>> From around the globe, it's theCUBE with coverage of KubeCon and CloudNativeCon Europe 2020, Virtual. Brought to you by Red Hat, The Cloud Native Computing Foundation and Ecosystem partners. >> Welcome back, I'm Stu Miniman and this is CUBEs coverage of KubeCon CloudNativeCon 2020 in Europe, the virtual edition and of course one of the things we love when we come to these conferences is to get to the actual practitioners, understanding how they're using the various technologies especially here at the CNCF show, so many projects, lots of things changing and really excited. We're going to talk about security in a slightly different way than we often do on theCUBE so happy to welcome to the program from Sera4 I have Jeff Klink who's the Vice President of Engineering and Cloud. Jeff, thanks so much for joining us. >> Thanks too, thanks for having me. >> All right so I teed you up there, give us if you could just a quick thumbnail on Sera4, what your company does and then your role there. >> Absolutely so we're a physical hardware product addressing the telco markets, utility space, all of those so we kind of differentiate herself as a Bluetooth lock for that higher end space, the highest security market where digital encryption is really an absolute must. So we have a few products including our physical lock here, this is a physical padlock, it is where door locks and controllers that all operate over the Bluetooth protocol and that people can just use simply through their mobile phones and operate at the enterprise level. >> Yeah, I'm guessing it's a little bit more expensive than the the padlock I have on my shed which is getting a little rusty and needs a little work but it probably not quite what I'm looking for but you have Cloud, you know, in your title so give us if you could a little bit you know, what the underlying technology that you're responsible for and you know, I understand you've rolled out Kubernetes over the last couple of years, kind of set us up with what were the challenges you were facing before you started using that? >> Absolutely so Stu We've grown over the last five years really as a company like in leaps and bounds and part of that has been the scalability concern and where we go with that, you know, originally starting in the virtual machine space and, you know, original some small customers in telco as we build up the locks and eventually we knew that scalability was really a concern for us, we needed to address that pretty quickly. So as we started to build out our data center space and in this market it's a bit different than your shed locks. Bluetooth locks are kind of everywhere now, they're in logistics, they're on your home and you actually see a lot of compromises these days actually happening on those kind of locks, the home security locks, they're not built for rattling and banging and all that kind of pieces that you would expect in a telco or utility market and in the nuclear space or so you really don't want to lock that, you know, when it's dropped or bang the boat immediately begins to kind of fall apart in your hands and two you're going to expect a different type of security much like you'd see in your SSH certificates, you know, a digital key certificate that arrives there. So in our as we grew up through that piece Kubernetes became a pretty big player for us to try to deal with some of the scale and also to try to deal with some of the sovereignty pieces you don't see in your shed locks. The data sovereignty meeting in your country or as close to you as possible to try to keep that data with the telco, with the utility and kind of in country or in continent with you as well. That was a big challenge for us right off the bat. >> Yeah, you know Jeff absolutely, I have some background from the telco space obviously, there's very rigorous certifications, there's lots of environments that I need to fit into. I want to poke at a word that you mentioned, scale. So scale means lots of things to lots of different people, this year at the KubeCon CloudNativeCon show, one of the scale pieces we're talking about is edge just getting to lots of different locations as opposed to when people first thought about, you know, scale of containers and the like, it was like, do I need to be like Google? Do I have to have that much a scale? Of course, there is only one Google and there's only a handful of companies that need that kind of scale, what was it from your standpoint, is it you know, the latency of all of these devices, is it you know, just the pure number of devices, the number of locations, what was what was the scale limiting factor that you were seeing? >> It's a bit of both in two things, one it was a scale as we brought new customers on, there were extra databases, there was extra identity services, you know, the more locks we sold and the more telcos we sold too suddenly what we started finding is that we needed all these virtual machines and sources in some way to tie them together and the natural piece to those is start to build shared services like SSO and single sign on was a huge driver for us of how do we unite these spaces where they may have maintenance technicians in that space that work for two different telcos. Hey, tower one is down could you please use this padlock on this gate and then this padlock on this cabinet in order to fix it. So that kind of scale immediately showed us, we started to see email addresses or other on two different places and say, well, it might need access into this carrier site because some other carrier