Jim Harris, International Best Selling Author of Blindsided & Carolina Milanesi, Creative Strategies
>> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (intro music) >> Good afternoon, everyone. Welcome back to "theCUBE's" day three coverage of MWC23. Lisa Martin here in Spain, Barcelona, Spain with Dave Nicholson. We're going to have a really interesting conversation next. We're going to really dig into MWC, it's history, where it's going, some of the controversy here. Please welcome our guests. We have Jim Harris, International Best Selling Author of "Blindsided." And Carolina Milanese is here, President and Principle Analyst of creative strategies. Welcome to "theCUBE" guys. Thank you. >> Thanks. So great to be here. >> So this is day three. 80,000 people or so. You guys have a a lot of history up at this event. Caroline, I want to start with you. Talk a little bit about that. This obviously the biggest one in, in quite a few years. People are ready to be back, but there's been some, a lot of news here, but some controversy going on. Give us the history, and your perspective on some of the news that's coming out from this week's event. >> It feels like a very different show. I don't know if I would say growing up show, because we are still talking about networks and mobility, but there's so much more now around what the networks actually empower, versus the network themselves. And a little bit of maybe that's where some of the controversy is coming from, carriers still trying to find their identity, right, of, of what their role is in all there is to do with a connected world. I go back a long way. I go back to when Mobile World Congress was called, was actually called GSM, and it was in Khan. So, you know, we went from France to Spain. But just looking at the last full Mobile World Congress here in Barcelona, in pre-pandemic to now, very different show. We went from a show that was very much focused on mobility and smartphones, to a show that was all about cars. You know, we had cars everywhere, 'cause we were talking about smart cities and connected cars, to now a show this year that is very much focused on B2B. And so a lot of companies that are here to either work with the carriers, or also talk about sustainability for instance, or enable what is the next future evolution of computing with XR and VR. >> So Jim, talk to us a little bit about your background. You, I was doing a little sleuthing on you. You're really focusing on disruptive innovation. We talk about disruption a lot in different industries. We're seeing a lot of disruption in telco. We're seeing a lot of frenemies going on. Give us your thoughts about what you're seeing at this year's event. >> Well, there's some really exciting things. I listened to the keynote from Orange's CEO, and she was complaining that 55% of the traffic on her network is from five companies. And then the CEO of Deutsche Telecom got up, and he was complaining that 60% of the traffic on his network is from six entities. So do you think they coordinated pre, pre-show? But really what they're saying is, these OTT, you know, Netflix and YouTube, they should be paying us for access. Now, this is killer funny. The front page today of the show, "Daily," the CO-CEO of Netflix says, "Hey, we make less profit than the telcos, "so you should be paying us, "not the other way around." You know, we spend half of the money we make just on developing content. So, this is really interesting. The orange CEO said, "We're not challenging net neutrality. "We don't want more taxes." But boom. So this is disruptive. Huge pressure. 67% of all mobile traffic is video, right? So it's a big hog bandwidth wise. So how are they going to do this? Now, I look at it, and the business model for the, the telcos, is really selling sim cards and smartphones. But for every dollar of revenue there, there's five plus dollars in apps, and consulting and everything else. So really, but look at how they're structured. They can't, you know, take somebody who talks to the public and sells sim cards, and turn 'em in, turn 'em in to an app developer. So how are they going to square this circle? So I see some, they're being disrupted because they're sticking to what they've historically done. >> But it's interesting because at the end of the day, the conversation that we are having right now is the conversation that we had 10 years ago, where carriers don't want to just be a dumb pipe, right? And that's what they are now returning to. They tried to be media as well, but that didn't work out for most carriers, right? It is a little bit better in the US. We've seen, you know, some success there. But, but here has been more difficult. And I think that's the, the concern, that even for the next, you know, evolution, that's the, their role. >> So how do they, how do they balance this dumb pipe idea, with the fact that if you make the toll high enough, being a dumb pipe is actually a pretty good job. You know, sit back, collect check, go to the beach, right? So where, where, where, where does this end up? >> Well, I think what's going to happen is, if you see five to 15 X the revenue on top of a pipe, you know, the hyperscalers are going to start going after the business. The consulting companies like PWC, McKinsey, the app developers, they're... So how do you engage those communities as a telco to get more revenue? I think this is a question that they really need to look at. But we tend to stick within our existing business model. I'll just give you one stat that blows me away. Uber is worth more than every taxi cab company in North America added together. And so the taxi industry owns billions in assets in cars and limousines. Uber doesn't own a single vehicle. So having a widely distributed app, is a huge multiplier on valuation. And I look to a company like Safari in Kenya, which developed M-Pesa, which Pesa means mo, it's mobile money in Swahili. And 25% of the country's GDP is facilitated by M-Pesa. And that's not even on smartphones. They're feature phones, Nokia phones. I call them dumb phones, but Nokia would call them "feature phones." >> Yeah. >> So think about that. Like 25, now transactions are very small, and the cut is tiny. But when you're facilitating 25% of a country's GDP, >> Yeah. >> Tiny, over billions of transactions is huge. But that's not the way telcos have historically thought or worked. And so M-Pesa and Safari shows the way forward. What do you think on that? >> I, I think that the experience, and what they can layer on top from a services perspective, especially in the private sector, is also important. I don't, I never believe that a carrier, given how they operate, is the best media company in the world, right? It is a very different world. But I do think that there's opportunity, first of all, to, to actually tell their story in a different way. If you're thinking about everything that a network actually empowers, there's a, there's a lot there. There's a lot that is good for us as, as society. There's a lot that is good for business. What can they do to start talking about differently about their services, and then layer on top of what they offer? A better way to actually bring together private and public network. It's not all about cellular, wifi and cellular coming together. We're talking a lot about satellite here as well. So, there's definitely more there about quality of service. Is, is there though, almost a biological inevitability that prevents companies from being able to navigate that divide? >> Hmm. >> Look at, look at when, when, when we went from high definition 720P, very exciting, 1080P, 4K. Everybody ran out and got a 4K TV. Well where was the, where was the best 4K content coming from? It wasn't, it wasn't the networks, it wasn't your cable operator, it was YouTube. It was YouTube. If you had suggested that 10 years before, that that would happen, people would think that you were crazy. Is it possible for folks who are now leading their companies, getting up on stage, and daring to say, "This content's coming over, "and I want to charge you more "for using my pipes." It's like, "Really? Is that your vision? "That's the vision that you want to share with us here?" I hear the sound of dead people walking- (laughing) when I hear comments like that. And so, you know, my students at Wharton in the CTO program, who are constantly looking at this concept of disruption, would hear that and go, "Ooh, gee, did the board hear what that person said?" I, you know, am I being too critical of people who could crush me like a bug? (laughing) >> I mean, it's better that they ask the people with money than not consumers to pay, right? 'Cause we've been through a phase where the carriers were actually asking for more money depending on critical things. Like for instance, if you're doing business email, then were going to charge you more than if you were a consumer. Or if you were watching video, they would charge you more for that. Then they understood that a consumer would walk away and go somewhere else. So they stopped doing that. But to your point, I think, and, and very much to what you focus from a disruption perspective, look at what Chat GTP and what Microsoft has been doing. Not much talk about this here at the show, which is interesting, but the idea that now as a consumer, I can ask new Bing to get me the 10 best restaurants in Barcelona, and I no longer go to Yelp, or all the other businesses where I was going to before, to get their recommendation, what happens to them? You're, you're moving away, and you're taking eyeballs away from those websites. And, and I think that, that you know, your point is exactly right. That it's, it's about how, from a revenue perspective, you are spending a lot of money to facilitate somebody else, and what's in it for you? >> Yeah. And to be clear, consumers pay for everything. >> Always. Always. (laughs) >> Taxpayers and consumers always pay for everything. So there is no, "Well, we're going to make them pay, so you don't have to pay." >> And if you are not paying, you are the product. Exactly. >> Yes. (laughing) >> Carolina, talk a little bit about what you're seeing at the event from some of the infrastructure players, the hyperscalers, obviously a lot of enterprise focus here at this event. What are some of the things that you're seeing? Are you impressed with, with their focus in telco, their focus to partner, build an ecosystem? What are you seeing? >> I'm seeing also talk about sustainability, and enabling telco to be more sustainable. You know, there, there's a couple of things that are a little bit different from the US where I live, which is that telcos in Europe, have put money into sustainability through bonds. And so they use the money that they then get from the bonds that they create, to, to supply or to fuel their innovation in sustainability. And so there's a dollar amount on sustainability. There's also an opportunity obviously from a growth perspective. And there's a risk mitigation, right? Especially in Europe, more and more you're going to be evaluated based on how sustainable you are. So there are a lot of companies here, if you're thinking about the Ciscos of the world. Dell, IBM all talking about sustainability and how to help carriers measure, and then obviously be more sustainable with their consumption and, and power. >> Going to be interesting to see where that goes over the years, as we talk to, every company we talk to at whatever show, has an ESG sustainability initiative, and only, well, many of them only want to work with other companies who have the same types of initiative. So a lot of, great that there's focus on sustainability, but hopefully we'll see more action down the road. Wanted to ask you about your book, "Blind," the name is interesting, "Blindsided." >> Well, I just want to tag on to this. >> Sure. >> One of the most exciting things for me is fast charging technology. And Shalmie, cell phone, or a smartphone maker from China, just announced yesterday, a smartphone that charges from 0 to 100% in five minutes. Now this is using GAN FEST technology. And the leader in the market is a company called Navitas. And this has profound implications. You know, it starts with the smartphone, right? But then it moves to the laptops. And then it'll move to EV's. So, as we electrify the $10 trillion a year transportation industry, there's a huge opportunity. People want charging faster. There's also a sustainability story that, to Carolina's point, that it uses less electricity. So, if we electrify the grid in order to support transportation, like the Tesla Semi's coming out, there are huge demands over a period. We need energy efficiency technologies, like this GAN FEST technology. So to me, this is humongous. And it, we only see it here in the show, in Shalmie, saying, "Five minutes." And everybody, the consumers go, "Oh, that's cool." But let's look at the bigger story, which is electrifying transportation globally. And this is going to be big. >> Yeah. And, and to, and to double click on that a little bit, to be clear, when we talk about fast charging today, typically it's taking the battery from a, not a zero state of charge, but a relatively low state of charge to 80%. >> Yep. >> Then it tapers off dramatically. And that translates into less range in an EV, less usable time on any other device, and there's that whole linkage between the power in, and the battery's ability to be charged, and how much is usable. And from a sustainability perspective, we are going to have an avalanche of batteries going into secondary use cases over time. >> They don't get tossed into landfills contrary to what people might think. >> Yep. >> In fact, they are used in a variety of ways after their primary lifespan. But that, that is, that in and of itself is a revolutionary thing. I'm interested in each of your thoughts on the China factor. Glaringly absent here, from my perspective, as sort of an Apple fanboy, where are they? Why aren't they talking about their... They must, they must feel like, "Well we just don't need to." >> We don't need to. We just don't need to. >> Absolutely. >> And then you walk around and you see these, these company names that are often anglicized, and you don't necessarily immediately associate them with China, but it's like, "Wait a minute, "that looks better than what I have, "and I'm not allowed to have access to that thing." What happens in the future there geopolitically? >> It's a pretty big question for- >> Its is. >> For a short little tech show. (Caroline laughs) But what happens as we move forward? When is the entire world going to be able to leverage in a secure way, some of the stuff that's coming out of, if they're not the largest economy in the world yet, they shortly will be. >> What's the story there? >> Well, it's interesting that you mentioned First Apple that has never had a presence at Mobile World Congress. And fun enough, I'm part of the GSMA judges for the GLOMO Awards, and last night I gave out Best Mobile Phone for last year, and it was to the iPhone4 Team Pro. and best disruptive technology, which was for the satellite function feature on, on the new iPhone. So, Apple might not be here, but they are. >> Okay. >> And, and so that's the first thing. And they are as far as being top of mind to every competitor in the smartphone market still. So a lot of the things that, even from a design perspective that you see on some of the Chinese brands, really remind you of, of Apple. What is interesting for me, is how there wouldn't be, with the exception of Samsung and Motorola, there's no one else here that is non-Chinese from a smartphone point of view. So that's in itself, is something that changed dramatically over the years, especially for somebody like me that still remember Nokia being the number one in the market. >> Huh. >> So. >> Guys, we could continue this conversation. We are unfortunately out of time. But thank you so much for joining Dave and me, talking about your perspectives on the event, the industry, the disruptive forces. It's going to be really interesting to see where it goes. 'Cause at the end of the day, it's the consumers that just want to make sure I can connect wherever I am 24 by seven, and it just needs to work. Thank you so much for your insights. >> Thank you. >> Lisa, it's been great. Dave, great. It's a pleasure. >> Our pleasure. For our guests, and for Dave Nicholson, I'm Lisa Martin. You're watching, "theCUBE," the leader in live and emerging tech coverage coming to you day three of our coverage of MWC 23. Stick around. Our next guest joins us momentarily. (outro music)
SUMMARY :
