easily framed to hold as if we're just being outsourced right? So, I think getting past that barrier first and really getting through to folks and letting them know that really this is good for you. This is not bad for you. You're going to be learning a new skill, very valuable skill, and you're going to be more effective at your job. So, I think that's the first thing. After that, once you start moving to the Cloud, then, the thing that becomes apparent very quickly is cost control. So, you know, the thing with public Cloud is you know, before you had this really kind of narrow range of what IT could cost. Now with the traditional data center, now we have this huge range. And yes, it can be cheaper than it was before. But, it can also be far more expensive than it was before. >> So, service is sprawled or just not paying attention? Both? >> Well, you essentially you're giving your engineers a blank check. So, you need to have some governance and, you know, you really need to think about things that you didn't have to think about before. You're paying for consumption. So, you really have to watch your consumption. >> So, take me thorough the mental model of D duplication in the Cloud. Because I'm trying to like visualize it or grok it a little bit. Okay, so, the Cloud is out there, data's everywhere. And do I move the compute to the data? How does the backup and recovery and data management work? And does D Doup change with Cloud? Because some people think I got my D Doup already and I'm on premise. I've been doing these old solutions. How does D Doup specifically change in the Cloud or does it? >> I know scale changes. You're looking at, you know, the best D Doup systems, if you look historically, you know, were 100 terabyte, 200 terabyte, Dedup indexes, data domain. The scale changes, you know, customers expect massive scale in Cloud. Our largest customer had 10 perabyte in a single Dedup index. It's 100x scale difference compared to what traditional systems could do. Number two, you could create a quality of service which is not really bound by a fixed, you know, algorithm like variable lent or whatever. So, you can optimize a Dedup very clearly for the right workload. The right Dedup for the right workload. So, you may Dedup off of 365 differently than your VMware instances, compared to your Oracle databases or your Endpoint workload. So, it helps you that as a service business model helps you create a custom, tailored solution for the right data. And bring the scale. We don't have the complexity of scale. But, to get the benefit of scale. All, you know, simply managing the cloud. >> Jake, what's it like working with Druve? What's the benefit that they bring to you guys? >> Yeah, so, specifically around backups for our enterprise systems, you know, that's a difficult challenge to solve natively in the Cloud. Especially if you're going to be limited to using Cloud native tools. So, it's really it's a really perfect use case for a third party provider. You know, people don't think about this much but in the old days, in the data center, you know, our backups went offsite into a vault. They were on tapes. It was very difficult for us to lose those or for them to be erased accidentally or even intentionally. Once you go into the Cloud, especially if you're all in with the Cloud, like we are. Everything is easier. And so, accidents are easier also. You know, deleting your data is easier. So, you know, what we really want and what a lot of enterprises want. >> And security too is a potential >> Absolutely, yeah. And so, what we want is we want to get some of that benefit, you know, back that we had from that inefficiency that we had beforehand. We love all the benefits of the Cloud. But, we want to have our data protected also. So, this is a great role for a company like Druva to come in and offer a product like Phoenix and say, you know, we're going to handle we're going to handle your backups for you essentially. So, you're going to put it in a safe place. We're going to secure it for you. And we're going to make sure it's secure for you. And doing it software is a service like Druva does with Phoenix. I think is the absolute right way to go. It's exactly what you need. >> Well, congratulations Jake Burns, Vice President in Cloud services. >> Thank you. >> At Live Nation entertainment. Jaspreet Singh, CEO of Druva, great to have you on. Congratulations on your success. >> Thank you. >> Inside the tornado called Cloud computing. A lot more stuff coming. More CUBE coverage coming up after this short break. Be right back. (electronic music)

(upbeat music) >> Narrator: Live from the Seattle, Washington, it's the Cube on the ground, covering KubeCon 2016. Brought to you by the Linux Foundation and Red Hat. Here's your host, John furrier. >> Hello, everyone. Welcome to the Cube special on the ground coverage of KubeCon or CloudNativeCon, this is an event. Seattle booming with attendance, great growth from last year, and we are here in Seattle covering it all. And my next guest is Dan Kohn, who's the executive director of the CNCF, which stands for the Cloud Native Computing Foundation. It's a mouthful, but it's super important part of the Linux foundation. Welcome. >> Thanks so much, really glad to be here. >> Yeah, so big fan of what's happening here. One, the event's awesome. Great uptake from last attendance from last year. >> Yeah, unfortunately, maybe a little too much. We're a little crowded in the foyer and a little bumping on the way into getting in the restroom and everything, but it's one of the challenges of fast growing technology space is trying to figure out a year ahead of time, what size space to get? >> And how many people to squeeze in without getting the fire marshal on your back. >> Exactly. >> Certainly this is going to be a great one because the hallway