OK, we're going to get going. Thank you very much for turning out. Welcome to Beyond the Buzzword. So my name's Duncan Nguyen. I'm a platform architect for Pivotal. And joining me today, we have Sean Keary and Keith Streeney, also from Pivotal, some of my colleagues I've worked with for a number of years. So just so we understand our audience, who here has been using Cloud Foundry for a couple of years? And who here would say they're a Cloud Foundry operator down in the weeds actually operating Cloud Foundry? Anyone? A couple of people. And so last question, who here feels the need to justify the value of using Cloud Foundry upline to somebody else in the business? Is anyone in that position? Perfect. So this talk is absolutely for you. So the premise of this talk is around a what if. And that's what if everything you do, everything you build, every function that your developers deliver, every bit of technical debt that you remove, you can measure the value. And by value, we're not talking about sentiment. So for example, my customer is delighted with this feature. We're actually talking about a quantitative value, a hard line return on investment or revenue. And obviously, we're at a Cloud Foundry conference. So we're going to talk about how Cloud Foundry plays into actually delivering that value and how using service level indicators and feedback loops, you can start to measure and quantify the value that you deliver to your organization. And so we've spent some time, Sean, Keith, and I, working with a number of companies. And for someone who's seen my previous talk I gave last year around buzzword bingo, we spend a lot of time talking about buzzwords and explaining what these terms mean. And we don't just mean the literal definition, but we actually dig into the sentiment, the value, that each term is trying to deliver. So for example, with agile, we do standups and we do sprints. But the key thing about agile is getting features into the hands of end users very quickly so you can get that feedback. A lot of people talk about DevOps. And for us, it's really establishing a culture of DevOps, not just a set of tools. And so once you understand these terms and the value that these terms are trying to bring, assuming you've taken these things on board and you've been through this cloud native journey, your environment probably looks something like this. You have Cloud Foundry. You've defined the IS layer that you want Cloud Foundry to run on. Maybe it's a hybrid strategy of public private cloud. You have a container strategy. You have your build packs locked down. Maybe you're using Docker images. You're deploying your applications to Cloud Foundry. You probably have some set of microservices. You've defined your messaging tier, your database tier. And then with your organizational structure, you have agile delivery teams. You have a culture of DevOps. You have a centralized platform operations team. And you pipeline absolutely everything. You pipeline your applications. And you pipeline the deployment of your platform itself. If you get here, if this is where you're at, you probably feel really good about yourself because you're effectively a cloud native Jedi status at this point if you've done all of this. But to get here, it's not for free. It actually comes at a cost. It's time consuming and it uses resource. And so when you go down this journey and when you get to this environment, it's right that you ask the question, what is the value of doing all of this stuff? And this is a question you should ask every day for everything you do. As you work, as you output in life, you need to understand the value you're producing. And if you can't answer that question, or at least if you can't get to the answer through assessing it, then you should really evaluate whether you should be spending your time on that activity. So going back to our cloud native environment, the platform operator, they're often really pleased about this environment and they pleased on the way they structured it. So they go and tell the developers, maybe this is a single line of business, maybe this is central IT, they talk to the other lines of business and developers start to use it. And at this point, the executive team stand back and they ask, what's the value of this environment? What other apps and what other lines of business should come onto this platform? And how do we justify the work to lift and shift some of our applications over to this platform? So it's a really legitimate question. So to answer that question, we have to spend a bit of time exploring what value really means. So value is both a verb and a noun if you look up the dictionary definition. The noun is to do with the importance, the worth. Some things are really hard to quantify, like we all have self-worth. So how do you quantify worth? And when you listen to other CF Summit talks and I've been to a number of CF Summit conferences, most talks stop at this level. They talk about faster time to market, innovation, delighted customers. These are all key tenants of Cloud Foundry. They're all really important aspects of why we adopt and use Cloud Foundry, but they don't go on to the next stage of quantifying the return on that investment. The verb is to do with estimating the monetary worth of Cloud Foundry. And that gives you that tangible, hard return on investment. And that's to do with things like removing spend, taking out cost, consolidating on your hardware, removing middleware licenses, removing OS licenses, getting better automation. And on those aspects, you can start to put a dollar value amount to that and that's really what this talk is primarily focused on. So within Pivotal and the companies we've worked with, we've seen this cloud native ROI continuum. And with each of these pillars, we'll be delving in deep, so don't worry too much about the detail at this stage. But it starts with waste reduction and the activities that go into that. It looks at things like paired programming and the fact that if you find a defect during development, it's a lot cheaper to fix a defect at that point versus in production. And it also looks at a platform and consolidation and automation, all that good stuff.  It lets you look at service level indicators. It lets you assess the customer feedback so you can identify if you're building the right thing. And you couple those two together that should give you an effective use of CAPEX. Now, when we built these slides, Keith and I went back and forth on this use of CAPEX. We've worked with a number of CFOs and CTOs and they tend to interchange OPEX and CAPEX depending on how they've structured their budget. So I wanted to abstract up and just call it spend. Forget about CAPEX, effective use of spend. The reason why we kept it in is when you look at your operations, your operations is really on the left-hand side. That's your waste reduction. For argument's sake, let's say it costs a million dollars to deploy your applications to date. And using a platform, using automation, getting a DevOps culture, you can get that down to 100,000. That's an amazing saving and that should be celebrated, but there's a flaw to how low you can go. You can't go beyond zero for your operational expenditure. But your CAPEX, the investment into your development teams and your designers, that can have this exponential effect as you start to really resonate with your customers. And so your waste reduction's really good, but what we're really trying to do is progress to the effective use of CAPEX. So let's start with waste reduction. Waste reduction has come out of lean theory. So lean theory, for anyone who's not familiar with it, it's a management methodology, and it allows us to map out all of the steps and the processes responsible for software delivery or for delivering something. So you start with a request, and then you move on to delivery, and it lets you just map out the current state, identify areas of waste and try and get to a more desirable future state. Now it came out of so, as we look to identify waste there, within lean theory, there's this concept of mooder. So mooder is really any process that consumes more resources than needed produces waste. Now the key thing here is there's two different types of waste. Type one is non-value add activity, but there's still stuff necessary for the end user, and that's to do with things like packaging your application and the way you release it. And arguably you can get rid of that waste through things like automation. The second is non-value add activity, stuff that's unnecessary for the end user, and that's the stuff you absolutely need to remove from your organization. So lean started in manufacturing, but within DevOps communities, it's become really, really impactful. And that's because you look at this anti-DevOps pattern, this siloed organization. When it comes to software delivery, there's many different teams involved in delivering that software. You look at something as simple as provisioning a VM, and there's all these teams involved. When they're siloed, you get these crazy flows of interaction, you get tickets, handoffs, delays, and you get a lot of waste. By realigning these skills into a cross-functional team, you immediately develop a culture which eliminates a lot of that waste. And so lean manufacturing or lean methodology has become really impactful within these communities. You couple that with Cloud Foundry, and you get further waste reduction. And so there's nothing new here, but I wanna cover some of the high topics. So the waste reduction we see with Cloud Foundry, absolutely with speed and deployment of your environment. So if I was to say, in your organization, please can I have a brand new VM? How long would it take for you to give me a new VM? Anyone wanna shout out? Timescales? Four weeks. Four weeks. Any advance on four weeks? Two days. Two days. Anyone else? Either end of the spectrum. So you speak to an operator, and often I speak to operators, and they say they can provision a VM in minutes or several hours, depending on how hard on that VM is, what they're putting onto that VM, how much middleware, et cetera. You speak to a developer, and they tend to measure getting a VM in days, weeks, or months. The worst I've ever seen is, I was in a meeting with a customer, and someone walked in and spoke to their colleague and said that VM you requested 18 months ago, it's finally arrived. I'm thinking 18 months lead time just to get a VM. That's really painful for productivity. And so by having pre-provisioned environments, self-service, using containers, you get rid of a lot of that operational