>>Hi everybody. This is Dave Volante. Welcome to this cube conversation where we're going to go back in time a little bit and explore the early days of Kubernetes. Talk about how it formed the improbable events, perhaps that led to it. And maybe how customers are taking advantage of containers and container orchestration today, and maybe where the industry is going. Matt Provo is here. He's the founder and CEO of storm forge and Chandler Huntington hoes. Hoisington is the general manager of EKS edge and hybrid AWS guys. Thanks for coming on. Good to see you. Thanks for having me. Thanks. So, Jenny, you were the vice president of engineering at miso sphere. Is that, is that correct? >>Well, uh, vice-president engineering basis, fear and then I ran product and engineering for DTQ masons. >>Yeah. Okay. Okay. So you were there in the early days of, of container orchestration and Matt, you, you were working at a S a S a Docker swarm shop, right? Yep. Okay. So I mean, a lot of people were, you know, using your platform was pretty novel at the time. Uh, it was, it was more sophisticated than what was happening with, with Kubernetes. Take us back. What was it like then? Did you guys, I mean, everybody was coming out. I remember there was, I think there was one Docker con and everybody was coming, the Kubernetes was announced, and then you guys were there, doc Docker swarm was, was announced and there were probably three or four other startups doing kind of container orchestration. And what, what were those days like? Yeah. >>Yeah. I wasn't actually atmosphere for those days, but I know them well, I know the story as well. Um, uh, I came right as we started to pivot towards Kubernetes there, but, um, it's a really interesting story. I mean, obviously they did a documentary on it and, uh, you know, people can watch that. It's pretty good. But, um, I think that, from my perspective, it was, it was really interesting how this happened. You had basically, uh, con you had this advent of containers coming out, right? So, so there's new novel technology and Solomon, and these folks started saying, Hey, you know, wait a second, wait if I put a UX around these couple of Linux features that got launched a couple of years ago, what does that look like? Oh, this is pretty cool. Um, so you have containers starting to crop up. And at the same time you had folks like ThoughtWorks and other kind of thought leaders in the space, uh, starting to talk about microservices and saying, Hey, monoliths are bad and you should break up these monoliths into smaller pieces. >>And any Greenfield application should be broken up into individuals, scalable units that a team can can own by themselves, and they can scale independent of each other. And you can write tests against them independently of other components. And you should break up these big, big mandalas. And now we are kind of going back to model this, but that's for another day. Um, so, so you had microservices coming out and then you also had containers coming out, same time. So there was like, oh, we need to put these microservices in something perfect. We'll put them in containers. And so at that point, you don't really, before that moment, you didn't really need container orchestration. You could just run a workload in a container and be done with it, right? You didn't need, you don't need Kubernetes to run Docker. Um, but all of a sudden you had tons and tons of containers and you had to manage these in some way. >>And so that's where container orchestration came, came from. And, and Ben Heineman, the founder of Mesa was actually helping schedule spark at the time at Berkeley. Um, and that was one of the first workloads with spark for Macy's. And then his friends at Twitter said, Hey, come over, can you help us do this with containers at Twitter? He said, okay. So when it helped them do it with containers at Twitter, and that's kinda how that branch of the container wars was started. And, um, you know, it was really, really great technology and it actually is still in production in a lot of shops today. Um, uh, more and more people are moving towards Kubernetes and Mesa sphere saw that trend. And at the end of the day, Mesa sphere was less concerned about, even though they named the company Mesa sphere, they were less concerned about helping customers with Mesa specifically. They really want to help customers with these distributed problems. And so it didn't make sense to, to just do Mesa. So they would took on Kubernetes as well. And I hope >>I don't do that. I remember, uh, my, my co-founder John furrier introduced me to Jerry Chen way back when Jerry is his first, uh, uh, VC investment with Greylock was Docker. And we were talking in these very, obviously very excited about it. And, and his Chandler was just saying, it said Solomon and the team simplified, you know, containers, you know, simple and brilliant. All right. So you guys saw the opportunity where you were Docker swarm shop. Why? Because you needed, you know, more sophisticated capabilities. Yeah. But then you, you switched why the switch, what was happening? What was the mindset back then? We ran >>And into some scale challenges in kind of operationalize or, or productizing our kind of our core machine learning. And, you know, we, we, we saw kind of the, the challenges, luckily a bit ahead of our time. And, um, we happen to have someone on the team that was also kind of moonlighting, uh, as one of the, the original core contributors to Kubernetes. And so as this sort of shift was taking place, um, we, we S we saw the flexibility, uh, of what was becoming Kubernetes. Um, and, uh, I'll never forget. I left on a Friday and came back on a Monday and we had lifted and shifted, uh, to Kubernetes. Uh, the challenge was, um, you know, you, at that time, you, you didn't have what you have today through EKS. And, uh, those kinds of services were, um, just getting that first cluster up and running was, was super, super difficult, even in a small environment. >>And so I remember we, you know, we, we finally got it up and running and it was like, nobody touch it, don't do anything. Uh, but obviously that doesn't, that doesn't scale either. And so that's really, you know, being kind of a data science focused shop at storm forge from the very beginning. And that's where our core IP is. Uh, our, our team looked at that problem. And then we looked at, okay, there are a bunch of parameters and ways that I can tune this application. And, uh, why are the configurations set the way that they are? And, you know, uh, is there room to explore? And that's really where, unfortunately, >>Because Mesa said much greater enterprise capabilities as the Docker swarm, at least they were heading in that direction, but you still saw that Kubernetes was, was attractive because even though it didn't have all the security features and enterprise features, because it was just so simple. I remember Jen Goldberg who was at Google at the time saying, no, we were focused on keeping it simple and we're going from mass adoption, but does that kind of what you said? >>Yeah. And we made a bet, honestly. Uh, we saw that the, uh, you know, the growing community was really starting to, you know, we had a little bit of an inside view because we had, we had someone that was very much in the, in the original part, but you also saw the, the tool chain itself start to, uh, start to come into place right. A little bit. And it's still hardening now, but, um, yeah, we, as any, uh, as any startup does, we, we made a pivot and we made a bet and, uh, this, this one paid off >>Well, it's interesting because, you know, we said at the time, I mean, you had, obviously Amazon invented the modern cloud. You know, Microsoft has the advantage of has got this huge software stays, Hey, just now run it into the cloud. Okay, great. So they had their entry point. Google didn't have an entry point. This is kind of a hail Mary against Amazon. And, and I, I wrote a piece, you know, the improbable, Verizon, who Kubernetes to become the O S you know, the cloud, but, but I asked, did it make sense for Google to do that? And it never made any money off of it, but I would argue they, they were kind of, they'd be irrelevant if they didn't have, they hadn't done that yet, but it didn't really hurt. It certainly didn't hurt Amazon EKS. And you do containers and your customers you've embraced it. Right. I mean, I, I don't know what it was like early days. I remember I've have talked to Amazon people about this. It's like, okay, we saw it and then talk to customers, what are they doing? Right. That's kind of what the mindset is, right? Yeah. >>That's, I, I, you know, I've, I've been at Amazon a couple of years now, and you hear the stories of all we're customer obsessed. We listened to our customers like, okay, okay. We have our company values, too. You get told them. And when you're, uh, when you get first hired in the first day, and you never really think about them again, but Amazon, that really is preached every day. It really is. Um, uh, and that we really do listen to our customers. So when customers start asking for communities, we said, okay, when we built it for them. So, I mean, it's, it's really that simple. Um, and, and we also, it's not as simple as just building them a Kubernetes service. Amazon has a big commitment now to start, you know, getting involved more in the community and working with folks like storm forage and, and really listening to customers and what they want. And they want us working with folks like storm florigen and that, and that's why we're doing things like this. So, well, >>It's interesting, because of course, everybody looks at the ecosystem, says, oh, Amazon's going to kill the ecosystem. And then we saw an article the other day in, um, I think it was CRN, did an article, great job by Amazon PR, but talk about snowflake and Amazon's relationship. And I've said many times snowflake probably drives more than any other ISV out there. And so, yeah, maybe the Redshift guys might not love snowflake, but Amazon in general, you know, they're doing great three things. And I remember Andy Jassy said to me, one time, look, we love the ecosystem. We need the ecosystem. They have to innovate too. If they don't, you know, keep pace, you know, they're going to be in trouble. So that's actually a healthy kind of a dynamic, I mean, as an ecosystem partner, how do you, >>Well, I'll go back to one thing without the work that Google did to open source Kubernetes, a storm forge wouldn't exist, but without the effort that AWS and, and EKS in particular, um, provides and opens up for, for developers to, to innovate and to continue, continue kind of operationalizing the shift to Kubernetes, um, you know, we wouldn't have nearly the opportunity that we do to actually listen to them as well, listen to the users and be able to say, w w w what do you want, right. Our entire reason for existence comes from asking users, like, how painful is this process? Uh, like how much confidence do you have in the, you know, out of the box, defaults that ship with your, you know, with your database or whatever it is. And, uh, and, and how much do you love, uh, manually tuning your application? >>And, and, uh, obviously nobody's said, I love that. And so I think as that ecosystem comes together and continues expanding, um, it's just, it opens up a huge opportunity, uh, not only for existing, you know, EKS and, uh, AWS users to continue innovating, but for companies like storm forge, to be able to provide that opportunity for them as well. And, and that's pretty powerful. So I think without a lot of the moves they've made, um, you know, th the door wouldn't be nearly as open for companies like, who are, you know, growing quickly, but are smaller to be able to, you know, to exist. >>Well, and I was saying earlier that, that you've, you're in, I wrote about this, you're going to get better capabilities. You're clearly seeing that cluster management we've talked about better, better automation, security, the whole shift left movement. Um, so obviously there's a lot of momentum right now for Kubernetes. When you think about bare metal servers and storage, and then you had VM virtualization, VMware really, and then containers, and then Kubernetes as another abstraction, I would expect we're not at the end of the road here. Uh, what's next? Is there another abstraction layer that you would think is coming? Yeah, >>I mean, w for awhile, it looked like, and I remember even with our like board members and some of our investors said, well, you know, well, what about serverless? And, you know, what's the next Kubernetes and nothing, we, as much as I love Kubernetes, um, which I do, and we do, um, nothing about what we particularly do. We are purpose built for Kubernetes, but from a core kind of machine learning and problem solving standpoint, um, we could apply this elsewhere, uh, if we went that direction and so time will tell what will be next, then there will be something, uh, you know, that will end up, you know, expanding beyond Kubernetes at some point. Um, but, you know, I think, um, without knowing what that is, you know, our job is to, to, to serve our, you know, to serve our customers and serve our users in the way that they are asking for that. >>Well, serverless obviously is exploding when you look again, and we tucked the ETR survey data, when you look at, at the services within Amazon and other cloud providers, you know, the functions off, off the charts. Uh, so that's kind of an interesting and notable now, of course, you've got Chandler, you've got edge in your title. You've got hybrid in, in your title. So, you know, this notion of the cloud expanding, it's not just a set of remote services, just only in the public cloud. Now it's, it's coming to on premises. You actually got Andy, Jesse, my head space. He said, one time we just look at it. The data centers is another edge location. Right. Okay. That's a way to look at it and then you've got edge. Um, so that cloud is expanding, isn't it? The definition of cloud is, is, is evolving. >>Yeah, that's right. I mean, customers one-on-one run workloads in lots of places. Um, and that's why we have things like, you know, local zones and wavelengths and outposts and EKS anywhere, um, EKS, distro, and obviously probably lots more things to come. And there's, I always think of like, Amazon's Kubernetes strategy on a manageability scale. We're on one far end of the spectrum, you have EKS distro, which is just a collection of the core Kubernetes packages. And you could, you could take those and stand them up yourself in a broom closet, in a, in a retail shop. And then on the other far in the spectrum, you have EKS far gate where you can just give us your container and we'll handle everything for you. Um, and then we kind of tried to solve everything in between for your data center and for the cloud. And so you can, you can really ask Amazon, I want you to manage my control plane. I want you to manage this much of my worker nodes, et cetera. And oh, I actually want help on prem. And so we're just trying to listen to customers and solve their problems where they're asking us to solve them. Cut, >>Go ahead. No, I would just add that in a more vertically focused, uh, kind of orientation for us. Like we, we believe that op you know, optimization capabilities should transcend the location itself. And, and, and so whether that's part public part, private cloud, you know, that's what I love part of what I love about EKS anywhere. Uh, it, you know, you shouldn't, you should still be able to achieve optimal results that connect to your business objectives, uh, wherever those workloads, uh, are, are living >>Well, don't wince. So John and I coined this term called Supercloud and people laugh about it, but it's different. It's, it's, you know, people talk about multi-cloud, but that was just really kind of vendor diversity. Right? I got to running here, I'm running their money anywhere. Uh, but, but individually, and so Supercloud is this concept of this abstraction layer that floats wherever you are, whether it's on prem, across clouds, and you're taking advantage of those native primitives, um, and then hiding that underlying complexity. And that's what, w re-invent the ecosystem was so excited and they didn't call it super cloud. We, we, we called it that, but they're clearly thinking differently about the value that they can add on top of Goldman Sachs. Right. That to me is an example of a Supercloud they're taking their on-prem data and their, their, their software tooling connecting it to AWS. They're running it on AWS, but they're, they're abstracting that complexity. And I think you're going to see a lot, a lot more of that. >>Yeah. So Kubernetes itself, in many cases is being abstracted away. Yeah. There's a disability of a disappearing act for Kubernetes. And I don't mean that in a, you know, in an, a, from an adoption standpoint, but, uh, you know, Kubernetes itself is increasingly being abstracted away, which I think is, is actually super interesting. Yeah. >>Um, communities doesn't really do anything for a company. Like we run Kubernetes, like, how does that help your bottom line? That at the end of the day, like companies don't care that they're running Kubernetes, they're trying to solve a problem, which is the, I need to be able to deploy my applications. I need to be able to scale them easily. I need to be able to update them easily. And those are the things they're trying to solve. So if you can give them some other way to do that, I'm sure you know, that that's what they want. It's not like, uh, you know, uh, a big bank is making more money because they're running Kubernetes. That's not, that's not the current, >>It gets subsumed. It's just become invisible. Right. Exactly. You guys back to the office yet. What's, uh, what's the situation, >>You know, I, I work for my house and I, you know, we go into the office a couple of times a week, so it's, it's, uh, yeah, it's, it's, it's a crazy time. It's a crazy time to be managing and hiring. And, um, you know, it's, it's, it's, it's definitely a challenge, but there's a lot of benefits of working home. I got two young kids, so I get to see them, uh, grow up a little bit more working, working out of my house. So it's >>Nice also. >>So we're in, even as a smaller startup, we're in 26, 27 states, uh, Canada, Germany, we've got a little bit of presence in Japan, so we're very much distributed. Um, we, uh, have not gone back and I'm not sure we will >>Permanently remote potentially. >>Yeah. I mean, w we made a, uh, pretty like for us, the timing of our series B funding, which was where we started hiring a lot, uh, was just before COVID started really picking up. So we, you know, thankfully made a, a pretty good strategic decision to say, we're going to go where the talent is. And yeah, it was harder to find for sure, especially in w we're competing, it's incredibly competitive. Uh, but yeah, we've, it was a good decision for us. Um, we are very about, you know, getting the teams together in person, you know, as often as possible and in the safest way possible, obviously. Um, but you know, it's been a, it's been a pretty interesting, uh, journey for us and something that I'm, I'm not sure I would, I would change to be honest with you. Yeah. >>Well, Frank Slootman, snowflakes HQ to Montana, and then can folks like Michael Dell saying, Hey, same thing as you, wherever they want to work, bring yourself and wherever you are as cool. And do you think that the hybrid mode for your team is kind of the, the, the operating mode for the, for the foreseeable future is a couple of, >>No, I think, I think there's a lot of benefits in both working from the office. I don't think you can deny like the face-to-face interactions. It feels good just doing this interview face to face. Right. And I can see your mouth move. So it's like, there's a lot of benefits to that, um, over a chime call or a zoom call or whatever, you know, that, that also has advantages, right. I mean, you can be more focused at home. And I think some version of hybrid is probably in the industry's future. I don't know what Amazon's exact plans are. That's above my pay grade, but, um, I know that like in general, the industry is definitely moving to some kind of hybrid model. And like Matt said, getting people I'm a big fan at Mesa sphere, we ran a very diverse, like remote workforce. We had a big office in Germany, but we'd get everybody together a couple of times a year for engineering week or, or something like this. And you'd get a hundred people, you know, just dedicated to spending time together at a hotel and, you know, Vegas or Hamburg or wherever. And it's a really good time. And I think that's a good model. >>Yeah. And I think just more ETR data, the current thinking now is that, uh, the hybrid is the number one sort of model, uh, 36% that the CIO is believe 36% of the workforce are going to be hybrid permanently is kind of their, their call a couple of days in a couple of days out. Um, and the, the percentage that is remote is significantly higher. It probably, you know, high twenties, whereas historically it's probably 15%. Yeah. So permanent changes. And that, that changes the infrastructure. You need to support it, the security models and everything, you know, how you communicate. So >>When COVID, you know, really started hitting and in 2020, um, the big banks for example, had to, I mean, you would want to talk about innovation and ability to, to shift quickly. Two of the bigger banks that have in, uh, in fact, adopted Kubernetes, uh, were able to shift pretty quickly, you know, systems and things that were, you know, historically, you know, it was in the office all the time. And some of that's obviously shifted back to a certain degree, but that ability, it was pretty remarkable actually to see that, uh, take place for some of the larger banks and others that are operating in super regulated environments. I mean, we saw that in government agencies and stuff as well. >>Well, without the cloud, no, this never would've happened. Yeah. >>And I think it's funny. I remember some of the more old school manager thing people are, aren't gonna work less when they're working from home, they're gonna be distracted. I think you're seeing the opposite where people are too much, they get burned out because you're just running your computer all day. And so I think that we're learning, I think everyone, the whole industry is learning. Like, what does it mean to work from home really? And, uh, it's, it's a fascinating thing is as a case study, we're all a part of right now. >>I was talking to my wife last night about this, and she's very thoughtful. And she w when she was in the workforce, she was at a PR firm and a guy came in a guest speaker and it might even be in the CEO of the company asking, you know, what, on average, what time who stays at the office until, you know, who leaves by five o'clock, you know, a few hands up, or who stays until like eight o'clock, you know, and enhancement. And then, so he, and he asked those people, like, why, why can't you get your work done in a, in an eight hour Workday? I go home. Why don't you go in? And I sit there. Well, that's interesting, you know, cause he's always looking at me like, why can't you do, you know, get it done? And I'm saying the world has changed. Yeah. It really has where people are just on all the time. I'm not sure it's sustainable, quite frankly. I mean, I think that we have to, you know, as organizations think about, and I see companies doing it, you guys probably do as well, you know, take a four day, you know, a week weekend, um, just for your head. Um, but it's, there's no playbook. >>Yeah. Like I said, we're a part of a case study. It's also hard because people are distributed now. So you have your meetings on the east coast, you can wake up at seven four, and then you have meetings on the west coast. You stay until seven o'clock therefore, so your day just stretches out. So you've got to manage this. And I think we're, I think we'll figure it out. I mean, we're good at figuring this stuff. >>There's a rise in asynchronous communication. So with things like slack and other tools, as, as helpful as they are in many cases, it's a, it, isn't always on mentality. And like, people look for that little green dot and you know, if you're on the you're online. So my kids, uh, you know, we have a term now for me, cause my office at home is upstairs and I'll come down. And if it's, if it's during the day, they'll say, oh dad, you're going for a walk and talk, you know, which is like, it was my way of getting away from the desk, getting away from zoom. And like, you know, even in Boston, uh, you know, getting outside, trying to at least, you know, get a little exercise or walk and get, you know, get my head away from the computer screen. Um, but even then it's often like, oh, I'll get a slack notification on my phone or someone will call me even if it's not a scheduled walk and talk. Um, uh, and so it is an interesting, >>A lot of ways to get in touch or productivity is presumably going to go through the roof. But now, all right, guys, I'll let you go. Thanks so much for coming to the cube. Really appreciate it. And thank you for watching this cube conversation. This is Dave Alante and we'll see you next time.