conversation has been spectacular, and normally the excitement's pretty strong at tech events like this because they're developers, so there's a lot of collaboration going on. But you have a kind of an air of really forward-thinking entrepreneurial kind of thinking going on here. And I haven't seen that in a while and I think that's one of the main things that we're seeing that came out of the containers, Kubernetes. I would say the unveiling and the clarity of at least a path. >> Yes, absolutely. >> And the importance of that. So that's been super important to (indistinct) community. Now making that a part of a foundation, an open source, has challenges. So that's what you're doing. So give us the plan, what's the strategy? >> Sure, so I'm actually relatively new to the space. I just became an executive director five months ago, and this is somewhat of a coming out party. This is the first big event that we've run as the first CloudNativeCon. And it's really just been extraordinary. I'm thrilled to see the range where we're getting some of the biggest companies in the world of the Cisco's, and Wallway's, and IBM's, Red Hat's and such. And then tons of startups, and a lot of real diversity in the end-users as well. Of startups looking at Kubernetes, massive companies, just saw a great presentation from Ticketmaster, about them having 50 year old technology that they're moving forward and putting into containers. >> So in the growth of the market, one of the challenges is to kind of, you know, not so much be a chess player, but be a gardener if you will, kind of like let the flowers bloom, if you will. And that's a challenge cause opensource is very opinionated, but there's also a lot of passion involved. So how do you look at, what's your philosophy on establishing kind of a rules of engagement? How do you foster the innovation? Certainly the market drivers are for more growth, but people have inhibitors on the enterprise that we hear about, support and these things of that nature. So how do you enable that? What's your strategy and what's your view? >> Sure, so CNCF is a very new organization. And my goal on it is to look at a lot of the giants that have come before us of like the Internet Engineering Task Force and the Apache Software Foundation and OpenStack. And my goal is to try and learn from them and ideally to try and make entirely new and different mistakes as opposed to the ones that they may have made in the past. So one of the things that's a little unusual in our setup is that we very much separate all of the technology decisions from the business decisions. We have a governing board of a bunch of the biggest technology companies in the world, the ones I mentioned, plus Google and Samsung just joined, which we're very excited about, a number of others. But they can't actually adopt projects in. So we have a separate group called the technical oversight committee, which is some of the top architects in the cloud space. So we have folks like Ben Hindman of Mesosphere, and Solomon Hykes of Docker, Brian Gantt of Google, and six others, and that's the group that looks at new projects and evaluates them and talks to them and decides whether to adopt them into CNCF or not. And we feel that that separation is really critical so that the technology decisions are not being biased by the business one. >> Yeah, it's always hard to foster growth in the innovation around business models, conflicting with the technology enablement, that's really key. Great to see that decoupling. So on the business model side, thoughts on things that you've learned and observed, learnings that you've had in your past career and applying that now, I mean, the Bait, the rage is on, Open Core to Apache, GPL, you saw some things going on there. So there's like all kinds of different approaches. Are there any thoughts of the winds blowing any which way or the other? >> Sure, I was previously the chief operating officer at the Linux Foundation between '06 and '10, and I definitely think you can, CNCF as part of the Linux Foundation, we took that model of saying, "the technology decisions "need to be separate from business ones." One thing that's interesting to me is that when I was last in this space 10 years ago, people were perfectly fine. Linux Journals, GPL, people were fine with free licenses like MIT and BSD. Since then, and for this group, there is an enormous focus on the Apache license. And the reason why, is the fear of submarine patents. And so the whole goal of CNCF is for us to be an intellectual property no fly zone. That you can have all of these companies that compete very hard in the marketplace, but they can come together and collaborate and share their ideas and their technology without the belief that a couple years later, someone's going to be able to trick someone else in with a lawsuit, and win that. And the Apache license is really the industry consensus right now for best practices. >> It's interesting cause that no fly zone gives the freedom for the creation and the invention side of it. The patent thing is always worrisome, but in general, there's also the business model down the road kind of approach. Which is, "let's go innovate." Apache has done great on packaging. Have someone get some traction. It fosters the community aspect as well as a startup. Maybe not thinking about packaging. >> No, we have an advantage that we're not, unlike OpenStack as an example, we're not trying to come up with the projects ourself. What we're actually doing is scouring the Cloud Native landscape, talking to different groups and saying, "Oh, what do we think is "the best in class project out there?" And in some cases it's more than one, but today we just announced the fourth project that's added to the CNCF. So we have Kubernetes, we have Prometheus, which is a monitoring