waste. And that theme is progressed throughout Cloud Foundry. So stability. The fact that Cloud Foundry self-heals means that the operator doesn't have to be as involved with bringing applications or components back up. And it also gives the developers the ability to adopt patterns of things like blue-green deployments, canaries, the use of feature flags, routing services, so they have more control on how and what they deploy. Scalability. So the fact that Cloud Foundry has dynamic routing in place means that there's less operational concern. You can autoscale the applications. You can autoscale the platform. And the same for security. We have the three R's. The fact that you can rotate and repave a new stem cell and get Cloud Foundry to do that rolling upgrade so you can easily roll out a CVE means there's less operational overhead there. So all of those combined, they're loosely focused for those savings on resource consolidation, software and license reduction, and more automation. So going back to our value stream, what we're doing is we're eliminating waste as we discussed and we're trying to get our lead time to match our process time as closely as possible. And so this is really the end state that we're trying to achieve. And when you start to factor in all the different disciplines that go into delivering software, you find that you have different value streams for different stages within the process. So provisioning an environment, coding, releasing, data operations, each one of these aspects has their own value stream and you can spend time mapping out the detail that goes into that and what that would look like using Cloud Foundry versus doing an outside Cloud Foundry. And so we've actually been through this process now with a number of the companies we've worked with. And so just to give you an indication, this is some feedback on provisioning a VM. Now in this case, there's something like 30 people involved in provisioning this VM. There's a huge lead time of roughly 20 weeks and then there's approximately 50 to 90 hours of effort just to get a VM. So the cost involved in this is $15,000 per VM. Now if you have six apps or six different environments a year, you can start to scale up. So you get these crazy exponential scaling issues of just in terms of sunk cost and sunk resource. If you compare that to what it's like in Cloud Foundry, within Pivotal at least, what we do is we do something called a platform dojo where we stand up a hardened version of Cloud Foundry, we integrate it into backend services and between sort of one to six to eight weeks we can get a hardened version of Cloud Foundry. You do that once for all of your apps. So you have a demonstrable time saving there. And the other end of the spectrum with patching. So if you look at a typical environment you have maybe 130 CVEs per month. For this particular company I worked with it was five hours per CVE to roll out across their organization. You basically gave it to an operator with an Excel spreadsheet. They took a script, ran the script, scripts failed, they raised a support ticket. It's very, very time consuming and very painful. Coupled that with Bosch where this release engineering tool chain can now dynamically roll out the new stem cell and dynamically provision that with a rolling upgrade. So this is taken from the CF Summit earlier this year. This is one of the companies we've worked with. When you look at these value streams on aggregate you see some fantastic results, something like 283% transaction growth, 50% reduction in time to market. And so I think it's absolutely worse spending the time to look at how you actually map out your process where you can eliminate waste and how you can get to these tangible savings. So Sean's now gonna talk about measuring the value and how we do that. Thanks Duncan. So my name is Sean Curie. Guten Morgen. Bonjour. G'day. I am the Minister of Chaos at Pivotal. I've been working with customers for the last five years in the industrial, financial and healthcare spaces helping them measure operational value. One of the things that we prefer to do instead of just measuring that value is to avoid the waste. So I'm gonna talk about how we can do that. So how many of you are familiar with the concept of site reliability engineering? Nobody. Oh, one couple. All right. So a system operator, system admin, it's just a crappy name, sorry. Site reliability engineering is the new buzzword. We're talking about buzzwords here if you guys weren't paying attention. It's something that Google has put together they like everyone to be an engineer. So what it involves is your operations team, not just being button pushers and ticket readers, but going out there and being proactive and making sure your systems are doing the things that Duncan talked about, security, secure, scalable, some other things he talked about. We'll come back to it. I'm gonna tie back to his concept in the value stream of waste with the seven types of waste from the Toyota production system. In Lean, there's the Google site reliability book. Talks about four golden signals for a platform. Anybody know what those are? No, there's just a couple people. We'll get to them. I'm gonna map those back to waste to kind of bring you guys into the 21st century out of the Lean 20th century stuff. But first we're really gonna focus on monitoring incident responses here, okay? So monitoring, we have our service level indicators. These are our lowest level measures of some aspect that the level of service is provided. So the number of people in this room may be an indicator. Anybody have a guess on how many people in this room? Come on. 40, okay. So then I have a service level objective, right? It's the target value or the range of values for a service that's measured by an SLI. So Duncan thought we could get 40. Our stretch goal was 50, right? So then we had a service level agreement, right? It's the contract between Duncan and myself in this case, for consequences of meeting or missing the SLAs. So what I have to do now is shave my beard because we didn't get to 50. 60, all right, good, thanks. Thanks for someone for actually counting. Okay, so I like to talk about test-driven operations. Moving forward, we have our site reliability engineers or our platform reliability engineers, depending upon where you are in the process chain. And those guys use a process of continuous experimentation, right? I don't know what those SLOs should be, right? I'm gonna monitor and measure the SLIs all the time. But when I compare the SLIs to the SLOs, I really have to decide whether I need to take some action. Right? If I got to 50, I have to shave my beard. If not, I need to do something else, right? If that action is needed, we need to figure out is that an automated action? Is it paging someone? What's the case, right? Take the action and then review. It's part of this continuous experimentation feedback cycle. Okay. So as part of the whole value stream, where I'm gonna talk about the part now is more your internal business or operations side of things, platform specific. Okay, Keith's gonna bring you more onto the customer side a little bit later. Okay, so avoiding waste is gonna give us value. So our first waste, golden signal number one is latency. We're gonna tie this back to the Muda style of waiting or delays in the process. Okay, so our service level agreement would be something like real time readiness of the platform. Our indicator would be sell rep time and I'm sorry for you guys who aren't familiar with these very technical terms. But these are just examples. Please do not use these numbers in production. Okay, auctioneer task placement failures, right? The number on the right is gonna be our objective, right? We want this number to always be greater than 0.5. The next one we'll talk about is errors in our system. In this case, we're gonna talk about errors specifically to help us avoid security issues, right? Anybody ever have any security issues with their platform? Okay, I did once, cost me $300,000 Amazon bill. Okay, let's monitor the security stuff. Number of authorization errors, failed logins, failed SSH attempts, right? And on the right, we've got our objectives, okay? In this case, 10 attempts could be 10 attempts per minute, 10 attempts per second, 10 attempts per hour. That's something you guys need to work out for yourself, right? Anybody have any other examples of a security indicator that they use? And none of you guys are operators. You're killing me. All right, moving on, saturation of our system, okay? So we wanna proactively scale our platform, okay? So if the number of unhealthy cells is zero, that's pretty good, I like that number. Someone who maybe cost them many, many millions of dollars because they didn't scale their systems correctly was Target, Black Friday, their system went down. That's the day after Thanksgiving for you guys who don't know that, very big shopping day in the US. All their systems went down, I'm sure it cost them millions of dollars. Anybody have any examples here, scaling, anything else you'd like to see up here on this slide? And then the final one is gonna be traffic, right? So we wanna proactively scale our apps that are running on the platform as well. So we wanna look at our router throughput, right? In this case, I have 10,000 requests per second, a little more specific than the first couple slides, right? But with Cloud Foundry, with our platform, we can also look at the requests per application instance to make sure our traffic is going the right way. And microservice architectures in our cloud native world, we can look at each application function. An example of a traffic problem. You guys familiar with the circuit breaker pattern? In your house, nobody has a circuit breaker? Come on, I know you guys have circuit breakers. In software, circuit breaker says if my behind the scenes system is off, right? Flip all the traffic and send it someplace else, right? So if you don't have that pattern in place, you can DDOS yourself, deny yourself service because you're making so many requests to the same point, you just overload it, right? This system can't even scale fast enough. So this is something you wanna try and avoid, that's waste, right? Traffic waste or transportation is the traditional mood of term there. So I just talked about in our continuous experimentation cycle, just a small part, the top part, right? Your process. Keith is gonna take it now to the next level and talk about how that fits into your product development process and your enterprise value stream. Hello, I'm Keith Strinney. I'm the Pivotal Federal Practice Lead. So this literally has nothing to do with my day job, but it's something that's very interesting to me and I really like this stuff. And so I've worked, Duncan's brought me in on a couple of customer engagements in which we worked out some of these different pieces. So one of the things that we wanna kinda revisit the slide and kinda say why? So we dropped down in the weeds, we looked at indicators, we looked at objectives, we talked about overall waste reduction and that's great. But as you heard Duncan say, there's a floor there, right? You never can drop, there's always gonna be some minimal operating expense. And if you, let's say fictitiously, you do go from a million down to 100,000, that 900,000, it looks great year one, but then year two, there's not that big drop anymore, right? So how do you continue to justify the use of the platform and why we're doing all this effort to make things dynamically scalable? Where is this all going? So the idea here is that we look at what the future value is. So how many of you feel like operation expenses is something sexy to talk about? Right, it's not. It's important, absolutely a fundamental step, but it's not that sexy, right?  