hi everyone my name is alia özkan I'm the data operations data ops global marketing leader at IBM we are all going through challenging times especially the industry we cannot control what we cannot but there are still things that we can control there are two things that we can do if unless you are on the front lines the first thing is social distancing it's a personal accountability the second thing is skills improvement we can do improve our skills for today and tomorrow whether whether that is new normal or the normal iBM is helping you to improve your skills by bringing you IBM's biggest events think for free virtually it is starting on May 5th and 6:00 we will be covering areas such as IT resiliency business continuity Casas risk management digital transformation journey to AI multi cloud environments and how we are all connected with the digital platforms hope you can join us to sharpen your skills for sure you will need it more and more today and tomorrow please please stay safe stay well thank you

hi everybody thank you for joining us today for the virtual Vertica BBC 2020 today's breakout session is entitled Vertica and en mode at the trade desk my name is su LeClair director of marketing at Vertica and I'll be your host for this webinar joining me is Ron Cormier senior Vertica database engineer at the trade desk before we begin I encourage you to submit questions or comments during the virtual session you don't have to wait just type your question or comment in the question box below the slides and click submit there will be a Q&A session at the end of the presentation we'll answer as many questions as we're able to during that time any questions that we don't address we'll do our best to answer them offline alternatively you can visit vertical forums to post your questions there after the session our engineering team is planning to join the forums to keep the conversation going also a quick reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide and yes this virtual session is being recorded and will be available to view on demand this week we'll send you a notification as soon as it's ready so let's get started over to you run thanks - before I get started I'll just mention that my slide template was created before social distancing was a thing so hopefully some of the images will harken us back to a time when we could actually all be in the same room but with that I want to get started uh the date before I get started in thinking about the technology I just wanted to cover my background real quick because I think it's peach to where we're coming from with vertically on at the trade desk and I'll start out just by pointing out that prior to my time in the trade desk I was a tech consultant at HP HP America and so I traveled the world working with Vertica customers helping them configure install tune set up their verdict and databases and get them working properly so I've seen the biggest and the smallest implementations and everything in between and and so now I'm actually principal database engineer straight desk and and the reason I mentioned this is to let you know that I'm a practitioner I'm working with with the product every day or most days this is a marketing material so hopefully the the technical details in this presentation are are helpful I work with Vertica of course and that is most relative or relevant to our ETL and reporting stack and so what we're doing is we're taking about the data in the Vertica and running reports for our customers and we're an ad tech so I did want to just briefly describe what what that means and how it affects our implementation so I'm not going to cover the all the details of this slide but basically I want to point out that the trade desk is a DSP it's a demand-side provider and so we place ads on behalf of our customers or agencies and ad agencies and their customers that are advertised as brands themselves and the ads get placed on to websites and mobile applications and anywhere anywhere digital advertising happens so publishers are what we think ocean like we see here espn.com msn.com and so on and so every time a user goes to one of these sites or one of these digital places and an auction takes place and what people are bidding on is the privilege of showing and add one or more ads to users and so this is this is really important because it helps fund the internet ads can be annoying sometimes but they actually help help are incredibly helpful in how we get much much of our content and this is happening in real time at very high volumes so on the open Internet there is anywhere from seven to thirteen million auctions happening every second of those seven to thirteen million auctions happening every second the trade desk bids on hundreds of thousands per second um so that gives it and anytime we did we have an event that ends up in Vertica that's that's one of the main drivers of our data volume and certainly other events make their way into Vertica as well but that wanted to give you a sense of the scale of the data and sort of how it's impacting or how it is impacted by sort of real real people in the world so um the uh let's let's take a little bit more into the workload and and we have the three B's in spades late like many many people listening to a massive volume velocity and variety in terms of the data sizes I've got some information here some stats on on the raw data sizes that we deal with on a daily basis per day so we ingest 85 terabytes of raw data per day and then once we get it into Vertica we do some transformations we do matching which is like joins basically and we do some aggregation group buys to reduce the data and make it clean it up make it so it's more efficient to consume buy our reporting layer so that matching in aggregation produces about ten new terabytes of raw data per day it all comes from the it all comes from the data that was ingested but it's new data and so that's so it is reduced quite a bit but it's still pretty pretty high high volume and so we have this aggregated data that we then run reports on on behalf of our customers so we have about 40,000 reports per day oh that's probably that's actually a little bit old and older number it's probably closer to 50 or 55,000 reports per day at this point so it's I think probably a pretty common use case for for Vertica customers it's maybe a little different in the sense that most of the reports themselves are >> reports so they're not it's not a user sitting at a keyboard waiting for the result basically we have we we have a workflow where we do the ingest we do this transform and then and then once once all the data is available for a day we run reports on behalf of our customer to let me have our customers on that that daily data and then we send the reports out you via email or we drop them in a shared location and then they they look at the reports at some later point of time so it's up until yawn we did all this work on on enterprise Vertica at our peak we had four production enterprise clusters each which held two petabytes of raw data and I'll give you some details on on how those enterprise clusters were configured in the hardware but before I do that I want to talk about the reporting workload specifically so the the reporting workload is particularly lumpy and what I mean by that is there's a bunch of work that becomes available bunch of queries that we need to run in a short period of time after after the days just an aggregation is completed and then the clusters are relatively quiet for the remaining portion of the day that's not to say they are they're not doing anything as far as read workload but they certainly are but it's much less reactivity after that big spike so what I'm showing here is our reporting queue and the spike is is when all those reports become a bit sort of ailable to be processed we can't we can't process we can't run the report until we've done the full ingest and matching and aggregation for the day and so right around 1:00 or 2:00 a.m. UTC time every day that's when we get this spike and the spike we affectionately called the UTC hump but basically it's a huge number of queries that need to be processed sort of as soon as possible and we have service levels that dictate what as soon as possible means but I think the spike illustrates our use case pretty pretty accurately and um it really as we'll see it's really well suited for pervert icky on and we'll see what that means so we've got our we had our enterprise clusters that I mentioned earlier and just to give you some details on what they look like there they were independent and mirrored and so what that means is all four clusters held the same data and we did this intentionally because we wanted to be able to run our report anywhere we so so we've got this big queue over port is big a number of reports that need to be run and we've got these we started we started with one cluster and then we got we found that it couldn't keep up so we added a second and we found the number of reports went up that we needed to run that short period of time and and so on so we eventually ended up with four Enterprise clusters basically with this with the and we'd say they were mirrored they all had the same data they weren't however synchronized they were independent and so basically we would run the the tailpipe line so to speak we would run ingest and the matching and the aggregation on all the clusters in parallel so they it wasn't as if each cluster proceeded to the next step in sync with which dump the other clusters they were run independently so it was sort of like each each cluster would eventually get get consistent and so this this worked pretty well for for us but it created some imbalances and there was some cost concerns that will dig into but just to tell you about each of these each of these clusters they each had 50 nodes they had 72 logical CPU cores a half half a terabyte of RAM a bunch of raid rated disk drives and 2 petabytes of raw data as I stated before so pretty big beefy nodes that are physical physical nodes that we held we had in our data centers we actually reached these nodes so so it was on our data center providers data centers and the these were these these were what we built our business on basically but there was a number of challenges that we ran into as we as we continue to build our business and add data and add workload and and the first one is is some in ceremony can relate to his capacity planning so we had to prove think about the future and try to predict the amount of work that was going to need to be done and how much hardware we were going to need to satisfy that work to meet that demand and that's that's just generally a hard thing to do it's very difficult to verdict the future as we can probably all attest to and how much the world has changed and even in the last month so it's a it's a very difficult thing to do to look six twelve eighteen eighteen months into the future and sort of get it right and and and what people what we tended to do is we reach or we tried to our art plans our estimates were very conservative so we overbought in a lot of cases and not only that we had to plan for the peak so we're planning for that that that point in time that those number of hours in the early morning when we had to we had all those reports to run and so that so so we ended up buying a lot of hardware and we actually sort of overbought at times and then and then as the hardware were days it would kind of come into it would come into maturity and we have our our our workload would sort of come approach matching the demand so that was one of the big challenges the next challenge is that we were running on disk you can we wanted to add data in sort of two dimensions the only dimensions that everybody can think about we wanted to add more columns to our big aggregates and we wanted to keep our big aggregates for for longer periods of time so both horizontally and vertically we wanted to expand the datasets but we basically were running out of disk there was no more disk in and it's hard to add a disc to Vertica in enterprise mode not not impossible but certainly hard and and one cannot add discs without adding compute because enterprise mode the disk is all local to each of the nodes for most most people you can do not exchange with sands and other external rays but that's there are a number of other challenges with that so um adding in order to add disk we had to add compute and that basically meant kept us out of balance we're adding more compute than we needed for the amount of disk so that was the problem certainly physical nodes getting them the order delivered racked cables even before we even start such Vertica there's lead times there and and so it's also long commitment since we like I mentioned me Lisa hardware so we were committing to these nodes these physical servers for two or three years at a time and I mentioned that can be a hard thing to do but we wanted to least to keep our capex down so we wanted to keep our aggregates for a long period of time we could have done crazy things or more exotic things to to help us with this if we had to in enterprise mode we could have started to like daisy chain clusters together and that would have been sort of a non-trivial engineering effort because we would need to then figure out how to migrate data source first to recharge the data across all the clusters and we had to migrate data from one cluster to another cluster hesitation and we would have to think about how to aggregate run queries across clusters so if you assured data set spans two clusters it would have had to sort of aggregated within each cluster maybe and then build something on top the aggregated the data from each of those clusters so not impossible things but certainly not easy things and luckily for us we started talking about two Vertica about separation of compute and storage and I know other customers were talking to Vertica as we were people had had these problems and so Vertica inyeon mode came to the rescue and what I want to do is just talk about nyan mode really briefly for for those in the audience who aren't familiar but it's basically Vertigo's answered to the separation of computing storage it allows one to scale compute and or storage separately and and this there's a number of advantages to doing that whereas in the old enterprise days when you add a compute you added stores and vice-versa now we can now we can add one or the other or both according to how we want to and so really briefly how this works this slide this figure was taken directly from the verdict and documentation and so just just to talk really briefly about how it works the taking advantage of the cloud and so in this case Amazon Web Services the elasticity in the cloud and basically we've got you seen two instances so elastic cloud compute servers that access data that's in an s3 bucket and so three three ec2 nodes and in a bucket or the the blue objects in this diagram and the difference is a couple of a couple of big differences one the data no longer the persistent storage of the data the data where the data lives is no longer on each of the notes the persistent stores of the data is in s3 bucket and so what that does is it basically solves one of our first big problems which is we were running out of disk the s3 has for all intensive purposes infinite storage so we can keep much more data there and that mostly solved one of our big problems so the persistent data lives on s3 now what happens is when a query runs it runs on one of the three nodes that you see here and assuming we'll talk about depo in a second but what happens in a brand new cluster where it's just just spun up the hardware is the query will will run on those ec2 nodes but there will be no data so those nodes will reach out to s3 and run the query on remote storage so that so the query that the nodes are literally reaching out to the communal storage for the data and processing it entirely without using any data on on the nodes themselves and so that that that works pretty well it's not as fast as if the data was local to the nodes but um what Vertica did is they built a caching layer on on each of the node and that's what the depot represents so the depot is some amount of disk that is relatively local to the ec2 node and so when the query runs on remote stores on the on the s3 data it then queues up the data for download to the nodes and so the data will get will reside in the Depot so that the next query or the subsequent subsequent queries can run on local storage instead of remote stores and that speeds things up quite a bit so that that's that's what the role of the Depot is the depot is basically a caching layer and we'll talk about the details of how we can see your in our Depot the other thing that I want to point out is that since this is the cloud another problem that helps us solve is the concurrency problem so you can imagine that these three nodes are one sort of cluster and what we can do is we can spit up another three nodes and have it point to the same s3 communal storage bucket so now we've got six nodes pointing to the same data but we've you isolated each of the three nodes so that they act as if they are their own cluster and so vertical calls them sub-clusters so we've got two sub clusters each of which has three nodes and what this has essentially done it is it doubled the concurrency doubled the number of queries that can run at any given time because we've now got this new place which new this new chunk of compute which which can answer queries and so that has given us the ability to add concurrency much faster and I'll point out that for since it's cloud and and there are on-demand pricing models we can have significant savings because when a sub cluster is not needed we can stop it and we pay almost nothing for it so that's that's really really important really helpful especially for our workload which I pointed out before was so lumpy so those hours of the day when it's relatively quiet I can go and stop a bunch of sub clusters and and I will pay for them so that that yields nice cost savings let's be on in a nutshell obviously engineers and the documentation can use a lot more information and I'm happy to field questions later on as well but I want to talk about how how we implemented beyond at the trade desk and so I'll start on the left hand side at the top the the what we're representing here is some clusters so there's some cluster 0 r e t l sub cluster and it is a our primary sub cluster so when you get into the world of eon there's primary Club questions and secondary sub classes and it has to do with quorum so primary sub clusters are the sub clusters that we always expect to be up and running and they they contribute to quorum they decide whether there's enough instances number a number of enough nodes to have the database start up and so these this is where we run our ETL workload which is the ingest the match in the aggregate part of the work that I talked about earlier so these nodes are always up and running because our ETL pipeline is always on we're internet ad tech company like I mentioned and so we're constantly getting costly running ad and there's always data flowing into the system and the matching is happening in the aggregation so that part happens 24/7 and we wanted so that those nodes will always be up and running and we need this we need that those process needs to be super efficient and so what that is reflected in our instance type so each of our sub clusters is sixty four nodes we'll talk about how we came at that number but the infant type for the ETL sub cluster the primary subclusters is I 3x large so that is one of the instance types that has quite a bit of nvme stores attached and we'll talk about that but on 32 cores 240 four gigs of ram on each node and and that what that allows us to do I should have put the amount of nvme but I think it's seven terabytes for anything me storage what that allows us to do is to basically ensure that our ETL everything that this sub cluster does is always in Depot and so that that makes sure that it's always fast now when we get to the secondary subclusters these are as mentioned secondary so they can stop and start and it won't affect the cluster going up or down so they're they're sort of independent and we've got four what we call Rhian subclusters and and they're not read by definition or technically they're not read only any any sub cluster can ingest and create your data within the database and that'll all get that'll all get pushed to the s3 bucket but logically for us they're read only like these we just most of these the work that they happen to do is read only which it is which is nice because if it's read only it doesn't need to worry about commits and we let we let the primary subclusters or ETL so close to worry about committing data and we don't have to we don't have to have the all nodes in the database participating in transaction commits so we've got a for read subclusters and we've got one EP also cluster so a total of five sub clusters each so plus they're running sixty-four nodes so that gives us a 320 node database all things counted and not all those nodes are up at the same time as I mentioned but often often for big chunks of the days most of the read nodes are down but they do all spin up during our during our busy time so for the reading so clusters we've got I three for Excel so again the I three incidents family type which has nvme stores these notes have I think three and a half terabytes of nvme per node we just rate it to nvme drives we raid zero them together and 16 cores 122 gigs of ram so these are smaller you'll notice but it works out well for us because the the read workload is is typically dealing with much smaller data sets than then the ingest or the aggregation workbook so we can we can run these workloads on on smaller instances and leave a little bit of money and get more granularity with how many sub clusters are stopped and started at any given time the nvme doesn't persist the data on it isn't persisted remember you stop and start this is an important detail but it's okay because the depot does a pretty good job in that in that algorithm where it pulls data in that's recently used and the that gets pushed out a victim is the data that's least reasons use so it was used a long time ago so it's probably not going to be used to get so we've got um five sub-clusters and we have actually got to two of those so we've got a 320 node cluster in u.s. East and a 320 node cluster in u.s. West so we've got a high availability region diversity so and their peers like I talked about before they're they're independent but but yours they are each run 128 shards and and so with that what that which shards are is basically the it's similar to segmentation when you take those dataset you divide it into chunks and though and each sub cluster can concede want the data set in its entirety and so each sub cluster is dealing with 128 shards it shows 128 because it'll give us even distribution of the data on 64 node subclusters 60 120 might evenly by 64 and so there's so there's no data skew and and we chose 128 because the sort of ginger proof in case we wanted to double the size of any of the questions we can double the number of notes and we still have no excuse the data would be distributed evenly the disk what we've done is so we've got a couple of raid arrays we've got an EBS based array that they're catalog uses so the catalog storage location and I think we take for for EBS volumes and raid 0 them together and come up with 128 gigabyte Drive and we wanted an EPS for the catalog because it we can stop and start nodes and that data will persist it will come back when the node comes up so we don't have to run a bunch of configuration when the node starts up basically the node starts it automatically joins the cluster and and very strongly there after it starts processing work let's catalog and EBS now the nvme is another raid zero as I mess with this data and is ephemeral so let me stop and start it goes away but basically we take 512 gigabytes of the nvme and we give it to the data temp storage location and then we take whatever is remaining and give it to the depot and since the ETL and the reading clusters are different instance types they the depot is is side differently but otherwise it's the same across small clusters also it all adds up what what we have is now we we stopped the purging data for some of our big a grits we added bunch more columns and what basically we at this point we have 8 petabytes of raw data in each Jian cluster and it is obviously about 4 times what we can hold in our enterprise classes and we can continue to add to this maybe we need to add compute maybe we don't but the the amount of data that can can be held there against can obviously grow much more we've also built in auto scaling tool or service that basically monitors the queue that I showed you earlier monitors for those spikes I want to see as low spikes it then goes and starts up instances one sub-collector any of the sub clusters so that's that's how that's how we we have compute match the capacity match that's the demand also point out that we actually have one sub cluster is a specialized nodes it doesn't actually it's not strictly a customer reports sub clusters so we had this this tool called planner which basically optimizes ad campaigns for for our customers and we built it it runs on Vertica uses data and Vertica runs vertical queries and it was it was wildly successful um so we wanted to have some dedicated compute and beyond witty on it made it really easy to basically spin up one of these sub clusters or new sub cluster and say here you go planner team do what you want you can you can completely maximize the resources on these nodes and it won't affect any of the other operations that were doing the ingest the matching the aggregation or the reports up so it gave us a great deal of flexibility and agility which is super helpful so the question is has it been worth it and without a doubt the answer is yes we're doing things that we never could have done before sort of with reasonable cost we have lots more data specialized nodes and more agility but how do you quantify that because I don't want to try to quantify it for you guys but it's difficult because each eon we still have some enterprise nodes by the way cost as you have two of them but we also have these Eon clusters and so they're there they're running different workloads the aggregation is different the ingest is running more on eon does the number of nodes is different the hardware is different so there are significant differences between enterprise and and beyond and when we combine them together to do the entire workload but eon is definitely doing the majority of the workload it has most of the data it has data that goes is much older so it handles the the heavy heavy lifting now the query performance is more anecdotal still but basically when the data is in the Depot the query performance is very similar to enterprise quite close when the data is not in Depot and it needs to run our remote storage the the query performance is is is not as good it can be multiples it's not an order not orders of magnitude worse but certainly multiple the amount of time that it takes to run on enterprise but the good news is after the data downloads those young clusters quickly catch up as the cache populates there of cost I'd love to be able to tell you that we're running to X the number of reports or things are finishing 8x faster but it's not that simple as you Iran is that you it is me I seem to have gotten to thank you you hear me okay I can hear you now yeah we're still recording but that's fine we can edit this so if I'm just talking to the person the support person he will extend our recording time so if you want to maybe pick back up from the beginning of the slide and then we'll just edit out this this quiet period that we have sir okay great I'm going to go back on mute and why don't you just go back to the previous slide and then come into this one again and I'll make sure that I tell the person who yep perfect and then we'll continue from there is that okay yeah sound good all right all right I'm going back on yet so the question is has it been worth it and for us the answer has been a resounding yes we're doing things that we never could have done at reasonable cost before and we got more data we've got this Y note this law has nodes and in work we're much more agile so how to quantify that um well it's not quite as simple and straightforward as you might hope I mean we still have enterprise clusters we've got to update the the four that we had at peak so we've still got two of those around and we got our two yawn clusters but they're running different workloads and they're comprised of entirely different hardware the dependence has I've covered the number of nodes is different for sub-clusters so 64 versus 50 is going to have different performance the the workload itself the aggregation is aggregating more columns on yon because that's where we have disk available the queries themselves are different they're running more more queries on more intensive data intensive queries on yon because that's where the data is available so in a sense it is Jian is doing the heavy lifting for the cluster for our workload in terms of query performance still a little anecdotal but like when the queries that run on the enterprise cluster the performance matches that of the enterprise cluster quite closely when the data is in the Depot when the data is not in a Depot and Vertica has to go out to the f32 to get the data performance degrades as you might expect it can but it depends on the curious all things like counts counts are is really fast but if you need lots of the data from the material others to realize lots of columns that can run slower I'm not orders of magnitude slower but certainly multiple of the amount of time in terms of costs anecdotal will give a little bit more quantifying here so what I try to do is I try to figure out multiply it out if I wanted to run the entire workload on enterprise and I wanted to run the entire workload on e on with all the data we have today all the queries everything and to try to get it to the Apple tab so for enterprise the the and estimate that we do need approximately 18,000 cores CPU cores all together and that's a big number but that's doesn't even cover all the non-trivial engineering work that would need to be required that I kind of referenced earlier things like starting the data among multiple clusters migrating the data from one culture to another the daisy chain type stuff so that's that's the data point now for eon is to run the entire workload estimate we need about twenty thousand four hundred and eighty CPU cores so more CPU cores uh then then enterprise however about half of those and partly ten thousand of both CPU cores would only run for about six hours per day and so with the on demand and elasticity of the cloud that that is a huge advantage and so we are definitely moving as fast as we can to being on all Aeon we have we have time left on our contract with the enterprise clusters or not we're not able to get rid of them quite yet but Eon is certainly the way of the future for us I also want to point out that uh I mean yawn is we found to be the most efficient MPP database on the market and what that refers to is for a given dollar of spend of cost we get the most from that zone we get the most out of Vertica for that dollar compared to other cloud and MPP database platforms so our business is really happy with what we've been able to deliver with Yan Yan has also given us the ability to begin a new use case which is probably this case is probably pretty familiar to folks on the call where it's UI based so we'll have a website that our customers can log into and on that website they'll be able to run reports on queries through the website and have that run directly on a separate row to get beyond cluster and so much more latent latency sensitive and concurrency sensitive so the workflow that I've described up until this point has been pretty steady throughout the day and then we get our spike and then and then it goes back to normal for the rest of the day this workload it will be potentially more variable we don't know exactly when our engineers are going to deliver some huge feature that is going to make a 1-1 make a lot of people want to log into the website and check how their campaigns are doing so we but Yohn really helps us with this because we can add a capacity so easily we cannot compute and we can add so we can scale that up and down as needed and it allows us to match the concurrency so beyond the concurrency is much more variable we don't need a big long lead time so we're really excited about about this so last slide here I just want to leave you with some things to think about if you're about to embark or getting started on your journey with vertically on one of the things that you'll have to think about is the no account in the shard count so they're kind of tightly coupled the node count we determined by figuring like spinning up some instances in a single sub cluster and getting performance smaller to finding an acceptable performance considering current workload future workload for the queries that we had when we started and so we went with 64 we wanted to you want to certainly want to increase over 50 but we didn't want to have them be too big because of course it costs money and so what you like to do things in power to so 64 nodes and then the shard count for the shards again is like the data segmentation is a new type of segmentation on the data and the start out we went with 128 it began the reason is so that we could have no skew but you know could process the same same amount of data and we wanted to future-proof it so that's probably it's probably a nice general recommendation doubleness account for the nodes the instance type and and how much people space those are certainly things you're going to consider like I was talking about we went for they I three for Excel I 3/8 Excel because they offer good good Depot stores which gives us a really consistent good performance and it is all in Depot the pretty good mud presentation and some information on on I think we're going to use our r5 or the are for instance types for for our UI cluster so much less the data smaller so much less enter this on Depot so we don't need on that nvm you stores the reader we're going to want to have a reserved a mix of reserved and on-demand instances if you're if you're 24/7 shop like we are like so our ETL subclusters those are reserved instances because we know we're going to run those 24 hours a day 365 days a year so there's no advantage of having them be on-demand on demand cost more than reserve so we get cost savings on on figuring out what we're going to run and have keep running and it's the read subclusters that are for the most part on on demand we have one of our each sub Buster's is actually on 24/7 because we keep it up for ad-hoc queries your analyst queries that we don't know when exactly they're going to hit and they want to be able to continue working whenever they want to in terms of the initial data load the initial data ingest what we had to do and now how it works till today is you've got to basically load all your data from scratch there isn't a great tooling just yet for data populate or moving from enterprise to Aeon so what we did is we exported all the data in our enterprise cluster into park' files and put those out on s3 and then we ingested them into into our first Eon cluster so it's kind of a pain we script it out a bunch of stuff obviously but they worked and the good news is that once you do that like the second yon cluster is just a bucket copy in it and so there's tools missions that can help help with that you're going to want to manage your fetches and addiction so this is the data that's in the cache is what I'm referring to here the data that's in the default and so like I talked about we have our ETL cluster which has the most recent data that's just an injected and the most difficult data that's been aggregated so this really recent data so we wouldn't want anybody logging into that ETL cluster and running queries on big aggregates to go back one three years because that would invalidate the cache the depot would start pulling in that historical data and it was our assessing that historical data and evicting the recent data which would slow things out flow down that ETL pipelines so we didn't want that so we need to make sure that users whether their service accounts or human users are connecting to the right phone cluster and I mean we just created the adventure users with IPS and target groups to palm those pretty-pretty it was definitely something to think about lastly if you're like us and you're going to want to stop and start nodes you're going to have to have a service that does that for you we're where we built this very simple tool that basically monitors the queue and stops and starts subclusters accordingly we're hoping that that we can work with Vertica to have it be a little bit more driven by the cloud configuration itself so for us all amazon and we love it if we could have it have a scale with the with the with the eight of us can take through points do things to watch out for when when you're working with Eon is the first is system table queries on storage layer or metadata and the thing to be careful of is that the storage layer metadata is replicated it's caught as a copy for each of the sub clusters that are out there so we have the ETL sub cluster and our resources so for each of the five sub clusters there is a copy of all the data in storage containers system table all the data and partitions system table so when you want to use this new system tables for analyzing how much data you have or any other analysis make sure that you filter your query with a node name and so for us the node name is less than or equal to 64 because each of our sub clusters at 64 so we limit we limit the nodes to the to the 64 et 64 node ETL collector otherwise if we didn't have this filter we would get 5x the values for counts and some sort of stuff and lastly there is a problem that we're kind of working on and thinking about is a DC table data for sub clusters that are our stops when when the instances stopped literally the operating system is down and there's no way to access it so it takes the DC table DC table data with it and so I cannot after after my so close to scale up in the morning and then they scale down I can't run DC table queries on how what performed well and where and that sort of stuff because it's local to those nodes so we're working on something so something to be aware of and we're working on a solution or an implementation to try to suck that data out of all the notes you can those read only knows that stop and start all the time and bring it in to some other kind of repository perhaps another vertical cluster so that we can run analysis and monitoring even you want those those are down that's it um thanks for taking the time to look into my presentation really do it thank you Ron that was a tremendous amount of information thank you for sharing that with everyone um we have some questions come in that I would like to present to you Ron if you have a couple min it your first let's jump right in the first one a loading 85 terabytes per day of data is pretty significant amount what format does that data come in and what does that load process look like yeah a great question so the format is a tab separated files that are Jesus compressed and the reason for that could basically historical we don't have much tabs in our data and this is how how the data gets compressed and moved off of our our bidders the things that generate most of this data so it's a PSD gzip compressed and how you kind of we kind of have how we load it I would say we have actually kind of a Cadillac loader in a couple of different perspectives one is um we've got this autist raishin layer that's homegrown managing the logs is the data that gets loaded into Vertica and so we accumulate data and then we take we take some some files and we push them to redistribute them along the ETL nodes in the cluster and so we're literally pushing the file to through the nodes and we then run a copy statement to to ingest data in the database and then we remove the file from from the nodes themselves and so it's a little bit extra data movement which you may think about changing in the future assisting we move more and more to be on well the really nice thing about this especially for for the enterprise clusters is that the copy' statements are really fast and so we the coffee statements use memory but let's pick any other query but the performance of the cautery statement is really sensitive to the amount of available memory and so since the data is local to the nodes literally in the data directory that I referenced earlier it can access that data from the nvme stores and the kabhi statement runs very fast and then that memory is available to do something else and so we pay a little bit of cost in terms of latency and in terms of downloading the data to the nose we might as we move more and more PC on we might start ingesting it directly from s3 not copying the nodes first we'll see about that what's there that's how that's how we read the data interesting works great thanks Ron um another question what was the biggest challenge you found when migrating from on-prem to AWS uh yeah so um a couple of things that come to mind the first was the baculum the data load it was kind of a pain I mean like I referenced in that last slide only because I mean we didn't have tools built to do this so I mean we had to script some stuff out and it wasn't overly complex but yes it's just a lot of data to move I mean even with starting with with two petabytes so making sure that there there is no missed data no gaps making and moving it from the enterprise cluster so what we did is we exported it to the local disk on the enterprise buses and we then we push this history and then we ingested it in ze on again Allspark X oh so it's a lot of days to move around and I mean we have to you have to take an outage at some point stop loading data while we do that final kiss-up phase and so that was that was a challenge a sort of a one-time challenge the other saying that I mean we've been dealing with a week not that we're dealing with but with his challenge was is I mean it's relatively you can still throw totally new product for vertical and so we are big advantages of beyond is allow us to stop and start nodes and recently Vertica has gotten quite good at stopping in part starting nodes for a while there it was it was it took a really long time to start to Noah back up and it could be invasive but we worked with with the engineering team with Yan Zi and others to really really reduce that and now it's not really an issue that we think that we think too much about hey thanks towards the end of the presentation you had said that you've got 128 shards but you have your some clusters are usually around 64 nodes and you had talked about a ratio of two to one why is that and if you were to do it again would you use 128 shards ah good question so that is a reference the reason why is because we wanted to future professionals so basically we wanted to make sure that the number of stars was evenly divisible by the number of nodes and you could I could have done that was 64 I could have done that with 128 or any other multiple entities for but we went with 128 is to try to protect ourselves in the future so that if we wanted to double the number of nodes in the ECL phone cluster specifically we could have done that so that was double from 64 to 128 and then each node would have happened just one chart that it had would have to deal with so so no skew um the second part of question if I had to do it if I had to do it over again I think I would have done I think I would have stuck with 128 we still have I mean so we either running this cluster for more than 18 months now I think especially in USC and we haven't needed to increase the number of nodes so in that sense like it's been a little bit extra overhead having more shards but it gives us the peace of mind that we can easily double that and not have to worry about it so I think I think everyone is a nice place to start and you may even consider a three to one or four to one if if you're if you're expecting really rapid growth that you were just getting started with you on and your business and your gates that's a small now but what you expect to have them grow up significantly less powerful green thank you Ron that's with all the questions that we have out there for today if you do have others please feel free to send them in and we will get back to you and we'll respond directly via email and again our engineers will be available on the vertical forums where you can continue the discussion with them there I want to thank Ron for the great presentation and also the audience for your participation in questions please note that a replay of today's event and a copy of the slides will be available on demand shortly and of course we invite you to share this information with your colleagues as well again thank you and this concludes this webinar and have a great day you

>>Live from Denver, Colorado. It's the cube covering comm vault. Go 2019 brought to you by Combolt. >>Hey, good morning. Welcome to the cubes coverage of combo go 19 I'm Lisa Martin and it was stupid man. Hey Sue. Hey Lisa. Are you ready? I was going to ask you. Yes. Are you ready? >>I believe the statement this morning was, we're born ready. >>We are born ready? Yes. That was a big theme this morning. It's the theme of the event here at con Volvo 19 in Colorado and great parody this morning of all these old video clips of all these actors including the Lego movie stars from saying I'm ready. Even SpongeBob. That one got me, so we had a great day. Yesterday's to love some news came out Monday and Tuesdays a lots of great stuff to talk about. We had there a lot of their C level execs and let a new changes a call yesterday. Really got the vibe of, Hey, this is a new Combalt. >>It's interesting Lisa, because one of the things we've been talking about is the 20 years of pedigree that the company has. This Andre Mirchandani said yet they're doing some new items. I was talking to some of the partners in there like how come metallics like a separate brand, don't you worry about brand spread? We knew a thing about having too many brands on the program so it is the history, the experience, the lessons learned, the war chest as they said of all of the things that have gone wrong over the years and I sure know that from my time living on the vendor side is there's no compression algorithm for all the experience you've had and like, Oh we fixed something in that stays in the code as opposed to there's something brand new might need to work through things over time but metallic a separate brand but leveraging the partnerships and the go to market and the experience of Convolt overall. >>So if you want, my quick take is, you know metallic. I definitely, I think coming out of here is the thing we will be talking the most about their SAS plus model. I want to see how that plays in the marketplace. As I probed Rob, when we interviewed him, customers, when you think about SAS, it should just be, I worry about my data and I get up and running and they said they have a very fast up and running less than 15 minutes. That's great. But some of that optionality that they built in, Oh well I can bring this along or I can add this and do this. It's always worried that a wait, do I have to remember my thing? And as it changes down the road, do I have everything set up right? Those are things that we're trying to get away from when we go to a SAS or cloud model. >>And to your point, another theme of the show has been about operational simplification, not just what Combolt is doing internally to simplify their operations, but what they need to deliver to customers. Customers want simplicity rates. Do we, we talk about that at every show regardless of industry, but there is this, this line, and maybe it's blurring, >>like we talked a lot about blurred lines yesterday of too much choice versus simplification. Where's the line there? >> Yeah and a great point Lisa, so one of the items Sandra Mirchandani said yesterday in his keynote was that blurring the line between primary and secondary storage and I probed him on our interview is Convolt going into the primary storage market with Hedvig. Hedvig has got a, you know, a nice offering, strong IP, good engineering team. I think they want to make sure that customers that have bought head vigor want to keep buying Hedvig we'll do it, but it really, I think two years from now when you look back at is that core IP, how does that get baked into the solution? That's why they bought it. That's where it's going to be there. I don't think we're going to be looking two years from now and saying, Oh wow know Convolt they're going up against all the storage star Walton competing a bit gets HCI and everything. >>They have a strong partnership, so I think I got clarity on that for the most part, even though the messaging will will move over time on that, it will move over time on that. >> That's a good point that the song blurred lines kept popping into my head yesterday as we were talking about that. But one of the things that was clear was when we spoke with Rob Kalusi and about metallic, we spoke with Avinash Lakshman about Hedvig Sanjay as well as Don foster. They're already working on the technical integration of of this solutions and we even spoke with their VP of pricing. So from a customer, from a current Hedvig customer perspective, there is focus on that from Combolt's perspective. It's not just about integrating the technologies and obviously that has to be done really well, but it's also about giving customers that consistency and really for combo kind of a new era of transparency with respect to pricing. >>And another thing we talked about some of that transformation of the channel and Mercer row came on board only a couple of days officially on the job. He's helped a number of companies get ready for multicloud and absolutely we've seen that change in the channel over the last five to 10 years. Know back in his days when he was at VM world at VMware there the channel was, Oh my gosh, you know, when Amazon wins we all lose and today we understand it as much more nuance there. The channel that is successful partners with the hyperscale cloud environments, they have practices built around it. The office three 65 and Microsoft practices are an area that Convolt in their partners should be able to do well with and the metallic will tie into as well as of course AWS. The 800 pound gorilla in this space will be there. Combolt plays into that and you know, setting the channel up for that next generation with the SAS, with the software and living in a broader multicloud environment is definitely something to watch you a lot of news about the channel, not just from a leadership standpoint but also so metallic for the mid market >>really delivered exclusively through the channel but also the new initiative that they have. And we talked a little bit about this yesterday about going after and really a big focus with global systems integrators on the largest global enterprises. And when we spoke with their GTM chief of staff yesterday along with Mercer with Carmen, what they're doing, cause I said, you know, channel partners, all the channel partners that they work with work with their competitors. So you have to really deliver differentiation and it can't just be about pricing or marketing messaging goes all the way into getting those feet on the street. And that's another area in which we heard yesterday Combolt making strategic improvements on more feet on the street co-selling with partners, really pulling them deeper into enablement and trainings and to them that's one of the key differentiators that they are delivering to their partners. Yeah >>and Lisa, he, we got to speak to a number, a couple of customers we have more coming on today. It's a little bit telling that you know the average customer you talk to, they have five 10 years of experience there. They are excited about some of the new offerings, but as we've said many times metallic, the new Hedvig we want to talk to the new logos that they're going to get on board. That is something that for the partners has been an incentive. There were new incentives put in place to help capture those new logos because as we know, revenue was actually down in the last fiscal year a bit and Convolt feels that they have turned the corner, they're all ready to go. And one other note I'd like to make, the analogy I used last year is we knew a CEO was canoe CEO search was happening, a lot of things were in motion and it's almost as if you were getting the body ready for an organ transplant and you make sure that the antibodies aren't going to reject it. And in conversation with Sanjay, he was very cognizant of that. His background is dev offs and he was a CIO. We went for it, he was the CEO of puppet. So he's going to make things move even faster. And the pace of change of the last nine months is just the beginning of the change. And for the most part I'm not hearing grumbling underneath the customer seem fully on board. The employees are energized and definitely there was good energy last year, but a raise of the enthusiasm this year. >>Well Stu, first of all, you have just been on fire the last two days comparing their CEO transition to getting a body ready for a transplant. It's probably one of the best things I've heard in a long time. That was awesome. But you're right, we've heard a lot of positivity. Cultural change is incredibly difficult. You talked a minute ago about this as a 20 year old company and as we all have all experience and the industries in which we're in, you know, one of the things that's important is, is messaging that experience and talking about the things that that worked well, but also the things that didn't work well, that they've learned from that message was carried through the keynote this morning. That three customers on stage that we saw before we had to come to the side. And I, I had, my favorite was from Sonic healthcare. Matthew McCabe's coming on in shortly with us and I always appreciate, you know, I think the voice of the customer is the best brand validation that you can get. However, what's even better is a customer talking about when the technologies that they're using fail because it does happen. How are they positioned with the support and the training and the education that is giving them to make those repairs quickly to ensure business continuity and ensure disaster recovery. I think that to me that speaks volumes about the legacy, the 20 years of experience that combo has. >>Yeah, no, Lisa, you're absolutely right. There's certain products out there that we talk about uptime in 100% in this space. You, I believe the stat was about 94% success rate and we had NASA in the keynote yesterday talking about success versus partial success versus failures and Convolt really embraces that and has customers that we'll talk about that because there are times that things will happen and there are things that you need to be able to recover from ransomware. Often it is not a question of if, when it is going to be happened, at least. The other thing I want to get your comment on Jimmy chin who is the director and one of the, the cameraman of the free solo Oscar-winning free solo documentary definitely gave me a little bit of, Oh my gosh, look at some of the Heights and I was nervous just looking at some of this stuff they're doing. I like a little bit of lightweight hiking. I'm not a mountain climber, nothing like that. But he talked about when the camera goes on, there's that added pressure that goes on and it's sitting there. It's like, yeah, you know, we sit here live all day doing that. There's that, that energy to perform. But you know, we all appreciate the everybody watching and understanding that we're all human here and every time, every once in awhile a word or a mistake gets in there, but we keep going summit. Yeah, >>that's life. But also Jimmy chin, phenomenal. I think at 2018 they just won the Oscar just earlier this year for free. Solo. I have to watch that this weekend. But a couple of things that he talked about is that failure is a huge part of preparation. Couldn't agree more. What a simplified statement for somebody that not only has has skied Everest, the climbed Meru, I think they call it the shark fin of India, but what you talked about with what he documented with free solo and all of the thousands of sequences and he talked about that, Alex, I'm forgetting his last name, the guy who closed, who free soloed, El Capitan, all of these different failure scenarios that he rehearsed over and over again in case he encountered any of them, he would immediately be to remedy that situation and get himself back on track. I thought that message to me, failure is a good F-word if you use it properly. You know NASA, you mentioned yesterday and NASA was famous for coining in the 60s failure is not an option and I always say onto that cause I used to work for NASA, but it's a distinct possibility. And so what Jimmy chin shared this morning was electrified, but it also was a great understatement of what Combolt is helping their customers. We have to help you prepare for this. We can't help you prepare for all of it. As you mentioned, ransomware, it's not if but when. >>Well, right and both NASA and when the climbing is understanding where something could go wrong and therefore what the failures scenarios are. So you know rockets today you can't have a failure and by failure they mean look, if the rocket isn't going to work or something goes wrong, we need to make sure we don't have loss of life. That is something that if you look at blue origin and SpaceX that is pre eminent in there is we can't have another challenger disaster. We can't have some of these environments where we have the loss of human life. So that is number one. Some of the other ones, sometimes we know that the unknown happens or things don't go quite right. So being prepared to understand if something goes wrong, how do we recover from that? And that brings us back to the whole data protection and recovery of the environment because the best laid architecture, eventually something will happen and therefore we need to make sure that that data, the lifeblood of the company is able to be recovered and used and that the business can go forward even if some piece of infrastructure or some attack got through. >>There are, and there's inherent risk in every industry, whether you're talking about healthcare data, we talked with AstraZeneca yesterday, you know, genetics, clinical data, or you're talking about a retailer, doesn't matter. There's an inherent risks with every business and one of the most important things that I got out of the NASA talk yesterday, Jimmy Chin's talked today, some of the customers, is that preparation is key. You can't be over prepared. You really can't act fact. He said that you can't be overprepared in his line of work, but I think it applies to the inherent risks that any business has. Managing data. As we talk about Sue all the time, it's the lifeblood. It's the new oil. It is. It has to be available, accessible 24 by seven if it isn't and can't be. Businesses are massive risk in this day and age. Competitive competitors who have maybe better risk fault tolerance scenario in play. >>So that risk that they have to mitigate comes a preparation. We're going to be talking with Sandra Hamilton in just a few minutes about who leads customer success for combo. Really want to dig into the training, the support. We've heard that articulated from customers on stage that I don't wake up in the middle of the night anymore because I have this support from my trusted vendor combo and that is critical to any business staying up. Absolutely. We're going to hear from number of customers. I'm sure they're ready and we are ready for day two. We are ready. See, let's have a great day. Yeah, thanks. All right, so Sue and I will be right back with our first guest on day two of our coverage of comm Volkow for Stu. I'm Lisa Martin. We'll be right back.