application. OpenTracing is a tracing, and then today we just added Fluentd, which is a logging solution. And this is the idea that if you have dozens or hundreds of different applications and projects that are each producing a log stream, and then you have a perhaps dozens of other applications that are consuming it, you don't want to have an M times N problem of creating adapters for all of them. Instead, you can plug them all into Fluentd, it has over 300 adapters for different solutions out there. And that provides one comprehensive approach. But what's interesting is that we don't need to win over the community and say, "Oh, here's this project you may not have heard of." There's actually over 2000 users of that today. But by having them here at CNCF, showing how it plugs into other technologies of ours, we think we can hope-- >> You're cross-pollinating? >> Dan: Exactly. >> You're letting it bubble up and you're not being a-- >> Dan: That's exactly the metaphor. >> (laughs) A dictator. Okay, and back to the project side, this is awesome. So you have some gravity around these projects. Is there any cadence or expectation, or is it free for all in terms of the velocity of adoption of projects that the technical committee will oversight? >> We would love to be at the pace of one a month. And I don't know that we'll quite get that fast. One big change that we're hoping to make in the next month or two. When our first two projects were Kubernetes and Prometheus, those are two of the fastest growing best respected projects on GitHub right now. We didn't want to have such a high milestone for every other project we considered. So we're adopting what we think we're going to call an inception stage of earlier projects that we're going to sort of try out, but they have to essentially prove themselves within 12 months. And hopefully that'll allow us to keep a pretty good velocity where we think there's a fantastic number of projects, we think as a community, we can-- >> Yeah, let people fight it out, surface stuff and let people kick the tires, right? That's the incubation period basically. What about the forking and all the battle cage matches that go on, how do you want to handle that or you just let nature take its course? Is that philosophy there? >> Thankfully, when we look at the space and this is really coming out of the Linux Space as well, anyone can fork, and of course it has a slightly different connotation now with GitHub, where when you make a change, you fork it, but there's also just a massive centripetal force pushing people together. And when you have a really high velocity of changes, the idea of forking and you would lose out on that, becomes a lot less appealing. And so, so far thankfully, all of our members and everyone in the community has really been on board on having a single head on working together to have that consultation. >> We just had Richard Kaufman on from, I think Robert Kaufman, I mean, from Samsung, he was talking about that the number two contributor is other. >> Dan: Yes. >> Which is a nice balance to the whole critical mass. >> It's an incredible accomplishment cause for Google to pull in enough people that they're no longer the majority contributor, is something that we're thrilled with. >> Yeah, it's great to see you have Richard Kaufman. Google is the number one contributor, are you worried about that? Maybe, they've been certainly good actors in the community. I mean, they had MapReduce and let Cloudera run with it, look at what happened with that? So, we kind of all know this backstory of Kubernetes, they're kind of letting it bloom on its own. That's consistent with their current posturing? >> Well, I don't think they want to have another Cloudera. >> Which is why they embraced Kubernetes. >> But I definitely don't think it's fair to say that they're doing it on their own. They're still the largest contributor of any one company and they have a massive amount of resources, and I think they see it as a really key technology, it's something they mentioned-- >> What I was referring to is that Cloudera kind of took MapReduce under their wing and made a commercial venture out. >> Dan: Oh yeah, absolutely. >> I think Google didn't want that. >> No, and they, I mean, the way I think about it is, they had this technology a few years ago. This is definitely oversimplified. They could have kept it as a proprietary in the house thing, like Amazon Elastic Container Service. They could have made it an internal open source project, like Go, or they could have just created a Kubernetes foundation that allowed other people in, but they still controlled it. But instead they were really interested in working with the Linux Foundation and creating this Cloud Native Computing Foundation that was always designed to be much more than just Kubernetes. And that really was about trying to push the project out of the nest. But I will say that my understanding is they're still see that as an absolutely core for their business. >> Yeah, I got to give Google props out there for that because they did do the right thing there. they put it out in the open, they did a line, and they could have land grabbed that, in a different way, I mean, certainly not the way that one was above. Final question on this event, KubeCon or KubernetesCon, KubeCon, it's KubeCon, however people call it. Not to confuse with the Cube, this Cube product which is seven years and might be trademark infringement but yeah, we'll get that later. >> Dan: With a K. (both laughing) >> It's still causing a lot of confusion. But that aside, CloudNativeCon also is in conjunction, this is part of the expansion you were mentioning. Talk about the vision for the events, you got one in Berlin coming up, and certainly you could have had probably at least a few more thousand people here for sure. >> Oh well, certainly