that drive human progress. We're going to have a really So great to be here. People are ready to be back, And so a lot of companies that are here to So Jim, talk to us a little So how are they going to do this? It is a little bit better in the US. check, go to the beach, right? And 25% of the country's GDP and the cut is tiny. But that's not the way telcos is the best media company "That's the vision that you and I no longer go to Yelp, consumers pay for everything. Always. so you don't have to pay." And if you are not (laughing) from some of the infrastructure and enabling telco to be more sustainable. Wanted to ask you about And this is going to be big. and to double click on that a little bit, and the battery's ability to be charged, contrary to what people might think. each of your thoughts on the China factor. We just don't need to. What happens in the future When is the entire world for the GLOMO Awards, So a lot of the things that, and it just needs to work. It's a pleasure. coming to you day three
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Keynote Analysis with Sarbjeet Johal & Chris Lewis | MWC Barcelona 2023
(upbeat instrumental music) >> TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (uplifting instrumental music) >> Hey everyone. Welcome to Barcelona, Spain. It's theCUBE Live at MWC '23. I'm Lisa Martin, Dave Vellante, our co-founder, our co-CEO of theCUBE, you know him, you love him. He's here as my co-host. Dave, we have a great couple of guests here to break down day one keynote. Lots of meat. I can't wait to be part of this conversation. Chris Lewis joins us, the founder and MD of Lewis Insight. And Sarbjeet Johal, one of you know him as well. He's a Cube contributor, cloud architect. Guys, welcome to the program. Thank you so much for joining Dave and me today. >> Lovely to be here. >> Thank you. >> Chris, I want to start with you. You have covered all aspects of global telecoms industries over 30 years working as an analyst. Talk about the evolution of the telecom industry that you've witnessed, and what were some of the things you heard in the keynote that excite you about the direction it's going? >> Well, as ever, MWC, there's no lack of glitz and glamour, but it's the underlying issues of the industry that are really at stake here. There's not a lot of new revenue coming into the telecom providers, but there's a lot of adjustment, readjustment of the underlying operational environment. And also, really importantly, what came out of the keynotes is the willingness and the necessity to really engage with the API community, with the developer community, people who traditionally, telecoms would never have even touched. So they're sorting out their own house, they're cleaning their own stables, getting the cost base down, but they're also now realizing they've got to engage with all the other parties. There's a lot of cloud providers here, there's a lot of other people from outside so they're realizing they cannot do it all themselves. It's quite a tough lesson for a very conservative, inward looking industry, right? So should we be spending all this money and all this glitz and glamour of MWC and all be here, or should would be out there really building for the future and making sure the services are right for yours and my needs in a business and personal lives? So a lot of new changes, a lot of realization of what's going on outside, but underlying it, we've just got to get this right this time. >> And it feels like that monetization is front and center. You mentioned developers, we've got to work with developers, but I'm hearing the latest keynote from the Ericsson CEOs, we're going to monetize through those APIs, we're going to charge the developers. I mean, first of all, Chris, am I getting that right? And Sarbjeet, as somebody who's close to the developer community, is that the right way to build bridges? But Chris, are we getting that right? >> Well, let's take the first steps first. So, Ericsson, of course, acquired Vonage, which is a massive API business so they want to make money. They expect to make money by bringing that into the mainstream telecom community. Now, whether it's the developers who pay for it, or let's face it, we are moving into a situation as the telco moves into a techco model where the techco means they're going to be selling bits of the technology to developer guys and to other application developers. So when he says he needs to charge other people for it, it's the way in which people reach in and will take going through those open APIs like the open gateway announced today, but also the way they'll reach in and take things like network slicing. So we're opening up the telecom community, the treasure chest, if you like, where developers' applications and other third parties can come in and take those chunks of technology and build them into their services. This is a complete change from the old telecom industry where everybody used to come and you say, "all right, this is my product, you've got to buy it and you're going to pay me a lot of money for it." So we are looking at a more flexible environment where the other parties can take those chunks. And we know we want collectivity built into our financial applications, into our government applications, everything, into the future of the metaverse, whatever it may be. But it requires that change in attitude of the telcos. And they do need more money 'cause they've said, the baseline of revenue is pretty static, there's not a lot of growth in there so they're looking for new revenues. It's in a B2B2X time model. And it's probably the middle man's going to pay for it rather than the customer. >> But the techco model, Sarbjeet, it looks like the telcos are getting their money on their way in. The techco company model's to get them on their way out like the app store. Go build something of value, build some kind of app or data product, and then when it takes off, we'll take a piece of the action. What are your thoughts from a developer perspective about how the telcos are approaching it? >> Yeah, I think before we came here, like I said, I did some tweets on this, that we talk about all kind of developers, like there's game developers and front end, back end, and they're all talking about like what they're building on top of cloud, but nowhere you will hear the term "telco developer," there's no API from telcos given to the developers to build IoT solutions on top of it because telco as an IoT, I think is a good sort of hand in hand there. And edge computing as well. The glimmer of hope, if you will, for telcos is the edge computing, I believe. And even in edge, I predicted, I said that many times that cloud players will dominate that market with the private 5G. You know that story, right? >> We're going to talk about that. (laughs) >> The key is this, that if you see in general where the population lives, in metros, right? That's where the world population is like flocking to and we have cloud providers covering the local zones with local like heavy duty presence from the big cloud providers and then these telcos are getting sidetracked by that. Even the V2X in cars moving the autonomous cars and all that, even in that space, telcos are getting sidetracked in many ways. What telcos have to do is to join the forces, build some standards, if not standards, some consortium sort of. They're trying to do that with the open gateway here, they have only eight APIs. And it's 2023, eight APIs is nothing, right? (laughs) So they should have started this 10 years back, I think. So, yeah, I think to entice the developers, developers need the employability, we need to train them, we need to show them some light that hey, you can build a lot on top of it. If you tell developers they can develop two things or five things, nobody will come. >> So, Chris, the cloud will dominate the edge. So A, do you buy it? B, the telcos obviously are acting like that might happen. >> Do you know I love people when they've got their heads in the clouds. (all laugh) And you're right in so many ways, but if you flip it around and think about how the customers think about this, business customers and consumers, they don't care about all this background shenanigans going on, do they? >> Lisa: No. >> So I think one of the problems we have is that this is a new territory and whether you call it the edge or whatever you call it, what we need there is we need connectivity, we need security, we need storage, we need compute, we need analytics, and we need applications. And are any of those more important than the others? It's the collective that actually drives the real value there. So we need all those things together. And of course, the people who represented at this show, whether it's the cloud guys, the telcos, the Nokia, the Ericssons of this world, they all own little bits of that. So that's why they're all talking partnerships because they need the combination, they cannot do it on their own. The cloud guys can't do it on their own. >> Well, the cloud guys own all of those things that you just talked about though. (all laugh) >> Well, they don't own the last bit of connectivity, do they? They don't own the access. >> Right, exactly. That's the one thing they don't own. So, okay, we're back to pipes, right? We're back to charging for connectivity- >> Pipes are very valuable things, right? >> Yeah, for sure. >> Never underestimate pipes. I don't know about where you live, plumbers make a lot of money where I live- >> I don't underestimate them but I'm saying can the telcos charge for more than that or are the cloud guys going to mop up the storage, the analytics, the compute, and the apps? >> They may mop it up, but I think what the telcos are doing and we've seen a lot of it here already, is they are working with all those major cloud guys already. So is it an unequal relationship? The cloud guys are global, massive global scale, the telcos are fundamentally national operators. >> Yep. >> Some have a little bit of regional, nobody has global scale. So who stitches it all together? >> Dave: Keep your friends close and your enemies closer. >> Absolutely. >> I know that saying never gets old. It's true. Well, Sarbjeet, one of the things that you tweeted about, I didn't get to see the keynote but I was looking at your tweets. 46% of telcos think they won't make it to the next decade. That's a big number. Did that surprise you? >> No, actually it didn't surprise me because the competition is like closing in on them and the telcos are competing with telcos as well and the telcos are competing with cloud providers on the other side, right? So the smaller ones are getting squeezed. It's the bigger players, they can hook up the newer platforms, I think they will survive. It's like that part is like any other industry, if you will. But the key is here, I think why the pain points were sort of described on the main stage is that they're crying out loud to tell the big tech cloud providers that "hey, you pay your fair share," like we talked, right? You are not paying, you're generating so much content which reverses our networks and you are not paying for it. So they are not able to recoup the cost of laying down their networks. By the way, one thing actually I want to mention is that they said the cloud needs earth. The cloud and earth, it's like there's no physical need to cloud, you know that, right? So like, I think it's the other way around. I think the earth needs the cloud because I'm a cloud guy. (Sarbjeet and Lisa laugh) >> I think you need each other, right? >> I think so too. >> They need each other. When they said cloud needs earth, right? I think they're still in denial that the cloud is a big force. They have to partner. When you can't compete with somebody, what do you do? Partner with them. >> Chris, this is your world. Are they in denial? >> No, I think they're waking up to the pragmatism of the situation. >> Yeah. >> They're building... As we said, most of the telcos, you find have relationships with the cloud guys, I think you're right about the industry. I mean, do you think what's happened since US was '96, the big telecom act when we started breaking up all the big telcos and we had lots of competition came in, we're seeing the signs that we might start to aggregate them back up together again. So it's been an interesting experiment for like 30 years, hasn't it too? >> It made the US less competitive, I would argue, but carry on. >> Yes, I think it's true. And Europe is maybe too competitive and therefore, it's not driven the investment needed. And by the way, it's not just mobile, it's fixed as well. You saw the Orange CEO was talking about the her investment and the massive fiber investments way ahead of many other countries, way ahead of the UK or Germany. We need that fiber in the ground to carry all your cloud traffic to do this. So there is a scale issue, there is a competition issue, but the telcos are very much aware of it. They need the cloud, by the way, to improve their operational environments as well, to change that whole old IT environment to deliver you and I better service. So no, it absolutely is changing. And they're getting scale, but they're fundamentally offering the basic product, you call it pipes, I'll just say they're offering broadband to you and I and the business community. But they're stepping on dangerous ground, I think, when saying they want to charge the over the top guys for all the traffic they use. Those over the top guys now build a lot of the global networks, the backbone submarine network. They're putting a lot of money into it, and by giving us endless data for our individual usage, that cat is out the bag, I think to a large extent. >> Yeah. And Orange CEO basically said that, that they're not paying their fair share. I'm for net neutrality but the governments are going to have to fund this unless you let us charge the OTT. >> Well, I mean, we could of course renationalize. Where would that take us? (Dave laughs) That would make MWC very interesting next year, wouldn't it? To renationalize it. So, no, I think you've got to be careful what we wish for here. Creating the absolute clear product that is required to underpin all of these activities, whether it's IoT or whether it's cloud delivery or whether it's just our own communication stuff, delivering that absolutely ubiquitously high quality for business and for consumer is what we have to do. And telcos have been too conservative in the past. >> I think they need to get together and create standards around... I think they have a big opportunity. We know that the clouds are being built in silos, right? So there's Azure stack, there's AWS and there's Google. And those are three main ones and a few others, right? So that we are fighting... On the cloud side, what we are fighting is the multicloud. How do we consume that multicloud without having standards? So if these people get together and create some standards around IoT and edge computing sort of area, people will flock to them to say, "we will use you guys, your API, we don't care behind the scenes if you use AWS or Google Cloud or Azure, we will come to you." So market, actually is looking for that solution. I think it's an opportunity for these guys, for telcos. But the problem with telcos is they're nationalized, as you said Chris versus the cloud guys are still kind of national in a way, but they're global corporations. And some of the telcos are global corporations as well, BT covers so many countries and TD covers so many... DT is in US as well, so they're all over the place. >> But you know what's interesting is that the TM forum, which is one of the industry associations, they've had an open digital architecture framework for quite some years now. Google had joined that some years ago, Azure in there, AWS just joined it a couple of weeks ago. So when people said this morning, why isn't AWS on the keynote? They don't like sharing the limelight, do they? But they're getting very much in bed with the telco. So I think you'll see the marriage. And in fact, there's a really interesting statement, if you look at the IoT you mentioned, Bosch and Nokia have been working together 'cause they said, the problem we've got, you've got a connectivity network on one hand, you've got the sensor network on the other hand, you're trying to merge them together, it's a nightmare. So we are finally seeing those sort of groups talking to each other. So I think the standards are coming, the cooperation is coming, partnerships are coming, but it means that the telco can't dominate the sector like it used to. It's got to play ball with everybody else. >> I think they have to work with the regulators as well to loosen the regulation. Or you said before we started this segment, you used Chris, the analogy of sports, right? In sports, when you're playing fiercely, you commit the fouls and then ask for ref to blow the whistle. You're now looking at the ref all the time. The telcos are looking at the ref all the time. >> Dave: Yeah, can I do this? Can I do that? Is this a fair move? >> They should be looking for the space in front of the opposition. >> Yeah, they should be just on attack mode and commit these fouls, if you will, and then ask for forgiveness then- >> What do you make of that AWS not you there- >> Well, Chris just made a great point that they don't like to share the limelight 'cause I thought it was very obvious that we had Google Cloud, we had Microsoft there on day one of this 80,000 person event. A lot of people back from COVID and they weren't there. But Chris, you brought up a great point that