so two points max first off ideas aren't worth a damn ever he's got ideas all right I could give a holy hoot about about ideas I mean I I I got people throw ideas at me all the friggin time you know I don't give a shit I just truly told give a shit right I want actions show me how I'm gonna turn something into an action how am I gonna make something better right and I I want to know ahead of time what that something is am I trying to improve customer attention trying to improve recovery time for an athlete who's got back-to-back games right III I know what I'm trying to do and I want to focus on that where ideas become great and you said it really well max is ideas are something I want to test so but I know what I want to test these of the event what outcome I'm trying to drive so it isn't just it is an ideation for the I eat for the sake of ideation its ideation around the idea that I need to drive an outcome I need to have athletes that are better prepare for the next game who can recover faster who are stronger and can you know it can play through a longer point of the season here we are in March Madness and we know that by the way that the teams that tend to rise to the top are the teams that have gone through a more rigorous schedule played tougher teams right they're better prepared for this and it's really hard for a mid-major team to get better prepared because they're playing a bunch of lollipop teams in their own conference so it's it's ideas really don't excite me ideation does around an environment that allows me to test ideas quickly fail fast in order to find those you know variables or metrics those data sources it just might be better predictors of performance yeah I like the idea of acting quickly failing quickly and learning quickly right you have this loop and what happens is and then I think every strand coach in the world is probably guilty of this is we get an idea and we just apply it you go home you know I think eccentric trainings this great idea and we're going to do an eccentric training block and I just apply it to my athletes and you don't know what the hell happened because you don't have any contextual metrics that you base your test on to actually learn from so you at the day go I think it worked you know they jump high but you're not comparing that to anything right they jump they've been the weight room for three months my god I hope they jump higher I hope they're stronger like I can sit in the weight room probably get stronger for three months and my thought is but let's have context and it's um I call them anchor data points they were always reflecting back on so for example if I have a key performance metric where I want to jump high I'll always track jumping high but then I can apply different interventions eccentric training power training strength training and I can see the stress response of these KPIs so now I've set an environment that we have our charter still there my charter being I'm going to improve my athletic development and that's my goal I'm basing that charter on the KPI of jumping high so key performance indicator of jumping high now I can apply different blocks and interventions with that anchor point over and over again and the example I give is I don't come home and ask my girlfriend how she's doing once every month I ask her every day and that's my anchor point right and I might try different things I might try cookie and I might try making dinner I might do the dishes I might stop forgetting our dates I might actually buy groceries for once well maybe she gets happier then I'll continue to buy groceries maybe I'll remember it's her birthday March 30th I remember that that's my put it on there right and so but the idea is we have in life the way life works we have these modular points where we call anchor points where we were self-reflect and we reflect off of others and we understand our progress in our own life environment based on these anchor points and we progress and we apply different interventions I want this job maybe I'll try having this idea outside of here maybe I'll play in a softball league and we're always reflecting it's not making me happier is that making me feel fulfilled and I don't understand why we don't take what we do every day and like subconsciously and apply it into the sports science world but lava is because it happens unconsciously because that's how our body has learned to evolve we have anchor points I want to survive I want to have kids lots of kids strong kids and I and I die so my kids can have my food and that's what we want as a body right your bison care about anything else and so that's why you walk with a limp after you get hurt you don't want perfect again it's a waste of energy to walk perfect right you can still have kids with a limp I hate to break it to you right we're not running from animals anymore and so we have all these anchor points in life let's apply that same model now and like you said it's like design thinking and actually having that architecture to outline it whether it's in that hypothesis canvas to force us to now consciously do it because we're not just interacting with ourselves now we're interacting with other systems other nodes of information to now have to work together in use in to achieve our company's charter interesting max there's a lot of a lot of key points in there the one that strikes me is measurement John Smail at Procter & Gamble I was there you still I say you are what you measure and you measure what you reward that was his way of saying as an organization that the compensation systems are critical and the story just walked through about what Kelsey right and what you guys are doing and how you increase your your happiness level right now here's the damnest your work I mean that is that is how you're rewarded right if you are rewarded by happiness and so you you learn to measure if you're smart right that you don't miss birthdays that you do dishes you you you help up around the house you do things and when you do those things the happiness meter goes up and when you don't do those things happiness meter goes down and you know because you're you're you're probably pulling not just once a day but as you walk by her throughout the day are on a weekend you're you're constantly knowing right if if you're liking your mom you know when mom's not happy you don't need to be a day to sign this and know mom's not happy and so then you you know you re engineer about okay what did I do wrong that causes unhappiness right and so life is a lot of there's a lot of life lessons that we can learn that we can apply to either our business our operations or sports whatever it might be that your your profession is in about the importance of capturing the right metrics and understanding how those metrics really drive you towards a desired outcome and the rewards you're gonna receive from those outcomes yeah and with those it's the right metrics right that's what not metrics the right metrics if I want to know if someone was happy I wouldn't go look at the weather I wouldn't you know check gas prices especially if I'm curious they're happy with me well maybe they might reflect if they're happy in general if they're happy with me right now I'm contextualizing I'm actually trying to look at I know a little bit more about what I should look at I don't know everything and so you might have metrics that you say you know I know science says this metric is good this metric is good maybe we want to explore of these couple of metrics over here because we think that either aid they're related to one of these metrics or they related to the main outcome itself and that gives you a way to then I have these key and core metrics that's not stacking the deck but it's no one you're gonna get insights out of it and then I have these exploratory metrics over here but you're gonna allow me then to dive and explore elsewhere and if you're a company those can be trade secrets they can be proprietary information if you're a trainer it can be ways to learn how different athletes adapt to make yourself better and again we're talking about a company and we're talking about trainer there's no difference when it comes to trade secrets right trainers keep their trade secrets and companies keep their trade secrets and as we talk about this it's really easy to see how these two environments where they're talking about company athletic development sports science personal training health and wellness are really universally governed by the same concepts because life itself is typically governed by these concepts and when we're playing those kind of home iterations to it you can really begin to quickly learn what's going on and whether or not those metrics that you we're good ARCA and whether or not you can learn new metrics and from that max you raise an interesting question or made a point here that's I might be very different in the sports world than it is in the business world and that is the ability to test and what I mean by that is you know the business world is full of concepts like a bee testing and see both custody and simulations and things like that when you're dealing with athletes individually I would imagine it's really hard to test athlete a with one technique and athlete B with another technique when both these athletes are trying to maximize their performance capabilities in order to maximize you know the money there can they can they can generate how do you deal with that so yes no one wants to get the shitty program yes that's correct yeah for the most part people don't and this I'll take people don't test like that and but here's my solution to us I think being a critic without solutions called being an asshole my solution to that is making it very agile and so we're not going to be able to you know test group a versus group B but what you can do if you're a coach and you have faith in because there are a lot of programs coaches use coaches probably use you know every offseason they might try a new program so there's no real difference in all honesty to try a new program on you know these seven athletes versus and then try a different one that you also trust on these seven athletes and part of that comes from the fact that we have science and evidence to show that both these programs are really good right but there's no one's actually broken down the minutiae of it and so yes you probably could do a and B testing because you have faith in both programs so it's not like either athletes getting the wrong program they're both getting programs that are going to probably elicit an outcome of performing better but who wants to perform the best the second asks the second aspect would be what kind of longitudinal data that you can collect very easily to understand typical progression of athletes for example if you coach and you coach for eight years you'll have you know eight different freshman classes theoretically and you'll begin to understand how a freshman typically progresses to a sophomore in what their key performance indicators typically trend ass and so you can now say okay last year we did this this year we do this I'm gonna see if my freshman class responds differently is this going to give us the perfect answer absolutely not no but without data you're just another person with an opinion that's not my quote I stole that quote but it's true because if we don't try and audit ourselves and try to understand the process of how is someone developing then we're just strictly relying on confirmation bias I mean my program was great you know Pat some guys in the back that jumped higher and we did awesome if we're truly into understanding what's best then we'll actually try and you know measure some of these progress some of this some of these KPIs over time in the example I give and it's unfortunate and fortunate I don't mean anything bad by this either we're on a salary right and so what happens when you're on a salary is no matter really what happens assuming you're doing your job you're gonna keep your job but if you look at a start-up a startup has one option and that's to make money or go out of business right they don't really have the luxury of oh we're just gonna you know hang out and not saying coaches hang up or not we're just gonna you know keep this path we're going on as a coach you know how do I apply a similar model well I start up the bank my startup is you can go from worth zero dollars to worth a hundred you know million two billion dollars in one year at the coach we don't have that same environment because we're not producing something tangible which doesn't always it doesn't have the same capitalistic Drive right the invisible hand pushing us the same way the free market does with you know devices and so we don't always follow the same path that these startups have done yet that same path and same model might provide better insights so max you've hit something I found very interesting confirmation bias if if you don't take the time before you execute a test understand the variables that you're gonna test what happens is if you after the test is over you go back and try to triage what the drivers were that impact and confirmation bias and revisionist history and all these other things that make humans really poor decision-makers get in the way and so but before as a coach I would imagine before as a coach what you'd want to do is is set up ahead of time we're gonna test the following things to see if they have impact by thoroughly like the hypothesis development canvas right they'll really understand against what you're really going to test and then when you've done that test you you will you would have much more confidence in the results of that test versus trying to say wow Jimmy Jimmy jumped two inches higher this year thank God what did he do let's figure out and revision it wasn't what he ate was it where he slept oh he played a lot of video games that must be it he is the video games made him jump higher right so it's I think a lot of sports in particular even more than the business for a lot of sports is based on on heuristics and gut feel it's run by a priesthood of former athletes who are were great because of their own skills and capabilities and it maybe had very little do with her development and I don't want to pick on Michael Jordan but no Michael Jordan was notoriously a poor coach and a poor judge of talent he made some of the most industries when the worst draft choices industry has ever seen and that's because he mistakenly thought that everybody was like him that he revision history about well what made me great were the following thing so I'm gonna look for people like that instead of reversing the course and saying okay let's figure out ahead of time what makes what will make you a better plant player and then trying these tests across a number of different players to figure out okay which of these things actually had impact so sports I think has gotten much better Moneyball sort of opened that people's eyes to it now we're seeing now more and more team who are realizing that that data science is as a discipline it's not something you apply after the fact but in order to really uncover what's the real drivers of performance you have to sit down before you do the test to really understand what it is you're testing because then you can learn from the tests and and let's be honest right learning is a process of exploring and failing and if you don't try and fail enough times if you don't have enough might moments you'll never have any break to a moment and I think what people don't understand is they hear the word fail and assumed oh we did a six-month program and failed nope failure can occur in one day and that's okay right you can use for example I'm going to use this piece of technology as motivation for biofeedback to increase my athletes and tint and the amount of effort they put into the weight room that's right hypothesis you can test that in one day you print out that piece of technology the athletes don't respond well you'd have learned something now okay that technology didn't bring about the motivation I thought why was that you can do reflect and that revision because you had the infrastructure beforehand on maybe notes that you may have taken and scribbled down on your pad or observations from the coaches I am I but you know what the athletes weren't very invested because the technology took too long to set up right it wasn't the technology's fault it was the process of given technology available to act and utilize on so maybe you retest again with it set up beforehand or a piece of technology that's much easier to use and the intent increases so now you say okay it's not the technology's fault it's the application of how we're using the technology at the same time we hear a lot of things like I'm gonna take a little bit of pivot not too far though is in the baseball world you see technology being more used more and more as a tool and it's helping guide immediate actions on the field whether it's not it's a you know spin rates its arm velocities with accelerometers or some sort of measurement they decide to use but that's not necessarily collecting data that's using technology as a performance tool and I think there's a distinction between the two the two are not mutually exclusive you can still use it as a performance tool but that performance data if the infrastructure is not there to store a file and reflect and analyze it's only being used one-sided and so people think oh we're doing sports science we're doing data science because we're collecting data well that's not I can go count ants that's collecting data but that's not you know I don't unless I count ants every day and say oh my game populations decreasing right and kind of a here's a really easy way to think of it in my opinion you have cookies in the fridge right and every day I go and every week will say my mom makes cookies this doesn't happen I wish it did be very cool but I love your mom and we didn't eat cookies every week but in the fridge I go when I count how many cookies there were right and using data I'd say oh twelve cookies if there's any cookies at all I can eat right that's using technology and that moment but doing data Sciences well you know what she's gonna make you know twelve and a couple of days and I have two days left and there's six cookies I can eat three today and three tomorrow because now you're doing prescriptive analytics right because you are prescribing an action based on the information you collected it's based on historical data because you know that every seventh day the cookies are coming no I just take it as I'm using technology as a tool I might only eat one cookie and forever be leaving six cookies on the table right and so there's hid don't want to do that no we don't but we trick ourselves I think we see that not saying baseball does is but I'm saying we've see that in all domains where we use technology we say oh technology good we had someone use technology that's data science no that's not data science that's using technology to help Tripp augment training using data Sciences understand the information that happened during the training process looking at it contextually to them prescribed saying I'm going to do this exercise or this exercise based on the collection and maturation of the information so instead of cookies here I eat one cookie it's a historic Lee I know there's going to be twelve cookies every seven days I have two days left I can eat three cookies now I can hide two and tell my sister Amelia oh there's only one left very weird I don't know who ate data - well let max let me let me let me wrap up with a very interesting challenge that I think all all data scientists face wellmaybe all citizens of data science face and I say did as citizens of data science I mean people who understand how to use the results of data science not necessarily people who are creating the data science and here's here's the challenge that if you if you make your decisions just based on the numbers alone you're likely to end up with suboptimal results and the reason why that happens is because there's lots of outside variables that have huge influence especially when it comes to humans and even machines to a certain extent let me give you an example know baseball is is infatuated with cyber metrics and numbers right everybody is making decisions we're seeing this now in the current offseason you know who was signing contracts and who has given given money and they're using they're using the numbers to show you know how much is that person really worth and and organizations are getting really surgical and their ability to figure out that that person is not worth a you know a six year contract for you know 84 million dollars they're worth a two-year contract for 36 and that's the best way I'm gonna you know pay but minimize my risks and so then the numbers are really drive and allow that but it isn't just the big data that helps to make decisions and in fact I would argue the insights carried from the small data is equally important especially in sports and I think this is a challenge in other parts of the business is the numbers itself the data itself doesn't tell the full story and in particular think about how does an organization leverage the small data the observed data to really help make a better decision so right now in baseball for example in this offseason the teams became infatuated with using numbers to figure out who were they going to offer contracts to how much they were going to pay him for how long and we saw really the contracts in most cases really shrinking and value in size cuz people are using the numbers and comparing that to say always so and so it only got this you're only going to get this and numbers are great but they miss some of the smaller aspects that really differentiate good athletes from great athletes and those are things like fortitude part you know effort resilience these these kind of things that aren't you can't find that in the number so somebody's ability to a closer write who goes out there in the eighth-inning and and just has a shit performance gets beat up all over the place comes back in it still has to lead and and does that person have the guts the fortitude to go back out there after us bad eighth-inning and go do it again who can fight through when they're tired it's late in the game now you've been playing it's a you know 48 minute game you've been playing forty minutes already you've hardly had a break and you're down by two the balls in your hand a three-pointer is gonna win it what are you gonna do my numbers don't measure that it's theirs these these these other metrics out there like fortitude at heart and such that you actually can start to measure they don't show up a numbers where they come from the inside some subject matter experts to say yeah that person has fight and in fact there's one pro team that actually what they do in the minor leagues they actually put their players into situations that are almost no win because they want to see what they're gonna do do they give up or do they fight back and and you know what you again you can't batting average then tell you that if somebody's gonna get up and that you're gonna give up it's a ninth in and you think you've lost you know what I don't want that person out there and so think about in sports how do you complement the data that you can see coming off of devices with the data that experience coach can say that that person's got something extra there they got the fight they have the fortitude they have the resilience when they're down they keep battling they don't give up and you know from experience from from playing and coaching I know from playing and coaching the guy is going to give up you know who they are I don't want them on the court right it made me the best player from a numbers perspective hell if that was the case Carmelo Anthony would be an all-star every time his numbers are always great the guide lacks heart but he doesn't know how to win so think about how as an organization a sporting organization you use the metrics to help give you a baseline but don't forget about the the soft metrics the servable things that you got to tell you that somebody has something special that is an awesome way to bring this together because subject matter experts those are people who have been in the trenches who see it firsthand date is here to augment you in your decisions it's not here to override you it's not here to take your place and so in coaches fear data it's the silliest thing ever because it's giving more ammo to a gunslinger that's all it does right it's not going to win the battle right it's just the bullets you got to still aim it in fire and so when we look at it in regards to performance and athletic development all these numbers they'll never be right ever they'll never be 100% perfect but neither will you and so what we're trying to do is help your decisions with more information that you can process into your brain that you might otherwise not be able to quantify so it's giving that paintbrush not just the color red but given all the colors to you and so now you can make whatever painting you want and you're not constrained by things you can't measure yourself I could add one point max to bill on that data won't make a shitty coach good but it will make a good coach great yeah yeah I couldn't agree more well dad thank you for being on here I really appreciate and for everyone who's listening this is going on prime March Madness time and so to pull away the dean of big data from March Madness who for people listening he made his bracket on the Google cloud using AI and so it only he so I was thanking him to come here and only he would be the one to I guess take I don't say take the fun out of it but try and grid the family bracket for used it all augmented decision-making he possibly can like it the data will make won't make somebody shitty good and I'm still not good Google Cloud couldn't help me I still at the bottom of the family pool it's great to have you in I guess every minute here is worth double being that's March Madness time thanks max for the opportunity it's a fun conversation alright thank you guys for listening really appreciate it and [Music] [Applause] [Music] you

>> No, that makes total sense. You've got me thinking a little bit. You see some of this right now going on general fitness and these thirty six minute classes will fit thirty six is awesome there. Big group No. One, their trainers. And they do a really good job of it. But the onset of maybe not such, um, high intensity aspects that you're doing. But you're promoting motor patterns, right? So it's not like, Okay, let's train for thirty six minutes. Generally was trained for forty five minutes. Let's train for an hour. But let's have a specific program that we're picking on to develop an athlete and push him in direction. So I mean by that is, I kind of see this in this is my attempt to digest cores. Mind not break it down and bring her with me. I thought you'd like to roost e a seven day period. And then you said in this period, I want to accomplish, you know, thiss five sets off total or five sets of ten reps and back squat and then your micro dose in mind like you, you slice it up, and so all of a sudden it doesn't become a five by ten because fifty total wrapped trying to get you won't take that ten reps here and twenty wraps here and maybe five reps here, and you put it in different ways. So if you look at it holistically, it's this very on the certainly first. See, it looks almost just organized, but looks like a lot happening at once. When you take us back, you look at a full truck, the full pies there, and so people they come and see me one of your workout So they see on Instagram that, oh, it's just Korea Doing, you know, appears to be basic patterns that kind of seem random. But really, you said, Okay, this is my goal. This is what I want from these guys and you're taking a step back. You applied it in a very strategic way. So it's not just people say, Oh, it's a fitness class. No, First off, Micro does seem just That's if I like, you know, a thirty minute workout. It's a thirty minute directed work out with the candle quantifiable goal over Baghdad, a period of time. Is that a fair assessment? I dove into the brain of Cory. No, my deal >> looked like this. Lookit. Let's look at another population. We look at prisoners when they go to the yard. How much time do they have a day? All right, >> You know what, >> Right. That's what I'm saying. Like, it's not a lot like they're locked up in a cell for the whole day. So when they go to the yard, they go ham on whatever's available, it ain't like they got this nice little hole like, Okay, we're going to do from squads. And they were gonna go to bench and they were going to Arlo, and we're going to do no. They pick something that is available and they go ham on it for an hour, and they're on really terrible food and really terrible environments, but tend to get really strong. Okay, well, that makes sense. So and you know what? They do it again the next day and the next day and the next day. So I'm not saying we're trained like prisoners, But what I'm saying is there's a reason why if I was to tell any elite level lifter, OK? All you can do today for thirty minutes is squad. What do you think's gonna happen? They're going to go heavy often. And they're going to be able to be fresh the next day to do the same thing. I mean, no one leaves a power lifting meet the next day saying, Oh, time to go train again. No, their body is trashed, right? Because of all the intensity that they didn't through multiple movements. Same idea, right? All I'm doing is isolating it. So, for instance, I'm looking for a specific response. If I want to train relative string, I want to find a movement that they can move a lot of way, obviously not through a high speed. And that's the movement we're going to do. If I want a absolute velocity, for instance, Woodchuck and Tendo terms, I want them to be very elastic. Reactive owned him to move very, very fast. Then I'm gonna pick a movement, say, like a barbell squad job. Maybe it's a credible swing. Maybe it's throws and then they're going to go ham on that. But if you just take that one isolated lift, I don't care. If you do tend doubles at it, you're not going to be that sword, especially if you've been doing this for over a year. First start the preseason. We gotta look at stress holistically. The biggest stress they have is basketball. So the last thing I'm going to do is beat them down. And here I'm just going to make sure that we'Ll stay on the cart. So you look at our total volume. It looks something like four sets of four. But by the time we're at the end of the season January, February, March, we're hitting our P R's and reason why we're hit Rp. Ours is because we've made this huge reservoir of stress that they're able tto handle. So now practises cut in half. So I have more reserves in the weight room. So that force that's afore we were hitting for those compound movements in preseason. Well, now they look like ten sets of doubles or twelve sets of singles because they have that reservoir. So now we're expressing in a controlled environment faster weights have your weights at the time of year that we're looking for those adaptations so that now we're quote unquote stronger and faster. We're trying to win the championship, not tryingto win it and the summer, which you generally