That's always the question that comes. And so this is what we say, we need to innovate, we need this new cloud foundry piece, buck the status quo, but then we go right back to how do we reduce OPEX? And then we run into more brick walls. And so the idea is we need to move the conversation forward from the reduction of software capitalization to the effective use of CAPEX. And what that means is, is everybody familiar with what capital expenditures? I mean, it is loosely used. It also does it depending on your accounting method, but does everybody kind of understand what that means? So, okay, great, awesome. All right, so this is excellent for the outfit. But in general, what it means is for every new investment, I have a certain number of resources that I have to use in order to just get that initial idea up in the software world, right? And so what we really wanna do is look at what the true promise of this conversation is. And so what happens when we get this, right? So we get the platform, your company has bought it, let's say, they love it, and they're like, okay, this is gonna solve world hunger, right? So what if, what we want you to do is we want you to, now that you have the platform, we want to acquire and retain new customers. But we're not gonna give you any new money, we just, you got the platform, it's magic, make it work. Well, so what we really have to do is, we have to do that first fundamental step, which is let's say fictitiously, we could get 40% reduction in OPEX, which would be phenomenal, right? And then let's say by eliminating the waste and the delivery of the software, we're able to see 20% more efficient use of our CAPEX for new customer acquisition. So what do we do with that money, right? So yay, we saved all this money, but what do we do? Any company that quickly disappears. So what we wanna do is we wanna have a plan. We wanna have a plan for how we're gonna use that first fundamental step to feed growth and capture and retain new market share. And so here, we see the company comes out with a second objective. Now that you have this cloud-native architecture and you can move at the speed of the market and do all these great things that we heard in the sales talk, we want you to do three new revenue-generating products per quarter, revenue-generating, meaning we're not counting the failed ones. We want just things that are gonna make money that actually are producing. And we also wanna prove that we get 10% lower churn in the new user base versus what our existing churn numbers are. So normally you would freak out, like how are we even supposed to do this, right? This doesn't, you just want fairy dust and everything is just flying off and magical. So we have to really look at this. It becomes work, right? All this is, is simply work. And what we wanna get to is, how many have heard of Blue Ocean Strategy? All right, so great concept, right? Don't fight in existing markets. Go find new markets and be the only fish in that market. Awesome, right? It's a great idea. The problem is that jump right now seems magical, right? It's very difficult to get into a new market that nobody's in. Otherwise everybody would do it. So what we do is we wanna be, we wanna make continuous experimentation on the product side of first class citizen in the actual development cycle, in the platform, and in the way we look at the user base. And so what that means is we have to observe users dealing with problems. We have to create assumptions about what we think they're doing to solve those problems. And then we have to continuously validate those assumptions to understand, is our hypothesis right or is it wrong? Either way is valuable. But how do we steer so we find that actual new market? And then from there, now suddenly new revenue streams open up, new customer acquisitions are possible, and we can actually see more than that 900,000 savings in year one, we see infinite number of revenue growth in all different market verticals. And the other thing that this does is, it has a, you look at the flip side of this and we look at technical debt. Well one aspect of technical debt are features that don't resonate, but we still have to maintain because if we rip them out, something's bad, it's gonna happen to the rest of the actual software. So what Cloud Native gives us, this kind of clean modularity that minimizes that ripple to be able to sunset things that we see in the data isn't generating any resonance. And by getting rid of that and reallocating those resources and teams, now suddenly we have new resources to put on new hypothesis to drive for new markets that we're looking for that we wanna expand into and generate new revenue. And all of this is, by pruning this technical debt early, we can reallocate these new resources to new hypothesis. And what this really allows us to do, as you can see, the idea of opinions, informed, non-informed opinions and going down these paths that don't necessarily lead anywhere. And so what we really wanna do is we wanna make it so that the product development can remain objective and more focused on what the user is telling them rather than guessing product directions based on unvalidated assumptions. And that's really the key here, right? Is what's the difference between speed and velocity? Anybody? It's a vector, right? So if I run, I wanna exit, my goal is to exit and I'm running this direction, it doesn't matter how fast because I'm in the wrong direction. So the idea is, how do we know we're going towards a new market, the correct market direction and how do we know the product is evolving along the right lines? We need actual velocity, not necessarily speed. We need to know that what we're doing is the right thing based on what our customers are telling us is what they want in our product and are willing to pay for it. So when we look at this, that's what we're trying to get to, effective use of CAPEX or expenditures. And this is what we have today, right? This is typically what happens, right? The idea is like the product teams get together, they huddle up, we got this great new products, we're gonna put it out and suddenly it's not doing anything, right? And we see this all the time and then that's why it becomes more art than science because we're guessing. I mean, ultimately that's what happens. You have all these product surveys and things out there and you have all these different biases that come into play but what really ends up happening is you're not really talking or engaging with the right folks. And it doesn't necessarily mean actual user centric, which is we do a lot of user centric design, which is awesome and it builds great products but not everybody can get that level of interaction with a customer. So what we also wanna do is we wanna instrument the platform and instrument the microservices and instrument end-to-end in the cloud native architecture to really understand these types of user interactions. And what that does is it liberates either the product or the company and enables them to evolve with customer changes in both their value and preferences. And the reason why this is important is because even after product features have been delivered, the granularity of microservice design promises clean modularity to minimize or to continuously collaborate with that customer and how they evolve. And we all know circumstances change and that means the user needs change. And instead of having to constantly restart with a new product, we are now able to evolve the existing product which is less money, costs less money because you have a core baseline there. And that initial product can remain relevant long after the initial launch of it. And so we look at this, we say, well, how do we do this? Does everybody know what Elf on the Shelf is? Anybody? So in the States, this is creepy little thing. It's a, you put it on your shelf and it's supposed to watch you all year and that's how Santa knows who gets presents and who doesn't get presents. That's the general synopsis. But the idea here is that we have, we actually have the ability to monitor the traffic that comes through the platform, to look at what the customer interactions look like and to try to derive things from those. And what we have, we have tools, very specific tools for this, like AB deployments or blue-green deployments that allow us to do some of these things. But those really tell us yes or no, right? That's what we're looking at, yes or no. But by building these up, they can also start to tell us the why. We can use these in conjunction with the granularity of cloud-native architectures in order to build up and distribute a tracing so we can see the shared context across interactions to really look at what is going on in the platform. And these primitives help us build up these very complex hypotheses and then which we can look at a number of different factors and validate behavior or invalidate behavior that we expect in. So what is this like? Anybody know what Choose Your Own Adventures are? Anybody remember those books? All right, so basically they were these books that, all right, so the object is a book, right?  