has a equipment on that site as well. So the scale started to pick up pretty quickly as well as the space where they started to unite together in a way that we said, well, we kind of have to scale to parts, not only the individuals databases and servers and identity and the storage of their web service data but also we had to unite them in a way that was GDPR compliant and compliant with a bunch of other regulations to say, how do we get these pieces together. So that's where we kind of started to tick the boxes to say in North America, in Latin America, South America we need centralized services but we need some central tie back mechanism as well to start to deal with scale. And the scale came when it went from Let's sell 1000 locks to, by the way, the carrier wants 8000 locks in the next coming months. That's a real scalability concern right off the bat, especially when you start to think of all the people going along with those locks in space as well. So that's the that's the kind of first piece we had to address and single sign on was the head of that for us. >> Excellent, well you know, today when we talk about how do i do container orchestration Kubernetes of course, is the first word that comes to mind, can you bring us back though, how did you end up with Kubernetes, were there other solutions you you looked at when you made your decision? What were your kind of key criteria? How did you choose what partners and vendors you ended up working with? >> So the first piece was is that we all had a lot of VM backgrounds, we had some good DevOps backgrounds as well but nobody was yet into the the container space heavily and so what we looked at originally was Docker swarm, it became our desktop, our daily, our working environment so we knew we were working towards microservices but then immediately this problem emerged that reminded me of say 10, 15 years ago, HD DVD versus Blu-ray and I thought about it as simply as that, these two are fantastic technologies, they're kind of competing in this space, Docker Compose was huge, Docker Hub was growing and growing and we kind of said you got to kind of pick a bucket and go with it and figure out who has the best backing between them, you know from a security policy, from a usage and size and scalability perspective, we knew we would scale this pretty quickly so we started to look at the DevOps and the tooling set to say, scale up by one or scale up by 10, is it doable? Infrastructure as code as well, what could I codify against the best? And as we started looking at those Kubernetes took a pretty quick change for us and actually the first piece of tooling that we looked at was Rancher, we said well there's a lot to learn the Kubernetes space and the Rancher team, they were growing like crazy and they were actually really, really good inside some of their slack channels and some of their groups but they said, reach out, we'll help you even as a free tier, you know and kind of grow our trust in you and you know, vice versa and develop that relationship and so that was our first major relationship was with Rancher and that grew our love for Kubernetes because it took away that first edge of what am i staring at here, it looks like Docker swarm, they put a UI on it, they put some lipstick on it and really helped us get through that first hurdle a couple years ago. >> Well, it's a common pattern that we see in this ecosystem that you know, open source, you try it, you get comfortable with it, you get engaged and then when it makes sense to roll it into production and really start scaling out, that's when you can really formalize those relationships so bring us through the project if you will. You know, how many applications were you starting with? What was the timeline? How many people were involved? Were there, you know, the training or organizational changes, you know, bring us through under the first bits of the project. >> Sure, absolutely. So, like anything it was a series of VMs, we had some VM that were load balanced for databases in the back and protected, we had some manual firewalls through our cloud provider as well but that was kind of the edge of it. You had your web services, your database services and another tier segregated by firewalls, we were operating at a single DCs. As we started to expand into Europe from the North America, Latin America base and as well as Africa, we said this has got to kind of stop. We have a lot of Vms, a lot of machines and so a parallel effort went underway to actually develop some of the new microservices and at first glance was our proxies, our ingresses, our gateways and then our identity service and SSL would be that unifying factor. We honestly knew that moving to Kubernetes in small steps probably wasn't going to be an easy task for us but moving the majority of services over to Kubernetes and then leaving some legacy ones in VM was definitely the right approach for us because now we're dealing with ingressing around the world. Now we're dealing with security of the main core stacks, that was kind of our hardcore focus is to say, secure the stacks up front, ingress from everywhere in the world through like an Anycast Technology and then the gateways will handle that and proxy across the globe and we'll build up from there exactly as we did today. So that was kind of the key for us is that we did develop our micro services, our identity services for SSO, our gateways and then our web services were all developed in containers to start and then we started looking at complimentary pieces like