see like thereby sent PR is before they go home and summer. Well, that's great. And then they go into their maintenance program for the season, which last six months. Can you maintain anything for longer than six? No, you can't, like, maybe your oil, but you've not wantto patients, you know? I'm saying so. You know, that's that's where it really came down to is I'm trying to find the best means to produce performance, >> so I'm on times Lower standard. Yeah. Please do not mind around it. So I get it correct. Nowhere earthly it's looking at How do we given work out at that? Fits? The current state needed the athlete, so Okay, there begin the year, right? Their capacity only so localize outside stressors to fit in the workout around the other twenty three hours. Right? And then you're applying a stressor that's heavy enough, but not too light. And you do it. I'm not not overly fatigued them, but at least stimulate them. So you working guide rails? Not a written in stone. A type of thing, >> right? Yeah. So yeah. Yeah. How Basically how I how I keep the best part of the best way to put it is what I've done this year that I haven't done in the past is abuse Tendo Units, I'm just That's my way of just monitoring. How about speed? Okay, Cool, because load is one thing. But once again, how do you move that load now? We're not We're not dicing up like, Oh, it's point seven. You're supposed to hit point five like up. You know, add thirty kilos or vice versa, right? Like you're not exact. But if you're within a range, it gives me a whole lot of details, all right? And then you're basically all we do from that point is record the wait, not the speed. I just keep them in a certain zone. Stay within this. You, for instance, our strength speed or a relative strength and strength. Speed movements can't go anything more than triples our speed, strength and are absolute velocity. You can't go anything over five reps. If you hit quote unquote those triples or those fives, then the next time you come in, guess what we get to upload if you're not above that was going to stick with the same load. And if you prove it within your early work sex, then we'LL have a little bit alert. But that's our way of day to day, keeping them on the road, if you will. >> No, that makes sense. Do I couldn't agree more. I see it carrying over so well. Universally way you looked at the origins of strength training and we're like Oh, came from Russia and even your ever pashanski for those people aren't nerds like myself. Russian sports science even started like appeared ization. It's kind of a made up thing, right? So one hundred percent made up haven't made up and it kind of came from the four years cycle of Russia itself. America takes that andan. What happens is you get the the non athlete world's intelligent public world. Everything is monetized, right? So it's like, Okay, we know that training really heavy every days and probably a good. So we're going to make these things called, you know, in small little workouts that might last twenty five minutes are our six minutes, you know, have a shrink it as Lois and possibly can. But no, let's make it not necessarily difficult, but challenging. Um and we make money office. We labeled something different and you see different fitness fads come off when I come and go. But a lot of because I got the capitalistic market monetization. People try to make money off of things. But that really does him from, like the athletic side. If you're thinking about Hey, I'm Cory. I'm dealing with Alex. I don't know how they're going to walk into my door today. I don't know if they're going to be high lower, you know, just normal. How can I then give myself the opportunity to provide environment where they can work successfully and and what you do, which is really cool, And I find it really inspiring kind of cheesy word. But you give a lot of ownership to all your athletes when it comes to selection of exercises and movements. And I find that to be something that we don't say. We as in the general world of anything sports, science and fitness don't always like to do. Um, and you say Okay, you know, credit. I'm wrong, Corey to I don't want take worth mountains, him incorrectly. Just so you know, here's a pattern and maybe select one of these three exercises that you feel like gets you ready. And what's so great about that? It removes the constraints of this exercise is the best. You know, this is the golden exercise and really, I mean you and I know it, but we want to feel good. We would always have a bench press when I came in town, but absolutely, it's like, Okay, let's let's really understand that it's not really a difference between Aback Squad versus upfront squad versus may be something of a trap, our poll, especially if you're using it to get the athlete ready. So talk. If you could talk a little bit about how you decide some of that and what led you down that path and giving those athletes that kind of ownership and understanding of you know, I want to do this versus I have to >> do this right? I mean, to me, autonomy is everything, because what you generally see and it's to me, it's almost criminal is everyone gets the piece of paper. They fill it out with me you get, then you do the same thing, right? You get that piece of paper the next day, fill it out. Get that piece of paper. Next thing, fill it out. And then four years later you go. Well, I'm leaving now. Where's my piece of paper For the rest of my life. Oh, so you didn't really learn how to train, did you? You didn't really learn what worked for you. You didn't really In the really issue is like I deal with crazy, different levers. I mean, I got guys that are five eight all the way to seven foot. So you can't tell me there's a golden exercise that it doesn't exist in my world. >> I >> like knowing you're on. I would love to have everybody do the exact same thing. They love doing it. And they all do it very, very well so that I can have my little lab and I can have my control and I can show. Hey, guys, look how much better we got this year because of my implementation. Bax Wass What? What does that say? That says that I care more about what I'm doing more than what's best for that athlete and what they're doing if you really the real reason why I got to this autonomy stage is when I realized what I do is such a small percentage of their overall success and the reason why I say that I'm not necessarily saying I agree with hit or disagree with Hit, but you could have a hit program. You could have an Olympic based program. You could have your holistic based program, whatever you want to say, and I see the hit program Win a national championship and I'm like, what happened? Like I don't agree with that program, but they won well, it's all about it's all about the dude's. So if I can give quote unquote my dudes the best training environment that works for them. So what I mean by that is Look, here's a squad. You hate doing back squats because the bar on your back, it's jerking the hell out of your shoulders because you don't like to be an external rotation will. Then maybe I'm just going to hate. How about this Bar safety squad bar that feel better? Cool court. My knees are super tender away. It's basketball. Everybody's needs at some point this season, every a super tender last thing I'm going to do is put them in an environment. Teo, flame up those tendons so that they can't perform at a higher level on the basketball court. So what are we going to do? Well, let's Hinch, how about we just do some already? L stay. How about we do some kettle bell swings? Maybe some tribe are dead. Lift. It doesn't necessarily have to be this golden exercise that everybody fits in. And I think really what it stands from is that strength coaches got approved to their sport coaches that we'll look at, our numbers go up and they have to have a control to do that. And the exact opposite. It's a sport. Coaches coming down saying one of our guys bench. Well, if our sport coaches cares so much about bench press, well, then what do you think I got to do? Well, I gotta bench my guys so we could get those numbers so I could look like, you know, I'm validated my job. Well, how about we take something that's oh, universally accepted. So how about a counter movement? Jump out force plate. Now, I'm not saying everybody has forced plates, but you could just use jump height. Friend sits. Who cares how you got there? As long as you are trending right, that's all that matters. Why should we be fixated to a certain methodology or a certain pattern or not? Pattern but exercise. Just give them a pattern, let him choose. And to be honest with you, if it feels right, it's going to fly, right? If it feels good to do attract bar squat, opposed to doing a front squat well, they're probably gonna put more load and they put more load that I'm going to get the stress response adaptation. If I don't like the front squat because it's choking me the hell out. Well, then I'm probably not going to put his much load on it. Now, I have a negative connotation now have all these internal stress is going on, and then I'm gonna have a weird as look atyou, saying I don't like what we're doing in here. So now you think the quote unquote Byeon is going to be there. So now we're not getting any stresses that are going to give me that positive adaptation I'm looking for. So at the end of the day, if I can give them the education tto, learn how to do these movements and how to choose for themselves, well, then now it's not just what they did here for four years. I just gave them skills for the rest of their life. And if they're good enough to play pros now, they can take that and they can articulate it to the next coaching stuff so they could do a better >> job. No, that's that's awesome, man like this. A lot of things I want. I head into their I'LL keep it all Diamond all nine hundred promised. But I couldn't agree more and one of things that you say, you know, let's have a king P I They said jump high, for example, a point of reference. Then let's not care what we d'Oh, to the extent I mean not care. But let's not constrain ourselves of what we dio in order to improve that k p I. So the way I think about it, it's kind of like you ever use waze before that? Yes, that we got right. It knows to things and knows where you are. It knows where you were. If you're driving, it knows where you're going. Road. And then as okay, all I care about getting to point B So it will take you on detours left and right. Little Granny is driving slow in front of you for the pothole. If whatever is going to find the best way to get there, it doesn't care how it gets there, right, Right. And so work that it's say, OK, let's get the sevens environment where we can learn. And we know we need to get to be for me. And I'm not gonna say to go in a straight line because you might go through building and crashing hit pedestrians. We're gonna find a way to get to be. We're going to find a way that makes sense for the athlete and yourself. So my teaching them, you know, let's have you like and learn to do some of these movements then don't know taking a left at this next stop light to get to point B will be quicker than you saying go straight because they're the one in the driver's seat, right? And if that educational environment where you start to look at this a really complex system, her planting a really simple abie model and apply it to something as complex as the human body so that we can learn. And the example I give. It's like, you know, the ways part like, that's the more complex and assumptions we make more room for aeri half All right, we'Ll screw this. We assume that the sumo gets here. Well, if we assume in order to get to A to B, we got a one a two a three a four, a five. But any point on the line that, you know, assumption breaks, we don't get to be all right, you guys, you stuck at a whatever and doing. You know, we have to follow this waterfall method. It's very much a living method where things come in, things come out, things make you change. But you know what? You want to go? I >> mean, it's we work in team sports. Like the only objective we are the only objective that matters is wins and losses, period. Right? So if I wasn't a stopwatch sport, maybe my mind would change a little bit, right? Maybe I got okay. We need to drift towards this because literally it's did you get faster? Did you not get faster? Right? Swimming whatever you're doing, maybe these are the things we need to do more often to make that happen. But I'm dealing with incompetent. I mean great human beings, but just physically incompetent. There's still learning about their bodies were still growing into their bodies. I think it's the most arrogance thing that a strength coach could do is to say, Here's a program that's gonna get you better for six weeks. What? What is that? Even here's a block that's going to get youto point me. How do you know Like, till you know Saddamist like, can you honestly tell me that following this six week plan is doing that? Hey, they got sport practice. They got exams, they got pick up your tell me none of those factors could potentially there off your little plan or that your little plan can go up. They're KP eyes, if you will, or their Their goal is just a play basketball. So that to me, that's where as this thing, it's like the most arrogant thing in our field and it just drives me up the wall. But the other day, like I got a sport coach who has all the faith in the world of me gives me the keys to the castle. He just tells me, Do what you think is best. I I report the numbers that he doesn't even know he needs. That's what's awesome about he's like Chord. I just trust you like these were things that I want to see my guys do. We want a quote unquote play fast. Well, okay, here's some standards that we can set And these Airways that we know we got quote unquote faster. Now, from the technical tactical aspect, that's where you guys come in and you guys got it. Apply what you think is best to make that happen, right? But I gave you the physical requirements. I told you exactly what you need to get done and how we got there. Now you guys apply the technical tactical aspect. And then there we go. Now we have a happy marriage is long as I can supply valuable information. It doesn't matter what the information ISS, and that's where everybody gets stuck on these controlled environment numbers like like looking, swatting inventions like Who cares? Like Who cares about written load? Load gets you to here right after that, it's all about It's all about speed. It's all about rhythm coordination, your vestibular system that there's so many things that go into making. You better not just, uh, put three fifteen on the back squat suite. No, >> that's you know. Yes, yes, I agree. I'm not going to deviate too far. My ma, you know how I work or my mind races and I don't go in straight lines. I apologized immediately. Good. I was thinking about your friend mentioned earlier. It was everything that this lately, too. People who've been the private sector's I work in personal training, and I worked in exercise clinic for two and a half years. Iowa State, where don't older adults randall off cool testing on them. But ultimately they showed up because they enjoy it. And one things that I think we I don't mean We have everybody some people forget is that it needs to be enjoyable back. And when you're in a private sector and you're literally your food is the ability for something to come back to you. Hey, it's really different and you start. You said Okay, you know what exercise and movement do you like, and then you manipulate How do I make that exercise the most effective exercise for that person? And that's what you kind of mentioned with the educational process for your athletes. You're taking this approach. Where? How did you get them to win? Firstly, they gotta want to be here, but they don't want to be who I try hard. And secondly, no Adam, take ownership of these movements. I really like that concept because it's really melting in the world of Hey, you're here. You have to get better. But everyone knows when you want to get better. Vs have to get better, right? The be out a little different and unusual marks Lefton excited to move. I just keep thinking about that from like the private side. That's really where, like the general public, and you could deal with great Alan to deal with a lot of athletes who really want to be there. But unfortunately, majority the world doesn't want to work out like they're they're not interested, and I hate to make an assumption, but it's hard not to think that it's either them not knowing or them intimidated that have to do something in there, right? Right. I'm like that mindset a beam to apply. Okay, let's have an ownership model that drives it, because if you talk to people, her successful personal trainers, they have a way to make sure people come back. Oh, for should join a box in a way that a strength coach you're no environment might not even have to be exposed to just because it's the nature of >> well, for me, like the off season. I mean, when I get a freshman, that's a great thing about basketball. But I get a freshman. I mean, maybe they picked up some weights like a B. There's still just such a greenhorn in the weight room. They don't know what's good and what's bad, right? So, essentially the off season is a little bit of dictatorship like Sorry, I'm to tell you what to do because you don't know shit, right? But the goal is to earn that autonomy as well. So, you know, my guys that are kind of like slaps like for the whole offseason. Well, their leashes a lot tighter like Nah, bro, you're going to do this because I know you need to do this. You have earned the right to have that a top. So I want to make sure that that's, like pretty clear, too, because if you just give autonomy all day and there's going to run over you. But the one aspect that I think that is so important with our autonomy is it's my biggest performance enhancer, and I actually had dated Approve it. Like if I just look at my C M J members from our force plates once again. Yes, there are some maybe eight sets of doubles or six sets of triples or whatever, right? But once again, that is Tendo based, like to a certain agree with most of our movement. So you know, it could be a triple. It could be a double. It could be a single. It depends on where they fall in on along those lines, but essentially the flexibility of the sets and wraps, the unbelievable latitude of the movement pattern that they're doing. But yet counter movement jumps in February. They are p r ng, not season. P R's. I'm talking life top ers Guys that have been here for three years are hidden from nineteen point one to twenty six point four. I can't say names the twenty six point four in February. So what does that say? It says that my biggest performance enhancer is the kids saying I want to do that. Cool. That's what we're going to do. >> No, I love it that zik perfect. If you want to be there, you're intense. Going to be high. You're going to try harder. You're going toe actually care about what you d'oh and that mindset really house dr an aspect of performance that otherwise we can't because all internal right korea we really started wrapping up towards the end you buy a couple questions for you before you go yourself thank you i appreciate it it's always good to have you next way clich a weekly cycle korea >> will make a >> record you know fire i slowly thanks for having you guys we wanted to come with because you're a scientist I mean, if you had to share a bitter fight and this is to anybody and this isn't their coach, Jenny, where nobody is looking to enhance their fitness, their performance, um, their overall well being You that with activity, right? How is what would you advise someone to get into and regards Tio training our house to someone Initiate That's on top of the micro dose in a kind of giving that much of credit here, obviously some e How does someone injured? I heard it put that way and I'll get straight to the point that one look into into exercise probably should do some form of micro dose in to see if you even like it everyone to overdose. How do they start that process if they're not athletes per se how they decide where they began? >> Well, essentially is what do you want to end up like, What's the what's the point beyond ways, right? Do you just want to look aesthetically better? How aesthetically do you want to look? Do you wanna look like a big body voter? Do you want to look like a swimmer? What do you want to look like? And I think that the vein than fan ity. And I mean, that's what drives my basketball players there in tank tops here around. Of course, they want nice arms. Right? So there's certain things that you gotta know. Like, I want to look like this. Now, some of the performance guys, Maybe I wantto sprint faster or jump higher. Like that's a whole another aspect. But we're talking about general population number one. What do you wanna look like? Okay, so if I'm three hundred pounds and I want to lose some body fat for my own general health and I want to, you know, be more presentable, if you will. And smaller clothing. Well, then maybe just walking ten minutes every day, and then you start adding layers to it, So Okay, You know what I mean? Killing these walks. How about we go Stairmaster? Okay, that's a little tougher. Okay, how about we introduce maybe some med ball exercises because that's not necessarily too complex to do that. I can do it through different ranges. It's easy to manipulate. Okay, Now, let's take a dumb bill or kettle bill. Then we work our way to a bar bill and now. Oh, man, what do you know? I just dropped one hundred pounds and in them. Oh, before all of that eating. But like, we're just talking about the physical aspects, but as far as that, where do you want to be? Okay, I want to look like Brad Pitt. OK, for one, get plastic surgery. But if you want to look cool air at Brad Pitt and Fight Club Okay, well, these are the things that I need to do. So let's reverse into near the process, okay? He cut his little jack, so that means he's got muscular strength. OK, cool. So that means weights are going to get involved at some point we'll he got really lean for this too. So my general fitness sucks. Maybe I just need to start with walking. Maybe a jump rope, maybe just medicine Ball toss is something that's super easy. The number one. What's going to make me more consistent? What consistency is goingto win? It's not. They'll work out you do that's going to make you go from a counter movement jumped a nineteen point one to twenty six point for It's the consistency that got you there. All right. That was a two year process for that kid. Just to get to that point, right? If you try to hijack the system, if you try to go, I want to get from point A to point Z like that. Well, you're going to run into multiple things. One possibly injury and two. What's the real reason why you're Russian? The real reason why you Russians, Because I don't want to be there in first place. Now you've just ruined the whole concept. Now you've just ruined the journey. To me, that is much more important. Like when I used to be a fake body motor, if you will, that when I try to get ready for shows. I don't remember the show at all. The only thing I remembered was those nights where I was damn hungry those mornings where I had to get up, do my quote unquote fasted cardio meal prep backs without remember only big. How I was on stage for forty five seconds like that was twelve weeks for forty five seconds. Right? So that's where you gotta understand like it's the beauty or what is it that Jake whole line of the beauty is in the is in the cash. Basically what? The thing that you want to fall in love with the most is the adversity that they were going to fall in love with the most is the stressful points. That's what's going to create the beauty, if you will remember that Jake Colon. But essentially, that Google >> search really quick pressure that the Brad Pitt Fight Club I >> mean, that dude was solid, Man, that was a solid right. May like Brad Pitt. He was a pretty boy until fight club. And I was like, Yo, that is some white trash. I would not mess with him. He can go. >> Uh, great. I love it. Lastly, Yeah. Course lesson. Where do we find you? On social media and other venues? Assault media were coming here more than beauty and wonder himself. >> Yeah. So Instagram is probably what you can find me on the most slash strength as C h L E s strength. You could find me there pretty active on it. You want to see so naked cats? So to sphinx, with my beautiful wife and ah, multiple podcast. I'm on a lot of different podcast that you just Google. I, too, are goingto iTunes type in my name. You'LL find many other platforms where I go into a lot more depth about how we train on And then, of course, speaking engagements. I do multiple speaking, engage with the nationally and internationally. And so there's opportunities to meet me in person there. >> There's beauty in the struggle. >> There is beauty in the struggle. This beauty >> I got my end. >> Yes, there is beauty in the struggle. That's when they >> get here in Britain, right? Right there. Where >> you Brooks. But there's beauty in the struggle >> A lasting well, Korea appreciate you have coming on here. I mean, I hope something useful. I >> was one hundred percent. My pleasure, Max. I love working with you, man. >> Now you do. And anybody curious about Corey? I mean, I really encourage checking out his social media. Yeah, I know. It's a lot of crazy stuff on Instagram that is really thought provoking. Put it that way and I can't believe it. Oh, my goodness. I can't let you escape Korea quite yet. >> Well, what you got? >> Uh, whole off the exit. Give me five minutes on it. I was going to ask his social media is going to ask. Yeah, way rehab itself. Yeah, to spring loaded monster man who means you want to share a little bit on this because I know you have been doing this yourself. Yeah, this is it in chorus singer based Achilles program. I love some of the actors. I love thee, not the unloaded foot contact under your hand motion who was seen Alice into this isn't the course in a chair, and he's for lack of better words. Words. MacInnis foot on the floor like a pogo stick and doing extremely extremely unloaded movements early on that site, too early on but in the rehab process itself to introduce low level plyometrics, He's doing band assisted jumps. He's doing isometrics. He's doing heavy squads. He's doing some bar bell curls. All things important for the curies. >> Sure are. Absolutely yeah beyond you. My understandings of the lower leg complex is off the charts because of my injury. So for the viewer's eye, tor macula or a ruptured my Achilles tendon with a full rupture but right at the insertion, which is the very atypical tear because I've been dealing teno sis for over a year before I tore it. So they had it cut me up top to bring me down low, if you will. So usually Achilles ruptures that all they do is bring it together and then tie it. There are. So it through the mind was at the very bottom. So essentially, they had to cut me up top toh length and me and then, uh, suitors through. So is very atypical, which sucks only that that part sucks. Spike. Um, it's not that I am Well, maybe a little bit arrogant, but I honestly want to take full control of my physical therapy because I think that intuitively I understand the process not just of rehab, but of how to increase performance. So all I did was watered down as much of that is possible and truly started as soon as I got to the pain free. And so, yeah, with all the unloaded stuff, it just made sense to me like that's something you just don't see in physical therapy to It's kind of blows. My mind is what's the first thing to go like when you get older? What happens? Will you lose your ability to do very forceful things or to lose power or the ability to generate power. So that's the first thing that came in my mind when I rupture. Or when a Torme Achilles was okay. I need to go back and not be old because essentially, I'm staying still. So if I'm staying still, it's like use it or lose it protocol. So from that perspective, I told myself, I need to move fast at some point. So I started with all my available limbs at the time, just moving fast. Then I progress toe when my suitors seal or excuse me with my I want my wound healed. I got into the pool, so that's the most is about is unloaded. You should get, and all it did was just frail. My leg and there a cz muchas I could through different planes and of course, he has fold up. But of course, it's going to like your adding a stress. And so I just did it Mohr or Mohr. And so I just Kim. Training fast, even though, is the most unloaded way you can do it. And then, like Max was talking about, I got to a seated position and I just started doing be most unloaded pogo jumps you've ever seen or ankle pops or whatever you want to call it. So then I transition to standing on it isometrics, then putting more force into the forefoot isometrics. And then I started using the bands I mean super heavy bands and then just started like Pogo's and then start lighting the bands I went to arm went the body weight. To me, it's like super common sense, but I don't know, maybe the physical world. It doesn't really look at it that way. They look at it and isolation opposed to global. So to me, I knew if I could quickly get back to global patterns that I will be able to promote healing faster. And so, like Chase talked about, his last one ought to be a far protocols. Luckily, I had him as a resource to help me with my healing process, but right now, on that four and a half months, almost five months, and I'm doing some pretty cool things if just to give you a point of reference. Dez Bryant, wide receiver. He tore his a week after mine, and essentially, you guys Essentially, he's What's a similar athletes level athlete? You know, very someone. Uh, actually, he's going to be up until eight to nine months. John Wall tour has a few months after mine. He's going to be an entire year for his process. Boog, Golden State warriors took him a whole year to get back on my goal. If I can get it back and lesson seven months, that means I did something, right? >> No, I love it. Well, that's tough stuff. Get to see if you check out his instagram page. So me, please, dear, do yourself a service. Go check out the man. He's a good dude, Tio. So sometimes no kid. Don't >> you know you're right there, e >> I don't want call corps on a bad day. >> You >> know, it's all good now. I really appreciate it, man. Thanks for being on here. And, uh, again we follow sometime in near future. I feel I'm expecting that shirt. By the way, where is my core bighead T shirt? >> You know, I want to find one of my earlier body building picks, and I'm gonna put it on a T shirts and, Tio, >> I love it. How I rocked the hell out of it. Man, >> you're beard in a most >> and be right here. Yes, right behind. Maybe my postal records slash proposing bronze and gold. You're welcome. You're welcome. An absolutely huge in that >> purple banana hammock to >> Wouldn't ask for another way. What? The full real deal. Korean stage. Ready, you know. Awesome. Well armed man up that thing. You guys, Listen, I appreciate it. Great South Korea on. If we're curious about finding more, check him out on instagram and look for Teo. No doing more. These in near future. >> Awesome. Thanks, Max.