But the difference in it is the way you navigated the book. It allows you, you'd read something in the adventure and then on this one it'd say, if you decide yes, go to page 13. If you decide no, go to page 30. And then it would take these alternate courses. And the idea was that now I'm using the book how I want to use the book, which is what made them so popular versus somebody telling me and dictating how that story is going to occur. So it worked like this, right? You had these pathways that you could go down. In this particular example, what we're looking at is let's say we're a company and we have basically three kinds of things. We sell physical media, we do electronic purchases of physical products and we stream audio books and movies. So we have streaming media. And then we have these different pathways. Let's say in the streaming media we have two hypotheses we wanna test. Automated recommendation engines, manual recommendation engines. Let's say on the e-purchases we look at tailored sales, not tailored sales, manual recommendation, automated recommend, targeted ads, not targeted ads. So we have all these things. And inside the company we don't know which way to go. We say, you know what? Streaming is the way of the future. We think that everybody's gonna do streaming. Let's just drop our e-purchases. And you know what? Big data is awesome. It solves all the problems in the world. Let's just do nothing but automated recommendation engines. But then when we run the trace and we look at the data, we say, well, wait a minute. Assumption one and assumption two are almost even. Maybe physical things, physical products is not necessarily going away. Maybe it's just as popular. But then we see the secondary where we see the blue-green deployment and we see that 60% use manual recommendation engines and 40% use automated recommendation engines. What's going on there? But then we also see that if they pass to that automated recommendation engines, we're seeing almost 100% conversion. What is really going on here? It immediately points us to possibilities and new hypothesis that then we can test with new sort of layouts. And for instance, in this particular case, if we think about streaming media, you know, streaming media is sort of a media gratification. I wanna watch a movie. Let me put it on. So maybe it's that our recommendation engines aren't very accurate. And if we spend a little bit more research in R&D and how those recommendation engines should work, we're gonna get higher conversion rates. Maybe not. Maybe it's that down here, everything is kind of split even. Maybe it's when people look at physical products, they look at more than one source of research, right? Because I don't need it right away. I'm buying a bicycle.  I'm looking at different things. So whether or not you target me or not, whether or not it's automated recommendation engine or not, is kind of irrelevant because I've already done my research. But in streaming that night, I wanna sit down and watch a movie. I'm gonna be more likely to look at recommendations. And so it gives us sort of points as to how the user wants to use our application and do further hypothesis testing to figure out where we can get the most ROI out of every dollar we're spending in engineering and engineering on the platform and the actual product line. So this opens up a whole new world of customer understanding. Things like, where are our highest bounce rates? Where do we see the most people exiting the conversion funnel? Where do we see what's most valuable in the product? Where do we see the highest level conversion? Well, what about the layouts? If we try different AB deployments of different layout structures and workflows, maybe it's just a user experience. Maybe in certain countries it's different than other countries and so we need to actually do some sort of localization but on the entire workflow rather than just language localization. So all different things that the data points us to and looks at and by instrumenting the entire platform we can see a shared context across the entire interaction. And the idea is this, we don't wanna be fads. You know, when we do product development it's significant R&D, it's significant investment dollars. It's significant the level of effort that we put into these things to try to figure out what's going on. What we don't want is something to get hot and then sunset and then all that investment that we put in is wasted. So we really wanna avoid fads. We wanna see long term how people are using it and then if maybe it needs to evolve and maybe it needs to continually to evolve the continuous experimentation process where we get these feedback loops that tell us how to evolve. And this is really what the panacea of all the cloud native buzz is, is how do you get into those new markets and expand that customer user base infinitely which is much greater than a $900,000 savings year one, right? You're talking about millions and millions of dollars that you could potentially bring back to your CFO and say this is why you wanna invest in like cloud native architecture. This is why we bought the platform. This is why we're doing the things that we do. Why we changed our whole DevOps culture. This is the whole point of this is because we know we can get to these blue oceans and we now have a physical way to do it. There's actual a step-by-step process in order to get there, it's no longer magic. And so the ultimate is to take away of this is to look at step one, waste reduction. Let's get our deliveries more streamlined. Step two, let's get closer interaction and feedbacks with our customers. And then ultimately what we really want is data-driven decisions that move forward in the future, higher customer spend ratios per investment dollar, lower overall subscription turn if you're a subscription model and then less restarts and more evolution which ultimately is less costly to evolve a core base and sunset and get rid of the technical debt because of microservices and the ability to segment those things and get rid of them without causing a ripple effect through the baseline. And so these are all the tenants that you've heard throughout the conference in these different pieces and this is ultimately who we wanna get to. That's all I have. Thank you. Questions? Thank you.


Islam, being the guide to our lives and how to live it, discusses many important topics and subjects. One important subject are table manners and how we should conduct ourselves with food and drink. We know what we are allowed to eat and drink, yet what other codes of practice should we uphold. In the West, we have what we call table manners. Some of these rules you may be familiar with. For example, no elbows on the table, put your mobile phones away. Islam and the Holy Prophet, sallallahu alayhi wa alayhi, also teach us the way to behave around food and drink. Especially with guests, let us discuss O you who have believed, eat from the good things which we have provided for you and be grateful to Allah if it is indeed Him that you worship. He has only forbidden to you dead animals, blood, the fresh of swine and that which has been dedicated to other than Allah. But whoever is forced by necessity, neither desiring it nor transgressing its limits, there is no sin upon Him. Indeed, Allah is forgiving and merciful. Surah al-Baqarah verse 172 to 173. Allah has allowed us to benefit from food as there is goodness in it. We are allowed and encouraged to eat and drink wholesome, delicious, clean and pure food. One should eat and drink lawful things that are allowed, like vegetables, fruit and juices. There are great hadiths that say not to overeat. But what is overeating? Overeating is eating when you are full.  In fact, a full stomach is a source of rebellion and corruption. Not to eat breakfast and dinner is bad for the health, but to eat in between breakfast and dinner is also harmful to the health. You must keep a balance. The Holy Prophet, sallallahu alayhi waalih, would start and finish his meal with a little salt. He would also wash his hands and mouth before and after eating a meal. It is good to have vegetables and vinegar at the table and it is forbidden to take meals on a dining table where wine is served.  It is the right of a guest to a toothpick and that he be accompanied to the door. Using gold and silver utensils for eating and drinking is strictly forbidden. There are many more advices given by the Ahlul Bayt in regards to table manners. Take small morsels and chew them well and do not stare at the faces of others. Feeding the brethren in faith and inviting them to a feast in a very virtuous act. We as Muslims and family members have many parties and get-togethers and feasts on many occasions. It is recommended to give a feast at the following five occasions. Marriage, Aqeeqah, circumcision of one's son, purchasing or constructing a new house and returning home from travels. When it comes to eating and drinking, there are eight types of persons that are worthy of being cursed. A person who joins the meal uninvited, a guest who orders his host, one who expects a good turn from one's enemy, one who hoards his mean and stingy and still expects to be obliged by others, one who interferes when two people are having a talk or a secret, one who does not give due respect to those in authority, one who sits in the company of those not worthy of, and finally one who talks with a person and does not give due attention to his words. Drinking water is an essential part of our lives without which only survive three days. Imam al-Sadr says cold water lessens temperature, cures vomiting, digest food and is an anecdote for hot heat. And also stated that boiled water is good for all types of pains and is not harmful in any way. It is said that water from the Zamzam, rain water and the water of the Euphrates carry many benefits and excellences. It is recommended to drink water standing during the day and sitting at night, saying Bismillah before and Alhamdulillah after drinking something. One should drink his water slowly and do not fill your mouth with it. Also do not gulp water in one single drop. Instead drink it in three stages minimum. While drinking water it is recommended to recite the following supplication. Salawatullah al-Husayn wa ala ahli baytih wa ala ashabih. Wa la'natullah ala qatali wa a'da'ih. May Allah shower his blessings on Imam Hussain, peace be upon him, the grandson of the holy prophet and his ahli al-bayt and his companions. May he also shower his curses on the murderers and the enemies of Imam Hussain, peace be upon him. It is important for a Muslim to always maintain a high level of akhlaq and also carry himself very well when eating and drinking. Make sure you are polite and eloquent in your mannerisms as people are very quick to judge. You don't want to look a mess with food and stains all over yourself.  Ma'asalam and thank you for watching.