email notification mechanisms, text notification, any of those that could be containerized later, which is dealt with a single one off restful services were moved at a later date. All right. >> So Jeff, yeah absolutely. What to understand, okay, we went through all this technology, we did all these various pieces, what does this mean to your your business projects? So you talked about I need to roll out 8000 devices, is that happening faster? Is it you know, what's the actual business impact of this technology that you've rolled out? >> So here's the key part and here's a differentiator for us is we have two major areas we differentiate in and the first one is asymmetric cryptography. We do own the patents for that one so we know our communication is secure, even when we're lying over Bluetooth. So that's kind of the biggest and foremost one is that how do we communicate with the locks on how do we ensure we can all the time. Two is offline access, some of the major players don't have offline access, which means you can download your keys and assign your keys, go off site do a site to a nuclear bunker wherever it may be and we communicate directly with the lock itself. Our core technology is in the embedded controllers in the lock so that's kind of our key piece and then the lock is a housing around it, it's the mechanical mechanism to it all. So knowing that we had offline technology really nailed down allowed us to do what many called the blue-green approach, which is we're going down for four hours, heads up everybody globally we really need to make this transition but the transition was easy to make with our players, you know, these enterprise spaces and we say we're moving to Kubernetes. It's something where it's kind of a badge of honor to them and they're saying these guys, you know, they really know what they're doing. They've got Kubernetes on the back end, some we needed to explain it to but as soon as they started to hear the words Docker and Kubernetes they just said, wow, this guys are serious about enterprise, we're serious about addressing it and not only that they're forefront of other technologies. I think that's part of our security plan, we use asymmetric encryption, we don't use the Bluetooth security protocol so every time that's compromised, we're not compromised and it's a badge of honor we were much alongside the Kubernetes. >> Alright, Jeff the thing that we're hearing from a lot of companies out there is that that transition that you're going through from VMs to containerization I heard you say that you've got a DevOps practice in there, there's some skill set challenges, there's some training pieces, there's often, you know, maybe a bump or two in the road, I'm sure your project went completely smoothly but what can you share about, you know, the personnel skill sets, any lessons learned along the way that might help others? >> There was a ton. Rancher took that first edge off of us, you know, cube-cuddle, get things up, get things going, RKE in the Rancher space so the Rancher Kubernetes engine, they were kind of that first piece to say how do I get this engine up and going and then I'll work back and take away some of the UI elements and do it myself, from scheduling and making sure that nodes came up to understanding a deployment versus a DaemonSet, that first UI as we moved from like a Docker swarm environment to the the Rancher environment was really kind of key for us to say, I know what these volumes are, I know the networking and I all know these pieces but I don't know how to put core DNS in and start to get them to connect and all of those aspects and so that's where the UI part really took over. We had guys that were good on DevOps, we had guys are like, hey how do I hook it up to a back end and when you have those UI, those clicks like your pod security policy on or off, it's incredible. You turn it on fine, turn on the pod security policy and then from there, we'll either use the UI or we'll go deeper as we get the skill sets to do that so it gave us some really good assurances right off the bat. There were some technologies we really had to learn fast, we had to learn the cube-cuddle command line, we had to learn Helm, new infrastructure pieces with Terraform as well, those are kind of like our back end now. Those are our repeatability aspects that we can kind of get going with. So those are kind of our cores now is it's a Rancher every day, it's cube-cuddle from our command lines to kind of do those, Terraform to make sure we're doing the same thing but those are all practices we, you know, we cut our teeth with Rancher, we looked at the configs that are generated and said, alright, that's actually pretty good configure, you know, maybe there's a team to tolerance or a tweak we could make there but we kind of work backwards that way to have them give us some best practices and then verify those. >> So the space you're in, you have companies that rely on what you do. Security is so important, if you talk about telecommunications, you know, many of the other environments they have, you know, rigid requirements. I want to get to your understanding from you, you're using some open source tools, you've been working with startups, one of your suppliers Rancher was just acquired by SUSE, how's that relationship between you know, this ecosystem? Is that something that is there any concerns from your end user clients and what are your own comfort level with the moves and changes that are happening? >> Having gone through acquisitions myself and knowing