kind of made me think, maybe you're right. Maybe they're in the afternoon keynote, they want their own time- >> You think GSMA invited them? >> I imagine so. You'd have to ask GSMA. >> I would think so. >> Get Max on here and ask that. >> I'm going to ask them, I will. >> But no, and they don't like it because I think the misconception, by the way, is that everyone says, "oh, it's AWS, it's Google Cloud and it's Azure." They're not all the same business by any stretch of the imagination. AWS has been doing loads of great work, they've been launching private network stuff over the last couple of weeks. Really interesting. Google's been playing catch up. We know that they came in readily late to the market. And Azure, they've all got slightly different angles on it. So perhaps it just wasn't right for AWS and the way they wanted to pitch things so they don't have to be there, do they? >> That's a good point. >> But the industry needs them there, that's the number one cloud. >> Dave, they're there working with the industry. >> Yeah, of course. >> They don't have to be on the keynote stage. And in fact, you think about this show and you mentioned the 80,000 people, the activity going on around in all these massive areas they're in, it's fantastic. That's where the business is done. The business isn't done up on the keynote stage. >> That's why there's the glitz and the glamour, Chris. (all laugh) >> Yeah. It's not glitz, it's espresso. It's not glamour anymore, it's just espresso. >> We need the espresso. >> Yeah. >> I think another thing is that it's interesting how an average European sees the tech market and an average North American, especially you from US, you have to see the market. Here, people are more like process oriented and they want the rules of the road already established before they can take a step- >> Chris: That's because it's your pension in the North American- >> Exactly. So unions are there and the more employee rights and everything, you can't fire people easily here or in Germany or most of the Europe is like that with the exception of UK. >> Well, but it's like I said, that Silicone Valley gets their money on the way out, you know? And that's how they do it, that's how they think it. And they don't... They ask for forgiveness. I think the east coast is more close to Europe, but in the EU, highly regulated, really focused on lifetime employment, things like that. >> But Dave, the issue is the telecom industry is brilliant, right? We keep paying every month whatever we do with it. >> It's a great business, to your point- >> It's a brilliant business model. >> Dave: It's fantastic. >> So it's about then getting the structure right behind it. And you know, we've seen a lot of stratification where people are selling off towers, Orange haven't sold their towers off, they made a big point about that. Others are selling their towers off. Some people are selling off their underlying network, Telecom Italia talking about KKR buying the whole underlying network. It's like what do you want to be in control of? It's a great business. >> But that's why they complain so much is that they're having to sell their assets because of the onerous CapEx requirements, right? >> Yeah, they've had it good, right? And dare I say, perhaps they've not planned well enough for the future. >> They're trying to protect their past from the future. I mean, that's... >> Actually, look at the... Every "n" number of years, there's a new faster network. They have to dig the ground, they have to put the fiber, they have to put this. Now, there are so many booths showing 6G now, we are not even done with 5G yet, now the next 6G you know, like then- >> 10G's coming- >> 10G, that's a different market. (Dave laughs) >> Actually, they're bogged down by the innovation, I think. >> And the generational thing is really important because we're planning for 6G in all sorts of good ways but actually what we use in our daily lives, we've gone through the barrier, we've got enough to do that. So 4G gives us enough, the fiber in the ground or even old copper gives us enough. So the question is, what are we willing to pay for more than that basic connectivity? And the answer to your point, Dave, is not a lot, right? So therefore, that's why the emphasis is on the business market on that B2B and B2B2X. >> But we'll pay for Netflix all day long. >> All day long. (all laugh) >> The one thing Chris, I don't know, I want to know your viewpoints and we have talked in the past as well, there's absence of think tanks in tech, right? So we have think tanks on the foreign policy and economic policy in every country, and we have global think tanks, but tech is becoming a huge part of the economy, global economy as well as national economies, right? But we don't have think tanks on like policy around tech. For example, this 4G is good for a lot of use cases. Then 5G is good for smaller number of use cases. And then 6G will be like, fewer people need 6G for example. Why can't we have sort of those kind of entities dictating those kind of like, okay, is this a wiser way to go about it? >> Lina Khan wants to. She wants to break up big tech- >> You're too young to remember but the IT used to have a show every four years in Geneva, there were standards around there. So I think there are bodies. I think the balance of power obviously has gone from the telecom to the west coast to the IT markets. And it's changing the balance about, it moves more quickly, right? Telecoms has never moved quickly enough. I think there is hope by the way, that telecoms now that we are moving to more softwarized environment, and God forbid, we're moving into CICD in the telecom world, right? Which is a massive change, but I think there's hopes for it to change. The mentality is changing, the culture is changing, but to change those old structured organizations from the British telecom or the France telecom into the modern world, it's a hell of a long journey. It's not an overnight journey at all. >> Well, of course the theme of the event is velocity. >> Yeah, I know that. >> And it's been interesting sitting here with the three of you talking about from a historic perspective, how slow and molasseslike telecom has been. They don't have a choice anymore. As consumers, we have this expectation we're going to get anything we want on our mobile device, 24 by seven. We don't care about how the sausage is made, we just want the end result. So do you really think, and we're only on day one guys... And Chris we'll start with you. Is the theme really velocity? Is it disruption? Are they able to move faster? >> Actually, I think invisibility is the real answer. (Lisa laughs) We want communication to be invisible, right? >> Absolutely. >> We want it to work. When we switch our phones on, we want it to work and we want to... Well, they're not even phones anymore, are they really? I mean that's the... So no, velocity, we've got... There is momentum in the industry, there's no doubt about that. The cloud guys coming in, making telecoms think about the way they run their own business, where they meet, that collision point on the edges you talked about Sarbjeet. We do have velocity, we've got momentum. There's so many interested parties. The way I think of this is that the telecom industry used to be inward looking, just design its own technology and then expect everyone else to dance to our tune. We're now flipping that 180 degrees and we are now having to work with all the different outside forces shaping us. Whether it's devices, whether it's smart cities, governments, the hosting guys, the Equinoxis, all these things. So everyone wants a piece of this telecom world so we've got to make ourselves more open. That's why you get in a more open environment. >> But you did... I just want to bring back a point you made during COVID, which was when everybody switched to work from home, started using their landlines again, telcos had to respond and nothing broke. I mean, it was pretty amazing. >> Chris: It did a good job. >> It was kind of invisible. So, props to the telcos for making that happen. >> They did a great job. >> So it really did. Now, okay, what have you done for me lately? So now they've got to deal with the future and they're talking monetization. But to me, monetization is all about data and not necessarily just the network data. Yeah, they can sell that 'cause they own that but what kind of incremental value are they going to create for the consumers that... >> Yeah, actually that's a problem. I think the problem is that they have been strangled by the regulation for a long time and they cannot look at their data. It's a lot more similar to the FinTech world, right? I used to work at Visa. And then Visa, we did trillion dollars in transactions in '96. Like we moved so much money around, but we couldn't look at these things, right? So yeah, I think regulation is a problem that holds you back, it's the antithesis of velocity, it slows you down. >> But data means everything, doesn't it? I mean, it means everything and nothing. So I think the challenge here is what data do the telcos have that is useful, valuable to me, right? So in the home environment, the fact that my broadband provider says, oh, by the way, you've got 20 gadgets on that network and 20 on that one... That's great, tell me what's on there. I probably don't know what's taking all my valuable bandwidth up. So I think there's security wrapped around that, telling me the way I'm using it if I'm getting the best out of my service. >> You pay for that? >> No, I'm saying they don't do it yet. I think- >> But would you pay for that? >> I think I would, yeah. >> Would you pay a lot for that? I would expect it to be there as part of my dashboard for my monthly fee. They're already charging me enough. >> Well, that's fine, but you pay a lot more in North America than I do in Europe, right? >> Yeah, no, that's true. >> You're really overpaying over there, right? >> Way overpaying. >> So, actually everybody's looking at these devices, right? So this is a radio operated device basically, right? And then why couldn't they benefit from this? This is like we need to like double click on this like 10 times to find out why telcos failed to leverage this device, right? But I think the problem is their reliance on regulations and their being close to the national sort of governments and local bodies and authorities, right? And in some countries, these telcos are totally controlled in very authoritarian ways, right? It's not like open, like in the west, most of the west. Like the world is bigger than five, six countries and we know that, right? But we end up talking about the major economies most of the time. >> Dave: Always. >> Chris: We have a topic we want to hit on. >> We do have a topic. Our last topic, Chris, it's for you. You guys have done an amazing job for the last 25 minutes talking about the industry, where it's going, the evolution. But Chris, you're registered blind throughout your career. You're a leading user of assertive technologies. Talk about diversity, equity, inclusion, accessibility, some of the things you're doing there. >> Well, we should have had 25 minutes on that and five minutes on- (all laugh) >> Lisa: You'll have to come back. >> Really interesting. So I've been looking at it. You're quite right, I've been using accessible technology on my iPhone and on my laptop for 10, 20 years now. It's amazing. And what I'm trying to get across to the industry is to think about inclusive design from day one. When you're designing an app or you're designing a service, make sure you... And telecom's a great example. In fact, there's quite a lot of sign language around here this week. If you look at all the events written, good to see that coming in. Obviously, no use to me whatsoever, but good for the hearing impaired, which by the way is the biggest category of disability in the world. Biggest chunk is hearing impaired, then vision impaired, and then cognitive and then physical. And therefore, whenever you're designing any service, my call to arms to people is think about how that's going to be used and how a blind person might use it or how a deaf person or someone with physical issues or any cognitive issues might use it. And a great example, the GSMA and I have been talking about the app they use for getting into the venue here. I downloaded it. I got the app downloaded and I'm calling my guys going, where's my badge? And he said, "it's top left." And because I work with a screen reader, they hadn't tagged it properly so I couldn't actually open my badge on my own. Now, they changed it overnight so it worked this morning, which is fantastic work by Trevor and the team. But it's those things that if you don't build it in from scratch, you really frustrate a whole group of users. And if you think about it, people with disabilities are excluded from so many services if they can't see the screen or they can't hear it. But it's also the elderly community who don't find it easy to get access to things. Smart speakers have been a real blessing in that respect 'cause you can now talk to that thing and it starts talking back to you. And then there's the people who can't afford it so we need to come down market. This event is about launching these thousand dollars plus devices. Come on, we need below a hundred dollars devices to get to the real mass market and get the next billion people in and then to educate people how to use it. And I think to go back to your previous point, I think governments are starting to realize how important this is about building the community within the countries. You've got some massive projects like NEOM in Saudi Arabia. If you have a look at that, if you get a chance, a fantastic development in the desert where they're building a new city from scratch and they're building it so anyone and everyone can get access to it. So in the past, it was all done very much by individual disability. So I used to use some very expensive, clunky blind tech stuff. I'm now using mostly mainstream. But my call to answer to say is, make sure when you develop an app, it's accessible, anyone can use it, you can talk to it, you can get whatever access you need and it will make all of our lives better. So as we age and hearing starts to go and sight starts to go and dexterity starts to go, then those things become very useful for everybody. >> That's a great point and what a great champion they have in you. Chris, Sarbjeet, Dave, thank you so much for kicking things off, analyzing day one keynote, the ecosystem day, talking about what velocity actually means, where we really are. We're going to have to have you guys back 'cause as you know, we can keep going, but we are out of time. But thank you. >> Pleasure. >> We had a very spirited, lively conversation. >> Thanks, Dave. >> Thank you very much. >> For our guests and for Dave Vellante, I'm Lisa Martin, you're watching theCUBE live in Barcelona, Spain at MWC '23. We'll be back after a short break. See you soon. (uplifting instrumental music)
SUMMARY :
that drive human progress. the founder and MD of Lewis Insight. of the telecom industry and making sure the services are right is that the right way to build bridges? the treasure chest, if you like, But the techco model, Sarbjeet, is the edge computing, I believe. We're going to talk from the big cloud providers So, Chris, the cloud heads in the clouds. And of course, the people Well, the cloud guys They don't own the access. That's the one thing they don't own. I don't know about where you live, the telcos are fundamentally Some have a little bit of regional, Dave: Keep your friends Well, Sarbjeet, one of the and the telcos are competing that the cloud is a big force. Are they in denial? to the pragmatism of the situation. the big telecom act It made the US less We need that fiber in the ground but the governments are conservative in the past. We know that the clouds are but it means that the telco at the ref all the time. in front of the opposition. that we had Google Cloud, You'd have to ask GSMA. and the way they wanted to pitch things But the industry needs them there, Dave, they're there be on the keynote stage. glitz and the glamour, Chris. It's not glitz, it's espresso. sees the tech market and the more employee but in the EU, highly regulated, the issue is the telecom buying the whole underlying network. And dare I say, I mean, that's... now the next 6G you know, like then- 10G, that's a different market. down by the innovation, I think. And the answer to your point, (all laugh) on the foreign policy Lina Khan wants to. And it's changing the balance about, Well, of course the theme Is the theme really velocity? invisibility is the real answer. is that the telecom industry But you did... So, props to the telcos and not necessarily just the network data. it's the antithesis of So in the home environment, No, I'm saying they don't do it yet. Would you pay a lot for that? most of the time. topic we want to hit on. some of the things you're doing there. So in the past, We're going to have to have you guys back We had a very spirited, See you soon.