You are part of the world's best fighting force that they have seen in 246 years, the United States Marines, the smallest fighting force in the United States, the most lethal fighting force in the United States, a moment to take a deep breath, sweat it out, and think about those Marines who have come before us. You guys earn every minute of tonight's celebration. Happy birthday Marines and enjoy.


From Barcelona, Spain, it's theCUBE, covering Cisco Live 2018. Brought to you by Cisco, Veeam, and theCUBE's ecosystem partners. Barcelona, Spain, it's theCUBE's exclusive coverage of Cisco Live 2018 in Europe. I'm John Furrier, co-host of theCUBE with my partner in crime this week, Stu Miniman, senior analyst at Wikibon, also co-host of many events across the world in terms of networking, storage, cloud, you name is Stu, he's on the developers with me. Stu, thanks for seeing you. Stefan Renner, technical director, Global Alliances at Veeam Software with us, with Darren Williams, Mr. Hyperflex, that's his Twitter handle, go check him out. Hyperflex, lead at Cisco. Guys, welcome to theCUBE. Thank you. I just want to love the Twitter handle. I live the brand. You've got to live the brand. That's got some longevity to it, so it's evergreen. So congratulations on that. You guys are together with Cisco Veeam. What's the story? What's going on in Europe with Cisco and Veeam? Yeah, I would say there is a lot of stuff going on between Cisco and Veeam, especially around the Hyperflex story, obviously sort of topic, topic of this session, right? So having integration, Hyperflex, having, I would say a good go-to market, having really a good relationship between the two companies. And we just choked about how often we've been in front of cameras talking about this exact same topic. So that shows that the relation between the two of us is really moving forward and in a good shape, huh? I think we're in good shape in terms of, you think about not just my product, Hyperflex, but you look at what Veeam can do for the rest of Cisco data center products and be that backup, a safe pair of hands around what we need in terms of that data protection layer, but also then what we can add in terms of that target to be the server of choice for backups so you get the benefits of the speed, performance, and more importantly, you get quicker restores because that's the important bit, you need to be able to do the quicker restores. Yeah, if we talk, you know, at Veeam we usually talk about availability, right? We don't talk about backup, sorry, recovery. Even if recovery is maybe the most important part of availability, still we talk more about availability than about anything else. And as he said, the good thing on Cisco is they actually can deliver what we need in terms of performance, in terms of capacity, in terms of compute resources. So yeah, that's really a benefit. It's such an interesting time. I mean, we look back at history, go back 10 years ago, maybe around or more, backup recovery, it's like, oh yeah, that's like, oh, we forgot to talk about that in our RFP, kind of bolted on, kind of retrofitted in, but now we've seen it come to the main center. But more importantly, with AI and cloud and all the action happening with DevOps on-premises, you hear CIOs and CXOs and developers saying, we're data-driven, okay? So if you're data-driven, you have to be data protection-driven too. So those things go hand in hand. So the question for you guys is, how does a data-driven organization, whether it's in the data center, all the way up to the business units or business processes, become data protection built in? How do they design in from day one, the data protection system up and down the stack? Yeah, so maybe I start to answer that question. I think when I'm going to customers, and I fully agree on what you just said, most customers 10 years ago, we were focusing on getting you with the realization platforms, getting you storage systems. It has been isolated projects, right? Now in those days, when I go to customers, I try to convince them to include data protection every project they do in data center, because at the end, as I said, data protection is one of the core elements. So 10 years back. So designing in early at the front end. Yeah, designing in early, right? So I say whenever you go about having a new hyper flex system, or whenever you talk about replacing your existing history environment, whatever you do, right? Just look into data protection, look into your availability story. Because right now, and you mentioned that, it's about the data services, right? We don't really talk about restoring a VM. We don't restore just a single file. It's about the customer wants to have a data availability in terms of a service availability. And that includes more than just a VM, and that includes more than just a single thing, right? So they need to include data protection and the design of that in the whole project. From the beginning. At your point. Yeah, we look at it from a similar thing in terms of where you've got changes happening in terms of the way people are looking at how they want to design their applications, where they want their data to live. And that's the whole message in around 3.0 is that multi-cloud readiness platform. Being able to think about an application to go, do I want to design in the public and house privately, or vice versa? Do I want to house the data of the application in a private location, and the actual application in the public? Having that being able to be transparent to a user in terms of the way they design it and then position. But also as we look at other applications, not all people on this journey are going to go, we're going to put everything in the cloud. They're going to look about, I'm going to have maybe a little bit in the cloud, a little bit of the traditional apps that we need to manage and protect. And it's all about that 3.0, that we've delivered the pre-multi-cloud offering around hyperconvergence. We've now brought the multi-cloud element. It's given you the choice of where you want to position things, where you want to house things, how you want to design things, and keeping it nice and simple for customers and the agility in performance. Darren, some really interesting points that you just had there. When I think back to a few years ago, hyperconverge, pretty strong in North America, but it was project-based. It was like, let's take a VDI or some virtualized environment. It wasn't a cloud discussion. Take us inside what you're seeing in Europe here, because today, hyperconverge is a lot about cloud, how that kind of hybrid or multi-cloud environment fits. So what do you hear from your customers? And I think if you look at what's happened in terms of hyperconvergence up to this point, it's the initial building block of this multi-cloud. And we're seeing more and more customers now. I think the latest IDC surveys, so in the 87% of all customers have a multi-cloud strategy. And we're seeing now more of the ability to think of hyperconvergence as that multi-cloud strategy and have that simplicity that people have done in terms of the initial thoughts around a simple application, how they can collapse the layers. They can now utilize that experience into the multi-cloud experience. And we're seeing more and more that... So we've now got 2,500 users around the world, around Hyperflex, and about 700 to 800 in Amir. And the majority of those are utilizing it as a private cloud experience. They're getting the benefits of what they've had in the cloud and getting away from the sovereignty issues and the shadow IT issues that they all face. They can now bring it back into their own data center. They can start small. They can spin up applications very quickly. They get the benefit of that cloud message, but locally now, yeah. And I think that perfectly aligns with the beam story because as you know, we are also focusing on the cloud. We recently changed our slogan to any app, any data, any cloud, and also did some acquisitions on the cloud side. So we are also moving forward in the cloud story, in the hyper cloud area. And that's more or less what CISCO multi-cloud story is all about, right? And I think one thing we should also mention here, coming a bit back to how to implement and how to design such solutions. As I said, having a more of a broader view on all the projects, I think one important thing for customers is the CVD CISCO have, right? And we do have CVDs also available to Veeam and CISCO on the data protection layer. So we try to make it really easy for customers and for partners to design, implement, and actually do the right decisions for those projects. Yeah, so Stefan, at Veeamon of course, a lot of partners, a lot of them talk about the multi-cloud. Of course Veeam has a long history of VMware, but what you talk about Microsoft, I believe there's some things you've been doing lately with Hyper-V and the like, what's the update? Yeah, so obviously with HyperFlex 3.0, there is Hyper-V coming, right? That's one of the biggest things coming in HyperFlex. Now for us, when we started to talk with CISCO, when actually CISCO told us that Hyper-V is next in 3.0, we said, you know, that's fine for us because as you said, we were dealing with Hyper-V like we deal with VMware since a couple of years. So there is no big difference in terms of features and what we can do with Hyper-V. Overall on the Microsoft side, obviously it's around Azure Stack, which also is a big story with CISCO and Veeam because there is an Azure Stack solution, so we try to get the Azure Stack fully integrated in the Veeam portfolio and it's about Azure, right? Getting, as we just talked about, making this cloud journey even easier for the customer, making sure we have data protection for Azure, or making sure you can actually use our cloud solutions in Azure to provide the full experience in the cloud. So the question on European audience, just looking at some Twitter tweets here, getting in some feedback, is