the SUSE team pretty well, I'd say actually it's a great thing to know that the startups are funded in a great source. It's great to hear internally, externally their marketing departments are growing but you never know if a startup is growing or not. Knowing this acquisitions taking place actually gives me a lot of security. The team there was healthy, they were growing all the time but sometimes that can just be a face on a company and just talking to the internals candidly as they've always done with us, it's been amazing. So I think that's a great part knowing that there's some great open source texts, Helm Kubernetes as well that have great backers towards them, it's nice to see part of the ecosystem getting back as well in a healthy way rather than a, you know, here's $10,000 Platinum sponsorship. To see them getting the backing from an open source company, I can't say enough for. >> All right, Jeff how about what's going forward from you, what projects you're looking at or what what additions to what you've already done are you looking at doing down the road? >> Absolutely. So the big thing for us is that we've expanded pretty dramatically across the world now. As we started to expand into South Africa, we've expanded into Asia as well so managing these things remotely has been great but we've also started to begin to see some latencies where we're, you know, heading back to our etcd clusters or we're starting to see little cracks and pieces here in some of our QA environment. So part of this is actually the introduction and we started looking into the fog and the edge compute. Security is one of these games where we try to hold the security as core and as tight as you can but trying to get them the best user experience especially in South Africa and serving them from either Europe or Asia, we're trying to move into those data centers and region as well, to provide the sovereignty, to provide the security but it's about latency as well. When I opened my phone to download my digital keys I want that to be quick, I want the administrators to assign quickly but also still giving them that aspect to say I could store this in the edge, I could keep it secure and I could make sure that you still have it, that's where it's a bit different than the standard web experience to say no problem let's put a PNG as close as possible to you to give you that experience, we're putting digital certificates and keys as close as possible to people as well so that's kind of our next generation of the devices as we upgrade these pieces. >> Yeah, there was a line that stuck with me a few years ago, if you look at edge computing, if you look at IoT, the security just surface area is just expanding by orders or magnitude so that just leaves, you know, big challenges that everyone needs to deal with. >> Exactly, yep. >> All right, give us the final word if you would, you know, final lessons learned, you know, you're talking to your peers here in the hallways, virtually of the show. Now that you've gone through all of this, is there anything that you say, boy I wish I had known this it would have been this good or I might have accelerated things or which things, hey I wish I pulled these people or done something a little bit differently. >> Yep, there's a couple actually a big parts right off the bat and one, we started with databases and containers, followed the advice of everyone out there either do managed services or on standalone boxes themselves. That was something we cut our teeth on over a period of time and we really struggled with it, those databases and containers they really perform as poorly as you think they might, you can't get the constraints on those guys, that's one of them. Two we are a global company so we operate in a lot of major geographies now and ETC has been a big deal for us. We tried to pull our ETC clusters farther apart for better resiliency, no matter how much we tweak and play with that thing, keep those things in a region, keep them in separate, I guess the right word would be availability zones, keep them make redundant as possible and protect those at all costs. As we expanded we thought our best strategy would do some geographical distribution, the layout that you have in your Kubernetes cluster as you go global for hub-and-spoke versus kind of centralized clusters and pods and pieces like that, look it over with a with an expert in Kubernetes, talk to them talk about latencies and measure that stuff regularly. That is stuff that kind of tore us apart early in proof of concept and something we had to learn from very quickly, whether it'll be hub-and-spoke and centralize ETC and control planes and then workers abroad or we could spread the ETC and control planes a little more, that's a strategy that needs to be played with if you're not just in North America, South America, Europe, Asia, those are my two biggest pieces because those are our big performance killers as well as discovering PSP, Pod Security Policies early. Get those in, lock it down, get your environments out of route out of, you know, Port 80 things like that on the security space, those are just your basic housecleaning items to make sure that your latency is low, your performances are high and your security's as tight as you can make it. >> Wonderful, well, Jeff thank you so much for sharing Sera4 for story, congratulations to you and your team and wish you the best luck going forward with your initiatives. >> Absolutely, thanks so much Stu. >> All right, thank you for watching. I'm Stu Miniman and thank you for watching theCUBE. (soft music)