a few more hundred. And we do feel a little bad that we didn't quite aim high enough. So our vision going forward is that we have CloudNativeCon that represents all of our projects, and that KubeCon represents the biggest part of CloudNativeCon. So it's multiple tracks. It's what a ton of folks go for but we think that it also gives us a chance to expose those people to our other projects, and by the time we get to Berlin, we're certainly hoping that we have another two or three or more projects-- >> And the date on Berlin? >> It's March 29th and 30th. And then we also announced that we're going to be in Austin, in early December. And I'll say that for both of those events, we're tripling the capacity from what we had last year. So we're hoping not to be so crowded. >> I was talking to Andy Jassy last night, we had a one-on-one with him and he's talking about the first Reinvent, he didn't think he can get 4,000 people there as packed. I think you might be, having to look at more capacity potentially. I mean, at this pace. >> It's the hard question is we'd actually like to be signing contracts for 2018, and it's just really hard to predict what the right size is to get for that, because I feel terrible about the fact that we did turn people away, especially end-users that we'd like to be introducing to this space. >> Yeah, well, I can attest people watching this, definitely a fire marshal issue, because it's really packed. That's why we're in a separate room here. There was sunlight in the background earlier. Normally, we're on the show floor with the Cube, but yeah, every space is taken, hallways are jamming. >> The other thing I'll mention though, is that we are also interested in going out and reaching customers and vendors where they are. So we're going to have a booth at AWS Reinvent, and we're looking at other conferences that we can be at to help spread the Cloud Native word. >> We're certainly going to be able to have a hundred events this year, so let us know where you're at, we'll certainly bring you guys on. Let me give you the final word. Tell the folks why Kubernetes is so important. Why is this movement, why are people so excited here? For the folks that couldn't make it, what's the vibe, why is it important, and what's the impact to customers in the industry? >> So the belief is that if you're deploying a new modern software application that, putting into containers, using an orchestration platform like Kubernetes, dividing your app up into microservices is a really such a benefit in terms of optimizing your resources, and tying into a whole rapid development process, continuous integration, continuous deployment, that not doing it almost makes it impossible to compete. And so we think there's just a ton of momentum around containerization and orchestration. >> And the speed of the innovation is one of those things if you're not on it, you fall further behind and it takes more energy to catch up if you try to do it by yourself. That's the benefit of the collective communities and it highlights open source. >> Right. >> Big time in terms of successes. Dan, thanks so much for coming on, sharing the perspective, congratulations and sorry for the folks who couldn't make it, hopefully this video will help. This is the Cube here in Seattle for special coverage of CloudNativeCon and KubeCon, here in Seattle. Thanks for watching, I'm John furrier. >> Dan: Thanks. (upbeat music)

hi everybody we are live at AWS reinvents in Las Vegas I'm Jeff Kelly with Wikibon org you're watching the cube silicon angles premiere live broadcast we go out to the technology events and as John foyer likes to say extract the signal from the noise so being here at the AWS show we were talk we're going to talk to a lot of AWS customers here a lot about what they're doing in in this case around analytics data warehousing and data integration so for this segment I'm joined by two customers Daniel heacock senior business systems analyst with a tix and Adam Cain's who's a data architect with federated sample welcome guys thanks for joining us on the cube Thanks your first time so we'll promise we'll make this as painless as possible so so you guys have a couple things in common we were talking beforehand some of the workflows are similar you work your you're using Amazon Web Services redshift platform for data warehousing you're using attunity for some of the data integration to bring that in from your for your operational transactional databases and using a bi tool on top to kind of tease out some of the insights from that data but why don't we get started Daniel we'll start with you tell us a little bit about etix kind of what you guys do and then we'll just kind of get into the use cases and talk to use AWS and the tuner need some of the other technologies you use it sure yeah so the company I work for is etix we are a primary market ticketing company in the entertainment industry we provide a box office solutions to venues and venue owners all types of events casinos fairs festivals pretty much you name and we sell some tickets in that industry we we provide a software solution that enables those menu owners to engage their customers and sell tickets so could kind of a competitor to something like ticketmaster the behemoth in the industry and you're definitely so Ticketmaster would be the behemoth in the industry and we are we consider ourselves a smaller sexier version that more friendly to the customer customer friendly more agile absolutely so Adam tell us a little bit about better a sample sure federated sample is a technology company in the market research industry and we aim to do is add an exchange layer between buyers and sellers so we facilitate the transaction between when a buyer or a company like coke would say hey we need to do a survey we will negotiate pricing and route