ask the GDPR question, which basically is code words for the sophistication between data protection, you know, the saying, we say as you get bitten in the butt if you don't prepare. And this is one of those things where, I mean, literally there's so much data out there, people can't even understand their own tables. I mean, if you have accounts, how do I know a user uses, you know, a certain name in this one, I got a certain name in this database? I mean, it's just a nightmare, even understand what data do you have, nevermind taking someone out of a database. So the challenges are massive. This is coming down and it really highlights the bigger trend is, what do I do with the data? What is my protection? What's my recovery? How do I engage in real time? GDPR issue is on HANA, so we'll talk about the GDPR issue and then what it really is going to mean for customers going forward. I think if you think about GDPR and people, I've got the misdemeanor that is just an EMEA thing, it's not, it's a worldwide thing. Any data that relates to a European citizen anywhere in the world is covered under the GDPR. So you've got to think about the multinationals we work with have to have this GDPR thoughts. Even if they're not based in EMEA, they may house data based around the European citizen. So it's a massive thing. Now, not one person or one organization can fix GDPR. We're all part of a bigger framework. So it looks like if you look at the Hyperflex offering, having self-encrypting drives, having good data protection and replication of the data, so it's protected, that protects the actual content of a record, but it doesn't solve everything around GDPR. There's no one organization that can do that. It's about having that framework of you do the right decisions around the architecture and the data protection, you'll get in there in terms of the protection. Yeah. Well, I mean, I'm just going to rant here and just say whoever came up with GDPR doesn't do anything about databases, okay? Yeah.  Absolutely. And so, yeah, just hand waving, hey, someone came to your website, well, did they come in anonymously? Did they log in? Which identity did they log in on? Is there's no seamless identity? I mean, it's a nightmare. This is a huge problem. Yeah. What do customers do? I think, you know, if you talk about GDPR, it's first of all not about a single solution, right? It's not an issue of just one company or one vendor or one solution. It goes across different databases, different applications, different software. So as you said, it's database solutions you need to delete maybe a single entry, a single table entry, which is almost impossible right now, especially if that's in a backup, right? How are you going to do that? And I think between Cisco and us, and he mentioned that one important part of GDPR is data protection itself, right? So the customers need to make sure they can actually promise and they can show to the government that they have a proper data protection in place so they can showcase, you know, what does my DR plan look like? How do I recover? What is my RTO, my RPO? So we can already solve those issues. Well, it changes your game because for you, it turns you into a insurance policy, to a proactive- Kind of. Because in order to do data protection, you actually have to know what the data is. So it kind of creates an opportunity to say, hey, this is an opportunity to say, we're going to start thinking about kind of a new e-discovery model. Yeah. And I was going to say, in terms of you, if you look at 3.0, the multi-cloud platform, we were discussing around how hyperconvergence started very small in certain apps. But when you actually then expand that out into the multi-cloud, security's a major pillar. And you've got to have the security elements and Cisco have some great security offerings in the data center and outside of the data center. They're all form part of that GDPR message, but it's been baked into multi-cloud 3.0 as a key component to allow customers that confidence. It's going to be a hyperconvergence of databases. So this is coming. Yeah. I mean, this is going to force, I think the compliance is going to be more of a shot across the bow, if you will. I mean, I don't know how hardcore they're going to be enforcing it, but... It's going to be interesting the first one. Because at the moment, I think a lot of customers are thinking, well, we'll wait till we see how big the fines are, and then we'll decide. They're going to create shell corporations on the Cayman Islands with... Yeah. All right, so we've talked a little bit about some of the headwinds that we're facing in IT. Talk about the tailwinds. A lot of things in the Hyperflex 3.0, we've got 700 or 800 customers. What's going to drive adoption, get that into thousands of customers here in 2018? So I think it's the simplicity message. Customers want ease of use of technology. They want to get away from what they've had before, where they've had tough times standing up applications, where they've had to invest time around different skill sets for the infrastructure, be it networking, be it storage, be it compute, having three teams battling against each other and change windows. So the simplicity message of Hyperflex is, you can have a three node cluster up and run in 34 minutes, including the network. We're the only ones that incorporate the network into the solution, and we do it for good reason, because we can get predictability and performance, and we can grow the solution very, very easily. And that's the whole point of what they're doing is, they want to be able to start small and add more nodes when required around what applications they're going to deploy on. We see, our tagline is, any application anywhere now, in either a private location or into that multi-cloud location. Gives customers choice, and I think, as we start seeing more and more customers, 700 in just under two years is a phenomenal amount in Amir and 2,500 worldwide. We've had some great traction, and it's just going to get faster and faster, yeah. Yeah, I think a lot of customers are obviously talking about moving to the cloud completely or at least the majority of the data. So for the customers that stay on-premise for them, and I talked about with some customers actually today, and they told me, no, for us right now, we can't focus any more on a data center itself. We do have much, much more difficult and more important topics to talk about and to cover in our IT business than the basic data center itself, right? That includes compute, that includes utilization. So it's great to hear you can actually set up a hyper-flex system, no matter if that's hyper-V or VMware, whatever, in less than an hour, right? And if I tell you now that if you add Veeam on that, on top of that to provide the availability for just installed hyper-flex environment, that's also less than an hour, right? So if you know how to configure that, you can be done in a couple of hours and you have more or less a whole data center setup. Bring up a really good point. What are customers concerned about? I have to worry about my application portfolio. I have my security issue, my whole cloud strategy piece. So if the infrastructure piece is just invisible and I don't have to touch it, tweak it and do that, I'm going to have time to actually grow my business. Yeah, yeah. The more integrated it is, the more easy it is to set up and to maintain and troubleshoot, by the way, that's also a important thing, right? What if it doesn't work? So if there is a consistent layer, a consistent way to get all this information and to get troubleshooting thing done, the barrier is for our customers because again, they don't want to care anymore what's happening in the back of this. And that's the next challenge we're addressing around our product or insight. It's taking that management solution into the cloud to make things easier for customers and being able to take a lot of the things we have in point products into a cloud model. So the likes of analytics, the likes of smart tack, not customers get fed up when if they have an issue, they have to go and roll the logs up into tack and then go and FTP them. They get away from that. They don't need to do that in insight. And it's all about, we're talking about the deployment of technology. Well, one of the first benefits of insight is hyperflex. We can roll out sites without even visiting them. You just do a cloud deployment and a cloud management and it's job done. And this is the whole point we were kind of getting at early connect back to the compliance issue. These agile like things are happening, it's throwing off data too. So now you got to organize the data. You can't protect what you don't understand. Correct. I mean, that is ultimately the bottom line for what's happening. Yeah, yeah. You can't protect what you don't understand. I think that's a good conclusion of the whole thing. And I think for us... By the way, when you guys use that tagline I want royalty. But it's true. I'm going to notice that. We'll get back to you on that. No, this is the big problem. Protection is inherently assuming you know where the data is. Yeah. Yeah, there it is. That's for sure the case. And one thing we were gone and, you know, we announced that a couple of months ago is to be mobility orchestrator, which is another layer on top of it. So he just talked about how they can deploy within the site, multiple sites of hyperflex very easily, right? And for us it's about, you know, getting the customer an easy solution all this disaster recovery and failover scenarios across the data centers with the availability orchestrator. Data is the competitive advantage. Data is messy. If you don't control it, reign it in. Of course theCUBE's doing their part in bringing the data to you guys here in theCUBE with Veeam and Cisco partnership. I'm John Furrier, Stu Miniman, breaking down here at Cisco Live in Europe 2018. Live coverage with theCUBE. We'll be back with more after the short break.