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, brought to you by Del Technologies and its ecosystem partners. >> Hello. Welcome back to the Cube. At least a market with Dave Alonso. We are at Del Technologies World. This is our third day of coverage. As John has been saying, This is a cannon double cannon of Q content. We are pleased to welcome back a couple of alumni to keep. We've got Beth failing Presidents data Protection division from Italians. It's great to have you back. And Sherrod Rastogi also welcome back S VP of data protection product management Guys, Lots of news. The last three days, fifteen thousand or so people. Lot of partners. We've been hearing nothing but tremendous amount of positivity and also appreciation from your customers and partners for all of this collaboration within the Della Technologies company with partners. Some of the news, though, that you were on the keynote stage give us some anecdotes that you've heard from customers and partners the last few days about where Del Technologies is going. >> Yeah, I'm happy too. And you know, a big announcements this week. We're a power protect software and the power protect extra hundred appliance. And what we're hearing from customers is this is exactly what we needed to do because the demands on data protection are changing with more more. Brooke look being distributed with data being more more important and with the risks being more more prevalent that they were looking for us to take a bold step and introduce this next generation software to find platform. And so the feedback you're getting is you've done what you needed to do, and they're looking forward to learning more. >> So I wonder if we could sort of explore a little bit this concept of data management. So data management lead needs different things to different people. Sure, if your database person maybe maybe different from a person who's doing data protection, what does it mean in a data protection context? I think >> first of all, you know, having visibility off your data all across your infrastructure that resides in the edge. The court a cloud across multiple applications in physical virtual environments, right? So having full facility that I think is one component second is not the ability to move the data across seamlessly across any socially target but it is on track in the cloud. Robert Cloud. I think that sort of a second element, the third and probably the most important is how do you actually get value from the data, right? Already, Actually, not only unable to protect it, but make it available at the right time, right place for the right application and be able to use it because, as you know, data is the fuel of the modern visual economy. On making it available is really, really critical. And that to me. So you're combining all of that is what I would consider it at management to be. >> So double click on that. I mean, could you be more specific about the attributes of, you know, a modern data management system? So I >> would say, you know, any modern technology may be modular FBI driven, you know, it really sort of the automate scale performance coverage. All those attributes, I think are very important for any more than data protection product and be able to meet the needs of our customers. You know, high scale hi coverage and rapidly, >> and that gives you a cloud like experience presumably allows you to scale out many a performance. I've seen some of the conversations that start associating with that or scale in place Bath. You talked about that? Yeah, Well, yeah. I want to explore a little bit about your business because you know who knew? Who would have predicted a few years ago? The data protection would always because all of a sudden become this hot space veces diving in hundreds and hundreds of millions of dollars being spent. And of course, you're the biggest player. So everybody wants a piece of your hide. And so and you got a portfolio. It goes back up llegado days. They have amar stuff data, domaine et cetera, et cetera. She had a sort of make sure that that was logical for your customers. Protect those customers that have made investment of you, but also shoma roadmap. Jeff Clark comes in, says, Okay, we're going to simplify, you know, marching orders. Your business in a very rapid time has transformed. Can you talk about that? What's what's taking place in your business? >> Absolutely, David, it's so interesting even comparing last year to this year, right? We're at this pivot point where we're building on the legacy of Trust and I T and knowledge and experience that we have. But we're now setting the foundation to be number one and data protection and data management for the next ten years. Introducing this new set of products were able to bring a customer's forward. We call it the path to power. So in addition to that, bring new customers into the family. We're looking for all those aspects of modern day to management, with simplicity, with multi cloud, with automation and with the new use cases where it's more than just back up. It's CCD are its analytics. It's testing toe. It's validation. So this is whole spectrum of things that we can expand into now that we have this new platform. It's really exciting. >> It is exciting. And yesterday the under Armour video was very cool, and one of the things that they set in there is that there they're leveraging data for brand reputation. I mean, they've got under Armour has incredible brand ambassadors Tom Brady, Steph Curry. But