our respondents to their surveys try to make that a more seamless process so they don't have to go out and find your very respond right everything online and right right absolutely got it so so let's talk a little bit about let's start with AWS so obviously we're here to reinvent a big show 9,000 people here so you guys you know talk about agile talk about cloud enabling kind of innovation and I'm gonna start with you what kind of brought you to AWS are you using red shift and I think you mentioned you're all in the cloud right just give us your impressions of the show in AWS and what that's meant your business right shows been great so far as to we were originally on-premise entirely at data center out in California and it just didn't meet our rapid growth we're a smaller company startup so we couldn't handle the growth so we need something more elastic more agile so we ended up moving our entire infrastructure into amazon web services so then we found that we had a need to actually perform analytics on that data and that's when we started the transition to you know redshift and so the idea being you're moving data from your transactional system which is also on AWS into redshift so using attunity for that they're clapping solution talk a little bit about that and and you know how that is differentiate from some of the other integration methods you could have chosen right so we started with a more conventional integration method a homegrown solution to move our data from our production sequel server into redshift and it worked but it was not optimal didn't have all the bells and whistles and it was prone to bad management being like not many people could configure it know how to use it so then we saw cloud being from attunity and they offered a native solution using secret survey replication that could tie into our native sequel server and then push that data directly into cloud being at a very fast rate so moving that data from from the sequel server it is essentially a real-time replication so that yes that's moving that data into redshifts of the year analysts can actually write when they're doing there the reporting or doing some real ad hoc kind of queries they can be confident they've got the most up-to-date data from your secret service right actual system right yeah nearly real-time and just to put in perspective the reports that we were running on our other system we're taking you know 10 15 minutes to run in redshift we're running those same reports in minutes 1 12 minutes right and if you're running those reports so quickly you know the people sometimes forget when you're talking about you know real time or interactive queries and reporting it's somewhat only as good as the data timeliness that you've got that you by Dave the timeless of the data you've got in that database because right trying to make some real-time decisions you've got a lag of depending on the workload and your use case even 15 minutes to an hour back might really impact you're ready to make those decisions so Adam talk a little bit about your use case is it is a similar cloud cloud architecture are you moving from upside Daniel moving from on-premise to so you're actually working with an on-premise data center it's an Oracle database and so we've basically we we ran into two limitations one regarding to our current reporting infrastructure and then to kind of our business intelligence capabilities and so as an analyst I've been kind of tasked with creating internal feedback loops within our organization as far as delivering certain types of KPIs and metrics to you know inform our our different teams or operations teams our marketing teams so that has been one of the kind of BI lms that we've been able to achieve because of the replication and the redshift and then the the other is actually making our reporting more I guess comprehensive we're able to run now that we're using redshift we're able to run reports that we were previously not be able to do to run on our on-premise transactional database so really we just are kind of embracing the power of redshift and it's enabling us and a lot of different types of ways yeah i mean we're hearing a lot about red shift at the show it's the amazon says the fastest-growing service AWS has had from a revenue perspective and it's six seven year history so clearly there's a lot of power in that platform it removes a lot of the concerns around having to manage that infrastructure obviously but the performance you know that's that's something I think when people are have their own data centers their own databases tuning those for the type of performance you're looking for is can be a challenge is that one of the drivers to kind of your move to redshift oh for sure the performance i I'm trying to think of a good example of a metric to compare but it's basically enabled us to develop a product or to develop products that would not have been possible otherwise there were certain i guess the ability to crunch data like you said in a specific time frame is very important for reporting purposes and if you're not able to meet a certain time frame then certain type of report is just not going to be useful so it's opening the door for new types of products within our organization well let's dig into that a little bit the different data types we're talking about so you've got a tea tix you're talking about customer transactions your custom are you talking about profiles of different types of customers tell us about some of the data sources that you're moving from your transactional system which i think is an Oracle database to to red shift and then you know what are some of those types of analytic workloads what kind of insights are you looking for sure so you know we're in the business of selling tickets and so one of our you know main concerns or I guess you