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Oracle Announces MySQL HeatWave on AWS
>>Oracle continues to enhance my sequel Heatwave at a very rapid pace. The company is now in its fourth major release since the original announcement in December 2020. 1 of the main criticisms of my sequel, Heatwave, is that it only runs on O. C I. Oracle Cloud Infrastructure and as a lock in to Oracle's Cloud. Oracle recently announced that heat wave is now going to be available in AWS Cloud and it announced its intent to bring my sequel Heatwave to Azure. So my secret heatwave on AWS is a significant TAM expansion move for Oracle because of the momentum AWS Cloud continues to show. And evidently the Heatwave Engineering team has taken the development effort from O. C I. And is bringing that to A W S with a number of enhancements that we're gonna dig into today is senior vice president. My sequel Heatwave at Oracle is back with me on a cube conversation to discuss the latest heatwave news, and we're eager to hear any benchmarks relative to a W S or any others. Nippon has been leading the Heatwave engineering team for over 10 years and there's over 100 and 85 patents and database technology. Welcome back to the show and good to see you. >>Thank you. Very happy to be back. >>Now for those who might not have kept up with the news, uh, to kick things off, give us an overview of my sequel, Heatwave and its evolution. So far, >>so my sequel, Heat Wave, is a fully managed my secret database service offering from Oracle. Traditionally, my secret has been designed and optimised for transaction processing. So customers of my sequel then they had to run analytics or when they had to run machine learning, they would extract the data out of my sequel into some other database for doing. Unlike processing or machine learning processing my sequel, Heat provides all these capabilities built in to a single database service, which is my sequel. He'd fake So customers of my sequel don't need to move the data out with the same database. They can run transaction processing and predicts mixed workloads, machine learning, all with a very, very good performance in very good price performance. Furthermore, one of the design points of heat wave is is a scale out architecture, so the system continues to scale and performed very well, even when customers have very large late assignments. >>So we've seen some interesting moves by Oracle lately. The collaboration with Azure we've we've covered that pretty extensively. What was the impetus here for bringing my sequel Heatwave onto the AWS cloud? What were the drivers that you considered? >>So one of the observations is that a very large percentage of users of my sequel Heatwave, our AWS users who are migrating of Aurora or so already we see that a good percentage of my secret history of customers are migrating from GWS. However, there are some AWS customers who are still not able to migrate the O. C. I to my secret heat wave. And the reason is because of, um, exorbitant cost, which was charges. So in order to migrate the workload from AWS to go see, I digress. Charges are very high fees which becomes prohibitive for the customer or the second example we have seen is that the latency of practising a database which is outside of AWS is very high. So there's a class of customers who would like to get the benefits of my secret heatwave but were unable to do so and with this support of my secret trip inside of AWS, these customers can now get all the grease of the benefits of my secret he trip without having to pay the high fees or without having to suffer with the poorly agency, which is because of the ws architecture. >>Okay, so you're basically meeting the customer's where they are. So was this a straightforward lifted shift from from Oracle Cloud Infrastructure to AWS? >>No, it is not because one of the design girls we have with my sequel, Heatwave is that we want to provide our customers with the best price performance regardless of the cloud. So when we decided to offer my sequel, he headed west. Um, we have optimised my sequel Heatwave on it as well. So one of the things to point out is that this is a service with the data plane control plane and the console are natively running on AWS. And the benefits of doing so is that now we can optimise my sequel Heatwave for the E. W s architecture. In addition to that, we have also announced a bunch of new capabilities as a part of the service which will also be available to the my secret history of customers and our CI, But we just announced them and we're offering them as a part of my secret history of offering on AWS. >>So I just want to make sure I understand that it's not like you just wrapped your stack in a container and stuck it into a W s to be hosted. You're saying you're actually taking advantage of the capabilities of the AWS cloud natively? And I think you've made some other enhancements as well that you're alluding to. Can you maybe, uh, elucidate on those? Sure. >>So for status, um, we have taken the mind sequel Heatwave code and we have optimised for the It was infrastructure with its computer network. And as a result, customers get very good performance and price performance. Uh, with my secret he trade in AWS. That's one performance. Second thing is, we have designed new interactive counsel for the service, which means that customers can now provision there instances with the council. But in addition, they can also manage their schemas. They can. Then court is directly from the council. Autopilot is integrated. The council we have introduced performance monitoring, so a lot of capabilities which we have introduced as a part of the new counsel. The third thing is that we have added a bunch of new security features, uh, expose some of the security features which were part of the My Secret Enterprise edition as a part of the service, which gives customers now a choice of using these features to build more secure applications. And finally, we have extended my secret autopilot for a number of old gpus cases. In the past, my secret autopilot had a lot of capabilities for Benedict, and now we have augmented my secret autopilot to offer capabilities for elderly people. Includes as well. >>But there was something in your press release called Auto thread. Pooling says it provides higher and sustained throughput. High concerns concerns concurrency by determining Apple number of transactions, which should be executed. Uh, what is that all about? The auto thread pool? It seems pretty interesting. How does it affect performance? Can you help us understand that? >>Yes, and this is one of the capabilities of alluding to which we have added in my secret autopilot for transaction processing. So here is the basic idea. If you have a system where there's a large number of old EP transactions coming into it at a high degrees of concurrency in many of the existing systems of my sequel based systems, it can lead to a state where there are few transactions executing, but a bunch of them can get blocked with or a pilot tried pulling. What we basically do is we do workload aware admission control and what this does is it figures out, what's the right scheduling or all of these algorithms, so that either the transactions are executing or as soon as something frees up, they can start executing, so there's no transaction which is blocked. The advantage to the customer of this capability is twofold. A get significantly better throughput compared to service like Aurora at high levels of concurrency. So at high concurrency, for instance, uh, my secret because of this capability Uh oh, thread pulling offers up to 10 times higher compared to Aurora, that's one first benefit better throughput. The second advantage is that the true part of the system never drops, even at high levels of concurrency, whereas in the case of Aurora, the trooper goes up, but then, at high concurrency is, let's say, starting, uh, level of 500 or something. It depends upon the underlying shit they're using the troopers just dropping where it's with my secret heatwave. The truth will never drops. Now, the ramification for the customer is that if the truth is not gonna drop, the user can start off with a small shape, get the performance and be a show that even the workload increases. They will never get a performance, which is worse than what they're getting with lower levels of concurrency. So this let's leads to customers provisioning a shape which is just right for them. And if they need, they can, uh, go with the largest shape. But they don't like, you know, over pay. So those are the two benefits. Better performance and sustain, uh, regardless of the level of concurrency. >>So how do we quantify that? I know you've got some benchmarks. How can you share comparisons with other cloud databases especially interested in in Amazon's own databases are obviously very popular, and and are you publishing those again and get hub, as you have done in the past? Take us through the benchmarks. >>Sure, So benchmarks are important because that gives customers a sense of what performance to expect and what price performance to expect. So we have run a number of benchmarks. And yes, all these benchmarks are available on guitar for customers to take a look at. So we have performance results on all the three castle workloads, ol DB Analytics and Machine Learning. So let's start with the Rdp for Rdp and primarily because of the auto thread pulling feature. We show that for the IPCC for attended dataset at high levels of concurrency, heatwave offers up to 10 times better throughput and this performance is sustained, whereas in the case of Aurora, the performance really drops. So that's the first thing that, uh, tend to alibi. Sorry, 10 gigabytes. B B C c. I can come and see the performance are the throughput is 10 times better than Aurora for analytics. We have done a comparison of my secret heatwave in AWS and compared with Red Ship Snowflake Googled inquiry, we find that the price performance of my secret heatwave compared to read ship is seven times better. So my sequel, Heat Wave in AWS, provides seven times better price performance than red ship. That's a very, uh, interesting results to us. Which means that customers of Red Shift are really going to take the service seriously because they're gonna get seven times better price performance. And this is all running in a W s so compared. >>Okay, carry on. >>And then I was gonna say, compared to like, Snowflake, uh, in AWS offers 10 times better price performance. And compared to Google, ubiquity offers 12 times better price performance. And this is based on a four terabyte p PCH workload. Results are available on guitar, and then the third category is machine learning and for machine learning, uh, for training, the performance of my secret heatwave is 25 times faster compared to that shit. So all the three workloads we have benchmark's results, and all of these scripts are available on YouTube. >>Okay, so you're comparing, uh, my sequel Heatwave on AWS to Red Shift and snowflake on AWS. And you're comparing my sequel Heatwave on a W s too big query. Obviously running on on Google. Um, you know, one of the things Oracle is done in the past when you get the price performance and I've always tried to call fouls you're, like, double your price for running the oracle database. Uh, not Heatwave, but Oracle Database on a W s. And then you'll show how it's it's so much cheaper on on Oracle will be like Okay, come on. But they're not doing that here. You're basically taking my sequel Heatwave on a W s. I presume you're using the same pricing for whatever you see to whatever else you're using. Storage, um, reserved instances. That's apples to apples on A W s. And you have to obviously do some kind of mapping for for Google, for big query. Can you just verify that for me, >>we are being more than fair on two dimensions. The first thing is, when I'm talking about the price performance for analytics, right for, uh, with my secret heat rape, the cost I'm talking about from my secret heat rape is the cost of running transaction processing, analytics and machine learning. So it's a fully loaded cost for the case of my secret heatwave. There has been I'm talking about red ship when I'm talking about Snowflake. I'm just talking about the cost of these databases for running, and it's only it's not, including the source database, which may be more or some other database, right? So that's the first aspect that far, uh, trip. It's the cost for running all three kinds of workloads, whereas for the competition, it's only for running analytics. The second thing is that for these are those services whether it's like shit or snowflakes, That's right. We're talking about one year, fully paid up front cost, right? So that's what most of the customers would pay for. Many of the customers would pay that they will sign a one year contract and pay all the costs ahead of time because they get a discount. So we're using that price and the case of Snowflake. The costs were using is their standard edition of price, not the Enterprise edition price. So yes, uh, more than in this competitive. >>Yeah, I think that's an important point. I saw an analysis by Marx Tamer on Wiki Bond, where he was doing the TCO comparisons. And I mean, if you have to use two separate databases in two separate licences and you have to do et yelling and all the labour associated with that, that that's that's a big deal and you're not even including that aspect in in your comparison. So that's pretty impressive. To what do you attribute that? You know, given that unlike, oh, ci within the AWS cloud, you don't have as much control over the underlying hardware. >>So look hard, but is one aspect. Okay, so there are three things which give us this advantage. The first thing is, uh, we have designed hateful foreign scale out architecture. So we came up with new algorithms we have come up with, like, uh, one of the design points for heat wave is a massively partitioned architecture, which leads to a very high degree of parallelism. So that's a lot of hype. Each were built, So that's the first part. The second thing is that although we don't have control over the hardware, but the second design point for heat wave is that it is optimised for commodity cloud and the commodity infrastructure so we can have another guys, what to say? The computer we get, how much network bandwidth do we get? How much of, like objects to a brand that we get in here? W s. And we have tuned heat for that. That's the second point And the third thing is my secret autopilot, which provides machine learning based automation. So what it does is that has the users workload is running. It learns from it, it improves, uh, various premieres in the system. So the system keeps getting better as you learn more and more questions. And this is the third thing, uh, as a result of which we get a significant edge over the competition. >>Interesting. I mean, look, any I SV can go on any cloud and take advantage of it. And that's, uh I love it. We live in a new world. How about machine learning workloads? What? What did you see there in terms of performance and benchmarks? >>Right. So machine learning. We offer three capabilities training, which is fully automated, running in France and explanations. So one of the things which many of our customers told us coming from the enterprise is that explanations are very important to them because, uh, customers want to know that. Why did the the system, uh, choose a certain prediction? So we offer explanations for all models which have been derailed by. That's the first thing. Now, one of the interesting things about training is that training is usually the most expensive phase of machine learning. So we have spent a lot of time improving the performance of training. So we have a bunch of techniques which we have developed inside of Oracle to improve the training process. For instance, we have, uh, metal and proxy models, which really give us an advantage. We use adaptive sampling. We have, uh, invented in techniques for paralysing the hyper parameter search. So as a result of a lot of this work, our training is about 25 times faster than that ship them health and all the data is, uh, inside the database. All this processing is being done inside the database, so it's much faster. It is inside the database. And I want to point out that there is no additional charge for the history of customers because we're using the same cluster. You're not working in your service. So all of these machine learning capabilities are being offered at no additional charge inside the database and as a performance, which is significantly faster than that, >>are you taking advantage of or is there any, uh, need not need, but any advantage that you can get if two by exploiting things like gravity. John, we've talked about that a little bit in the past. Or trainee. Um, you just mentioned training so custom silicon that AWS is doing, you're taking advantage of that. Do you need to? Can you give us some insight >>there? So there are two things, right? We're always evaluating What are the choices we have from hybrid perspective? Obviously, for us to leverage is right and like all the things you mention about like we have considered them. But there are two things to consider. One is he is a memory system. So he favours a big is the dominant cost. The processor is a person of the cost, but memory is the dominant cost. So what we have evaluated and found is that the current shape which we are using is going to provide our customers with the best price performance. That's the first thing. The second thing is that there are opportunities at times when we can use a specialised processor for vaccinating the world for a bit. But then it becomes a matter of the cost of the customer. Advantage of our current architecture is on the same hardware. Customers are getting very good performance. Very good, energetic performance in a very good machine learning performance. If you will go with the specialised processor, it may. Actually, it's a machine learning, but then it's an additional cost with the customers we need to pay. So we are very sensitive to the customer's request, which is usually to provide very good performance at a very low cost. And we feel is that the current design we have as providing customers very good performance and very good price performance. >>So part of that is architectural. The memory intensive nature of of heat wave. The other is A W s pricing. If AWS pricing were to flip, it might make more sense for you to take advantage of something like like cranium. Okay, great. Thank you. And welcome back to the benchmarks benchmarks. Sometimes they're artificial right there. A car can go from 0 to 60 in two seconds. But I might not be able to experience that level of performance. Do you? Do you have any real world numbers from customers that have used my sequel Heatwave on A W s. And how they look at performance? >>Yes, absolutely so the my Secret service on the AWS. This has been in Vera for, like, since November, right? So we have a lot of customers who have tried the service. And what actually we have found is that many of these customers, um, planning to migrate from Aurora to my secret heat rape. And what they find is that the performance difference is actually much more pronounced than what I was talking about. Because with Aurora, the performance is actually much poorer compared to uh, like what I've talked about. So in some of these cases, the customers found improvement from 60 times, 240 times, right? So he travels 100 for 240 times faster. It was much less expensive. And the third thing, which is you know, a noteworthy is that customers don't need to change their applications. So if you ask the top three reasons why customers are migrating, it's because of this. No change to the application much faster, and it is cheaper. So in some cases, like Johnny Bites, what they found is that the performance of their applications for the complex storeys was about 60 to 90 times faster. Then we had 60 technologies. What they found is that the performance of heat we have compared to Aurora was 100 and 39 times faster. So, yes, we do have many such examples from real workloads from customers who have tried it. And all across what we find is if it offers better performance, lower cost and a single database such that it is compatible with all existing by sequel based applications and workloads. >>Really impressive. The analysts I talked to, they're all gaga over heatwave, and I can see why. Okay, last question. Maybe maybe two and one. Uh, what's next? In terms of new capabilities that customers are going to be able to leverage and any other clouds that you're thinking about? We talked about that upfront, but >>so in terms of the capabilities you have seen, like they have been, you know, non stop attending to the feedback from the customers in reacting to it. And also, we have been in a wedding like organically. So that's something which is gonna continue. So, yes, you can fully expect that people not dressed and continue to in a way and with respect to the other clouds. Yes, we are planning to support my sequel. He tripped on a show, and this is something that will be announced in the near future. Great. >>All right, Thank you. Really appreciate the the overview. Congratulations on the work. Really exciting news that you're moving my sequel Heatwave into other clouds. It's something that we've been expecting for some time. So it's great to see you guys, uh, making that move, and as always, great to have you on the Cube. >>Thank you for the opportunity. >>All right. And thank you for watching this special cube conversation. I'm Dave Volonte, and we'll see you next time.