looking at it as not just a business ever. But this is actually tied to our brand reputation, did. It is so incredibly pivotal to the lifeblood of a business. It has to be protected. >> Yeah, and that's a big theme. And you probably something too. But, you know, in this day and time data is no longer something that maybe people in I'd worry about write It is now the lifeblood of most of our customers, corporations and at the same time list, like the threat of malware are very prevalent. And so things like what we've done with cyber recovery always were working with our customers to protect their data. In a survey we just did. With twenty two hundred I t professionals, twenty eight percent of them had had some data loss in the last twelve months. So the risk of data loss is real. And we take our responsibility very seriously to help our customers protect from that risk. >> So I like this message to any source. Any target, any s l a. I would I would had any workload and because on so talk about you're differentiation in the marketplace, that would be great, because it's hard sometimes, you know, squint through all the marketing. And so what makes you guys different specifically thinking >> about Delhi emcee Indiana production historically has its strength in dealing with complex work clothes at high scale, with high performance on having a wide coverage of work has been a strength and actually had very low cost, very efficient, right? So that string we sort of carry on into the future. And what we're adding on is I would say that the next degree off simplification and ease off ease off, install, upgrade use. Making those work was very, very simple, right? So I think that's another dimension. We are God. We're adding our dimension, what we call multi dimensional scale, which is both scale up and scale out the same time when you actually add more notes and more cubes, you are not only capacity, but he also improved performance, right? That's it, architecturally, a fundamentally different way in Harvey approach it. So I think that's an element of innovation, and I think on performance we're introducing our first all flash off Lions industry first, So we're super excited about that. And so I think it just helped our customers in terms of restore interactions store Do those work was a lot faster. Those are some other elements in which we continue innovating. >> That's great. Yeah, so you talk about the power protect X four hundred, which is your flesh. John Rose said something on stage. Beth, I want to ask you, Teo, sort of add some color. Hey, said this is not just secondary storage. It's protected. Managed infrastructure, >> huh? That's great face. >> What? What did he mean by that? And what should we take away? >> I mean, it shows how we're broadening the use cases that these products can help satisfy. And so much of what we're talking about Del Technologies is a simplified infrastructure across the board, not thinking about just point products, but giving the customer that experience of a seamless extendable infrastructure. So protected managed infrastructure means that your infrastructure, something you have, can confidence it's protected and that you also are not just dealing with all of these pieces and parts. But I can think of it has a managed whole. I think that that helps out and talk to John about that. But that's what I take away from what he's saying. >> If I can just add to that, I would say Like, you know, data management is sort of the perfect glue across the whole del technology infrastructure, but a server storage bm We're, you know, eighty, you know, infrastructure pivotal, right? Data management data productions are off, cuts across everything, and we can bring everything together. So >> I would like to add something to that if I make it. You know Beth on sure Art as well. Data protection Backup was always OK. We gotta back it up. Who's gonna? Okay, Bump bolted on. And what's happening is the lines are blurring. Primary storage, secondary storage. You're seeing back up in the e r. Use cases. You talked about analytics and, you know, so many new emerging. That's why it is so exciting. And so because those lines are blurring, you get more value out of the system. It goes beyond just insurance. And that means this could be a lot of money being made here >> if there is. And it is also a really important need, write one thing that we haven't touched on. But I also think it's really important is with our protect we're helping combine self service with centralized governance. So what I mean by that is, if you're a V a madman or Oracle Adnan or a sequel admin, you know, you could have control over protecting your data, but we pair that with a single, you know, governance model. So if I'm the person is responsible for my company's entire, you know, data set, I can still make sure that everything's happening is it should be. And there are no anomalies, so we're really making it as easy as possible, for the business is within our customers to protect and manage their data but not making it the Wild West. Because somebody in the end is accountable for saying I know where all the data is, and I know it's protected, so it's having both of those users. >> So as data protection has really elevated, the stay was saying to become its way beyond an insurance policy. This is absolutely table stakes because data has so much value and so much value that organisations haven't even been able to extract it right, how the conversation within the customer base changed. It's not just to the