should say we're in the business of helping our customers sell tickets and so we're always trying to figure out ways to improve their marketing efforts and so marketing segmentation is one of the huge ones appending data from large data services in order to get customer demographic information is something as you know easy to do in red shift and so we're able to use that information transaction information customer information I guess better engage our fans and likewise Adam could you maybe walk us through kind of a use case maybe your types of data you're looking at right that you're moving into red ship with attunity and then you know what kind of analytics are you doing on top of that what kind of insights are you gathering right so are our date is a little bit different than then ticketing but what we ultimately capture is is a respondent answers to questions so we try to find the value in a particular set of answers so we can determine the quality of the supply that's sent from suppliers so if they say that a person meets a certain demographic that we can actually verify that that person reads that demographic and then we can actually help them improve their supply that they push down to that respondent to it everybody makes more money because the completion rates go up so overall just business and analysis on that type of information so that we can help our customers and help ourselves so I wonder if we could talk a little bit about kind of the BI layer on top as well I think you're both using jaspersoft but you know beyond that you know one of the topics we've been covering on the cube another and on Wikibon is this whole analytics for all movement and we've been hearing about self service business intelligence for 20-plus years from some of the more incumbent vendors like business objects and cognos that others but really I mean if you look at a typical enterprise business intelligence usage or adoption rate kind of stalls out by eighteen percent twenty percent talk about how you've seen this kind of industry evolve a little bit maybe talk about jaspersoft specifically but what are some of the things that you think have to happen or some of the types of tools that are needed to really make business intelligence more consumable for analysts and more business use people who are not necessarily trained in statistics aren't data scientists Adam we start yes so one of the things that we're doing is with our jaspersoft we're trying to figure out you know certain we have a pis and we have traditional you know client server applications which ones our customers want to use the most because we're trying to push everybody towards an API oriented so we're trying to put that data into redshift with Jasper soft and kind of flip that data and look at it year-to-date or over a period of time to see where all of our money's coming from where others are rather than getting driven from and our business users are now empowered with jaspersoft to do that themselves they don't rely on us to pull data from they could just tie right into jaspersoft grab the data they need for whatever period of time they want and look at it in a nice pretty chart as a similar experience you're having any text definitely and I think one of the things I should emphasize about our use of Jasper's off and basically really any bi tool you choose to use in the Amazon platform is just the ability to launch it almost immediately and be able to play with data within 5-10 minutes of trying to launch it yeah it's pretty amazing what how quickly things can come from just a thought into action so well that's a good point because I mean you think about not just bitten telligence but the whole datawarehousing world it was you know the traditional method is you you know the business user a business unit goes to IT they say here are some of the requirements of the metrics we want on these reports IT then gun it goes away and builds it comes back six months later 12 months later here you go here's the report and next thing you know the business doesn't remember what they asked for this isn't necessarily going to serve our needs anymore and you've just essentially it's not a particularly useful model and Amazon really helps you kind of shorten that time frame significantly it sounds like between what you can do with redshift and some of their other database products and whatever bi to used to use is that kind of how you see this evolving oh definitely and the options I guess the the kind of plug and play workflow is is pretty pretty amazing and it's a it's given us the flexibility in our organization to be able to say well we can use this tool for now and there's a there's a chance we may decide there's something different in the future that we want to use and plugin in its place we're confident that that product will be there whenever the you know whenever the need is there right well that's the other thing you can you can start to use a tool and if it doesn't meet your need you can stop using it move to another tool so I think that puts you know vendors like jaspersoft than others puts them on their toes they've got to continually innovate and make their product useful otherwise you know they know that you know there were AWS customers can simply press the button stop using it press another button stop start using another tool so I think it's good in that sense but kind of you know when you talk about cloud and especially around data you get questions around privacy about data ownership who owns the data if it's in amazon's cloud is your data but you know it's on there in their data centers how do you feel about that Adam is there any concerns around either privacy or data ownership when it comes to using the cloud I mean you guys are all in in the cloud so right yeah so we've isolated a lot of our data into virtual