SUMMARY :
The company is now in its fourth major release since the original announcement in December 2020. Very happy to be back. Now for those who might not have kept up with the news, uh, to kick things off, give us an overview of my So customers of my sequel then they had to run analytics or when they had to run machine So we've seen some interesting moves by Oracle lately. So one of the observations is that a very large percentage So was this a straightforward lifted shift from No, it is not because one of the design girls we have with my sequel, So I just want to make sure I understand that it's not like you just wrapped your stack in So for status, um, we have taken the mind sequel Heatwave code and we have optimised Can you help us understand that? So this let's leads to customers provisioning a shape which is So how do we quantify that? So that's the first thing that, So all the three workloads we That's apples to apples on A W s. And you have to obviously do some kind of So that's the first aspect And I mean, if you have to use two So the system keeps getting better as you learn more and What did you see there in terms of performance and benchmarks? So we have a bunch of techniques which we have developed inside of Oracle to improve the training need not need, but any advantage that you can get if two by exploiting We're always evaluating What are the choices we have So part of that is architectural. And the third thing, which is you know, a noteworthy is that In terms of new capabilities that customers are going to be able so in terms of the capabilities you have seen, like they have been, you know, non stop attending So it's great to see you guys, And thank you for watching this special cube conversation.
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Jay Workman, VMware & Geoff Thompson, VMware | VMware Explore 2022
>>Hey everyone. Welcome back to the cubes day two coverage of VMware Explorer, 22 from San Francisco. Lisa Martin, back here with you with Dave Nicholson, we have a couple of guests from VMware. Joining us, please. Welcome Jay Workman, senior director, cloud partner, and alliances marketing, and Jeff Thompson, VP cloud provider sales at VMware guys. It's great to have you on the program. >>Ah, good to be here. Thanks for having us on. >>We're gonna be talking about a really interesting topic. Sovereign cloud. What is sovereign cloud? Jeff? Why is it important, but fundamentally, what is >>It? Yeah, well, we were just talking a second ago. Aren't we? And it's not about royalty. So yeah, data sovereignty is really becoming super important. It's about the regulation and control of data. So lots of countries now are being very careful and advising companies around where to place data and the jurisdictional controls mandate that personal data or otherwise has to be secured. We ask, we have to have access controls around it and privacy controls around it. So data sovereign clouds are clouds that have been built by our cloud providers in, in, in VMware that specifically satisfy the requirements of those jurisdictions and regulated industries. So we've built a, a little program around that. We launched it about a year ago and continuing to add cloud providers to that. >>Yeah, and I, I think it's also important just to build on what Jeff said is, is who can access that data is becoming increasingly important data is, is almost in it's. It is becoming a bit of a currency. There's a lot of value in data and securing that data is, is becoming over the years increasingly important. So it's, it's not like we built a problem or we created a solution for problem that didn't exist. It's gotten it's, it's been a problem for a while. It's getting exponentially bigger data is expanding and growing exponentially, and it's becoming increasingly important for organizations and companies to realize where my data sits, who can access it, what types of data needs to go and what type of clouds. And it's very, very aligned with multi-cloud because some data can sit in a, in a public cloud, which is fine, but some data needs to be secure. It needs to be resident within country. And so this is, this is what we're addressing through our partners. >>Yeah, I, yeah, I was just gonna add to that. I think there's a classification there there's data residency, and then there's data sovereignty. So residency is just about where is the data, which country is it in sovereignty is around who can access that data. And that's the critical aspect of, of data sovereignty who's got control and access to that data. And how do we make sure that all the controls are in place to make sure that only the right people can get access to that data? Yeah. >>So let's, let's sort of build from the ground up an example, and let's use Western Europe as an example, just because state to state in the United States, although California is about to adopt European standards for privacy in a, in a unique, in a unique, unique way, pick a country in, in Europe, I'm a service provider. I have an offering and that offering includes a stack of hardware and I'm running what we frequently refer to as the STDC or software defined data center stack. So I've got NEX and I've got vs N and I've got vSphere and I'm running and I have a cloud and you have all of the operational tools around that, and you can spin up VMs and render under applications there. And here we are within the borders of this country, what makes it a sovereign cloud at that? So at that point, is that a sovereign cloud or? >>No, not yet. Not it's close. I mean, you nailed, >>What's >>A secret sauce. You nailed the technology underpinning. So we've got 4,500 plus cloud provider partners around the world. Less than 10% of those partners are running the full STDC stack, which we've branded as VMware cloud verified. So the technology underpinning from our perspective is the starting point. Okay. For sovereignty. So they, they, they need that right. Technology. Okay. >>Verified is required for sovereign. Yes. >>Okay. Cloud verified is the required technology stack for sovereign. So they've got vSphere vs. A NSX in there. Okay. A lot of these partners are also offering a multitenant cloud with VMware cloud director on top of that, which is great. That's the starting point. But then we've, we've set a list of standards above and beyond that, in addition to the technology, they've gotta meet certain jurisdiction requirements, certain local compliance requirements and certifications. They've gotta be able to address the data re data residency requirements of their particular jurisdiction. So it's going above and beyond. But to your point, it does vary by country. >>Okay. So, so in this hypothetical example, this is this country. You a stand, I love it. When people talk about Stan, people talk about EMIA and you know, I, I love AMEA food. Isn't AIAN food. One. There's no such thing as a European until you have an Italian, a Britain, a German yep. In Florida arguing about how our beer and our coffee is terrible. Right. Right. Then they're all European. They go home and they don't like each other. Yeah. So, but let's just pretend that there's a thing called Europe. So this, so there's this, so we've got a border, we know residency, right. Because it physically is here. Yep. But what are the things in terms of sovereignty? So you're talking about a lot of kind of certification and validation, making sure that, that everything maps to those existing rules. So is, this is, this is a lot of this administrative and I mean, administrative in the, in the sort of state administrative terminology, >>I I'm let's build on your example. Yeah. So we were talking about food and obviously we know the best food in the world comes from England. >>Of course it does. Yeah. I, no doubt. I agree. I Don not get that. I do. I do do agree. Yeah. >>So UK cloud, fantastic partner for us. Okay. Whether they're one of our first sovereign cloud providers in the program. So UK cloud, they satisfied the requirements with the local UK government. They built out their cloud verified. They built out a stack specifically that enables them to satisfy the requirements of being a sovereign cloud provider. They have local data centers inside the UK. The data from the local government is placed into those data centers. And it's managed by UK people on UK soil so that they know the privacy, they know the security aspects, the compliance, all of that wrapped up on top of a secure SDDC platform. Okay. Satisfies the requirements of the UK government, that they are managing that data in a sovereign way that, that, that aligns to the jurisdictional control that they expect from a company like UK cloud. Well, >>I think to build on that, a UK cloud is an example of certain employees at UK, UK cloud will have certain levels of clearance from the UK government who can access and work on certain databases that are stored within UK cloud. So they're, they're addressing it from multiple fronts, not just with their hardware, software data center framework, but actually at the individual compliance level and individual security clearance level as to who can go in and work on that data. And it's not just a governmental, it's not a public sector thing. I mean, any highly regulated industry, healthcare, financial services, they're all gonna need this type of data protection and data sovereignty. >>Can this work in a hyperscaler? So you've got you, have, you have VMC AVS, right? GC V C >>O >>CVAs O CVS. Thank you. Can it be, can, can a sovereign cloud be created on top of physical infrastructure that is in one of those hyperscalers, >>From our perspective, it's not truly sovereign. If, if it's a United States based company operating in Germany, operating in the UK and a local customer or organization in Germany, or the UK wants to deploy workloads in that cloud, we wouldn't classify that as totally sovereign. Okay. Because by virtue of the cloud act in the United States, that gives the us government rights to request or potentially view some of that data. Yeah. Because it's, it's coming out of a us based operator data center sitting on foreign soil so that the us government has some overreach into that. And some of that data may actually be stored. Some of the metadata may reside back in the us and the customer may not know. So certain workloads would be ideally suited for that. But for something that needs to be truly sovereign and local data residency, that it wouldn't be a good fit. I think that >>Perspectives key thing, going back to residency versus sovereignty. Yeah. It can be, let's go to our UK example. It can be on a hyperscaler in the UK now it's resident in the UK, but some of the metadata, the profiling information could be accessible by the entity in the United States. For example, there now it's not sovereign anymore. So that's the key difference between a, what we view as a pro you know, a pure sovereign cloud play and then maybe a hyperscaler that's got more residency than sovereignty. >>Yeah. We talk a lot about partnerships. This seems to be a unique opportunity for a certain segment of partners yeah. To give that really is an opportunity for them to have a line of business established. That's unique from some of the hyperscale cloud providers. Yeah. Where, where sort of the, the modesty of your size might be an advantage if you're in a local. Yes. You're in Italy and you are a service provider. There sounds like a great fit, >>That's it? Yeah. You've always had the, the beauty of our program. We have 4,500 cloud providers and obviously not, all of them are able to provide a data, a sovereign cloud. We have 20 in the program today in, in the country. You you'd expect them to be in, you know, the UK, Italy, Italy, France, Germany, over in Asia Pacific. We have in Australia and New Zealand, Japan, and, and we have Canada and Latin America to, to dovetail, you know, the United States. But those are the people that have had these long term relationships with the local governments, with these regulated industries and providing those services for many, many years. It's just that now data sovereignty has become more important. And they're able to go that extra mile and say, Hey, we've been doing this pretty much, you know, for decades, but now we're gonna put a wrap and some branding around it and do these extra checks because we absolutely know that we can provide the sovereignty that's required. >>And that's been one of the beautiful things about the entire initiative