admin girl or guy anymore. Rightness is Are you saying this really leveled up Tio? Maybe a senior level C level challenge as our business imperative that the state of must be protected and readily accessible at any time. Who are you talking to? >> So answer quickly that I lied to you when we're talking to the eye to decision makers. So seo no, that level data protection strategy has become something that they have in their priority list, right? It's not really in any way what it was maybe five or ten years ago. Now it's something that there's cord of what they hold as their responsibilities, executives and and that's great. It's great to have those kind of conversations because it's strategic. >> Another conversation. Just an example from yesterday, while speaking with one of the chief architects at a major company, they're really talking about cyber security on How do you use Extend? You know what we offer into a full solution across their technology. Do address, you know, doesn't use case right. So I think it's expanding beyond just back up and protection to true protection off the data. Very most mission critical data is available and not just protected. They also want to talk about how can you recover that real quickly in very quick time, so that your operation, when you do have that cyber, if and when you have that attack So I think it's just expanding toe touch. A lot more customers, I would say Our people buying, buying decision makers across >> so that when I talk to people in division I sense a renewed energy. A renewed focus. I mean, GMC before Del. Tell'Em Steve always been really good. Taking engineering resource is to getting products out to the market. But But I I see again more focused effort here and one of the exam to keep pushing on. Is this notion of cloud model so beyond? Just okay, there's a target. How do we now get to that? You know, data protection is a service small. I know that you're working toward that. I know it's, you know, a lot of it's It's early days there, but you've got to be a leader in that, I presume. So. I want to keep watching that pushing that I won if you guys could comment on what coming >> on, both things that you said. First of all, there's absolutely a level of excitement and focus and confidence in what we're doing in the product groups. I'm really changing the way we're developing software so that we have a new customer value coming out every quarter. And they were having clarity between the top level strategies. White downs, what individual engineers are working on. So that's fun and excited because we're truly transforming the way we're developing Product says point one. And the second one, absolutely here, that theme throughout all of what we're talking about. You heard a nun day one, No, giving people that cloud that experience infrastructure has a service which certainly includes data management and data protection so they can consume it in a way that fit step business that scales with business That's automated, that doesn't require, you know, massive manual steps and is more what people expect today was a cloud like experience, even for them on from data centers. Clearly, that's where we're moving. And this one more point is you know, people really want automation they don't wanna have to think about. Did I remember to protect everything? They want the system to do that for them. So you'LL see more of that from us as well. You know how we helping them with machine learning? An A I an automation so they can have confidence that all of the assets are protected even if they haven't remember to do it all. >> I mean, I just add to it. I've bean at Delhi emcee for about a year. >> It >> has been a fantastic journey waiting. It's exciting. It's been awesome. Awesome experience. I totally see the >> focus. And I think that renewed focus the cloud like a model and the innovation. They all go hand in hand because the old waterfall model of okay, we're gonna develop properties shipment every year, eighteen months. Whatever it is that doesn't fly anymore. People want innovations, and now they want to push code every day. Right? So our baby, every quarter at least. >> Yeah. Yeah. Facing new energy to the engineers as well. >> So I mean, I understand that many of your team, if not your entire engineering team, has been trained in agile. Is that my getting it right? Is that right? >> Yeah, yeah, >> not just not just like internal train. You guys brought in outside people and really took him through some formal training. Right >> way have in multiple different kinds of training. And we have lots of communications inside to get people coaching. And it's not just a process book that we're following its really a different way of thinking about how you bring customer value in small increments, staying in a good known stay and making sure that we're maximizing our engineering capacity. >> That's big. And I wish we had more time cause that's cultural train. Yeah, yeah, that you guys are really driving. And we also didn't have time to touch on partners, but it can imagine there's a lot of excitement and your huge partner community about what you guys are doing This. Congratulations on all the announcement is gonna have to have you back because there's just so much more to dig into. But back Sherrod, Thank you for joining David me this afternoon on the you go. >> Thank you so much >> for our pleasure. For Dave Volonte and Lisa Martin. You're watching the Cube live from Day three of Del Technologies, World twenty nineteen on the Cube. Thanks for watching