live from Las Vegas it's the cube covering AWS reinvent 2018 brought to you by Amazon Web Services Intel and their ecosystem partners everyone welcome back to the cube day two of three days of wall-to-wall coverage here in Las Vegas for AWS Amazon Web Services reinvent 2018 start sixth year covering I was only meeting to all the reinvents except for the original one I'm John for a mykos David Lunz we got two sets here and set one we got another set over there's so much content set upstairs total of four sets here covering all the video covers and right now we're coming off the keynote with Andy Jesse full of action packed analysis Dave a pretty amazing set of keynotes they had so many announcements here at ABS they had to basically start releasing them they did media alerts before the show midnight madness just a lot of front-end releases that would be notable releases for any other other conference they're letting out early Andy Jesse only has two hours on stage he's giving the keynote finishing up huge amounts of news announcements Dave so you know you went to the analyst session I met with any Jesse last Monday night for the preview we've been following Amazon a lot of the stuff that we've been saying for the past multiple years is actually happening our original predictions and impact of a toes of business on the IT landscape is happened and happening and now new areas that they're focused on that extends their a leadership both technology and the business and on the business model side continue to come on they well yesterday launched as a preview satellites as a service you actually stand up some satellites which is the power of the IOT has so many implications so many things to talk about through the course of today a lot of interviews we're gonna have again day two let's analyze Andy's keynote and the company what's your take their business is strong their lead a lot of analysts are fooled by the numbers you're not we've been watching it huge number of lead in the cloud I think the first thing I want to say is I go to Silicon angle calm I read you're both of your pieces that are up there your piece on Forbes this is a preview and then the the number of announcements here is just overwhelming last two days at the analyst event they gave us previews of sixty-two announcements and that's only a partial list of the announcement 62 that they took us through it was like rapid-fire kool-aid injection unbelievable so silicon Hangul comm is covering as much of that as physically possible any chassis made a big point this morning and at the private analyst meeting of talking about the context of the cloud market he's very sensitive to people misinterpreting growth there are 26 27 billion dollar business exclusively focused on infrastructure as a service and they're totally transparent about the size of that business everybody else throws in SAS I mean you've made this point a bunch of times and all these other things they mix in hosting and you really can't tell what's what despite that Amazon is by far the leader infrastructure as a service with over 50% of the marketplace now they're only growing at 46% but there are 26 27 billion-dollar run rate business growing at 46% so you see sometimes like Microsoft growing it whatever 70% but they're much much smaller so the absolute number in terms of revenue growth is much much higher for Amazon so that's sort of point number one the thing that Jesse doesn't talk about is the profitability of the business they're transparent about it in their financials Amazon has 28 percent operating margins now just to put that in the context software companies like Oracle have 37 percent operating margins Microsoft 32 percent AWS I just mentioned 28 percent Cisco 25 percent VMware as a software companies with 22 percent operating margins IBM 15 percent Dell eight point seven percent HPE seven percent this underscores the power of the AWS model at scale where they're driving the marginal cost of new services and deploying new services down to zero and it just keeps getting stronger and stronger and stronger and as a great point as a flywheel effect totally Amazon like but it's a great point ago we've been saying it for a long time the competitive advantage is scale and speed and jessee's now adopting that into his rhetoric into his into his narrative totally true and the other thing to point out on that margin and the profitability is that they're plowing it back in so what what you have at scale I wrote this in my Forbes post and in space at all in Silicon angle transcripts are my my exclusive interview with Andy is that not only are they profitable so they have software like margins for what is what is a hardware business basically in the cloud the stack and in Iraq on their own servers they're making their own chips as they start getting the scale up Dave they are getting a competitive advantage I'm following the profitability back in to launch new services so as the trajectory and I kind of tease this out in my Forbes article I wrote which is as you have a trajectory of growth they're building on that trajectory and the competition is trying to copy Amazon and you can't you can't get the trajectory value by trying to meet Amazon's current trajectory that's called diseconomies of scale I teach you that in business school this economies of scales it's the unsub optimized execution model where you run you don't have the learning experience that you don't have the scale Amazon has that trajectory and as people try to match them they will lose and they're at risk of losing because the risk so the only play for the competition in my opinion like Google like Microsoft like others is to change the goalposts they got to change the playing field on Amazon this is the number one thing that the industry is looking at Amazon is making the market they continue to make the market as market makers that's gonna attract an ecosystem that's gonna attract value and and more companies the only competitive strike that I think one could make is to change the playing field they got to change the game on Amazon that is extremely difficult to do when you're operating at scale you got the profitability numbers you talked about so again that is the new bar and as Amazon I'm we've said this every year ever and every year they've done it they've doubled their aurora customers on the database lied every year they introduced more services last year they in 2018 they'll have introduced significantly new services and the number of eighteen hundred plus okay the year before is like fourteen hundred next year we're probably higher by introducing new services faster that keeps the pace and that's what's hard to copy it's hard for competition to try to match that and as they try to match it they have the diseconomies of scale this is a major advantage this is going to give Amazon customers a comfort because they know that that's that scale becomes more stable products becomes stable the flywheel kicks in and as data gets into the cloud it gets sticky so there's no real lock in spec you can move every in and out of Amazon all you want but why would you because the benefits of being in Amazon far outweigh benefits of actually doing anything else so again the lock-in spec doesn't exist anymore from a technology feature standpoint it's a lock in spec on business model oh it's better well and I want to add something some color to what you said about the the new services and the pace of innovation their services of substance and we know this because our developers you know they talked to us about Amazon Andy Jesse told the story about one of his his executives that was on a plane leaving Seattle he didn't mention Microsoft but clearly he was talking about Microsoft that somebody's laptop was up the PowerPoint presentation it said their strategies explicitly said just announce what Amazon announces don't worry about the functionality and and what so what Amazon what Microsoft is doing presumably it was Microsoft is basically saying check the box freeze the market we have that too so to your point competitors cannot compete with Amazon on pace of innovation no way what they have to do is in the case of Microsoft they have to rely on their software estate in the case of Google you know they've got their innovations ok fine but nobody can match Amazon head-to-head don't just lose every time I like Google's position I love I love Google's got the technology power but you're exactly right that competitive strategy of checking the boxes is an old way of competing and I think what's interesting is the competition has not yet woke up to the fact that the game has already changed on them look at the public sector work that Teresa Collins done the CIA deal really made Amazon a business model because people said hey the CIA could do it we could do it that's been that's come up in all my conversations across all Amazon executives but what's happening now the DoD isn't the only soul source or that's Oracle's business they try to compete I'm checking the boxes but because it's so easy to use the cloud and Andy talked about this as keynote is that you can't fake it till you make it hey you can't you got it you have the surge it's gotta be up and running so it's very easy to do a bake off so you know that it's so that whole way of competing of checking the boxes over the only way to compete is have value and that is a new flywheel that's what's happening right now and that's key so so let me set it up so when you get to reinvent I mean I'm so psyched to be here with you John it's been a while but but try that you try to absorb all the innovation so you so try to boil it down you got to look at it it to simplify it two dimensions one is the data exploiting data better use out of data and the other is developers and developer tools and making it simpler for developers their announcements the 60-plus announcement that they made I break them down into five categories storage and database services compute networking and security third is ease of use and abstractions making things simpler fourth is machine learning and developer tools and the fifth is these new growth frontiers like you mentioned satellite as a service and some other things like in hybrid that we're going to talk about so the announcements kind of fit into that framework but let's maybe highlight some of the announcement a lot of time at least just run down the announcements real quick to meet the main main ones at the place he started the bottom of the stack and move it has moved up storage deep glacier FX Windows file systems lustre for high-performance workloads the new persona developer he call it the Builder the right tool for the right job this is a constant refrain he's been saying control tower blueprints and guardrails template based approach to stand up new use cases for compliance all the kind of detailed manual things that they're automating signal from the noise I love that phrase he put on this put on the big screen that's our motto here it's looking angle security hub lake formation instant data lakes he kind of was pooh-poohing data Lake which I love them like oh shit it'll is just one piece of freedom around databases so one storage was a huge part of the innovation and they're expanding that front and compute and storage it's gonna be critical the database war is definitely happening open engines Aurora doubling customers DynamoDB runtime capacity on demand sensors and time series dais they have timestream block chaining address that straight up look at weekly we'd love blockchain if the customers wanted we're gonna look at and understand it then announce that they announced the quantum ledger database ql DB amazon manage blockchain service so you want etherium or hyper ledger got it and then he moved on to Mitch from databases to machine learning this is where sage maker comes in so you got s3 and ec2 that the pillars you got Aurora that's the key product and then sage making the other key product at the top of stack sets the tone for all these abstractions the machine learning trends tensorflow if you want to use it no problem we'll optimize it Amazon inference engine so elastic inferences blatant see they got a chip inference chip in inferential inferential so you put it the chip and the elasticity service together you have a powerful combination that comes out of the Annapurna acquisition from 2015 building and tuning and managing machine learning apps is gonna be a control area they're gonna add value on and the insights coming from this and marketplace capability so and then on and on and finally the Amazon on premise stack which is gonna be called outpost is the final nail of the coffin behind the strategy because think about it you're gonna put Amazon on premise with a hardware device that's gonna connect with the cloud with all the innovation and this is what amy jazzy talked about we're not just a software company we're hardware we're networking we're compute we're storage and we're software abstractions we're gonna bring that and put it in a device so we're gonna basically create a Amazon Cloud in a box search solution on premise to allow people to manage those Layton sees where network traffic is there this is not just a software only he's not gonna let people run their databases on their clouds it's the Amazon footprints I wanna make a comment about that I came in to reinvent with the with the statement that Amazon actually is winning in hybrid now they used to not talk about hybrid as you well know how can I make that statement when they don't really deliberately go after hybrid well the reason I made the statements cuz the entire ecosystem is connecting to Amazon s3 targets Direct Connect you've seen Amazon do things like snowball and VPC so now what Amazon is doing without post is they're putting the exact same hardware that Amazon uses in its data centers allowing customers to put that in their data centers the identical configuration control plane hardware etc that move is going to really even further extend yeah Amazon's lead and hybrid yeah and we're gonna have eme Jesse on Thursday day of coverage here we have Teresa Carlson on Andy Jessie all the topics David gave McCann you name all the AIG folks coming on as well we're gonna get into all this and extract the signal from all this signal out here of course RTS on VMware I forgot it the other way so there's so much this noise omus iot there IOT strategy is bottoms up and very very impressive gonna have Jerry Chen come on former VMware cloud guy now great little cray lakh venture capital partners over there he's gonna come on and analyze what's the only announcements we're gonna continue to tease out the announcement of course but a silicon angle comm check out all the news it's a tsunami of content coming out of reinvent totally game-changing days we're gonna wrap it up here we're gonna do more analysis so you'll see a bunch of videos on on our YouTube channel and stay tuned watching all week here for live coverage I'm chef Devaux want a stay with us we'll be right back [Music]

>> Live. From San Francisco, it's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. (techno music) >> Hello, everyone, welcome back to our day two of live coverage here in San Francisco, California for Google Next's conference called Next 2018, Google Next 2018 is the hashtag. I'm John Furrier with Dave Vellante. We're kickin' off day two. We just heard the keynotes, they're finishing up. Most of the meat of the keynote is out there, so we're going to just dive in and start the analysis. We got a tight schedule again, great guests, we have all the cloud-native folks comin' up from Google. We're going to hear from customers, and from partners. We're going to hear all the action. We're going to break it down for you. But first we want to do kind of a breakdown on the keynote, do analyze it and give some critical analysis, and also, things we think Google's doing great. Dave, day two, we've got three days of wall-to-wall coverage, go to the siliconangle.com for special journalism cloud series, a lot of articles hitting, a lot of CUBE videos, go to theCube.net, just check out those videos. That's our site, where all the videos are. Dave, day one, we had a great close yesterday; I thought it was phenomenal. But I thought we nailed it, today, too. And one of the things we were talkin' about in the first day close, editorially, was saying, hey, you know, this AI is super important. Today, in the keynote, more AI, more under the covers, more speed of announcements. Google kind of taking a playbook out of Amazon, let's get some announcements out there, I wouldn't say that the pace of announcements meets AWS, in terms of the announcements, but the focus is on a very few core things: AI, RollaData, Cloud-Native, Cloud Functions, Cloud Services Platform. This is the Google, that they're lifting the curtain. We're startin' to see some action. Your thoughts on the keynote... >> Well, I think you're absolutely right, I think Google realizes that it's got to compete with Amazon, from the keynote standpoint, demonstrating innovations, putting out a lot of function. I will say this, maybe it doesn't match Amazon's pace of innovation and announcements, but when you compare what these cloud-guys do with the traditional enterprise shows that we go to, there's no comparison. Even this morning, keynote day two, was drinking from a fire hose, there are dozens of announcements that Google made today. I would say just a couple of things, critical analysis, Google, everything is very scripted, as is all these shows, Amazon is very scripted as well, but they're reading everything, which I don't like, I would rather see them have a little bit more teleprompter, friendly, sort of presentation. So that's just sort of a little side comment. But the content is very good. The big themes I took away today, even though they didn't use this term, is really they're treating infrastructure as code. They're deploying infrastructure and microservices from code, as developers. So that was a theme that cut through the entire morning. Big announcement was the GA of Cloud Functions. It's been in beta, now it's Serverless, it's been in beta for a long time. And then a number of other announcements that we're going to go through and talk about, but those were some of the big highlights. But AutoML, I want to talk about that a little bit, talk a lot about developer agility. Threw out a couple of examples of customers, we heard from Chevron, we heard from Twitter, so they're starting to give examples, again, not as many Amazon, but real customers in the enterprise, customers like Mastercard, so, they're dropping some names... You're starting to see their belief manifest into actual adoption. But I'd like to ask you, John, what's your sense of the adoption bell curve, and the maturity curve, of the Google customer? >> Great question, I think for me, just kind of squinting through all of the noise, and looking at the announcements specifically, and how the portfolio of the show's going, it's very clear that Google is saying, we are here to play, we are here to win, we're going to take the long game on this cloud business. We have a ton to bring to the table, I call it the "bring out the Howitzers, the big guns." And they're doing that, they're bringing major technology, BigQuery, BigTable, Spanner, and a variety of other things, from the core Google business, bringing that out there and making it consumable; said that yesterday. Today, we looked at what's goin' on. You're seeing AI within G Suite. Leading by example, by demonstrating, look at it, this is how we use AI, you could use it, too, but not jamming AI and G Suite down the throats of the customer. AI and BigTable, I thought was pretty significant, because you can now bring machine learning and artificial intelligence, so to speak, into a data warehouse-like environment, where there's not a lot of data movement, data prep, it just happens. And then the Cloud Services Platform, the CSP, that Eyal Menor, the Vice President of Engineering, rolled out, I found interesting. The key move there was Cloud Functions. They now need to have Serverless up and running, and obviously Lambda's AWS. The uptake on the enterprise with Lambda has been significant, more than they thought. We heard that from Amazon, so I expect that Cloud Functions, and having this foundational layer with Kubernetes doubling down. The Kubernetes, Istio, and these Cloud Functions, represent that foundation. Knative open source projects, again, another arrow in their quiver around their open source contribution. This is Google, they're bringing the goods to the party, the open source party. This is an under-appreciated value proposition, in my opinion; I think a lot of people don't understand the implications of what's going to go on with this. This upstream contribution, and the downstream benefits that's going to come from their contra open source, is highly strategic. We used to call it, in the old days, "Kool-Aid injection." That's the way you ingratiate into the community with your software, ultimately the best software should win. There's not a lot of politics in open source, as there was once was, so I think that's fine. Now, to the question of migration, Google Cloud is showin' some customers up there, but I don't think they're going to, they're a long ways away from winning enterprises. What you see Google winning now is the AlphaTechies. The guys who were, and gals, who know tech, they know scale, and they can come in and appreciate the goodness of Google, they can appreciate the 10x advantages we heard from Danielle, with Spanner. These are what I call people with massive tech chops. They understand the tech, they've had problems, they need an aspirin, they need a steroid, and they need a growth hormone, right? They don't just need a pain-killer, they need solutions. These guys can make it happen. They jump in, take the machinery, and make that scale. The second level on the trajectory of their growth, on the adoption curve, is what I call, "Smart SMB, Smart enterprises." These are enterprises that have really strong technical people, where the internal conversations is not "if we should go to cloud," it's "how should we go to cloud?" And the DNA of the makeup of the technical people will decide the cloud they go with. And if it's engineering-led, meaning they have strong network operations, strong dev-team, then they have people who know what they're doing, they gravitate to Google Cloud. The third phase, which I think is not yet attainable, although aspirational, for Google, is the classic enterprise. "Man, I've been buying IT for years, oh my god, I'm like a straight-jacket of innovation, nothing's happening!" They're like, "we got to go to the cloud, how do we do it?" It's a groping for a strategy, right? So, Amazon gets those guys, because there's some things that shadow IT that Amazon can deliver, in more options, than what Google has. So I think I don't see Google knockin' that down in the short term, anytime soon. They can do plenty of business. Again, this is a trajectory that has an economy of scale to it, as an advantage, as a competitive advantage, by doing that. If Google tries to become Amazon, and meet their trajectory, the diseconomies of scale plays against Google. This is critical, Google does not want to do that, and they're not doing that, so I think the strategy of Google is right on the money. Nail the early adopters, the alpha geeks. Hit the engineering teams within the smartest companies, or small businesses, and then wait to hit that mainstream market, two, three years from now. So I think there's a multi-year journey for Google. Again, this diseconomies of scale is not what they want, they have tons of leverage in the tech, and the data, and the AI. So to me, they're right on track. They're now getting into the phase two. Smart. I give them credit for that. >> Let me pick up on a couple of things you said, and tie it into the keynotes from this morning. But I want to start with some of the conversations that you and I had last night, and around the show, with some of the GCP users. So, we've been asking them, okay, well how do you like GCP? Whaddya like? What don't you like? How does it compare with Azure? How does it compare with Amazon? And the feedback has been consistent. Tech is great, a lot of confidence in the tech. Obviously what Google's doing is they're using the tech internally, and then they're pointing it to the external world. It comes out in beta, and then they harden it, like they did today with Serverless and GOGA. The tech's great. Documentation has a little bit to be desired; we heard that as a consistence theme. Functionality not as rich in the infrastructure side as AWS, and not as enterprise app friendly as Azure, but very, very solid capabilities. This comes from people in financial services, people in healthcare, people from oil and gas. So, it's been consistent feedback that we've heard across the user base. You mentioned Knative; Knative is a new open source project, that brings Serverless to Kubernetes, and it was brought forth by Pivotal, IBM, RedHat, SAP, obviously Google, and others. Again, a big theme of the keynotes this morning was developer agility, bringing microservices, and services, and things like Kubernetes, to the developer community. Now, I want to talk about another example of a customer, Chevron. Is Google crushing it in traditional enterprise IT in the cloud? Well, no, you're bringing up the point that they're not. But, what they are doing, is doing well in places where people are solving data-oriented business problems with technology. Is that IT? It's not a traditional IT, but it's technology. Let me give you an example, Chevron was up on stage today, and they gave an example of they have thousands and thousands of docs, of topographical data points, and they use this thing called AutoML to ingest all the data into a model that they built, and visualize that data, to identify high-probability drilling zones and sites in the Gulf of Mexico. Dramatically compressed the time that it would have taken. In fact, they wouldn't have been able to do this. So they ingested the data, auto-categorized all the data to simplify it, put it into buckets, and then mapped it into their model, which was tuned over time, and identified the higher probability of sites for drilling. That's using tech to solve a business problem, drive productivity; Google crushes it with those type of data applications, really good example. >> And AutoML drives that, and this is where, again, a machine learning, AutoML, AI operation, we mentioned that yesterday, the IT operations sector is going to be decimated. But I think the big tell sign for me is when I look at the cloud shows, Amazon definitely has competition with Google, so that anyone who says Google's way far back in the market share, which you know I think is bastardized, I think those market share numbers don't mean anything because there's so much sandbagging going on; I could look at any one and say Microsoft's just sandbagging the numbers, and Amazon not really, if Amazon could probably sandbag the numbers even more by putting revenue from their partner ecosystem. Google throws G Suite in there, but they could throw AdWords in there and say technically that's running on their cloud, and be the number one cloud. What is a good cloud? When you have a cloud, if you can make a situation where you can take a customer and get them on the cloud easily, in a simplified, accelerated way, that is a success formula. What you heard on stage today was kind of, naw, I won't say underplayed, they certainly played it up and got some applause, is Velostrata and these services. They bought a company called Velostrata in May of this past year, and what they do is essentially the migration. We had a guest on, a user yesterday, migrating from Oracle to Spanner, 10x value, major reduction in price. They didn't say 10x, but significant; we'll try to get those numbers, she wouldn't say. But what Velostrata does is allows you to migrate to existing apps in a very easy, non-disruptive way, from on-prem to the cloud. This is the killer app for the leading clouds. They need tools to move workloads and databases to their cloud, because as clients and enterprises start to do taste tests, kick the tires in cloud, they're going to want to know what's the better cloud. So, the sales motto is simply go try it before you buy it. It's cloud. You can rent it. This is the value of the cloud. So, Amazon's done an extremely awesome job at this, Google has to step up, and I think Velostrata's one of many. I think the Kubernetes piece is critical, around managing legacy workloads, and adding new cloud natives. Between Velostrata, and the Knative, and the Cloud Functions, I think Google is shoring up their offerings, and it makes them a formidable competitor for certain workloads, and those early adopters, and that Stage Two, small, medium, or Smart enterprise, as a foundational element. I think that is a tell sign, and I got to give them props for that, and again, you can get an Oracle database into cloud, you're going to win a lot of business. If you can get an app workload running on Google Cloud seamlessly, in a very easy, meaningful way, it's just going to rain money. >> So let's talk about something we just talked about, how Google's not crushing it in traditional enterprise apps, but let's talk about some-- >> For now. >> of things we heard today, where they're trying to get into that space. So they announced today support on GCP for Oracle RAC, real application clusters, and exit data, and then SAP, via a partnership with Accenture. So Accenture does crush it with Oracle and SAP. Now, here's the problem: Oracle will play its licensing games, we've seen this with Amazon, where essentially, Oracle's license costs are double in AWS, they'll do the same thing for Google, I guarantee it, than they are in Oracle's cloud. So, 2x. It's already incredibly expensive. So, Oracle's going to use its pricing strategy to lock out competitors. So, that's a big deal, but we also saw some stuff on security: Cloud Armor, automatically defending against DDoS attacks, that's a big deal. We heard about shielded VMs, so secure VMs within GCP. These are things that traditional enterprises, it's going to resonate with traditional enterprises. >> Yeah, but here's the thing, then, we have one final point. I know we're going to run over a little bit of time, here, but I wanted to get it out there. You mentioned Oracle and the licenses. It's not just about Oracle, and their costs, and that disadvantage that could happen for a lot of people, and what cloud clearly has some benefits on a lot of cost. Here's the problem, like any Mafia business, Dave, we always talk about the cloud Mafias, and the on-premise Mafias. Oracle has an ecosystem of people who make a boatload of money around these licenses. So, you have a lot of perverse incentives around keeping the old stuff around, okay? So, as the global SIs, you mentioned Accenture, Deloitte, and others, those guys may salute the Google Cloud flag and the ecosystem, but at the end of the day, it's going to come down to money for them. So, if the perverse incentive is to stay in the old ways, saying "hey, okay, if we keep the license in there I get more better billing hours and I can roll out more deployments." Because what clouds do, and what Google's actually enabling, is enabling for the automation of those systems and those services, so you're going to see a future, very quickly, where half of the work that Accenture and Deloitte get paid on is going to be gone. From weeks to minutes; months, to weeks, to minutes. This is not a good monetization playbook for Accenture, and those guys. >> Well. >> So Google has to shift a ecosystem strategy that's smart and makes people money. At the end of the day-- >> No doubt. >> That's going to be a healthy ecosystem for every dollar of Google spend, it has to be at least 5 to 15x ecosystem dollars. I just don't see it right now. >> The big consultancies love to eat at the trough, as we like to say. But let's talk about the ecosystem, because you and I, we've walked the floor a couple times now. We mentioned Accenture, Cognizant is here, RedHead is here, KPMG, Salesforce, Marketo, Tata, everybody's here. UiPath, a startup in RPA; Cohesity's here. Rubrik's here, Intel's here, everybody's here, except AWS isn't here. >> Obviously. >> (chuckles softly) And Microsoft's not here. The other point that I think is worth mentioning, is again, big theme here is internally tested and then we point it at the market. Chevron, Autotrader, Mastercard, you're starting to see these names trickle out, other traditional enterprise. They announced today a partnership with NetApp for file sharing, for NFS workloads. So you're seeing NetApp lean in to the cloud in a big way. NetApps, back! You know you were seein' that. You saw Twitter on the Google Cloud. So you're seeing more and more examples of real companies, real businesses. >> I'll just end this segment by saying one thing quickly, the high IQ people in the industry, whether it's customers, partners, or vendors, are going to have to increase their 3D chess game, because as the money shifts around, the zero-sum game in my mind, it's going to shift to the value. Things are going to get automated either way, and that could be core businesses. So, the innovative dilemma is in play for many, many people. You got to be smart, and you got to land in a position, you got to know where the puck is going to be, skate to where the puck is going to be. It's going to require the highest IQ: tech IQ, and also business IQ, to make sure that you are making money as the world turns, because those dollars are up for grabs. The dollars are shifting as the new ecosystem rolls out. If you're relying on old ways to make money, you are in for a world of hurt if you don't have a plan. So, to me, that's the big story, I think, in the cloud that Google's driving. Google's driving massive acceleration, massive value creation, massive ecosystem opportunities, but it's not your grandfather's ecosystem, it's different. So we're going to see, we're going to test people, we're going to challenge it, we're going to have conversations here in TheCube. The day two of three days of live coverage. I'm John Furrier with Dave Vellante. Stay with us as we kick off day two. We'll be right back. (techno music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back to day two of theCube's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight along with my co-host James Kobielus. James, it's great to be here with you in the hosting seat again. >> Day two, yes. >> Exactly. So here we are, this conference, 2,100 attendees from 32 countries, 23 industries. It's a relatively big show. They do three of them during the year. One of the things that I really-- >> It's a well-established show too. I think this is like the 11th year since Yahoo started up the first Hadoop summit in 2008. >> Right, right. >> So it's an established event, yeah go. >> Exactly, exactly. But I really want to talk about Hortonworks the company. This is something that you had brought up in an analyst report before the show started and that was talking about Hortonworks' cash flow positivity for the first time. >> Which is good. >> Which is good, which is a positive sign and yet what are the prospects for this company's financial health? We're still not seeing really clear signs of robust financial growth. >> I think the signs are good for the simple reason they're making significant investments now to prepare for the future that's almost inevitable. And the future that's almost inevitable, and when I say the future, the 2020s, the decade that's coming. Most of their customers will shift more of their workloads, maybe not entirely yet, to public cloud environments for everything they're doing, AI, machine learning, deep learning. And clearly the beneficiaries of that trend will be the public cloud providers, all of whom are Hortonworks' partners and established partners, AWS, Microsoft with Azure, Google with, you know, Google Cloud Platform, IBM with IBM Cloud. Hortonworks, and this is... You know, their partnerships with these cloud providers go back several years so it's not a new initiative for them. They've seen the writing on the wall practically from the start of Hortonworks' founding in 2011 and they now need to go deeper towards making their solution portfolio capable of being deployable on-prem, in cloud, public clouds, and in various and sundry funky combinations called hybrid multi-clouds. Okay, so, they've been making those investments in those partnerships and in public cloud enabling the Hortonworks Data Platform. Here at this show, DataWorks 2018 here in San Jose, they've released the latest major version, HDP 3.0 of their core platform with a lot of significant enhancements related to things that their customers are increasingly doing-- >> Well I want to ask you about those enhancements. >> But also they have partnership announcements, the deep ones of integration and, you know, lift and shift of the Hortonworks portfolio of HDP with Hortonworks DataFlow and DataPlane Services, so that those solutions can operate transparently on those public cloud environments as the customers, as and when the customers choose to shift their workloads. 'Cause Hortonworks really... You know, like Scott Gnau yesterday, I mean just laid it on the line, they know that the more of the public cloud workloads will predominate now in this space. They're just making these speculative investments that they absolutely have to now to prepare the way. So I think this cost that they're incurring now to prepare their entire portfolio for that inevitable future is the right thing to do and that's probably why they still have not attained massive rock and rollin' positive cash flow yet but I think that they're preparing the way for them to do so in the coming decade. >> So their financial future is looking brighter and they're doing the right things. >> Yeah, yes. >> So now let's talk tech. And this is really where you want to be, Jim, I know you. >> Oh I get sleep now and I don't think about tech constantly. >> So as you've said, they're really doing a lot of emphasis now on their public cloud partnerships. >> Yes. >> But they've also launched several new products and upgrades to existing products, what are you seeing that excites you and that you think really will be potential game changers? >> You know, this is geeky but this is important 'cause it's at the very heart of Hortonworks Data Platform 3.0, containerization of more... When you're a data scientist, and you're building a machine learning model using data that's maintained, and is persisted, and processed within Hortonworks Data Platform or any other big data platform, you want the ability increasingly for developing machine learning, deep learning, AI in general, to take that application you might build while you're using TensorFlow models, that you build on HDP, they will containerize it in Docker and, you know, orchestrate it all through Kubernetes and all that wonderful stuff, and deploy it out, those AI, out to increasingly edge computing, mobile computing, embedded computing environments where, you know, the real venture capital mania's happening, things like autonomous vehicles, and you know, drones, and you name it. So the fact is that Hortonworks has made that in many ways the premier new feature of HDP 3.0 announced here this week at the show. That very much harmonizes with what their partners, where their partners are going with containerization of AI. IBM, one of their premier partners, very recently, like last month, I think it was, announced the latest version of IBM, what do they call it, IBM Cloud Private, which has embedded as a core feature containerization within that environment which is a prem-based environment of AI and so forth. The fact that Hortonworks continues to maintain close alignment with the capabilities that its public cloud partners are building to their respective portfolios is important. But also Hortonworks with its, they call it, you know, a single pane of glass, the DataPlane Services for metadata and monitoring and governance and compliance across this sprawling hybrid multi-cloud, these scenarios. The fact that they're continuing to make, in fact, really focusing on deep investments in that portfolio, so that when an IBM introduces or, AWS, whoever, introduces some new feature in their respective platforms, Hortonworks has the ability to, as it were, abstract above and beyond all of that so that the customer, the developer, and the data administrator, all they need to do, if they're a Hortonworks customer, is stay within the DataPlane Services and environment to be able to deploy with harmonized metadata and harmonized policies, and harmonized schemas and so forth and so on, and query optimization across these sprawling environments. So Hortonworks, I think, knows where their bread is buttered and it needs to stay on the DPS, DataPlane Services, side which is why a couple months ago in Berlin, Hortonworks made a, I think, the most significant announcement of the year for them and really for the industry, was that they announced the Data Steward Studio in Berlin. Tech really clearly was who addressed the GDPR mandate that was coming up but really did a stewardship as an end-to-end workflow for lots of, you know, core enterprise applications, absolutely essential. Data Steward Studio is a DataPlane Service that can operate across multi-cloud environments. Hortonworks is going to keep on, you know... They didn't have a DPS, DataPlane Services, announcements here in San Jose this week but you can best believe that next year at this time at this show, and in the interim they'll probably have a number of significant announcements to deepen that portfolio. Once again it's to grease the wheels towards a more purely public cloud future in which there will be Hortonworks DNA inside most of their customers' environments going forward. >> I want to ask you about themes of this year's conference. The thing is is that you were in Berlin at the last big Hortonworks DataWorks Summit. >> (speaks in foreign language) >> And really GDPR dominated the conversations because the new rules and regulations hadn't yet taken effect and companies were sort of bracing for what life was going to be like under GDPR. Now the rules are here, they're here to stay, and companies are really grappling with it, trying to understand the changes and how they can exist in this new regime. What would you say are the biggest themes... We're still talking about GDPR, of course, but what would you say are the bigger themes that are this week's conference? Is it scalability, is it... I mean, what would you say we're going, what do you think has dominated the conversations here? >> Well scalability is not the big theme this week though there are significant scalability announcements this week in the context of HDP 3.0, the ability to persist in a scale-out fashion across multi-cloud, billions of files. Storage efficiency is an important piece of the overall announcement with support for erasure coding, blah blah blah. That's not, you know, that's... Already, Hortonworks, like all of their cloud providers and other big data providers, provide very scalable environments for storage, workload management. That was not the hugest, buzzy theme in terms of the announcements this week. The buzz of course was HDP 3.0. Containerization, that's important, but you know, we just came out of the day two keynote. AI is not a huge focus yet for a lot of the Hortonworks customers who are here, the developers. They're, you know, most of their customers are not yet that far along in their deep learning journeys and whatever but they're definitely going there. There's plenty of really cool keynote discussions including the guy with the autonomous vehicles or whatever that, the thing we just came out of. That was not the predominant theme this week here in terms of the HDP 3.0. I think what it comes down to is that with HDP 3.0... Hive, though you tend to take it for granted, it's been in Hadoop from the very start, practically, Hive is now a full enterprise database and that's the core, one of the cores, of HDP 3.0. Hive itself, Hive 3.0 now is its version, is ACID compliant and that may be totally geeky to the most of the world but that enables it to support transactional applications. So more big data in every environment is supporting more traditional enterprise application, transactional applications that require like two-phase commit and all that goodness. The fact is, you know, Hortonworks have, from what I can see, is the first of the big data vendors to incorporate those enhancements to Hive 3.0 because they're so completely tuned in to the Hive environment in terms of a committer. I think in many ways that is the predominant theme in terms of the new stuff that will actually resonate with the developers, their customers here at the show. And with the, you know, enterprises in general, they can put more of their traditional enterprise application workloads on big data environments and specifically, Hortonworks hopes, its HDP 3.0. >> Well I'm excited to learn more here at the on theCube with you today. We've got a lot of great interviews lined up and a lot of interesting content. We got a great crew too so this is a fun show to do. >> Sure is. >> We will have more from day two of the.