is we're actually, we're learning a lot from our partners in these countries to Jeff's point have been doing this. They've been long time, VMware partners they've been doing sovereignty. And so collectively together, we're able to really establish a pretty robust framework from, from our perspective, what does data sovereignty mean? Why does it matter? And then that's gonna help us work with the customers, help them decide which workloads need to go and which type of cloud. And it dovetails very, very nicely into a multi-cloud that's a reality. So some of those workloads can sit in the public sector and the hyperscalers and some of 'em need to be sovereign. Yeah. So it's, it's a great solution for our customers >>When you're in customer conversations, especially as, you know, data sovereign to be is becomes a global problem. Where, who are you talking to? Are you talking to CIOs? Are you talking to chief data officers? I imagine this is a pretty senior level conversation. >>Yeah. I it's, I think it's all of the above. Really. It depends. Who's managing the data. What type of customer is it? What vertical market are they in? What compliance regulations are they are they beholden to as a, as an enterprise, depending on which country they're in and do they have a need for a public cloud, they may already be all localized, you know? So it really depends, but it, it could be any of those. It's generally I think a fair, fairly senior level conversation. And it's, it's, it's, it's consultancy, it's us understanding what their needs are working with our partners and figuring out what's the best solution for them. >>And I think going back to, they've probably having those conversations for a long time already. Yeah. Because they probably have had workloads in there for years, maybe even decades. It's just that now sovereignty has become, you know, a more popular, you know, requirements to satisfy. And so they've gone going back to, they've gone the extra mile with those as the trusted advisor with those people. They've all been working with for many, many years to do that work. >>And what sort of any examples you mentioned some of the highly regulated industries, healthcare, financial services, any customer come to mind that you think really articulates the value of what VMware's delivering through its service through its cloud provider program. That makes the obvious why VMware an obvious answer? >>Wow. I, I, I get there's, there's so many it's, it's actually, it's each of our different cloud providers. They bring their win wise to us. And we just have, we have a great library now of assets that are on our sovereign cloud website of those win wires. So it's many industries, many, many countries. So you can really pick, pick your, your choice. There. That's >>A good problem >>To have, >>To the example of UK cloud they're, they're really focused on the UK government. So some of them aren't gonna be referenced. Well, we may have indication of a major financial services company in Australia has deployed with AU cloud, one of our partners. So we we've also got some semi blind references like that. And, and to some degree, a lot of these are maintained as fairly private wins and whatnot for obvious security reasons, but, and we're building it and building that library up, >>You mentioned the number 4,500, a couple of times, you, you referencing VMware cloud provider partners or correct program partners. So VCP P yes. So 45, 4500 is the, kind of, is the, is the number, you know, >>That's the number >>Globally of our okay. >>Partners that are offering a commercial cloud service based at a minimum with vSphere and they're. And many of 'em have many more of our technologies. And we've got little under 10% of those that have the cloud verified designation that are running that full STDC, stack >>Somebody, somebody Talli up, all of that. And the argument has been made that, that rep that, that would mean that VMware cloud. And although some of it's on IAS from hyperscale cloud providers. Sure. But that, that rep, that means that VMware has the third or fourth largest cloud on the planet already right now. >>Right. Yep. >>Which is kind of interesting because yeah. If you go back to when, what 2016 or so when VMC was at least baned about yeah. Is that right? A lot of people were skeptical. I was skeptical very long history with VMware at the time. And I was skeptical. I I'm thinking, nah, it's not gonna work. Yeah. This is desperation. Sorry, pat. I love you. But it's desperation. Right. AWS, their attitude is in this transaction. Sure. Send us some customers we'll them. Yeah. Right. I very, very cynical about it. Completely proved me wrong. Obviously. Where did it go? Went from AWS to Azure to right. Yeah. To GCP, to Oracle, >>Oracle, Alibaba, >>Alibaba. Yep. Globally. >>We've got IBM. Yep. Right. >>Yeah. So along the way, it would be easy to look at that trajectory and say, okay, wow, hyperscale cloud. Yeah. Everything's consolidating great. There's gonna be five or six or 10 of these players. And that's it. And everybody else is out in the cold. Yeah. But it turns out that long tail, if you look at the chart of who the largest VCP P partners are, that long tail of the smaller ones seem to be carving out specialized yes. Niches where you can imagine now, at some point in the future, you sum up this long tail and it becomes larger than maybe one of the hyperscale cloud providers. Right. I don't think a lot of people predicted that. I think, I think people predicted the demise of VMware and frankly, a lot of people in the VMware ecosystem, just like they predicted the demise of the mainframe. Sure. The storage area network fill in the blank. I >>Mean, Jeff and I we've oh yeah. We've been on the, Jeff's been a little longer than I have, but we've been working together for 10 plus years on this. And we've, we've heard that many times. Yeah. Yeah. Our, our ecosystem has grown over the years. We've seen some consolidation, some M and a activity, but we're, we're not even actively recruiting partners and it's growing, we're focused on helping our partners gain more, share internally, gain, more share at wallet, but we're still getting organic growth in the program. Really. So it, it shows, I think that there is value in what we can offer them as a platform to build a cloud on. >>Yeah. What's been interesting is there's there's growth and there's some transition as well. Right? So there's been traditional cloud providers. Who've built a cloud in their data center, some sovereign, some not. And then there's other partners that are adopting VCP P because of our SA. So we've either converted some technology from product into SA or we've built net new SA or we've acquired companies that have been SA only. And now we have a bigger portfolio that service providers, cloud providers, managed service providers are all interested in. So you get resellers channel partners. Who've historically been doing ELAs and reselling to end customers. They're transitioning their business into doing recurring revenue and the only game in town where you really wanna do recurring revenues, VCP P. So our ecosystem is both growing because our cloud providers with their data center are doing more with our customers. And then we're adding more managed service providers because of our SA portfolio. And that, that, that combo, that one, two punch is creating a much bigger VCP P ecosystem overall. >>Yeah. >>Impressive. >>Do you think we have a better idea of what sovereign cloud means? Yes. I think we do. >>It's not Royal. >>It's all about royalty, >>All royalty. What are some of the things Jeff, as we look on the horizon, obviously seven to 10,000 people here at, at VMwares where people really excited to be back. They want to hear it from VMware. They wanna hear from its partner ecosystem, the community. What are some of the things that you think are on the horizon where sovereign cloud is concerned that are really opportunities yeah. For businesses to get it right. >>Yeah. We're in the early days of this, I think there's still a whole bunch of rules, regulatory laws that have not been defined yet. So I think there's gonna be some more learning. There's gonna be some top down guidance like Gaia X in Europe. That's the way that they're defining who gets access and control over what data and what's in. And what's out of that. So we're gonna get more of these Gaia X type things happening around the world, and they're all gonna be slightly different. Everyone's gonna have to understand what they are, how to interpret and then build something around them. So we need to stay on top of that, myself and Jay, to make sure that we've got the right cloud providers in the right space to capitalize on that, build out the sovereign cloud program over time and make sure that what they're building to support aligns with these different requirements that are out there across different countries. So it's an evolving landscape. That's >>Yeah. And one of the things too, we're also doing from a product perspective to better enable partners to, to address these sovereign cloud workloads is where we have, we have gaps maybe in our portfolio is we're partner partnering with some of our ISVs, like a, Konic like a Forex vem. So we can give our partners object storage or ransomware protection to add on to their sovereign cloud service, all accessible through our cloud director consult. So we're, we're enhancing the program that way. And to Jeff's point earlier, we've got 20 partners today. We're hoping to double that by the end of our fiscal year and, and just take a very methodical approach to growth of the program. >>Sounds great guys, early innings though. Thank you so much for joining Dave and me talking about what software and cloud is describing it to us, and also talking about the difference between that data residency and all the, all of the challenges and the, in the landscape that customers are facing. They can go turn to VMware and its ecosystem for that help. We appreciate your insights and your time. Guys. Thank >>You >>For >>Having us. Our >>Pleasure. Appreciate it >>For our guests and Dave Nicholson. I'm Lisa Martin. You've been watching the cube. This is the end of day, two coverage of VMware Explorer, 2022. Have a great rest of your day. We'll see you tomorrow.
SUMMARY :
It's great to have you on the program. Ah, good to be here. What is sovereign cloud? It's about the Yeah, and I, I think it's also important just to build on what Jeff said is, And that's the critical aspect of, of data sovereignty who's got control and access to So let's, let's sort of build from the ground up an example, and let's use Western I mean, you nailed, So the technology underpinning from Verified is required for sovereign. That's the starting point. So is, this is, this is a lot of this administrative and I mean, So we were talking about food and obviously we know the best food in the world comes I Don not get that. that enables them to satisfy the requirements of being a sovereign cloud provider. I think to build on that, a UK cloud is an example of certain employees at UK, Can it be, can, can a sovereign cloud be foreign soil so that the us government has some overreach into that. So that's the key difference between a, what we view as a pro you know, of the hyperscale cloud providers. to dovetail, you know, the United States. sit in the public sector and the hyperscalers and some of 'em need to be sovereign. Where, who are you talking to? And it's, it's, it's, it's consultancy, it's us understanding what their needs are working with It's just that now sovereignty has become, you know, And what sort of any examples you mentioned some of the highly regulated industries, So you can really pick, So we we've also got some semi blind references like that. So 45, 4500 is the, kind of, is the, is the number, you know, And many of 'em have many more of our technologies. And the argument has been made that, Right. And I was skeptical. can imagine now, at some point in the future, you sum up this long tail and it becomes Our, our ecosystem has grown over the years. So you get resellers channel I think we do. What are some of the things that you think are on the horizon Everyone's gonna have to understand what they And to Jeff's point earlier, we've got 20 partners today. all of the challenges and the, in the landscape that customers are facing. Having us. Appreciate it This is the end of day, two coverage of VMware Explorer, 2022.