Hi everyone, Joe for jaspy'scasewrakes.com. We've got 2021 hip parade football. It's a jaspy exclusive. 10 bucks, pick your team number eight, a random team break. Pretty awesome stuff here. We curated this product with hip parade. They help us secure some product, put it all together and all that sort of stuff. So big thanks to them for doing that. Take advantage of this jaspy exclusive. We have basketball edition as well. Now there's a couple of things we need to do. First of all, we need to give away that early bird spot before we do the break. We've got another hip parade case up, same format as we're doing here. We're gonna sell 18 spots and all 18 of those spots have a chance at an extra spot, the early bird spot. The other 13 remaining spots we've put into a filler. So that you can win a full spot for a fraction of the price, which would be awesome, I think. So check that out, give that a go. All right, now for the rest of, for this group here, let's give away an extra spot. Let's see who gets the worm. The early bird gets the worm. So thanks for getting your spot straight up.  Two for the price of one after 10 times. Six and a four. One, two, three, four, five, six, five, six, seven, eight, nine, and good luck. 10th and final time, name on top after 10 is Ryan Heights. There you go, Ryan. After 10 times, you are the early bird that gets the delicious, delicious worm. Now, and I guess Steven Schwartz technically has last spot mojo here. Let's put that star, last spot mojo, star next to his name. They say, Steven, according to legend, 70% of the time, last spot mojo hits 100% of the time. Let's see if that works out. All right, now let's roll it. Randomize names and teams 10 times again. Four and a six, 10 times. One, two, three, four, five, six, seven, eight, nine and 10th and final time. After 10, we got Chris down to Bennett. Four and a six, 10 times for the teams. One, two, three, four, five, six and a four, right? Yeah, seven, eight, nine and 10th and final time. After 10, we got the Buccaneers down to the Texans.  Let's alphabetize by team by column B, and I'm gonna pause the video. When we come back, we're gonna see if there's any trades and then we'll have this hip parade break. Stick around, BRB. All right, welcome back, ladies and gentlemen. There was a lot of trade chatter, but in the end, no deals were done, so that list right here remains the same. Thanks everyone for getting in, appreciate it. So there's Ryan's early bird spot. If you have a rooftop next to your name, that means you won that spot in a filler. If you have that star next to your name, that means last spot mojo. The JASPY science team says that 70% of the time that hits 100% of the time. He hits here, packed in. Good luck. You can see the rest of the boxes on the top camera right here. First one out of the gate. Yeah, Matt Gray, thank you. You've been waiting patiently for this break to fill, and this is his first time breaker here with us. So good luck. The new guy mojo, the new person mojo does exist, so good luck, Matt. If you're keeping track at home, Matthew Gray has the saints. He also tried to trade them too. There is a tried to trade, couldn't trade mojo. All right, first out of the gate. Oh, we were just talking cheese steaks in Philadelphia. Here is Philadelphia's Devonta Smith, three-color patch and autograph, 16 out of 20 from certified football. Nice, this dynamic playmaker going to Jimmy and the Iggles. Ross is saying he was in Philadelphia. Hi, Ross, for the holidays. He's saying Angela's Pizza, greater than any cheese steak. Next one, and we've got another one of these in the store, folks. Thesecasebreaks.com. Next up is Jonathan Taylor. What a season he's having. Prism Black rookie autograph, 62 out of 99. Colts, Patrick, who won the spot in the filler, got randomized, the Colts gets a nice Jonathan Taylor. Next up, good luck, everybody, is Ray Lewis, one out of 10. Is it two colors? Should we give that two-color patch? An autograph, an impeccable football. What time is it? Game time. Patrick with the Ravens. I always say I wish there was like a alarm clock app, where Ray Lewis yells at you and says, what time is it? Wake up time. That would get me up in the morning pretty quickly. All right, no worry. Oh, you must be Patrick, no worries. Easy. All right, next up is a Ram, Aaron Donald, 23 out of 25, Dominator's autograph. You don't see a lot of Aaron Donald autographs. He's pretty good. He's all right. Los Angeles Rams, Ryan Heintz with the spot that he won. Got randomized, the Rams gets the nice Aaron Donald. Rams trying to get their season back on track after dropping a few in a row. I'm at Cowboys at Saints, Thursday Night Football, coming up a little bit later tonight. Well, here's our first graded card. Beckett, grade nine. And that's raided rookie, Tua Tungovaloa. I think he's been looking all right too. 2020 Donner's Optic Football, graded at nine. We've got Centering Edge's corners are all at nine, five, just the surface at eight, five. 270 out of 299. Nice one for Miami, JC with the Dolphins. All right, next five. We've got another case loaded up in the store. If you're interested in running this fact tonight, jaskyscasebreaks.com. I'd love to do another one of these. Ooh, another graded card, feels like a PSA slab, yeah. PSA, it's a bird team. It is Russell Wilson, wow. PSA 10 from 2012, Panini Score Football. Rookie Russell Wilson. Seahawks struggling a little bit too, but this is still a nice card. And a really nice grade. PSA 10 is definitely a matter. Seahawks, that's for Carlos in the Seahawks. He won that spot and won that spot. Got randomized to Seahawks, ended up with a nice hit. All right, next up. Ross, let me answer that question after I finish up this break. Next up is Josh Jacobs. Tried to trade, couldn't trade Mojo from 2019 Panini Contenders Optic Football, blue parallel, nine, 10. It looks like it couldn't be done. And we've got Contenders Optic Basketball on the website. Ladies and gentlemen, if you want to run into that, 47 out of 75, centering in eight five corners and serve as a nine five edges of 10. Nice one for Tyler, who bought a spot straight up, got randomized My Raiders. Tried to trade, couldn't trade Mojo. All right, three boxes to go. And it's a Phillip Rivers, one out of five. Got a piece of the bolt right there. Phillip Rivers autograph right over there. There he is from 2019 Panini one. Hope I can see the one sticker right here as well. And who's got the Chargers, Barry Roberts, out of fives and under, get the train whistle, all aboard the Big Hit Express. Woo-woo! Two more to go, fingers crossed everybody. And like I said, don't sleep on this hip parade, Jaspy exclusive, a lot of nice hits in here. Let's get into the next one. All right, next up is, he had a nice game last weekend, T. Higgins, 11 out of 15. I think Jamar Chase is getting a lot of that number one cornerback attention. And now T. Higgins took advantage of that last weekend. I feel like Ryan Hines, Ron Cincinnati, rookie T. Higgins. Nice auto. And the last one, good luck everybody. Thanks for getting in. Remember you do have another opportunity to get in if you'd like. You can run this back pretty easily tonight. All right, last one is graded, feels like a, it is a PSA slab, what do we have here? We have a, nice. 79 out of 100, scripted in time, Larry Fitzgerald autograph. PSA 10, rookie Larry Fitzgerald, nice. And that'll go to David, David Lee and the Arizona football Cardinals. What a great break, ladies and gentlemen. Thanks again, everyone for getting in. I appreciate it. Again, like I said, we've got more in the store. I'd love to do more in the store. We also have a dual case basketball group break as well. All of that is on jaspyscasebreaks.com. Get into it and let's make some more happen tonight. I'm Joe. I'll see you next time for the next football break. Bye-bye.