>> From Orlando, Florida, it's theCUBE. Covering SAP SAPPHIRE NOW 2018. Brought to you by NetApp. >> Welcome to theCUBE, Lisa Martin with Keith Townsend. We are just wrapping up day two at SAP SAPPHIRE 2018. Keith, this event is enormous. We were just comparing our step goals. This event size is 16 American football fields. Enormous, 20,000 people. I think, combined, we have around 15,000 steps today. >> That sounds about right. >> Quite a few of them go to your longer legs than mine but this event is really been incredible, the energy that SAP's CEO Bill McDermott kicked off with yesterday morning has really been carried through this event and with our guests on the show for the last two days. >> No, we did 23, 24 interviews and every last one of them was high-energy. The guests were extremely excited about the products, the solutions, and the problems they're solving for, not just enterprise, but for society. I thought that was a really great theme of the guests today specifically. >> It's amazing, and you talk about, you know, the impact on society and SAP wants to be one of the top world's most valuable brands like Apple, Google, Coca Cola, who are all customers of SAP's and who all sell products that we can interact with, that we can taste, you know, Mercedes Benz, we can drive. They've got this invisible software product. They've been around for 46 years. And to your point, the stories that we have heard about how these invisible product, products, are transforming industries, are saving lives, was really something that I did not expect. >> Well when you make a great product that impact lives or... I compare it to making great content. theCUBE makes great content, that content would be found, people would take notice, you make a great product that impacts people's lives. It's no wonder that SAP is near the top of that brand recognition, brand value, 17th on the list. If they continue to do that, if they become the product, the ERP solution that you can talk to and you can ask a question, you know, not just business questions of what were the numbers the last quarter for Chicago, but you can ask a question, you know what, where is the best place to take my family to live in Eastern Europe during the summer months? That becomes value-add that people wouldn't be able to ignore. >> They've done a tremendous job building this partner ecosystem. There were hundreds of partner sessions alone. We've heard from a lot of their partners. We're in the NetApp booth, thanks to NetApp for having theCUBE here. NetApp is a customer and a partner of SAP and we heard a lot about how SAP is transforming to the cloud dramatically with the help of this massive partner ecosystem. >> You know what, we've had Microsoft, Fujitsu, SAP, NetApp, Nvidia, the list goes on and on of customers and partnerships of examples of companies that have come together and they've been consistent. In some areas, obviously Microsoft competes with SAP. In some areas, Microsoft competes with NetApp. But they recognize that without these alliances, without these partnerships, they can't solve these large, complex problems of ridding parts of Africa with mosquitoes. SAP can't do that by themselves. Microsoft can't do that by themselves. And this week was a great acknowledgement and a example of how the ecosystem works. >> They also talked a lot at this event about the intelligent enterprise where it's, you know, it's not just about digital transformation as table stakes. Companies that do it well have, or are working towards getting, this true 360-degree view of the customer which is essential. They talked about enabling that via certain things that they're leading in, or pioneering, which is connecting the demand chain and the supply chain. They really talked about enabling this new, this current SAP that's built for this fourth generation customer experience. Our lives as consumers have dramatically influenced business. We expect to have the ability to, you know, try and buy an app if we want it, right? And they're using that model very well to give customers in many industries, they have 390,000 customers, choice and flexibility. And the partner ecosystem is just part of that flexibility that they have to give. And they do a great job of listening to their customers who really are helping with a lot of the co-development in a very symbiotic way. >> Yeah, SAP is reentering this people-centric view of ERP, CRM, of data, saying that their relationship is about people. Bill McDermott spent a lot of time talking about trust. One of the reasons why people trust the brand of theCUBE is because we're on the ground, we're talking to the users, we're talking to the people. People can reach out and touch and feel you, there's a personal relationship between that brand and the community. The same thing with, got the same feel for what SAP is trying to do of, you know, obviously with over 20,000 people, I dunno if the number is 21,000, 22,000, but more than 20,000 people, a million people online watching the event, SAP the serious about this C/4HANA move, of being able to say, you know what, we are going to create a ecosystem of trust. We talked about trust with the app center and being able to validate applications on the platform. SAP has long been one of those companies that's serious about their partnerships and validation and certification of platforms. So whether it's HCI, storage with NetApp, the deep relationship with NetApp, SAP is going to put its brand upfront and say that if you're going to engage with one of our partnerships, there's a transient trust that goes from SAP to their partners. >> And we talked with a number of folks working in different groups within SAP focused on the customer. This morning we had on their Chief Customer, a guy from their Chief Customer Office who talked about these, kinda top 100 strategic accounts that they partner with who then also they take that information, those learnings and don't just improve the technologies but they also use them to influence much greater than a hundred customers. They're strategically utilizing that data. We talked yesterday with one of the gentlemen running the SAP four, S/4HANA community rather, and the Leonardo community and the amount of engagement that they have in that community, especially in Leonardo which has only been around for a year. The customer engagement is key but also their reaction to it, and I would say even, I think we heard a lot of how they're being proactive with creating content and enabling their customers to be able to learn at the same time as they're learning from their customers. >> Yeah some hero numbers that we heard this week: 6,000 people in that HANA, the S/4HANA community. While the Customer Success Group focuses on the top 100 customers, there were, I think 38,000 people following the Twitter account, so there's obviously outreached stretch. The Leonardo and S/4 communities have created a thousand videos on how-to. So obviously the impact of and the reach of SAP has ambitions of not just raising brand awareness and getting into that Top 10 with Apple and Google, they also have the ambitions of becoming a platform, a ecosystem. You know, we look at Microsoft as kinda one of the ultimate platform companies. Microsoft partners make more money off of Windows than Microsoft makes off of Windows. SAP seems to have the same goal of their partners, there's a hundred partners on the show floor, that should generate more revenue than SAP which would be impressive. SAP, I looked the other day, $136 billion market capital, not a small company at all. >> So you have an interesting perspective, for many reasons, but one you've run large SAP infrastructures before. And here you are now at SAPPHIRE from the press and media, the analyst perspective. What are some of the things that really surprised you in all of your experience as a user of SAP to now covering it from this angle. >> You know what, I don't know if it was a year ago. It was not even a full year, my anniversary for running my company is August. So less than a year ago I ran SAP for a large pharmaceutical. And we're in the throes of selecting where our next platform was gonna be hosted. Cloud was a possibility and it is amazing how the conversations have changed from my peers a year ago, or a year and a half or even a year ago, to now to how readily acceptable customers are of running mission-critical, the core of the business, 77% of the world's transactions, we heard today, goes through SAP, how willing customers are at running those work goals in the cloud. Second piece, which was probably a proof point, how much SAP has improved SAP in the cloud. SAP has marketed SAP HANA and SAP as cloud-ready applications, it was more of something that you... I took legacy application, I installed it on VMs in the cloud, cloud-ready. No we've given examples from the hyperscalers, specifically Google, of how, and Microsoft of how, customers are coming whipping their credit card up, spinning up instances of HANA, spinning them down. Google talked about how you can migrate your whole ECC on HANA to the cloud within 30 minutes to two hours, amazing movement in cloud. I think it's by far my biggest surprise coming to this show. I didn't expect SAP to accelerate their cloud adoption as fast as they have. >> I'm curious to your thoughts too about simplicity, simplicity of message, you know, what's their best-run businesses campaign? Best-run businesses run on SAP. Simplicity has long been part of their messaging. As we look at the SAP cloud platform and some of the announcements there today and you look at, they've got Ariba, and Concur, and Fieldglass, and SuccessFactors, with the C/4 announcement from yesterday, what is your impression on, have they been able to sort of simplify and kind of reduce customer confusion in terms of this breadth of products and technologies that SAP now delivers? >> You know, SAP is a big company and they have a lot of products. They've been around for 46 years. You know, we didn't talk about any legacy database stuff. They still own Siebel so they still own a traditional database company. It's easier said than done to simplify the message. When you come to... You know, we talked to interviewee after interviewee, customers are still overwhelmed when they look at a overall problem. They can even identify SAP as the potential partner to solve it, but 300 products is still 300 products. It's very... You can help simplify the message by throwing those products in categories, sales force, which product you lead with, so new customers, you know, sales force will help you with that. Traditional customers that don't have deep relationships with their sales force and solution providers, maybe, I think there's still a little difficulty around understanding the messaging around all of 300 products. I mean, it's 300 products. >> Well, there's always work to be done and well we have... There was a lot of product announcements, a lot of energy, and evangelicalism that you and I heard consistently throughout the event and on-set here. A third area that I think really struck me is, SAP has been very vocal about having an initiative to raise the profile of women in technology. They did an excellent job of getting women onstage during both keynote sessions, yesterday and today. From their CMO, Alicia Tillman, to Lindsey Vonn and a whole suite of women Olympic athletes that were yesterday in the general session, to some of the women that were doing some of these outstanding demos and I, I really tip my hat to SAP because for being as large and as lengthy of an incumbent as they are, they're really able to focus on some of these key areas and we at theCUBE love to cover that because it's something that really needs consistent awareness. >> Well, I dunno if people would notice but we probably, both of us, are very vested in diversity and Silicon Valley, in general, is always appreciated when companies go, not just acknowledge the challenge of diversity, it is a very, very difficult problem. It's probably one of the most difficult problems in our industry. So to actually put some meat on a bone, announce the problem, announce the challenge, and go forth and put, you know, obviously, extremely capable women and minorities in the forefront. >> Yeah. Well Keith, always a pleasure hosting with you. Thanks so much for working with me the last couple of days, it's been-- >> I always enjoy it. >> I do too. It's really been a really fun, energetic show so thanks for all of your help. >> Thank you. >> Keith and I wanna thank you for watching theCUBE. Lisa Martin for Keith Townsend, we're from SAP SAPPHIRE 2018. Thanks for watching. (energetic music)

>> Announcer: From Berlin, Germany, it's the Cube covering Datawork Summit Europe 2018. Brought to you by Hortonworks. (electronic music) >> Hello and welcome to the Cube on day two of Dataworks Summit 2018 from Berlin. It's been a great show so far. We have just completed the day two keynote and in just a moment I'll bring ya up to speed on the major points and the presentations from that. It's been a great conference. Fairly well attended here. The hallway chatter, discussion's been great. The breakouts have been stimulating. For me the takeaway is the fact that Hortonworks, the show host, has announced yesterday at the keynote, Scott Gnau, the CTO of Hortonworks announced Data Steward Studio, DSS they call it, part of the data plane, Hotronworks data plane services portfolio and it could not be more timely Data Steward Studio because we are now five weeks away from GDPR, that's the General Data Protection Regulation becoming the law of the land. When I say the land, the EU, but really any company that operates in the EU, and that includes many U.S. based and Apac based and other companies will need to comply with the GDPR as of May 25th and ongoing. In terms of protecting the personal data of EU citizens. And that means a lot of different things. Data Steward Studio announced yesterday, was demo'd today, by Hortonworks and it was a really excellent demo, and showed that it's a powerful solution for a number of things that are at the core of GDPR compliance. The demo covered the capability of the solution to discover and inventory personal data within a distributed data lake or enterprise data environment, number one. Number two, the ability of the solution to centralize consent, provide a consent portal essentially that data subjects can use then to review the data that's kept on them to make fine grain consents or withdraw consents for use in profiling of their data that they own. And then number three, the show, they demonstrated the capability of the solution then to execute the data subject to people's requests in terms of the handling of their personal data. The three main points in terms of enabling, adding the teeth to enforce GDPR in an operational setting in any company that needs to comply with GDPR. So, what we're going to see, I believe going forward in the, really in the whole global economy and in the big data space is that Hortonworks and others in the data lake industry, and there's many others, are going to need to roll out similar capabilities in their portfolios 'cause their customers are absolutely going to demand it. In fact the deadline is fast approaching, it's only five weeks away. One of the interesting take aways from the, the keynote this morning was the fact that John Kreisa, the VP for marketing at Hortonworks today, a quick survey of those in the audience a poll, asking how ready they are to comply with GDPR as of May 25th and it was a bit eye opening. I wasn't surprised, but I think it was 19 or 20%, I don't have the numbers in front of me, said that they won't be ready to comply. I believe it was something where between 20 and 30% said they will be able to comply. About 40% I'm, don't quote me on that, but a fair plurality said that they're preparing. So that, indicates that they're not entirely 100% sure that they will be able to comply 100% to the letter of the law as of May 25th. I think that's probably accurate in terms of ballpark figures. I think there's a lot of, I know there's a lot of companies, users racing for compliance by that date. And so really GDPR is definitely the headline banner, umbrella story around this event and really around the big data community world-wide right now in terms of enterprise, investments in the needed compliance software and services and capabilities are needed to comply with GDPR. That was important. That wasn't the only thing that was covered in, not only the keynotes, but in the sessions here so far. AI, clearly AI and machine learning are hot themes in terms of the innovation side of big data. There's compliance, there's GDPR, but really innovation in terms of what enterprises are doing with their data, with their analytics, they're building more and more AI and embedding that in conversational UIs and chatbots and their embedding AI, you know manner of e-commerce applications, internal applications in terms of search, as well as things like face recognition, voice recognition, and so forth and so on. So, what we've seen here at the show is what I've been seeing for quite some time is that more of the actual developers who are working with big data are the data scientists of the world. And more of the traditional coders are getting up to speed very rapidly on the new state of the art for building machine learning and deep learning AI natural language processing into their applications. That said, so Hortonworks has become a fairly substantial player in the machine learning space. In fact, you know, really across their portfolio many of the discussions here I've seen shows that everybody's buzzing about getting up to speed on frameworks for building and deploying and iterating and refining machine learning models in operational environments. So that's definitely a hot theme. And so there was an AI presentation this morning from the first gentleman that came on that laid out the broad parameters of what, what developers are doing and looking to do with data that they maintain in their lakes, training data to both build the models and train them and deploy them. So, that was also something I expected and it's good to see at Dataworks Summit that there is a substantial focus on that in addition of course to GDPR and compliance. It's been about seven years now since Hortonworks was essentially spun off of Yahoo. It's been I think about three years or so since they went IPO. And what I can see is that they are making great progress in terms of their growth, in terms of not just the finances, but their customer acquisition and their deal size and also customer satisfaction. I get a sense from talking to many of the attendees at this event that Hortonworks has become a fairly blue chip vendor, that they're really in many ways, continuing to grow their footprint of Hortonworks products and services in most of their partners, such as IBM. And from what I can see everybody was wrapped with intention around Data Steward Studio and I sensed, sort of a sigh of relief that it looks like a fairly good solution and so I have no doubt that a fair number of those in this hall right now are probably, as we say in the U.S., probably kicking the tires of DSS and probably going to expedite their adoption of it. So, with that said, we have day two here, so what we're going to have is Alan Gates, one of the founders of Hortonworks coming on in just a few minutes and I'll be interviewing him, asking about the vibrancy in the health of the community, the Hortonworks ecosystem, developers, partners, and so forth as well as of course the open source communities for Hadoop and Ranger and Atlas and so forth, the growing stack of open source code upon which Hortonworks has built their substantial portfolio of solutions. Following him we'll have John Kreisa, the VP for marketing. I'm going to ask John to give us an update on, really the, sort of the health of Hortonworks as a business in terms of the reach out to the community in terms of their messaging obviously and have him really position Hortonworks in the community in terms of who's he see them competing with. What segments is Hortonworks in now? The whole Hadoop segment increasingly... Hadoop is there. It's the foundation. The word is not invoked in the context of discussions of Hortonworks as much now as it was in the past. And the same thing for say Cloudera one of their closest to traditional rivals, closest in the sense that people associate them. I was at the Cloudera analyst event the other week in Santa Monica, California. It was the same thing. I think both of these vendors are on a similar path to become fairly substantial data warehousing and data governance suppliers to the enterprises of the world that have traditionally gone with the likes of IBM and Oracle and SAP and so forth. So I think they're, Hortonworks, has definitely evolved into a far more diversified solution provider than people realize. And that's really one of the take aways from Dataworks Summit. With that said, this is Jim Kobielus. I'm the lead analyst, I should've said that at the outset. I'm the lead analyst at SiliconANGLE's Media's Wikibon team focused on big data analytics. I'm your host this week on the Cube at Dataworks Summit Berlin. I'll close out this segment and we'll get ready to talk to the Hortonworks and IBM personnel. I understand there's a gentleman from Accenture on as well today on the Cube here at Dataworks Summit Berlin. (electronic music)

>> Narrator: Live, from Las Vegas, it's The Cube, covering IBM Think 2018. Brought to you by IBM. >> Hello, everyone and welcome back to our day two of coverage here in Las Vegas, where IBM Think 2018's The Cube's three days of wall-to-wall coverage day two. Yesterday, we had kick-off, kind of partner day. Today's really the kick-off of the event. CEO of IBM up on stage for the keynote. I'm John Furrier with Dave Vellante. Dave, we're doing seven years or so plus all these six shows coming down to one for IBM Think. It's a packed house; you can't even get through the hallways. Looks like they need to go to Sands Convention Center. >> Dave: (laughs) or Moscone. >> Or Moscone, or somewhere bigger, they need a bigger boat, but the keynote kicked off, Ginni Rometty was up there. Interesting, putting smart to work, quantam, blockchain, AI data and she kind of laid out the cloud strategy, you know, using data in public cloud and private. It's clear where they're going with the cloud. Your analysis of the keynote, what's your thoughts? >> Well, first of all, John, as viewers know, I mean, I'm a big fan if Ginni Rometty. I think she's been overly criticized, but I think she's a great presenter. When I compare Ginni's presentation skills with some of the other CEOs in the industry, I think she's far superior. She connects with the audience, she looks great, she's really cogent, she's well prepared, so, I really like her as a presenter and as an executive, and, you know, another women in tech, you know we love that. Yes, you're right, putting smarter to work was her theme. She's talkin' about 30 to 40,000 people at the event. There's too many people to count I guess. You can't really figure that out, and, so, it's big, it's packed. She also did a theater in the round which was different. I noticed last year ServiceNow did that. I really like that style, so that was kind of an interesting thing. Ginni talked about three exponential growth areas. So, I'll lay 'em out and then, we can talk about it. She said they come every 25 years. The first was Moore's law, and we all know what that is, and the second was Metcalfe's law, the value of the network increases exponentially if the nodes in network increase, and then, the third, which is upon us now, is data plus AI. Her supposition was that is going to usher in a next era of incremental growth, because you're going to out-learn the competition, and she used this term of incumbent disruptors, and I heard that and went okay, hold on, (Dave laughs) 'cause I don't see it that way. >> Yeah. >> I don't see the incumbents as the disruptors. So, that was my first reaction, and then, she brought up three customers, Verizon, and I'm like, "Verizon? "A big telco is a disruptor, come on! "They're gettin' a disruptor by over the top.", but the CEO came on, Lowell McAdam, talkin' about 5G, so we'll talk about that, and then, Maersk, IBM has a joint venture with Maersk, so, Michael White came up, he's the CEO of that. Now, Maersk is using blockchain, and Maersk we all know is the container company and they're attacking inefficiencies with blockchain, so I thought that was actually a really good example, and then, Royal Bank of Canada, RBC, came up. You know, banking, to me, is an industry that has not been disrupted yet, and, so, I, again, was initially negative toward this idea of incumbent disruptors, 'cause I don't think the incumbents are disruptors, and we'll talk about why I think that, but I thought IBM did a pretty good job of showing how incumbents can actually take AI and blockchain and, at least, defend against the disruptors. >> I mean, it's clear to me that she's obviously playing to the crowd with the digital debt transformation. I mean, we talk about these traditional companies, they need to transform, and she brings up Moore's law and Metcalfe's law kind of to take a view of the past, but to look forward, she's kind of saying, "Lookit, Moore's law make things smaller, faster, "cheaper, doubling every six months." That's just on the, I mean, this applies to IoT, quantum makes everything else. Metcalfe's law I think is very relevant, 'cause if you look at blockchains about decentralized internet, you're talkin' about decentralized applications, that's where blockchain will play the major enablement there, that's about network effects, so you bring network effects in with Metcalfe's law, Moore's law on the equipments on the hardware side, I like that, so, that worked for me. The disruptors, I think it's more of overplaying her hand on that, because I just haven't seen any evidence of any incumbents truly disrupting themselves. So, maybe you can talk with Microsoft, IBM's trying to transform, but at the end of the day, they got to look back and learn from the internet era. If you don't jump on these next waves, you could be driftwood, right? So, you got to surf the new waves, and I think that's what I heard her say is IBM is putting data at the center of the value proposition using AI as a front end for that, make it smarter, and then, using blockchain as an infrastructure and protocol level opportunity to take the IBM software and data plane and wrap 'em together. So, if you look at it, you got data at the center, blockchain on one side, and AI on the other, it's the innovation sandwich. That, for me, works for me, now, let's unpack that. How real is it, and that's going to be what we're going to talk about, and I think that's a good strategy. All the elements are in play. >> Well, I think the other piece of that sandwich, maybe it's the dressing on top, is the cloud, 'cause you have to have scale and network effects in order to achieve that innovation. I just want to mention, she talked about three other things that you are going to do as a customer. You're going to, one, leverage digital platforms, you're going to, two, embed learning in, virtually, every process that you do, and, three, you're going to empower humans. So, she put forth this idea of augmented intelligence, and, as I predicted yesterday, she, unlike Larry Olsen, she doesn't come right out and slam her competition, she does it in a classy way. She said, quote, "IBM is not "in conflict with your business." In other words, we're not taking your data and then, remonetizing it at the back end. That's a big deal, IBM makes a lot of noise about that. So, it's really augmenting humans, not in conflict with your business, and bringing advanced security to things like blockchain, >> Yeah. >> and cloud, and AI. >> I like her term security to the core, I like that, but that kind of gives the impression that's core to all things, but if you look at the megatrends that are impacting the incumbents and the people trying to do digital transformation, as well as the new startups, Dave, that are trying to get a new position in the landscape is clear. You got blockchain, you got decentralized apps, you got AI, but the data's critical, and she mentioned some cool things I like with the cloud which was she's saying, "Lookit, we'll make "the data a really big thing for you. "If you want it in public cloud, "you can have it in private cloud." So, she's looking at cloud as much more of a hybrid approach on private, kind of hinting at the GDPR problem that we know's out there. So, if you want to move your data around, that's a critical asset. Also, if you look at what's going on in the news today, these days, is Facebook is getting slammed because how they were hacked with the election, and other weaponization of data, this is a big deal for companies, and I think if IBM can play that card to leverage the data and have the confidence of the companies that they serve to say, "Lookit, data's got to be owned by you, "but has to be managed in a way that's dynamic, "whether it's a GDPR or some other regulatory issue.", and, believe me, blockchain's going to have some. So, you know, they could come out and get in the front of this new wave, and I think that's a good play. So, it wasn't just a recycled cloud show, it wasn't just AI Watson, I like how she put it together. >> So, just touching on a thing, you mentioned Facebook. So she talked about Moore's law ushering in this era of back office productivity. She didn't mention Wintel; I think it's still, probably, too painful for IBM to think about that. Metcalfe's law, she said ushered in, sort of, the Facebook era. I think that's fair, the network effect of Facebook, and then, she said, "Hopefully, you know, "they'll call this Watson's law." I don't know if that's going to happen, but that notion of, >> Wishful thinking. >> hey, hey, you got to be power of positive thinking, but that notion of exponential learning. I want to talk about cloud for a minute. You and I had some interesting debates yesterday in our open about cloud. Oracle announced its earnings yesterday, cloud growth 30%. I see Oracle and IBM as very similar in their cloud strategies; both companies would vehemently disagree with that, >> Yeah. >> but I think they are very similar in that sense. The street didn't like it, because Oracle cloud only grew at 30%, stock's down, okay, great, but, to me, IBM and Oracle are similar in that they're basically cloudifying their business. They're allowing their clients to onboard customers to the cloud, putting their applications portfolios, their SAS products, their middleware into the cloud, IBM putting mainframe class stuff in the cloud, they're putting power into the cloud, storage into the cloud, pretty much everything into the cloud if you want it. Now, that's not easy to do >> Yeah. >> if you've got, you know, legacy businesses, obviously, AWS has a blank sheet of paper, that was kind of your point yesterday, >> Yeah, yeah. >> but I like the differentiation that I see from the companies like IBM and Oracle, and there really aren't many others like that. >> Yeah. I mean, my point yesterday was the definition of cloud has been totally mangled, right? Like, it's different, if you're Amazon, they have a slew of services, they have more services than anyone else on the planet, and they have more people using those services, so, by that standard, Amazon is clearly kicking everyone's butt, but that's just their perspective. If you look at IBM, their services are applications, same with Oracle. So, if you look at what IBM's doing is they're taking the same approach. Services and applications are going to be IBM's view of the cloud, but IBM's taking a multicloud approach, and I think that's different, and, when you put the data as the central component of the architecture, you're basically saying, "I'm going to look "at the cloud as more of a commodity layer. "I'll let the customers decide which cloud to use.", and that's a better strategy, now, it's hard to do multicloud, so maybe they're buying some time, but I think that's a good, solid strategy to take if they're not going to be trying to push their own cloud as 100%, because not all customers will sole source cloud unless there's functionality that that cloud does. For instance, Amazon is winning the public sector business like it's nobody's business, because they have the only cloud that has the ability to do classified and non-classified cloud. Nobody else has it, so, from a log speck standpoint, they're winning everything and from the DOD, CIA, and government. What IBM has to do is go into customer requirement saying, "We're the only company that can provide this." That's a unique opportunity for IBM. I think that's a winning approach rather than going on a frontal arms race of services with Amazon, and that's what all the big guys are doing. Microsoft, Oracle, IBM are not taking on Amazon directly, because they're going to have to match feature for feature, and then, Amazon wins that game every time. >> So, I want to go back to something Sam Palmisano said when he was CEO of IBM in 2012 on his way out. HP was the hot company, Hurd was running the company, and he was asked, "Do you worry about HP?" He said, "I don't worry about HP, "'cause they don't invest in R&D. "I worry about Oracle, 'cause they invest in R&D.", and, again, what I like about Oracle and IBM, they both invest in R&D, IBM even, you know, core stuff around blockchain, certainly quantum computing and the like. So, I think that is a very positive dynamic for both of those companies. >> Well, I mean, IBM's R&D is a secret weapon, I think, for them; they don't overplay that much. They do talk about it, but we look at what blockchain potentially could be, and I think, you know, IBM's certainly doing the messaging on blockchain. It still has a bunch of ads on T.V., and they're trying to make that a kind of a global brand, but blockchain speaks to a new infrastructure, right? It's not just distributed computing, it's decentralized computing, and we were saying on the Cube and we've been reporting there is a new wave of software developers coming on the market that are going to be writing decentralized applications for token economics. The notion of tokens isn't about ICOs and those scams, although there's a lot of those going on. The notion of token economics fit with a mobile cloud decentralized architecture whether it's IoT, or end users, or applications, token economics is going to change the impact in efficiencies up and down the stat. So, to me, the developer community that's rushing into the market on the decentralized applications will be a major opportunity, but you got to nail the blockchain and that tech is just a moving train from a protocol standpoint to an infrastructure. So, to me, I like what IBM's doing with blockchain. I think that's going to be an opportunity to move the ball down the field. >> So, the exponential innovation formula, in my view of the next ten years, is going to, and you nailed it, going to combine data with artificial intelligence, or machine intelligence, and cloud economics, and there is a set of digital services emerging. >> Well, cloud and token economics, both, it's two. >> But, so, yes, but, so, and that's part of it, but there's a set of digital services emerging in this fabric, and they're not bespoke services, they're part of this integrated fabric. The extent to which people leverage those services, those digital services, to create new business models is going to determine success or failure. Data, at the core, is critical. >> Yeah, yeah. >> I think you're right on on that, but what I like is that IBM is trying to solve some hard problems with AI. >> I mean, lookit, I was tweeting yesterday all day on some highlights from my Puerto Rico trip on the cryptocurrency events we've been covering, and one thing that we reported was the killer app for blockchain and cryptocurrency and decentralized apps is money. Money is the killer app, and we see that with the hype cycle with the ICOs, but, if you look at what IBM's doing with the supply chain side of their business, perfect storm for supply chain innovation. Blockchain is about money, marketplaces, and nailing inefficient incumbents. So, if the incumbents want to be disruptive, they're going to have to disrupt themselves by removing inefficiencies out of the system. >> Well, and the Maersk example was a good one where there's inefficiencies, you know, 20% of the cost of moving containers is admin stuff. Sometimes the admin costs exceed the shipping costs. So, that was a good example, but, again, I see blockchain as one component in this fabric, in this puzzle. >> Day two, Cube here, kicking off wall-to-wall coverage. Three days of live broadcast talking to the thought leaders. Extracting the signal from the noise, the Cube, the number one leader in live tech coverage. Go to cube.net to check out all the footage and siliconangle.com to check out all of our articles. We're reporting and the team reporting all week, and that analysis of Ginni's keynote, well done, Dave. More coverage after this short break. (techno beat) >> Narrator: Robert Herjavec.