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OSCAR BELLEI, Agoraverse | Monaco Crypto Summit 2022
>>Okay, welcome back everyone. This is the Cube's coverage here. Monaco took a trip all the way out here to cover the Monaco crypto summit. I'm John feer, host of the cube, a lot of action happening presented by digital bits and this ecosystem that's coming together, building on top of digital bits and other blockchains to bring value at the application. These new app, super apps are emerging. Almost every category's gonna be decentralized. This is our opinion and the world believes it. And they're here as well. We've got Oscar ballet CEO co-founder of Agora verse ago is a shopping metaverse coming out soon. We'll get the dates, Oscar. Welcome to the cube. >>Thank you very much for having me. >>We were just talking before you came on camera. You're a young gun, young entrepreneur. You're a gamer. Yeah, a little bit too old to miss the eSports windows. You said, you know, like 25. It's great until that's you missed the window. I wish I was 25 gaming the pandemic with remote work, big tailwind acceleration around the idea of this new digital VI virtual hybrid world. We're living in where people want to have experiences that are similar to physical and virtual. You're doing something really cool around shopping. Yeah. Take a explain. What's going on when the, I know it's not out yet. It's in preview. Yeah. Take a minute to explain. >>Absolutely. So a goers really is a way to create those online storefront environments, virtual environments that are really much inspired by video games in their usage and kind of how the experience goes forward. We want to recreate the brand's theme, aesthetic storytelling or the NFT project as well. All of that created in a virtual setting, which is way more interesting than looking at a traditional webpage. And also you can do some crazy stuff that you can't do in real life, in a real life store, you know, with some crazy effects and lighting and stuff. So it's, it's a whole new frontier that we are trying to cover. And we believe that there is a real use case for shopping centric S experiences and to actually make the S a bit more than a buzzword than that. It is at the moment. >>Okay. So a Agora is the shopping. Metaverse a Agora verse is the company name and product name. You're on the Solona blockchain. Got my notes here, but I gotta ask you, I mean, people are trying to do this right now. We see a lot of high end clients like Microsoft showroom, showroom vibes. Yeah. Not so much. E-commerce per se, but more like the big, I mean it's low hanging fruit. Yeah. How do you guys compare to some other apps out there? Other metaverses? >>I think compared to the bigger companies, we are way more flexible and we can act way more quickly than they can. They still have a lot of ground to cover. And a lot of convincing to do with their communities of users metaverse is not really the most popular topic at the moment. It's still very much kind of looked at as a trend, as something that is just passing and they have to deal with this community interaction that is not really favorable for them. There are other questions about the metaverse that are not being talked about as often, but the ecological costs, for example, of running a metaverse like Facebook envisions it, of running those virtual headsets, running those environments. It's very costy on, on, on the ecological side of things and it's not as often mentioned. And I think that's actually their biggest challenge. >>Can you get an example for folks that don't are in the weeds on that? What's the what's what do you mean by that? The cost of build the headsets? Is it the >>Servers? It's more of the servers, really? You need to run a lot of servers, which is really costly on the environment and environmental questions are at the center of public debates. Anyways, and companies have to play that game as well. So they will have to find kind of this balance between, well, building this cool metaverse, but doing it in an ecological friendly manner as well. I think that's their toughest challenge. >>And what's your solution just using the blockchain? Well, an answer to that, cause some people say, Hey, that's not that's, that's not. So eco-friendly either, >>That's part of it. And it's also part of why we're choosing an ecosystem such as Lana as a starter. It's not limited to only Salana, but Salala is, is known as a blockchain. That is very much ecological. Inclined transactions are less polluting. And definitely this problem is, is tackled in the fact that we are offering this product on a case by case scenario brands come to us, we build this environment and we run something that is proper to them. So the scale of it is also way less important that what Facebook is trying to build. >>Yeah. They're trying to build the all encompassing. Yeah. All singing old dancing, as we say system, and then they're not getting a lot of luck. They just got slammed dunked this week on the news, I saw the, you know, FTC moved against them on the acquisition of the exercise app. >>It's it's a tough, it's a tough battle for them. Let's say they >>Still have, they got a headwind. I wouldn't say tailwind. They broke democracy. So they gotta pay for it. Right. Exactly. I always say definitely revenge going on there. I'm not a big fan of what they did. The FTC. I think that's bad move. They shouldn't block acquisitions, but they do buy, they don't really build much. That's well documented. Facebook really hasn't built anything except for Facebook. That's right. Mean what's the one thing Facebook has done besides Facebook. >>I mean, >>It's everything they've tried is failed except for Facebook. Yeah. >>So we'll see what's going on with the Methodist side. >>Well, so successful, not really one trick bony. Yeah. They bought Instagram. They bought WhatsApp, you know, and not really successful. >>That's true. They do have the, the means though, to maybe become successful with something. So >>You're walking out there, John just said, Facebook's not successful. I meant they don't. They have a one product company. They use their money to buy everything. Yeah. And that's some people don't like that, but anyway, the startups like to get bought out. Yeah. Okay. So let's get back to the metaverse it's coming out is the business model to build for others. Are you gonna have a system for users? What's what's the approach? How do you, how are we view viewing this? What's the, the business you're going after? >>So we are very much a B2B type of service where we can create custom kind of tailor made virtual environments for brands, where we dedicate our team to building those environments, which has been what we have been at the start to really kickstart the initiative. But we're also developing the tool that will allow antibody to develop their own shop themselves, using what we give them to do something kind of like the Sims for those that know, building their environment and building their shop, which will they, they, they will then be to put online and for anybody of their user base customers to have a look at. So it's, it's kind of, yeah, the tailor made experience, but also the more broader experience where we want to create this tool, develop this tool, make it accessible to the public with a subscription based model where any individual that has an idea and maybe a product that is interesting for the metaverse be able to create this virtual storefront and upload it directly. >>How long does it take to build an environment? Let's say I was, I wanna do a cube. Yeah. I go to a lot of venues all around the world. Yeah. MOSCON and San Francisco, the San convention center in Las Vegas, we're here in Monaco. How do I replicate these environments? Do I call you up and say, Hey, I need some artists. Do you guys render it? What's the take us through the process. >>Yeah. It's, it's basically a case by case scenario at the moment, very much. We're working with our partners that find brands that are interested in getting into the metaverse and we then design the shops. Well, it depends on the brands. Some have a really clear idea of what they want. Some are a bit more open to it and they're like, well, we have this and this, can you build something? >>I mean, I mean, I can see the apple store saying, Hey, you know, they're pretty standard apple stores. You got cases of iWatches. Yeah. I mean that's easily to, replicateable probably good ROI for them. >>Exactly. It's it's is that what you're thinking? Their team. Exactly. Yeah. It depends. And we, we want to add a layer of something cuz just replicating the store simply. Yeah. It's it's maybe not as interesting, you know, it just, oh, okay. I'm in the store. It's white, everywhere. It's apple. Right. It's like, oh I'm in at the dentist, but we want to add some video game elements to the, to those experiences. But very subtle ones, ones that won't make you feel, oh, I'm playing one of these games, you know? It's yeah. Very supple. >>You can, you can jump into immersive experience as defined by the brand. Yeah. I mean the brand will control the values. So you're say apple and you're at the iWatch table. Yeah. You could have a digital assistant pop in there with an avatar. Exactly. You can jump down a rabbit hole and say, Hey, I want this iWatch. I'm a bike mountain biker. For example, I could get experience of mountain biking with my watch on I fall off, ambulance sticks me up. I mean, all these things that they advertise is what goes >>On. Yeah. And we can recreate these experiences and what they're advertising and into a more immersive experience is what we're trying to our, our goal is to create experiences. We know that, you know, why does someone is someone spend so much at Disneyland? It's like triple the price of whatever, because you know, it's Mickey mouse around you. It's, that's the experience that comes around. And often the experience is more important than the product. Sometimes >>It's hard. It's really hard to get that first class citizen experience with the event or venue physical. Yeah. Which is a big challenge. I know the metaverse are gonna try to solve this. So I gotta ask you what's your vision on solving that? Okay. Cause that's the holy grail. That's what we're talking about here. Yeah. I got a physical event or place. I wanna replicate it in the metaverse but create that just as good first party citizen like experience. >>Yeah. I mean that's the whole event event type of business side of the metaverse is also a huge one. It's one that we are choosing to tackle after the e-commerce one. But it's definitely something that has been asked a lot by the brands where like we want to create, like, we want to release this store for an event that is in real life, but we want to make it accessible to the largest number. That's why we saw with Fortnite as well. All those events, the fashion week in the central land. And >>Sand's a Cub in the Fortnite too. >>There you go. And so the, the event aspect is super important and we want those meta shops to be places where a brand can organize an event. Let's say they want to make the entrance paid. They can do an NFD for that if they want. And then they have to, the user has to connect the NFD to access the event with an idea. Right. But that's definitely possible. And that's how we leverage blockchain as well with those companies and say, you know, you're not familiar with >>This method. You're badging, you know, you're the gaming where we were talking earlier. Yeah. Badging and credentials and access methods. A tech concept can be easily forwarded to NFTs. Yeah, >>Exactly. Exactly. And brands are interested in that. >>Sure. Of course. Yeah. By being the NFT. That's cool. Yeah. Yeah. So I gotta ask you the origination story. Take me through the, the, how this all started. Yeah. Was it a seat of an idea you and your friends get together? Yeah. It was an it scratch. And when you're really into this, what's the origination story and where you're at now. >>So we started off in January really with a, quite a, a different idea. It was called the loft business club. It's an NFT collection on the Salina blockchain. And the whole idea beyond it is that NFT holders would have access to their virtual apartments that we called the lofts. It got very popular. We got a really big following at the start. It was really the trend back in January, February. And we managed to, to sell out successfully the whole collection of 5,000 NFTs. And yeah, we started as a group of friends, really like-minded friends from my hometown in, in, met in France who are today, the co-founders and the associates with different backgrounds. Leo has the marketing side of things. A club has the 3d designing. We had all our different skills coming into it. Obviously my English was quite helpful as well cause French people in English it's, it's not often the best French English. Yeah. And I was, the COO has been doing amazing on the kind of the serious stuff. You know, the taxis lawyers >>Operational to all of trains running on time. >>Exactly >>Sure. People get their jobs done. >>Yeah, exactly. So >>It's well too long of a lunch cuz you know, French would take what, two hour lunches. Yeah. You >>Have to enjoy it. Yeah. >>Coffee and stuff. That's wine, you know about creative, >>But yeah, it's, it's a friend stuff that started as a, as a passion project and got so quick. And today I'm here talking to you in this setting. It's like, >>You're pretty excited. >>I mean it's super excited. It's such a we're you know, we feel like we're building something that's new and our developer team, we're now a team of 15 in total with developers based in Paris, mostly. And everybody is, is feeling like, you know, they're contributing to something new and that's, what's exciting about it. You know, it's something that's not really done or it's trying to be done, but nobody really knows the way >>It's pioneering days. But the, but the pandemic has shifted the culture faster because people like certainly the gen Zs are like, I don't wanna reuse that old stuff. Yeah. And, but they still want to go to like games or events or go to stores. Yeah. But once to go to a store, I mean, I go to apple store all the time where I live in Palo Alto, California. And it's like, yeah, I love that store. And I know it by heart. I don't, I don't have to go there. Yeah. Walking into the genius bar virtually I get the same job done. Yeah, >>Exactly. That's that's what we want to do. And the other pandemic is just it's it's been all about improving, you know, people's condition, life conditions at home, I think. And that's what kind of boosted the whole metaverse conversation and Facebook really grabbing onto it as well. It's just that people were stuck at home and for gamers, that's fine. We used to be stuck at home playing video games all day. Yeah. We survived the pandemic fine. But for other people it was a bit more of a new >>Experience. Well, Oscar, one of the cool things is that you said like mind you and your founding team, always the secret to success. But now you see a lot of old guys like me and gals coming in too, your smart people are like-minded they get it. Especially ones that have seen the ways before, when you have this kind of change, it's a cultural shift and technology shift and business model shift at the same time. Yeah. And to me there's gonna be chaos, but at the end of the day, >>I mean there's fun and >>Chaos. That's opportunity. There's a fun and fun and opportunity. >>It's fun and chaos, you know, and yeah. Likeminded people and the team has really been the driving factor with our company. We are all very much excited about what we're doing and it's been driving us forward. >>Well, keep in touch. Thanks for coming on the cube and sharing, sharing a story with us in the world. We really appreciate we'll keep in touch with you guys. Do love what you do. Oscar ballet here inside the cube Argo verse eCommerce shop. The beginning of this wave is happening. The convergence of physical virtual is a hybrid mode. It's a steady state. It is not gonna go away. It's only gonna get bigger, more cooler, more relevant than ever before. Cube covering it like a blanket here in Monaco, crypto summit. I'm John furrier. We'll be right back after this short break.
SUMMARY :
I'm John feer, host of the cube, a lot of action happening presented by digital bits big tailwind acceleration around the idea of this new digital VI virtual hybrid and kind of how the experience goes forward. You're on the Solona blockchain. And a lot of convincing to do with their It's more of the servers, really? Well, an answer to that, cause some people say, So the scale of it is also way less important that what Facebook is trying to build. news, I saw the, you know, FTC moved against them on the acquisition of the exercise It's it's a tough, it's a tough battle for them. I'm not a big fan of what they did. Yeah. you know, and not really successful. They do have the, the means though, to maybe become successful with something. the startups like to get bought out. idea and maybe a product that is interesting for the metaverse be able to create this virtual storefront MOSCON and San Francisco, the San convention center in Las Vegas, that are interested in getting into the metaverse and we then design the shops. I mean, I mean, I can see the apple store saying, Hey, you know, they're pretty standard apple stores. It's like, oh I'm in at the dentist, I mean the brand will control the values. the price of whatever, because you know, it's Mickey mouse around you. I know the metaverse are gonna try to solve this. But it's definitely something that has been asked a lot by the brands where like we want to create, like, we want to release this store for the event with an idea. You're badging, you know, you're the gaming where we were talking earlier. And brands are interested in that. So I gotta ask you the origination And the whole idea beyond it is that NFT holders would have access So It's well too long of a lunch cuz you know, French would take what, two hour lunches. Yeah. That's wine, you know about creative, And today I'm here talking to you in this setting. And everybody is, is feeling like, you know, they're contributing to something new and that's, what's exciting about it. like certainly the gen Zs are like, I don't wanna reuse that old stuff. And the other pandemic is just it's it's been all about improving, always the secret to success. There's a fun and fun and opportunity. It's fun and chaos, you know, and yeah. Thanks for coming on the cube and sharing, sharing a story with us in the world.