From theCUBE Studios in Palo Alto in Boston, 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, there's really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Cree and body is here as the CEO and founder of H2O. Cree, 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 thoughts 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 relentlessly saving lives across the country and the world. And whatever we're 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, we'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 diagnoses, machines in some cases are helping doctors make diagnoses. They're sometimes making even better diagnosis, at least in forming. There's been a lot of talk about the models, you know, how the, I know you've been working with a lot of healthcare organizations. You may probably familiar with that, you know, the medium posts, the hammer and the dance and if people criticize the models, of course they're just models, right? 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 machine intelligence informing the models, improving sort of what we know about this pandemic? I mean, it's changed literally daily. What are you seeing? Yeah, and I think it started with Wuhan and we saw the best application of AI trying to trace literally from Alipay to WeChat, tracked down the first folks who are 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. In the prime center is hospital staffing. How many ventilators? It's the first few weeks of the COVID crisis as it evolved in the US. We're busy predicting working with some of the local healthcare communities to predict how many hospital staffing in hospitals will work, how many PPE and ventilators will be needed and so on so forth. But that quickly, and when the peak surge will be just with the beginning problems and many of our customers are beginning to do these models and iterate and improve and kind of educate the community to do practice social distancing. And that led to a lot of flattening the curve and you're talking about 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 an semblance of order to the reaction to COVID, I think what we are beginning to figure out is there going to be a second surge? What elective procedures that are 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 surge in 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, the other side you saw the likes of Zoom and online 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, and obviously these things, 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 fight it out. So who are some of the people that you're working with that as a result of COVID-19, what's some of the work that H2O has done? I want to better understand what role you're playing. So one of the things we are kind of privileged as a company to come into the crisis with a strong balance and an ability to actually and 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 form of Kaggle grandmasters 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 by 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 have left the city, which places did they go to? And what's it 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 started 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 and testing to customers in healthcare who are trying to predict where the search is likely to come. But it's mostly about information, right? So the AI is at the end of it is all about intelligence and being prepared, predictive is all about being prepared. And then that's kind of what we did with the general, lots of 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 backtesting 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, it sounds like two, just in terms of the surge and the comeback, kind of what those models say based upon, we have some advanced information coming from the global market for sure, but it seems like every situation's 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 calm down a little bit.  What is the data telling you at this point in time without understanding that 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 the county I'm in, is different from San Francisco, right? So the 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 the US. You see the likes of Brazil, which we were 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 of the search 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 we 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 later this month. So I think that's a very, very local response, hyper-local response. No one country and no one community has a symmetrical with other ones. And I think we have a unique situation where in the 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 take Norway and apply it, or South Korea and apply it. Every situation is different. And then I want to ask you about the economy in terms of, how much can AI actually, how can it work in this situation where you have, for example, okay, so the Fed, yes, it started doing asset buys back in 2008, but still very hard to predict. I mean, at the time of this interview, the stock market's up 900 points. It's been very, very difficult to predict that. 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, it's sort of continuously learning containers. That's where the vision of the world is headed towards where data is meaning you build a model and then 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 into 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 found. And so upskilling and trying to kind of bring back these jobs back, both into kind of the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airline slash hotel industries. So trying to now bring back the sense of confidence and we'll take a lot more kind of testing, a lot more masking, a lot more social empathy. I think 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 pervade 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 doubly shaped pieces. But governments are trying very strong, very, very well. We're seeing stimulus dollars being pumped through banks. So some of the use cases we're looking for banks is which small, medium business, especially in unsecured lending, which business to lend to, and there's so many applications to that have come to banks across the world. It's not just in the U.S. And banks are caught up with the problem of which, what's going concern for this business to kind of, are they really accurate about the number of employees they are saying they have to then the next level problem around forbearance and mortgage outside of the things that 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 was far go, 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. We'll 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 historical comparisons and current comparison. Yeah, that's great. I think the last time we spoke, probably 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've essentially built a platform called Driverless AI, which is a licensed 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, which essentially allows them to build models without necessarily having the same level of talent as these great tackle grandmasters. And so that has democratized the 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 large organization, also across small organizations which don't have the access to the same kind of talent. Now, third level, what we 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 licensed software. And to give you an idea, Dave, from a small standpoint, now majority of our software sales is coming from closed-source software. 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, the open-source model was Red Hat, the one company that succeeded wildly and it was put it out there open-source, come up with a service to maintain the software. You got to buy the subscription, okay, fine. And everybody thought that you were going to do that. They thought that's what Databricks was going to do and that changed. But I want to take two examples. Hortonworks, which kind of took the Red Hat model in cloud era, which has 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 licensed software model, Databricks with a managed service. And so it's becoming clearer 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 closed-source platform I mentioned, we opened up the layers above in open-source as recipes. So for example, different companies build their zip codes differently, right? So 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 SAS 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 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 Red Hat model and the others 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. We were one of the first or second.ai domain and for us to see thousands of companies which are .ai and AI first and even more companies adopting AI and talking about AI as a major wave that was possible because of open source. If you had chosen closed source and many of our 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 fast and fast rise up to become unicorns. In that race, you're essentially sacrificed 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 it's much more difficult sales machine to scale. Instead of our open source business actually is reasonably positive with our 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 rate limiting step. And that's why we chose and also explaining to the investors how open source needs to be invested in as you go closer to the IPO markets. That's where we chose, let's go into licensed software model and scale that as a regular business. So I've said a few times, it's kind of ironic that this pandemic hit, this is 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 should 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 say just, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. 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 conversations are digital. And AI is eating software. I mean, a lot of cloud transitions are happening and they 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-first, data-driven applications that use AI. Instead of rule-based software, you're beginning to see pattern recognition, AI-based software, and you're seeing that in space. And of course, that is just the tip of the iceberg. AI has been with us for a hundred years and it's going to be ahead of us another hundred years, right? So as you see, the discovery rate at which, it is really fundamentally a math movement. And in that math movement at the beginning of every century, it leads to a hundred years of phenomenal discovery. So AI is essentially making discoveries faster. AI is producing entertainment, AI is producing music, AI is producing choreography. We're seeing AI in every walk of life. AI summarization of Zoom meetings, you're beginning to see a lot of the AI-enabled ETF picking of stocks. Actually, if you're beginning to see, we reprise 20,000 bonds every 15 seconds using H2 AI, corporate bonds. 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 a hospital looking at how the second half will look like to for physicians looking at what is the sentiment of what is the surge to expect to kind of what is the amount of money looking at the sentiment of the customers. AI is the ultimate money ball you can pay in business. And 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 invert every piece of software, every application or those tools going to have much use. Three, 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. Thank you guys, 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.