(upbeat electronic music) >> Narrator: Live from Nassau in the Bahamas, it's theCUBE! Covering Polygon '18, brought to you by Polyman. >> Welcome back everyone, we're live here at theCUBE in the Bahamas, this is the live coverage in the Bahamas for Polycon '18, I'm John Furrier, this is a wrap up of our day two. We're going to do show wrap up, brought in special analyst guest, Dave Vellante, they had to jump on a plane, head back to Boston, get out before the snow storm, to head to California. Al Burgio and I are going to wrap it up. Al, serial entrepreneur, founder of FuseChain, and CEO of FuseChain and DigitalBits, an open source project, had you on yesterday, we also were out scouring last night and getting all the data. You were the only Cube alumni at this event, now we add in another 20, good success, good to add more, thought leaders into the family, with Polycon, but big story here is the security token. I mean, I was talking to the founder of Polymath, and Genevieve with Grit Capital, and just my take is, looking at the ecosystem, it's been a sigh of relief on one hand, oh my god, finally, documents we understand accredited investors, no scams, a feel for a good, solid foundation to get funding, no rush to do a utility token, because although utility is super important, people were using utility tokens to get funding, using that money and running as fast as they can to build a product, sub-optimized kind of role there, so again, big news there. >> No, absolutely, it's been, it's the natural evolution and companies like Polymath and Secure Ties and others are helping with this natural progression and birth of the security token. There's clearly a lot of people here interested in that, lot of action, lot of new announcements at the event as well. >> John: What jumped out at you for news announcements? >> The news, I guess. >> John: Ecosystem news is big. >> If we go with the latest today, announcement with Barbados Stock Exchange, folks at Polymath, it's interesting. These emerging markets embracing new technology, it's the next wave and a lot of capital is going to be raised this way. >> What did you learn last night, I mean, first of all this event just for the folks watching, was a real interesting event, it was a 400 plus attendees, really an industry conference about, what the thought was, you had whales, billion dollars of whales here, called whales, which they have a net worth in billions and millions, hundreds of millions, then you have investors, variety of investor types and then entrepreneurs, all coming together. I heard a lot of different things last night, what did you hear? >> You know, it's interesting, I mean a lot of people were sharing their perspectives. Some are presenting different perspectives of the future, (laughing) >> Come on, spit it out! >> Others are, you know, really, in some cases, stating the obvious. But there's definitely a strong ecosystem that's coming together here, strong alignment on a number of things, irrespective of where everybody's sort of come from or the industry that they're in. A lot of people want to see this new ASA class, come and grow and be very successful. So, you had YouTuber influencers here, you had CEOs of well-established organizations, and up-and-coming CEOs of a lot of these blockchain emerging companies. There's definitely tremendous synergy amongst some of them as well, in terms of how they're sharing perspective, and how they're, in some cases, working together. >> Liquidity has been a big option, I heard people talk about liquidity. What's your take on that? What's your observation of how that's evolving? >> Well, I think there's a huge opportunity with areas where traditionally, they've lacked liquidity. Or there's been minimal liquidity, tremendous friction and challenges in terms of being able to leverage what one possesses. Blockchain really presents a huge opportunity to change the game there, as it relates to DigitalBits and what we're focused on, we see a huge opportunity in all things loyalty rewards. There's in a lot of cases, these centralized organizations, you can kind of think of them like a central bank, and people have had these difficulties in earning points, if it's a pair of golf clubs you want, you maybe have to earn points for maybe three years and you get tired after a year. >> That's your venture. >> Yeah. >> I mean FuseChain and DigitalBits specifically is solving a big problem. >> Big problem, there's tremendous lack of liquidity in all things loyalty rewards. >> What's your angle of attack there? Obviously disrupting the pre-existing and somewhat fragmented loyalty programs. I mean, I'm in so many, I don't even use the airlines things anymore. I get so many points, I never use them, I try to use the good ones that I use a lot, like Southwest or whatever, as an example, I use because my kids need to fly to an event or soccer or whatever. But other ones, I've lost all my points. I don't even know the number. I mean, where the hell is it? >> Well it's. >> What email address did I use? >> It's about perceived value, right, maybe you started off with some degree of enthusiasm and had a higher perceived value, but then towards the end it goes to nil. 'Cause it's really. >> John: But I can't get (mumbles) with my points. This is the problem I want to ask you. >> Traditionally, what you see now, a few weeks ago we saw announcement by Singapore Airlines, announcing by August their existing loyalty programs and we place them into a blockchain. We're seeing examples of this almost every week now, companies are embracing blockchain technology and what this allows for now is a more frictionless transfer of points. So, for those companies that are embracing blockchain technology, if you have points, and yeah you could potentially, after you have X number of points, go and redeem them for something you like, but in the meantime, you get discouraged, maybe you love Southwest, but maybe some of these other programs, you could trade them and hand them over to someone that actually could take advantage of it and get an alternative asset that you have a higher perceived value for. >> Digital currencies and gaming has been around for a while. We've seen the young guns get that, that's like a fish to water. Obviously loyalty has different assets than old school techniques, old stacks, technology, if that. So anyway, I ask you the question, how is blockchain disrupting the loyalty program that is the massive billions of dollars being spent and earned in that market? >> A third of points never get redeemed. There's a huge problem with many corporations, they have, as they're issuing points, it's a liability on their balance sheet. More points get issued, it's a hemorrhaging issue. It could potentially create solvency issues for companies. There's actually been professors from some reputable organizations that have really done a tremendous research in this area, it really evolves nicely into what blockchain can do. >> Like, give me an example, I mean what is the disruptive nature of it? Is it storing of the value? Is it trading on that value? Is it, I mean what is the real one thing that blockchain does to the loyalty program? >> The fact that it allows for a more frictionless transfer of points, so for the programs that are tokenizing their points on a block chain, it empowers the user to be able to directly transfer those points. >> So you guys of FuseChain and DigitalBits, you're tokenizing loyalty. >> We're supporting organizations, our big mission is to support organizations that have either existing loyalty programs or wishing to create new loyalty programs to be able to tokenize those on chain, and the ability to then allow the consumers, the users of these points programs, to, in addition to the traditional uses, redeeming them perhaps in a rewards store or what have you, the ability to transfer them for other assets that they like. >> John: So if I understand this correctly. >> Other points that they like. >> The trend that you like, or would like to see continue or happen, is retailers or loyalty programs would tokenize themselves. So, there'd be, literally, thousands and thousands of loyalty tokens and you would be the platform to support that? >> That's correct, absolutely. So, I've used the sort of red hat analogy, we have FuseChain as well that's really focused on helping support enterprises that maybe are struggling to spell blockchain. But they see all the value. >> That's everybody. >> Well from a technology perspective. Similar to Linux being born, enterprises needed to go to companies like a red hat, to support them with the integration, maintenance, so on and so forth of such technology. We're focused on having an evolving ecosystem of other organizations that can support enterprises that have loyalty programs, consume blockchain technology. >> You're a tech entrepreneur, I'm a tech entrepreneur. I have a media business, you're building another business, you sold your last business, you're very successful. You and I always talk about this, but I want to ask you here live on theCUBE, as a tech entreprenur, what is the opportunity that this ecosystem of tokenizing your business, using blockchain, how do you look at it and how would a solid tech entrepreneur look at this opportunity to integrate it, a new enabling technology, what's the orientation, what's your view on how tech entrepreneurs should look at it, and how do you look at it? >> Well, so, if we just, as it relates to the liquidity issue, this is a very powerful thing. Right now, perceived value for many points programs is very low. So, if the perceived value, you solve the liquidity issue or you create technology that can help solve the liquidity issue, the opportunity for the perceived value to be perceived in a more optimal light, everybody kind of wins. The merchant, the business that is issuing these points, they now have a more desirable asset that they're issuing, and as a result of that, consumers have an ever-growing desire to want to be part of these programs and earn points. So this is, it's fascinating when you start to think of it, in terms of. >> Technology is applying, 'cause it's the application of societal impact, whether it's a retailer or a non-profit, tokenization is happening. >> Absolutely, and it's happening obviously, not just in loyalty rewards, we've seen it happen, starting to happen now in other spaces, and with different. >> John: Your big takeaway, obviously. >> ASA classes. >> You've done a lot of work, and I know you can't talk about it 'cause you're in start-up mode and you're doing some financing right now, but just generally speaking, and I'm totally, the landscape of this ecosystem, health-wise, feels like the security token has been a good thing, utility token is still evolving, under observation, obviously SEC and other regulatory challenges, good, bad, ugly, I mean still scams out there? We're hearing the community loud and clear, we're going to stamp out the scams and flush that through the system, as fast as possible. Your take on this ecosystem? >> I think those that are taking their time to build great technology and doing it at the right pace will build great products and ideally do it at such a rate and in such an order that they'll stay out of trouble. (laughs) We're seeing a lot of great entrepreneurs come together, surround themselves with their own ecosystems and building great platforms. I think where we see others that are moving a little too quickly, they might trip on their shoelaces. >> Yeah and people don't, I mean the general consensus is "You're going to move fast, but you don't want to be in jail." Literally, I heard that quote here on theCUBE. (laughs) Investors we've been meeting, we've had on theCUBE but also we've chatted, I know I've seen you chatting, sidebars, I've had a lot of sidebars, Dave has as well, conversation among investors, not necessarily with you, I know you can't talk about it, 'cause that's, it's a hot deal, but I mean, in general, generally speaking, what's the conversations in the investor landscape that you're seeing and hearing here? >> Its interesting, everyone is trying to find their own point of view or speculating in terms of what's going to happen next. I've heard comments in terms of arbitrage as a result of income tax, people realizing that transferring between alt coins is actually likely taxable, and accountants making new investors in the space aware of these things, and having to potentially sell to be able to pay that bill. Then there's others where a lot of us are seeing this as an emerging technology, the actual use of certain, let's say, utility coins, it has not yet been demonstrated. That doesn't necessarily suggest that a particular project is bad, things do take time, I mean, we saw in the 90's with the internet, I mean, remember starting in that space, I call it the dial-up modem era, (laughs) You know, but we had these big visions of video, and theCUBE could not be possible at that time. But the vision of a Cube could be, you know, a wonderful thing, people could've bought into that. You kind of ride the trend, evolve your technology, and then you disrupt and you help change the game. >> Final question, obviously your business is, you're doing some things here, how did the show go for you here? You feel good about it? >> Absolutely. Obviously this is not like an Amazon, some of the other events we've been at but. >> It's more intimate. >> But. >> John: But there's money here, there's billionaires here. >> Absolutely, and look at any of those type of events, I mean they start with thousands, and tens of thousands, and the next year it's twenty thousand, we're going to see that kind of growth in this space as well. It's great to be involved in it early, but there's definitely quality, high-profiled individuals here, high net worth individuals, and they're investing their money in this space and they're going to help drive it forward. >> I remember the first show we did with Amazon and meeting Andy Jassy for the first time, first of all, really like him a lot, sports fan like me, but he's also really smart, a great operator, he made a comment that some of the best companies are ones that are misunderstood in the beginning, obviously we run a different kind of media business, people don't really understand us, cryptocurrency and blockchain is funny because everyone understands it, but doesn't understand it. (laughing) They understand how big it's going to be, and there's money involved, so that's the key learning that I had this week, was, yeah, we see the big opportunity, we can see money being made, but people still don't truly understand what it is. If you talk to all the smartest people, whether it's Jeremy, that came on at 26 years old, to Bill Tie, they say, "We're learning, everyday." The women in tech, the CryptoChicks came on and said, "This is learning environment, "this is still not understood." >> Absolutely. >> "And this is the big opportunity." >> It is a huge opportunity. In the early 90's, people didn't understand the internet, and there's a classic program episode of The Today Show, and I think it was Bryant Gumbel trying to understand what is the internet, you know, and so forth. Fast forward, here we are. Fascinating things, there's smart individuals that can see and embrace the vision right away, others were scratching their head but eventually, we'll all get there. (laughs) >> Al, great to see you and great to see a Cube alumni here too, I'm glad you were here, 'cause I get to know at least one person that I know intimately of Cube alumni. We added 20 more new Cube alumnis, the sun is setting here in theCUBE, day two of wall-to-wall coverage, I'm John Furrier, really excited to have been part of this event, it begins, kicks off our 2018 cryptocurrency tokenizing the world, blockchain, top events, theCUBE will be there, theCUBE is there, it's relevant, we're going to be tracking all the signal, and extracting it from the noise and sharing it with you. It's a wrap up of the cryptocurrency token economics decentralized internet at Polycon 18, here in the Bahamas, thanks for watching. I want to thank all the crew here, great job, and you guys watching. More to come! Stay tuned, check out siliconangle.com, thecube.net, and wikibon.com, of course, CubeCoin coming soon, stay tuned for what we're doing love to tokenize that business, everyone's doing it, it's really relevant and thanks for watching. (upbeat electronic music)

(techno music) >> Narrator: Live, from Barcelona, Spain, it's theCUBE, covering Cisco Live 2018. Brought to you by Cisco, Veeam, and theCUBE's ecosystem partners. (techno music) >> Okay, welcome back everyone. This is theCUBE's exclusive coverage. Day two. We're wrapping up the show here at Cisco Live 2018, in Europe. We're in Barcelona, Spain. The past two days we've been here. I'm John Furrier with Stu Miniman, talking to the most important people at Cisco, the top executives, some developers, and really kind of getting the lay of the land. It's the first time theCUBE has been at Cisco Live in its existence, so it's great to be here. Stu, Cisco Live, a lot of smart people. So it's great to have theCUBE. The Cube fits beautifully with Cisco Live because you've got people sharing, you have great, smart networking guys, but they are also doing applications. This is really an awesome opportunity because this is like the perfect storm for Cisco. This is an opportuity to galvanize their base, grow them into the new talent to move forward in this cloud edge world, where the network needs to be more intelligent. This is your wheelhouse. You have been covering this for a long time. >> So, John, yeah, I was looking forward to this. It's been years since I've attended Cisco Live in person. There is a term we haven't talked about a lot this week, but I think it fits. It's the digital transformation. And Cisco is in the midst of this transformation. I said in our open on day one, my barometer was going to be, look how is Cisco doing becoming a software company? Of course, things like IOS have been in the guts of what they did from networking, but being here in the DevNet Zone. DevNet, Susie Lee's team, really helping to drive some of that transformation. We had a great conversation with Rowan talking about the future. Talking about apps. Talking about so many of the different things that Cisco is doing to not just be boxes and ports. Hardware still an important piece. >> Yep. >> I'm actually concerned that maybe they have a little bit of that hardware holding them back a tiny bit because Cisco has skills there. They have lots of expertise. It might be mostly software, but even when they talk about things like collaboration there is hardware underneath a lot of that. >> Stu, Rowan Trollope, who is the SVP, general manager of the applications team, is the rising star. He is being promoted, and watch. This is a signal from Cisco. They recognize it. So, we heard from Andy Jassy at AWS re:Invent, there is the old guard meaning, they are talking about Oracle, and then the new guard, trying to obviously position themselves as the new guard to carry customers into the future. Rowan Trollope, on his keynote yesterday, who the big story cause the CEO wasn't here. He was the lead dog. So he's getting promoted. He was telling about the future. So the question I have for you is, as an analyst, is Cisco an old guard, or are they a new guard? Or, are they moving to be a new guard? What is your opinion? >> Yeah, too soon to say. Cisco was one of the four horsemen of the internet era. Absolutely, they should have a place going forward. But look, they're not one of the big public cloud providors. They don't sell a lot to the hyper-scale players. But, they have a very strong position in a lot of places. Still dominant in traditional networking. Do very well in collaboration. Have a lot of software pieces. They have made a number of acquisitions. The telecompany we had tracked before doing well inside of Cisco. AppD, lot of buzzwords going on. We got to learn a bunch about Spark this week, John. Heck, even little tidbits I got. There is these two colored globes sitting behind you. It's like, oh, it's Alexa apps. And there's been people doing developer labs this entire week. So Cisco, part of helping to educate and do that transformation. Other companies, like Pivotal, is a partner. Lots of partnerships. And not just the traditional infrastructure companies, but we heard about what they are doing with Google, with Apple, and others. So, I'm not ready to anoint Cisco as a winner in the new world. But, if multi-cloud, which I'd love to get your take on, What you think with the multi-cloud strategy is. But, Cisco at least has a right to be at the table. They've got strong customer relationships. Strong in the enterprise. Strong in service providers. >> But if Oracle is an old guard, then why isn't Cisco? I mean, Oracle is plumbing. They have these database deals. They're not going anywhere soon. So, you can make an argument that Oracle is not going to be displaced anytime soon, cause they have the massive deals. But a lot of people will say, and we even said, that Oracle's relevance is waning with new database growth happening outside the proprietary database. So, is Cisco relevant? >> Yeah, John. It's a good question. So for one piece, if you say okay how are they doing on the transition to becoming recurring revenue rather than boxes, they still have quite a ways to go. They are not far enough along that journey. But, my measuring stick was how much are they a software company? How much are they an infrastructure company? They're kind of straddling the line. They are moving up the stack. More than some of the other initiatives in the past. It's taken hold. Thousands of people, so I give them good marks, John. What's your take? >> I mean, I don't know. I think, here's my take on Cisco. Cisco knows the networking. You can't, like I was saying with Oracle, they're not going anywhere. No one is going to rip out Cisco and replace it. There's nothing else to replace it with. I mean, there is no other competition, really. The competition to Oracle, I mean Cisco, is not being on the right side of history. So to me, I think Cisco should be worried about one thing, making the bet wrong on architecture. So, they own the network. The other thing that people don't know about Cisco, that is a competitive advantage is, they know the edge of the network. They have been doing edge computing since it existed. So, okay sending it out to IOT is not a big deal, in my opinion. I think that is going to be an easy get for Cisco. Extending it to wireless, they have that with their deal with Jasper. That's interesting. That's going to be a game changer. But that's not going to be their problem. Wireless, human, cars, that's the new edge. That's just an extension for Cisco. That is a major advantage. So competitively speaking, I think that is a real point that they are going to really nail home that a lot of people don't understand. The second thing is that their DevNet program is showing that they're upgrading and advancing their capabilities up the stack and bringing along with them their entire developer consistencies, which were essentially network engineers. So, they were once the rock stars, those network engineers, of any enterprise. You go into any enterprise you say, the network engineers, they ran the show. Now, the threat is coming from alpha perspective from developers. So now you have this kind of dynamic going on Stu, where the network engineers need to move up the stack to meet the new developers, and that is where the rubbing is going on, right. That's where the action is. That's what DevNet's doing. They're doing a masterful job, in my opinion. They are not over driving, not overplaying their hand. They are in the cloud native rule with DevNet Create. So I think their best move is to just continue to march down that path, but they got to own the IOT edge. Without the IOT edge, Cisco could crumble. >> Yeah, so a couple comments on that, John. One, IOT, Cisco started messaging IOT really early, and they've gone through a couple of iterations, so that what they're talking about IOT wasn't what they were talking about a few years ago. I like their story much better today. Absolutely, both from a wireless standpoint, they have got the hardware gear like Meraki, they talked on stage. From the software standpoint, like Jasper. One of the areas we got feedback from the community, John, they are talking about containers and Kubernetes, sure. They're not involved with serverless yet. And that is a blindness. Is it something that the big public cloud's are going to do there? >> Well I have an opinion on that. >> They're, I'm sorry? >> I have an opinion on that. >> Okay. >> Cisco is running billion dollar partnerships. They're doing billions of dollars in revenue. So I think you can't really judge them there by their participation in these open source projects yet. I think they've got to bring something to the party quickly. I think it's too early to tell, I would agree with you on that point. On this piece, they've got to go to open source. And they've got to figure out a way to do it in a way that is not distracting from the core mission. If I am Cisco, if I'm advising the CEO, I'm like, march with the network as the value, maximize the software play, and don't blow off open source. They cannot blow off open source. Are they brilliant at open source right now? Outside of Lew Tucker, who do we see? >> Look, no. I mean, from a network standpoint, Cisco has been involved across lots of projects, not just open stack containers. We've talked about what they are doing with Kubernetes and Istio. >> Give them a grade, open source, give them a grade. A, B, C, or D, or F? >> You know, I tell you at least a strong B. >> Okay, that's decent. >> Yeah, I mean look, they are not monetizing open source. They're not rallying around the flag. They are doing great with developers, which John, I guess we say, is it contributing for contributing sake or how does it fit in the business model? We did a couple of interviews here where it said, no open source, we're not negative on it. They're not pushing against public cloud. They're not against these things. It just doesn't fit as much into their environment. >> I think the multi-cloud thing, well getting back to you're question about containers. So containers are being commoditized. Red Hat just bought Core OS. Docker's Docker. Docker's got a business model challenge. We've reported on that, Stu. And we're doing a feature report on it now. And so what are they going to do? But still, container is a goodness. People like containers. Is it super complicated? Not really. Is Kubernetes strategic and important? Yes, that's obvious. So the service mesh is interesting to me. And I think the net devops positioning that they announced here, Cisco is bringing this devops culture to the networking world. They are kind of creating a new devops ethos at a networking layer. I think that's going to be a really, really big deal. And that is either going to be a go big or go home situation. It is either going to work like a charm, or it's going to fail miserably. So, what do you think? I mean the smell, it lines up with Istio, it lines up with Service Mesh, programmable infrastructures, managing micro services. I mean, it kind of hangs together, Stu. What do you think? >> Yeah, I mean, John, it goes along with the whole trend we have been seeing. The people that were managing the network can't be managing devices, or even groups of devices. Intent based networking is one of the big items coming into here. It's how do I let the machine learning, the programmability help me in this environment because it is only going to get more complicated. The edge you talked about is critical. IOT keeps growing. And it's not something that people alone can do, it needs to be people plus machines. And I've seen nice maturation of how Cisco does this. Cisco, to be critical on Cisco for the last decade, is thy have thrived in complexity. And I think they are trying to get over that some and shift their model to more of a softer model. >> Well, Stu, I think you nailed that this. So here's my take. Software model allows them to scale. With machine learning, they can do what Facebook and Google has done. So if you go to Google, for instance, how they manage their data center, they have site reliability engineers. They have changed the IT model to scale the number of machines that they have. The number of devices that are coming on the network cannot be physically managed by people. So this means machine learning and software has to automate. That is Cisco's opportuity. I'm not seeing it clearly right now, but if that's what they're talking about, that to me will be the tell sign. If Cisco can create a site reliability engine, like what Google did for networks, that's a game changer. Alright Stu, final thoughts. Let's go through, let's riff on what we saw here. Obviously Barcelona great city. The weather's been phenomenal. It's been really great. Good food, good tapas. But Cisco, good vibe. Cube in the DevNet Zone, it's been really interesting to watch. People love the labs. It's very chill and relaxed, but very active. The keynote looking forward, not looking back. Notable point, the CEO wasn't here. So that to me-- >> It's the end of the quarter and he was just at Davos, and there is a bunch there. He didn't come last year either. >> John: Okay. >> But Chuck will be at the Orlando show. Hoping we'll have him on theCUBE when we go there. We're going to be at the Orlando show. We've got theCUBE at the DevNet Create show again. And John, chill I think was the right word. And part of me is wondering, is it because we are here in Barcelona and it is just a relaxed atmosphere of a city. I've really enjoyed it this week. Or, network people, it used to be a little bit uptight. I mean, it's the risk and fear are things that kind of ruled in networking before. And people seemed a little bit more chill here. >> Pros and cons, Stu. Or observations that were good and not so good? Observations to me were, on the good side, was a lot of activity in the DevNet Zone. A lot of energy in the hallway, and in Barcelona wise. There was a lot of European flavor. The signal I thought was good was the keynote was packed. You and I thought it might be empty, right. But people strolled in. They packed every seat. The other area is that you can just tell people were interested in the new direction. The critical analysis to me would be, I didn't hear enough data driven. I want to see more data driven, but I didn't want to hear AI is changing the world. I want to see real, practical examples of data-driven impact to data center and I wanted to see more meat on the bone on multicloud. Because I didn't really see much there, I just heard about it. It was almost like a, "we're going there," not a lot of data driven, not a lot of multicloud. Outside of that, I thought it was really, really a great conference. >> And John, we had some phenomenal guests here. So on the data driven piece, Michelle Dennedy, the Chief Privacy Officer, really good piece and she said, oh, you guys are missing it if you didn't hear the data-driven. And she drove home in the interview with us how Cisco is involved there. So, John, there is a lot going on. Cisco is a big company. Big show. There is a lot we are not going to be able to get. Reaz Rehan, got the IOT piece, seeing some new players. Really helping to shift along this transition. Love Susie Lee's discussion about the four year transformation that we are talking. And Rowan, strong executive, good bench at Cisco. Stock has been up, like most of the tech stocks the last few months. >> I mean, we forgot to mention that, good point, Stu. New sheriff in town on IOT, that was a great interview. Again, Susie's at DevNet's hit a home run here. She's got a great group she's developing. Awesome stuff. >> So last thing, John. If Chuck Robbins gave you a call and said, Hey John, I've got that 10, 20, 30 billion dollars that I might be able to play with. Any final advice for him? >> I would really sure up the collab stuff. I think there is a distraction there from the sense of that I get why its developing. But if you use WebEx or all these tools, you're biased. You don't understand, it's the tools you use. You're just going to use it. I think that is a great data. And I think that the collab apps, if you look at it not as a software play, but as an IOT edge device, data-driven device. That's a good play. So I like the direction. I would throw a lot of dough at the collab and make that an IOT edge feature. Cause they can cross connect great data from WebEx to Spark. And I think Spark feels like an app. I want to see, it's not an app. It's a platform. >> Look, it's a messy space. Who leads in those spaces tends to be a lot more the consumer companies that did this. Cisco killed most of their consumer stuff. Then they did, after they had Flip in the set top boxes. So very different Cisco. What assets do they have? >> But to answer your question, Stu, what I would say, I would say Chuck, own the edge. This is a strategic imperative. I would throw the kitchen sink at owning the edge of the network. That means from the core to the edge, and I'd push that edge all the way to the wearables. All the way to the implants in your brain in the future. Own it end to end. Lock that down. Make it dynamic. Make it programmable. That is a holy grail moment and to me, lock it down. And everything will fall into place. You'll have cloud traction. You'll have app traction. Everything will happen. >> And they don't need to be the owner of the public cloud to be successful in what you said, John. So good strategy, I like that. >> Alright, theCUBE, with all the strategy for the CEO, Chuck Robbins, who's watching. Chuck, good to see you. Thanks for having us at Cisco Live. Stu, great analysis. I want to thank all the guests, thank the crew here. Tony Day and the team, and Brendan and Brian, great job. And all the people back home at theCUBE network and theCUBE network operating center in Palo Alto and Boston. This is live coverage. This is our wrap-up from Barcelona, Spain. Cube is calling it a day here at Cisco Live 2018 in Europe. Thanks for watching. (techno music)