private clouds so with that segment of the network we feel much more comfortable putting our data in a public space because we do feel like it's secure enough for our type of data so that was one of the major concerns up front but you know after talking with Amazon and going through the whole process of migrating to we kind of feel way more comfortable with that if you expand on that a little so you've got a private instance essentially in amazon's rep right so we have a private subnet so it's a segmented piece of their network that's just for us okay so we're not you can't access this publicly only within our VPN client or within our infrastructure itself so we're segmented we're away from that everybody else interesting so they offer that kind of type of service when there's more privacy concern as a security concern definitely and of course a lot depends on the type of data i mean how sensitive that data is if it you know but personally identifiable data obviously is going to be more sensitive than if it's just a general market data that anyone could potentially access daniel is we'll talk about your concerns around that or did you have concerns definitely a more of a governance people process question than a technology question I think well I definitely a technology question to a certain extent I mean as a as a transaction based business we were obviously very concerned with security and our CTO is very adamant about that and so that was one of the first first issues that we address whenever we decided to go this route and I'm obviously AWS has has taken all the precautions we have a very similar set up to what Adam is describing as far as our security we are very much confident that it is a very robust solution so looking forward how do you see your use of both the cloud and kind of analytics evolving you know one of the things we've been covering a lot is the as use case to get more complex your kind of you've got to orchestrate more data flows you've got to move data for more places you mentioned you're using attunity to do some of that replication from your transactional database and some red shift you know what are some of the other potential data integration challenges you see fate you see yourselves facing as you kind of potentially get more complex deployments we've got more data maybe you start using more services on Amazon how do you look to tackle some of those eight integration challenges let me start that's a good question one of the things we're trying to do inside of you know our organization is I guess bring data from all the different sources that we have together we have you know we use Salesforce for our sales team we collect information from MailChimp from our digital marketing agency that that we'd like to tile that information together and so that's something we're working on attunity has been a great help there and they're you know they're their product development as far as their capabilities of bringing in information from other sources is growing so that's a you know we're confident that the demand is there and that the product will develop as we as we move forward well I mean it's interesting that we've got you know you two gentlemen up here one with a kind of a on premise to cloud deployment and one all in the cloud so I'm clearly tuning you can kind of gap both those right on premise and cloud roll but also work in the cloud environment Adam when we if you could talk a little bit about how you see this kind of evolving as you get more complex maybe bring in more systems are you looking to bring in more data sources maybe even third-party data sources outside data sources how are you how do you look at this evolve right President Lee we do have a Mongo database so we have other sources that we're doing now there's talks of even trying to stick that in dynamo DB which is a reg amazon offering and that ties directly into redshift so we could load that data directly into that using that key pair or however we want to use that type of data data Mart but one of the things that we're trying to work out right now is just distribution and you know being agile you know elasticity which I work those issues with our growing database so so our database grows rather large each month so working on scalability is our primary focus but other data sources so we look into other database technologies that we can leverage in addition to sequel server to help distribute that load you so we've got time just for one more question I wonder I always like to ask when we get customers and users on if you can give some advice to other practitioners for watching so I mean if you can give one piece of advice to somebody who might be in your position they're looking at maybe they've got an on-premise data warehouse or maybe they're just trying to figure out a way to to get make better use of their data I mean what would the we the one thing would it be a technology piece of advice maybe you know looked at something like red shift or and solutions like attunity but maybe it would be more of a you know cultural question around the use of data and I'm I instead of making data-driven decisions but with that kind of one piece of ice big I could put you on the spot okay I would say don't try to do it yourself when the experts have done it for I couldn't put it any more simpler than that very succinct but very powerful but for me my biggest takeaway would be just redshift I was kind of apprehensive to use it at first I was so used to other technologies but we can do so much with redshift now add you know half the cost so your good works pretty compelling all right fantastic well Adam pains Daniel heacock thank you so much for joining us on the cube appreciate it we'll be right back with our next guests we're live here at AWS reinvent in Las Vegas you're watching the cube the cute