>> Announcer: Live from Madrid, Spain, it's The Cube covering HPE Discover Madrid 2017. Brought to you by: Hewlett Packard Enterprise. >> Welcome back to HPE Discover, 2017 in Madrid. This is The Cube, the leader in live tech coverage, my name is Dave Vellante, I'm here to rap with my co-host, Peter Burris. >> Hey, Dave. >> Dave: Good couple a days. >> Oh, you know what I just discovered. I discovered The Cube is the antidote to jet lag. (laughs) >> That's right, when you get interesting people on. >> Oh, man. >> It pumps you up. >> Totally. Just unbelievable, exciting and it's all framed by... Well let's start where we talked about yesterday, we proposed that increasing what we're seeing in the industry is the new model of computing being established by Amazon and then the other poll, where it was known, we know that it's not all gonna be one cloud, it's not all gonna be a central cloud model, or essentialize cloud model. There's gonna be other places where data's gonna need to be processed. >> Dave: Well, that's what we believe. >> That's what we believe, and... There's physics behind that statement. There's legal regulations about data residency, behind that statement. But, we didn't know who was gonna step up and lead that other side and it's nice to see this conference indicate that HPE is in a position to help demonstrate, or help show the industry how cloud truly can go from centralized down to the edge. >> Yeah, and I think as I said a number of times, the strategy's coming into focus, you could debate it. You could say, "well, splitting it up was the wrong thing to do. "They lost their supply chain." But, Meg's argument, and then Antonio's argument always was, "look, we're gonna be more focused, "it's gonna allow us to do "a better job for our customers. "Yes, we're gonna be service's lead." They didn't say this. "Our margines are gonna be lower, "you don't have software anymore, "but that's okay, we can learn how to make money at that." And you know, the old HPE went through a similar transition. Kinda, got out of the HPEX business and got out of building it's own OS, and relying more on Microsoft and Intel and it made a lot of money. In those days. >> Peter: It did well. >> Did very well. It didn't invest under the herd regime the way it could have or should have and that hurt and then it spun out and made a lot of missteps but... Meg, to her credit, didn't make a lot of missteps. There was the initial entrance into the public cloud, they pulled back fast, they failed fast on that, good. Yeah, maybe there was some organizational issues early on but in general, the acquisitions have been solid, the strategy... >> And well integrated. >> And well integrated, absolutely. >> Peter: They've gotten value out of 'em. >> The strategies has been... I think clear internally, it wasn't always clear externally but they stayed calm about that, they didn't freak out about that. Helped that the stock price was going up a little bit, 'cause it was pretty depressed for a while. >> And shareholders weren't incontestable like they were for many years. >> That's right, and so, that gave them a little bit of time to bring it all together... It's finally here and I think Meg is stepping down at absolutely the right time. >> Or at a... She's stepping down at a good time, she's leaving a company that is much stronger than it was when she took it over. >> And that's what you want, one of the things I'm personally proud of when I left IDC it was in really good shape when I left, it wasn't a mess that I handed to somebody else. Had a lot of messes and IDC that I turned around as you well know. So, I think, I feel as though the company's in good shape and good hands. And, again, I think the... I don't know if you're a stock analyst or if you're pounding the table saying "buy this stock." 'Cause it is a relatively low margin business and there's a lot of competition, there's knife fights out there, it's not a high growth business, but on the flip side, it's clean, it throws off a lot of cash, they got a decent balance sheet and the customers love 'em. >> And that's the most important thing, it's the customers. Look, I... Disclosure, I actually did a significant consulting stint, here at HPE, right around the time of the compact acquisition and I saw what happened and for many years, the senior manager and team of HPE behaved as though they presumed that scale was it's own reward. If we get bigger, we'll find efficiencies, we'll find opportunities. Just being big, is the objective and I think that they have wandered in the desert trying to find those opportunities, that were the consequence of just being big and they never materialized. >> They weren't there. >> They never... It was like mirages on the horizon, they never materialized and I think if there's anything to your point that Meg has successfully done, is she's gotten the company to say, "don't chase the mirages, chase the customer. "Let's come back to what made HPE great for so long." And the idea that, if we stay focused on the customer and focus on technology, we can put them together in unique and interesting ways that will bind us to what customers are doing. And if you take a look at this event and the new messaging, and the things that they're focusing on it feels like, to me, that HPE is no longer wandering in the desert. You and I are smart guys, we are... Typically we can look at a company and we can see whether or not they know what they're doing and when you said, "well, you know what. "Maybe they had it all figured out inside, "and the rest of us couldn't see it." No, that's not the case. It was not figured out inside and that's what we saw but under Meg, it has become increasingly more figured out and the consequence of that... And it's been very, very plan full. She first was figured out and then she told Wall Street and Wall Street was happy with the numbers, and then she figured out and she started talking to customers when customers were there and now she's figuring it out, she's telling a broader market place. >> Well, and when she stopped by- >> And Antonio's got a great big story to tell. >> And both of those guys stopped by to see us. Meg spent 10 minutes with us, we were chatting here on the open mics and she was very good. Meg, one on one situation, in a small crowd is phenomenal. I've always said that about Meg. Not the greatest presence on stage, not a super dynamic speaker, she's not a Steve Jobs, obviously nobody is, but... But, man, is she credible in a one on one situation. One of the things she said to us was, "Y'know, we kinda got lucky..." My words, "with Aruba, we bought him "because we thought we could compete with "Cisco better, we bought him obviously "because it was a great business, a growth business," and boom all of a sudden, this intelligent edge thing hit. You sprinkle in a little Dr. Tom Bradicich and boom, off you go and you've got not only a great business, you got something that is becoming increasingly strategic for organizations. Great example, I mean the nimble acquisition. We heard, yesterday, Bill Philbin talking about, "well, when we got nimble-" was it Bill Philbin, no it was somebody else today it was... Alain Andreoli. He said, "we picked up nimble 'cause it was a great "flash company, but then we saw this inside thing, "we said, wow, we can spread this thing "across our entire portfolio." That's where- >> And the example he gave was: in six months, it's not running on... >> On three par and then it's gonna run... His goal, he says, "I'm not committing to this, "but my goal is by the end of the next year "it's gonna be running across the entire "server and storage and networking line." That would be a major accomplishment. If in fact, we'll see how much of this stuff is actually impactful to the business, how much it can actually save money you know, anticipate failures, I don't know. We'll see, it's AI, it's a perfect application. You guys have written a lot on the Wikibon team about AI for ITOM. >> Oh yeah, look... >> Dave: And this is a good example. >> I'm not the kinda guy, as you know, that gets all excited about technology for technology's sake. I like thinking about technology and how it's gonna be applied, more problems are gonna be solved and so as we, in Wikibon, started running around and getting all excited about AI, my challenge to the guys was: Well, show me the two concrete cases, where it's gonna have a material business impact and one of the most important cases is, it's got a material business impact and how IT runs itself because you cannot... IT cannot reduce the number of people it's got and take on these increasingly complex application, problems, and portfolios unless they get a lot of help and the best, most likely source of that help is by bringing a lot of these new AI technologies that are capable of taking concrete, real time action in response to what's happening within the infrastructure and the applications at any given time. >> Yeah, now... Couple other things, just observations. Ana Pinczuk came on, great leader, woman in tech, big proponent of advancing women's causes, especially in tech. She had mixed feelings about Meg stepping down, obviously you have a woman leader, I thought her comments there were... Were quite interesting, but she said, "But I am up for the challenge "to continue the mission." Which leads me to Antonio. Antonio is outwardly a humble guy, he may have a big ego I don't know, he's been on The Cube a number of times, but he certainly doesn't come across as a guy who's looking to get credit. He's a quiet but very competent leader, he knows the business very well. Really interesting to see what his relationship- >> Peter: Homegrown. >> Homegrown, which is 22 years at HPE, technology background, not a U.S... Born individual, now living in the U.S. obviously. But, somebody with international experience which is always been an attribute that's valued at HPE. Gonna be interesting to see what his relationship is with Wall Street. Will he be sort of a quiet leader that lets the CFO take front and center, which would be fine. Or will he slowly sort of advance, he's not been sitting on the earnings calls. I'm interested to see how he handles it, or he may just say, "you know what, "I'm gonna go execute in the business "and let the results speak for themselves." So, I'm kind of curious as to how that all... All plays out. It's a big job, it's a big role as you pointed out with me the other day. Big role for him, big job for him. Serious opportunities to make a mark in the industry. >> Again, and you raise a really great point. Meg had a very good reputation on Wall Street, the knock on her when she came on, was she didn't know customers. Antonio's got a great reputation with customers, you're asking the question: is he gonna get to know Wall Street? A great CEO has to be able to take care of customers and owners He seems very... Look, this is a, this whole simplification of how they're gonna bring cloud technologies to where their data's gonna require is apparently, based on what we heard, in large part Antonio's brain child. He conceived it, he invested in it, he nurtured it, he took risks for it, he put some skin in the game and now it's coming to fruition, that's great, and he's got customers lining up behind it. We'll see, this is another place where we'll see, but I don't think that there's... There's no reason to suspect, just looking at Antonio's track record, why Wall Street would abandon him. On the contrary, there's reasons to suspect that he will also be able to develop that set of skills that Wall Street needs to do their job. But, clearly this is a guy that's gonna turn on a lot of customers. >> Yeah, and as I say, it's gonna be interesting to see what his relationship, like look at a guy like Frank Slootman, who had a great relationship with Wall Street, everybody loved him 'cause he just performed but he's a hard-driving, in your face kinda guy, who developed close relationships with the street. It's gonna be, as they say, I gotta watch that, to see how Antonio interacts with them. I think it's important to have a relationship with... >> Peter: With your ownership, yeah it usually is. >> And I think that's the one big question mark here is, where has his presence been there but so we'll watch and I'm confident he'll step up to that. Okay. Let's see, The Cube... Next week? Cube-con? >> Peter: Yeah. >> Next week in Austin. Right, so development. You'll see The Cube expanding way beyond it's original infrastructure route, so obviously HPE Discover, big infrastructure show. But we're at Amazon Reinvent this week, it's our big cloud show. We obviously... All the IBM shows are being consolidated into one show called Think. This year The Cube will be there. But CES is gonna be January, we were there last year, likely be there again. Cisco live is on the radar, we're gonna be at Cisco live I think both in Barcelona and most likely in the states this year, so that's another big thing. A lot of developer shows, Docckercon, Kubecon, working with the Linux Foundation, developers are really the lynch pin, developers in cloud. Really big areas of growth. IOT, some IOT conferences that we're gonna be doin' this year. Obviously, our big data heritage we still do a lot of work there, so. It's been an unbelievable year, I think a 125 shows for The Cube. TheCube.net, new website, our new clipper tool, you see the clips that come out, so. A lot of innovation comin' out of Siliconangle Media, check out Siliconangle.com. Peter, the work that your team is doing on the Wikibon side, Wikibon.com. Unbelievable amounts of research that you guys are crackin' out. Digital business, AI, AI for ITOM stuff that we talked about, we still do some stuff in infrastructure, true private cloud. >> New computing architectures, memory based computer architectures. >> So, fantastic work there and... Yeah, so we're looking forward to another great year. Thanks everybody for these last two days, thanks to the crew, great job. Everybody at home. We're out. Dave Vellante for Peter Buriss from Madrid. Thanks for watching. (upbeat music)

>> Narrator: Live from Orlando, Florida it's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to sunny Orlando everybody. This is theCUBE, the leader in live tech coverage, and this is our second day covering PentahoWorld 2017. theCUBE was here in 2015 when Pentaho had just been recently acquired by Hitachi. We then, let's see, around September timeframe we saw Hitachi rebrand, Hitachi Data Systems rebrand as Hitachi Vantara, bringing together three components of its business, the Hitachi Data Systems business, the Hitachi Insights business, and of course, the Pentaho Analytics platform. We heard yesterday from Brian Householder, the president and COO of Hitachi Vantara, what the strategy was. I thought he was a very crisp, clear presenter. The strategy made a lot of sense, it resonated. Obviously a lot of execution to be done. And then subsequently at the last two days we've heard largely from Pentaho practitioners who are applying this end to end analytics platform to really transform their businesses, to really become data driven supporting those digital transformations. So pretty positive story overall. A lot of work to be done. We got to see how this whole edge to outcome plays out. Sounds good. There's got to be some execution there. We got to see the ecosystem grow for sure. These guys got a great story. This conference should explode. >> It's really a validation for Pentaho. They've been on the market for more than a decade now as the spearhead for the open source analytics revolution in business analytics, and in predictive modeling, and in data integration, all of it open source. And they've come very far and they're really a blue chip solution program. I think this show has been a great validation of Pentaho's portfolio presence in the market. Now Hitachi Vantara has a gem of a core asset. Clearly, the storage market, the data center converged infrastructure, the core Hitachi Data Systems product lines, are starting to experience the low growth that such a mature space experiences. And clearly they're placing a strong bet on Hitachi Vantara that the IoT, that the edge analytics market, will just boom wide open. Hitachi Insight Group, which was only created last year by their corporate parent, was chartered to explore opportunities in IoT. They've got the Lumata platform. They had, Hitachi Next, their conference last month, focused on IoT. I think that's really the capstone, the Lumata portfolio, in this overall story. Now, I think what we're hearing this week is that great, they've got the components, the building blocks, of potential growth, but I don't think they're going to be able to achieve takeoff growth until such time, Hitachi Vantara, they have a stronger, more credible reach out to the developer community, specifically the developers who are building the AI and machine learning for deployment to the edge. That will require to have credibility in that space. Clearly it's going to have to be the new set of frameworks, such as TensorFlow, and MXNet, and Fee-an-o, and so forth. They're going to need some sort of a modeling framework or abstraction from it that sits on top of the Pentaho platform or really across all of their offerings, including Lumata, and enables a developer to using, the mainstream application developer to use code, whether it be Python or R or Java, whatever, to build the deep learning and AI models at the highest level of abstraction, the business level of abstraction, then to automatically compile those models, which are computational graphs, down to formats that are optimized and efficient to run on devices of all sorts, chip sets of all sorts, that are increasingly resource constrained. They're not there yet. I'm not hearing that overall developer story at this show. I think they've got a lot of smart people, including Brian, pushing them in that direction. Hopefully next year's PentahoWorld or however they may rebrand this show, I think they'll probably have more of that put together, but we'll keep on waiting to see. >> And that's something that I pushed on a little bit this week. In particular, that requires a whole new go to market where the starting point is developers and then you're nurturing those developers. And certainly Pentaho has experience with community editions, but that was more to get enterprise buyers to kind of try before they buy. As you know well, Jim, the developer community is, they're very fickle, they're persnickety, they're demanding, and they're super smart, and they can be your best advocates or they'll just ignore you. That's just kind of the way it is with developers. And if you can appeal to them you can get a foothold in markets. We've seen it. Look at what Microsoft has done, look at what Amazon has done, certainly Docker, you know, on and on and on. >> Community marketing that's full bore (mumbles) user groups, developer days, hackathons, the whole nine yards, I'm not seeing a huge emphasis on community marketing in that really evangelistic sense. They need to go there seriously. They need to win the hearts and minds of the next generation developer, the next generation developer who actually won't care about whether it's TensorFlow backends or the other ones. What they will care is the high level framework, and really a collaborative framework, that's a solution provider gives them for their teams to collaborate on building and training and deploying all this stuff. I'm not hearing from this solution provider, devops really, here this year. Hopefully in the coming years there will be. Other vendors are a bit further along than they are. We see a bit further along IBM is. We see a bit further along like Cloudera and others are in putting together really a developer friendly ecosystem of components within a broader data lake framework. >> Yeah, and that's not been the historical Pentaho DNA. However, as you know, to reach out, have a community effort to reach out to developers requires resources and commitment, and it's not a one shot deal. But, it also requires a platform, and what we're seeing today is the formation of that. The reformation of Hitachi into Hitachi Vantara with a lot of resources that has a vision of a platform, of which Pentaho is a critical component, but it's going to take a lot of effort, a lot of cultivating. I presume they're having those conversations internally. They're not ready to have them externally, which is I presume why they're not having them. But that's something that we're going to certainly watch for in the coming years. What else? You gave a talk this afternoon. >> Yeah, AI is Eating the Edge, and it was well received. In fact, when I prepared my thoughts and my research about a month ago for this event I was thinking, "Am I way too far ahead?" This is Pentaho. I've been of course familiar with them since their inception. I thought, "Are there other users? "Are there developers? "Is their community going deep into AI "and all the IoT stuff?" And the last day or so here at this event it's like, "Whoa, everybody here is into that. "They know this stuff." So, not only was I relieved that I wouldn't have to explain the ABCs of all that, they were ahead of me in terms of the questions I got. The questions are, once again, what framework should we adopt for AI, the whole TensorFlow, all those framework wars, which I think are sort of overblown and they will be fairly soon, it'll be irrelevant, but those kinds of questions. Those are actually developer level questions that people are just here and they're coming to me with. >> Well, you know, I tell you, I'm no expert in frameworks, but my advice would be whatever framework you adopt you're probably not going to be using that same framework down the road. So you have to be flexible as an organization. A lot of technical leaders tell me this is look, technology is going to come and it's going to go. We got to have great people. We've got to be able to respond to the market requirements. We have to have processes that allow us to be proactive and responsive, and that your choice of framework should ensure that it doesn't constrict you in those areas. >> And you know, the framework that actually appeals to this crowd, including the people in my room, it's a wiki bot framework, it's also what Brian Hopkins of Forrester presented, the three tier architecture. There's the edge devices. There are the gateways or hubs. There's the cloud. We call them primary, secondary, tertiaries. Whatever you call them, you put different data, you put different analytics on each of those tiers. And then really in many ways in a modular fashion then you begin to orchestrate with Kubernetes and so forth these AI infused apps and these distributed architectures, like self driving vehicles or whatever. And the buzz I've been getting here, including in my session, everybody is saying, "Yeah, that's exactly the way to go." In other words, thinking in those terms prevents you as a developer from thinking that AI has to be some monolithic frigging stack on one single node. No, it actually has to be massively parallel and distributed, because these are potentially very compute intensive applications. I think there's a growing realization in the developer community that when you're talking about developing AI you're really talking about developing two core workloads. There's the inferencing, which is where the magic happens in terms of predictions and classifications, but even more resource consumptive is the training that has to happen in the cloud, and that's data, that's exabytes, petabytes intensive potentially. That's compute intensive. Very different workload. That definitely needs to happen in the cloud primarily. There's a little bit of federated training that goes out to the edge, but that's really the exception right now. So there's a growing realization in the developer community that boy, we better get a really good platform for training. And actually they could leverage, we've seen it in our research of wiki bot is that, many AI developers, many deep learning developers, actually leverage their Spark clusters for training of TensorFlow and so forth, because of in memory massive parallelism, so forth and so on. I think there will be a growing realization in the developer community that the investments they've been making in Hadoop and Spark will just be leveraged for this growing stack, for training if nothing else. >> Well, in 8.0 that was sort of the big buzz here. And you and I talked at the open with Rebecca, our other co-host, about 8.0 A lot of incremental improvements. But you know what, in talking to customers that's kind of what they want. They want Pentaho to do a good job of incorporating, curating, open source content, open source platforms and products, bringing them into their system, and making sure that their customers can take advantage of them. That's what they consistently kept asking for. They weren't freaked out about lack of AI and lack of deep learning and ML and Weka is fine. Now maybe it's a blind spot, I don't know. >> No, no, actually I've had 24 hours since they announced to chew on it. In fact, I have a SiliconANGLE article going up fairly soon with essentially my trip report and my basic takeaway. And actually what I like about 8.0 is that it focuses on streaming, bringing open source analytic streaming more completely into the Pentaho data integration platform, in other words, their stronger interoperability with Spark streaming, with Kafka, and so forth, but also they have the ability within 8.0 to better match realtime streaming workloads to execution engines in a distributed fabric. In other words, what I think that represents not only in terms of Hitachi Vantara's portfolio, but in terms of where the industry is going with all things to do with big data applications whether or not they involve AI is streaming is coming into the mainstream, pun intended, and data at rest platforms are starting to become marginalized in a lot of applications. In other words, Hadoop is data at rest par excellence, so are a fair number of other no SQL platforms. Those are not going away. Those are the core of your data lakes. But most development is being developed now, most AI and machine learning is being developed for streaming environments that increasingly are edge oriented. So Pentaho, Hitachi Vantara, for 8.0 have put in the right incremental features for the market that lies ahead. So in many ways I think that was actually a well thought out release for this particular event. >> Great. Okay, some of the highlights here. We had a lot of different industries, gaming, we had experts on autonomous vehicles, we had the NASDAQ guys on, that was a very interesting segment, the German police interview you did, the chief data officer of community colleges in Indiana. So, a lot of diversity, which underscores the platformness of Pentaho. It's not some industry specific system. It is a horizontal capabilities platform. Final thoughts on the show, some interesting things that you saw, things you learned? >> Yeah, on the show itself, they did a really good job. Hitachi Vantara, of course it's a new brand, but it's an old company, and it's even an old established set of product teams that have come together in a hurry essentially, though it's really been two years since the acquisition. They did a really good job of presenting a unified go to market message. That's a good start They've done a good job of the fact that they had these two shows in a rapid sequence, Hitachi Next, which was IoT and Lumata, but it was Hitachi Vantara, and now this one where it's all data analytics. The fact that here in the peak of fall event season they had these two shows really highlighting their innovations and their romance for those two core of their portfolio, and have done a good job of positioning themselves in each case, that shows that the teams are orchestrating well in terms of at least go to market presenting their value prop. I think in terms of the actual, we've had a lot of great customer and partner interviews on this show. And I think, you mentioned gaming first, I wasn't actually on the gaming related CUBE interview, but gaming is a hot, of course it's a hot, hot market for AI increasingly. A lot of AI that gets developed now for lots of applications involves simulations of whatever scenario you're building, including like autonomous vehicles. So gaming is in many ways a set of practices that are well established and mature that are becoming fundamental to development of all AI, because you're developing synthetic data based on simulation environments. The fact that Hitachi Vantara has strong presence as a data provider in the gaming market I think in many ways indicates that they've got ... It's a crowded marketplace. They have much larger competitors and deeper pocketed, but I think the fact is they've got all the piece parts needed to be a roaring success in this new era, and they've got strong and very loyal customers I'm discovering, not discovering, I've known this all along. But, since I've rejoined the analysts' space it's been revalidated that Pentaho how strong in blue chip they are. Now that they're a new brand in a new era, they're turning themselves around fairly well. I don't think that they'll be isolated by ... Clearly, I mean, with AI ... AI right now belongs to AWS and Microsoft and Google and IBM to some degree. We have to recognize that the Hitachi Vantaras of the world right now are still a second tier in that arena. They probably have to hitch their wagon to at least one of those core cloud providers as a core partner going forward to really prevail. >> Dave: Which they can do. >> Yeah, they can do. >> Alright. Jim, thanks very much for closing with me. Thanks to you all for watching. theCUBE puts out a lot of content. You can go to SiliconAngle.com to see all the news. theCUBE.net is where we host all these videos. Wikibon.com is our research site, so check that out, as well. We've got CrowdChats going on, CrowdChat.net. It's just unbelievable. >> Unbelievable. >> Rush of content. We're all about the data, we're all about sharing, so check those sites out. Thanks very much to the crew here. Great job. And next week a lot going on. We're in New York City. We've got some stuff going on there. Want to thank our sponsor, without whom this show, this CUBE show, would not be possible, Hitachi Vantara slash Pentaho. >> Thank you to sunny Orlando. It's great and wonderful. >> This has been theCUBE at PentahoWorld 2017. We'll see you next time. Thanks for watching. (techno music)