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Mike Palmer, Sigma Computing | Snowflake Summit 2022
>>Welcome back to Vegas guys, Lisa Martin and Dave Lanta here wrapping up our coverage of day two of snowflake summit. We have given you a lot of content in the last couple of days. We've had a lot of great conversations with snowflake folks with their customers and with partners. And we have an alumni back with us. Please. Welcome back to the queue. Mike Palmer, CEO of Sigma computing. Mike. It's great to see you. >>Thanks for having me. And I guess again >>Exactly. >>It's fantastic me. >>So talk to the audience about Sigma before we get into the snowflake partnership and what you guys are doing from a technical perspective, give us that overview of the vision and some of the differentiators. >>Sure. You know, you've over the last 12 years, companies have benefited from enormous investments and improvements in technology in particular, starting with cloud technologies, obviously going through companies like snowflake, but in terms of the normal user, the one that makes the business decision in the marketing department and the finance team, you know, in the works in the back room of the supply chain, doing inventory very little has changed for those people. And the time had come where the data availability, the ability to organize it, the ability to secure it was all there, but the ability to access it for those people was not. And so what Sigma's all about is taking great technology, finding the skillset they have, which happens to be spreadsheets. There are billion license spreadsheet users in the world and connecting that skillset with all of the power of the cloud. >>And how do you work with snowflake? What are some of the, the what's the joint value proposition? >>How are they as an investor? That's what I wanna know. Ah, >>Quiet, which is the way we like them. No, I'm just kidding. Snowflake is, well, first of all, investment is great, but partnership is even better. Right. You know, and I think snowflake themselves are going through some evolution, but let's start with the basics of technology where this all starts because you know, all of the rest doesn't matter if the product is not great, we work directly on snowflake. And what that means is as an end user, when I, when I sit on that marketing team and I want to understand and, and connect, how did I get a, a customer where I had a pay to add? And they showed up on my website and from my website, they went to a trial. And from there, they touched a piece of syndicated contents. All of that data sits in snowflake and I, as a marketer, understand what it means to me. >>So for the first time, I want to be able to see that data in one place. And I want to understand conversion rates. I want to understand how I can impact those conversion rates. I can make predictions. What that user is doing is going to, to Sigma accessing live data in snowflake, they're able to ask ad hoc questions, questions that were never asked questions, that they don't exist in a filter that were never prepped by a data engineer. So they could truly do something creative and novel in a very independent sort of way. And the connection with Snowflake's live data, the performance, the security and governance that we inherit. These are all facilitators to really expand that access across the enterprise. So at, at a product level, we were built by a team of people, frankly, that also were the original investors in snowflake by two amazing engineers and founders, Rob will and Jason France, they understood how snowflake worked and that shows up in the product for our end customers. >>So, but if I may just to follow up on that, I mean, you could do that without snowflake, but what, it would be harder, more expensive. Describe what you'd have to go through to accomplish that outcome. >>And I think snowflake does a good job of enabling the ecosystem at large. Right. But you know, you always appreciate seeing early access to understand what the architecture's going to look like. You know, some of the things that I will, you know, leaning forward that we've heard here that we're very excited about is snowflake going to attack the TP market, right? The transactional market, one of the transactional database market. I, yeah. Right. You know, one of the things that we see coming, and, and one of the bigger things that we'll be talking about in Sigma is not just that you can do analytics out of snowflake. I think that's something that we do exceptionally well on an ad hoc basis, but we're gonna be the first that allow you to write into snowflake and to do that with good performance. And to do that reliably, we go away from OAP, which is the terminology for data warehousing. >>And we go toward transactional databases. And in that world, understanding snowflake and working collaboratively with them creates again, a much better experience for the end customer. So they, they allow us into those programs, even coming to these conferences, we talk to folks that run the industry teams, trying to up level that message and not just talk database and, and analytics, but talk about inventory management. How do we cut down the gap that exists between POS systems and inventory ordering, right? So that we get fewer stockouts, but also that we don't overorder. So that's another benefit, >>Strong business use cases. >>That's correct. >>And you're enabling those business users to have access to that data. I presume in near real time or near real time, so that they can make decisions that drive marketing forward or finance forward or legal >>Forward. Exactly. We had a customer panel yesterday. An example of that go puff is hopefully most of the viewers are familiar with, as a delivery company. This is a complicated business to run. It's run on the fringes. When we think about how to make money at it, which means that the decisions need to be accurate. They need to be real time. You can't have a batch upload for delivery when they're people are on the street, and then there's an issue. They need to understand the exact order at that time, not in 10 minutes, not from five minutes ago, right. Then they need to understand, do I have inventory in the warehouse when the order comes in? If they don't, what's a replacement product. We had a Mike came in from go puff and walked us through all of the complexity of that and how they're using Sigma to really just shorten those decision cycles and make them more accurate. You know, that's where the business actually benefits and, >>And actually create a viable business model. Cuz you think back to the early, think back to the.com days and you had pets.com, right? They couldn't make any money. Yeah. Without chewy. Okay. They appears to be a viable business model. Right? Part of that is just the efficiencies. And it's sort of a, I dunno if those are customers that they may or may not be, but they should be if they're not >>Chewy is, but okay. You know, and that's another example, but I'll even pivot to the various REI and other retailers. What do they care about cohorts? I'm trying to understand who's buying my product. What can I sell to them next? That, that idea of again, I'm sitting in a department, that's not data engineering, that's not BI now working collaboratively where they can get addend engineer, putting data sets together. They have a BI person that can help in the analytics process. But now it's in a spreadsheet where I understand it as a marketer. So I can think about new hierarchies. I wanna know it by customer, by region, by product type. I wanna see it by all of those things. I want to be able to do that on the fly because then it creates new questions that sort of flow. If you' ever worked in development, we use the word flow constantly, right? And as people that flow is when we have a question, we get an answer that generates a question. We have, we just keep doing that iteratively. That that is where Sigma really shines for them. >>What does a company have to do to really take advantage of, of this? I, if they're kind of starting from a company that's somewhat immature, what are the sort of expectations, maybe even outta scope expectations so they can move faster, accelerate analytics, a lot of the themes that we've heard today, >>What does an immature company is actually even a question in, in and of itself? You know, I think a lot of companies consider themselves to be immature simply because for various constraint reasons, they haven't leveraged the data in the way that they thought possible. Good, >>Good, good definition. Okay. So not, not, >>Not, I use this definition for digital transformation. It very simple. It is. Do you make better decisions, faster McKenzie calls this corporate metabolism, right? Can you speed up the metabolism of, of an enterprise and for me and for the Sigma customer base, there's really not much you have to do once. You've adopted snowflake because for the first time the barriers and the silos that existed in terms of accessing data are gone. So I think the biggest barrier that customers have is curiosity. Because once you have curiosity and you have access, you can start building artifacts and assets and asking questions. Our customers are up and running in the product in hours. And I mean that literally in hours, we are a user in snowflake, that's a direct live connection. They are able to explore tables, raw. They can do joins themselves if they want to. They can obviously work with their data engineering team to, to create data sets. If that's the preferred method. And once they're there and they've ever built a pivot table, they can be working in Sigma. So our customers are getting insights in the first one to two days, you referenced some, those of us are old enough to remember pest.com. Also old enough to remember shelfware that we would buy. We are very good at showing customers that within hours they're getting value from their investment in Sigma. And that, that just creates momentum, right? Oh, >>Tremendous momentum and >>Trust and trust and expansion opportunities for Sigma. Because when you're in one of those departments, someone else says, well, you know, why do you get access to that data? But I don't, how are you doing this? Yeah. So we're, you know, I think that there's a big movement here. People, I often compare data to communication. If you go back a hundred years, our communication was not limited. As it turns out by our desire to communicate, it was limited by the infrastructure. We had the typewriter, a letter and the us postal service and a telephone that was wired. And now we have walk around here. We, everything is, is enabled for us. And we send, you know, hundreds and thousands of messages a day and probably could do more. You will find that is true. And we're seeing it in our product is true of data. If you give people access, they have 10 times as many questions as they thought they had. And that's the change that we're gonna see in business over the next few years, >>Frank Salman's first book, what he was was CEO of snowflake was rise of the data cloud. And he talked about network effects. Basically what he described was Metcalf's law. Again, go back to the.com days, right? And he, Bob Metcalf used the phone system. You know, if there's two people in the phone system, it's not that valuable, right. >>You know, exactly, >>You know, grow it. And that's where the value is. And that's what we're seeing now applied to data. >>And even more than that, I think that's a great analogy. In fact, the direct comparison to what Sigma is doing actually goes one step beyond everything that I've been talking about, which is great at the individual level, but now the finance team and the marketing team can collaborate in the platform. They can see data lineage. In fact, one of our, our big emphasis points here is to eliminate the sweet products. You know, the ones where, you know, you think you're buying something, but you really have a spreadsheet product here and a document product there and a slide product over there. And they, you know, you can do all of that in Sigma. You can write a narrative. You can real time live, edit on numbers. You, you know, if you want to, you could put a picture in it. But you know, at Sigma we present everything out of our product. Every meeting is live data. Every question is answered on the spot. And that's when, you know, you know, to your point about met cap's law. Now everybody's involved in the decision making. They're doing it real time. Your meetings are more productive. You have fewer of them because they're no action items, right. We're answering our questions there and we're, and we're moving forward. >>You know, view were meeting sounds good. Productivity is, is weird now with the, the pandemic. But you know, if you go back to the nineties here am I'm, I'm dating myself again, but that's okay. You know, you, you didn't see much productivity going on when the PC boom started in the eighties, but the nineties, it kicked in and pre pandemic, you know, productivity in the us and Europe anyway has been going down. But I feel like Mike, listen to what you just described. I, how many meetings have we been in where people are arguing about them numbers, what are the assumptions on the numbers wasting so much time? And then nothing gets done and they, then they, they bolt cut that away and you drive in productivity. So I feel like we're on a Renaissance of productivity and a lot of that's gonna be driven by, by data. Yeah. And obviously communications the whole 5g thing. We'll see how that builds out. But data is really the main spring of, I think, a new, new Renaissance in productivity. >>Well, first of all, if you could find an enterprise where you ask the question, would you rather use your data better? And they say, no, like, you know, show me, tell me that I'll short their stock immediately. But I do agree. And I, unfortunately I have a career history in that meeting that you just described where someone doesn't like, what you're showing them. And their first reaction is to say, where'd you get that data? You know, I don't trust it. You know? So they just undermined your entire argument with an invalid way of doing so. Right. When you walk into a meeting with Sigma where'd, where'd you get that data? I was like, that's the live data right now? What question do you want answer >>Lineage, right. Yeah. And you know, it's a Sen's book about, you know, gotta move faster. I mean, this is an example of just cutting through making decisions faster because you're right. Mike and the P the P and L manager in a meeting can, can kill the entire conversation, you know, throw FUD at it. Yeah. You know, protect his or her agenda. >>True. But now to be fair to the person, who's tended to do that. Part of the reason they've done that is that they haven't had access to that data before the meeting and they're getting blindsided. Right. So going back to the collaboration point. Yes. Right. The fact we're coming to this discussion more informed in and of itself takes care of some of that problem. Yeah. >>For sure. And if, and if everybody then agrees, we can move on and now talk about the really important stuff. Yeah. That's good. It >>Seems to me that Sigma is an enabler of that curiosity that you mentioned that that's been lacking. People need to be able to hire for that, but you've got a platform that's going here. You go ask >>Away. That's right in the we're very good. You know, we love being a SaaS platform. There's a lot of telemetry. We can watch what we call our mouse to Dows, you know, which is our monthly average users to our daily average users. We can see what level of user they are, what type of artifacts they build. Are they, you know, someone that creates things from scratch, are they people that tend to increment them, which by the way, is helpful to our customers because we can then advise them, Hey, here's, what's really going on. You might wanna work with this team over here. They could probably be a little better of us using the data, but look at this team over here, you know, they've originated five workbooks in the last, you know, six days they're really on it. There's, there's, you know, that ability to even train for the curiosity that you're referring to is now there, >>Where are your customer conversations? Are they at the lines of business? Are they with the chief data officer? What does that look like these days? >>Great question. So stepping back a bit, what, what is Sigma here to do? And, and our first phase is really to replace spreadsheets, right? And so one of the interesting things about the company is that there isn't a department where a spreadsheet isn't used. So Sigma has an enormous Tam, but also isn't necessarily associated with any particular department or any particular vertical. So when we tend to have conversations, it really depends on, you know, either what kind of investment are you making? A lot of mid-market companies are making best technology investments. They're on a public cloud, they're buying snowflake and they wanna understand what's, what's built to really make this work best over the next number of years. And those are very short sales for us because we, we prove that, you know, in, in minutes to hours, if you're working at a large enterprise and you have three or four other tools, you're asking a different question. >>And often you're asking a question of what I call exploration. We have a product that has dashboards and they've been working for us and we don't wanna replace the dashboard. But when we have a question about the data in the dashboard, we're stuck, how do we get to the raw data? How do we get to the example that we can actually manage? You can't manage a dashboard. You can't manage a trend line, but if you get into the data behind the trend line, you can make decisions to change business process, to change quality, accuracy, to change speed of execution. That is what we're trying to enable. Those conversations happen between the it team who runs technology and the business teams who are responsible for the decisions. So we are, you know, we have a cross departmental sale, but across every department, >>One of the things we're not talking about at this event, which is kind of interesting, cause it's all we've been talking about is the macro supply chain challenges, Ukraine, blah, blah, blah, and the stock market. But, but how are you thinking about that? Macro? The impacts you're seeing, you know, a lot of private companies being, you know, recapped, et cetera, you guys obviously very well funded. Yeah. But how do you think about, I mean, I asked Frank a similar question. He's like, look, it's a marathon. We don't worry about it. We, you know, they made the public market, they get 5 billion in cash. Yeah. Yeah. How are you thinking about it? >>You know, first of all, what's the expression, right? You never, never waste a good, you know, in this case recession, no, we don't have one yet, but the impetus is there, right. People are worried. And when they're worried, they're thinking about their bottom lines, they're thinking about where they're going to get efficiency and their costs. They're already dealing with the supply chain issues of inventory. We all have it in our personal lives. If you've ordered anything in the last six months, you're used to getting it in, you know, days to weeks. And now you're getting in months, you know, we had customers like us foods as a good example, like they're constantly trying to align inventory. They have with transportation that gets that inventory to their end customers, right? And they do that with better data accuracy at the end point, working with us on what we are launching. >>And I mentioned earlier, having more people be able to update that data creates more data, accuracy creates better decisions. We align that then with them and better collaboration with the folks that then coordinate the trucks with Prologis and the panel yesterday, they're the only commercial public company that reports their, their valuations on a quarterly basis. They work with Sigma to trim the amount of time it takes their finance team to produce that data that creates investor confidence that holds up your stock price. So I mean the, the importance of data relative to all the stakeholders in enterprise cannot be overstated. Supply chain is a great example. And yes, it's a marathon because a lot of the technology that drives supply chain is old, but you don't have to rip out those systems to put your data into snowflake, to get better access through Sigma, to enable the people in your environment to make better decisions. And that's the good news. So for me, while I agree, there's a marathon. I think that most of the, I dunno if I could continue this metaphor, but I think we could run quite far down that marathon without an awful lot of energy by just making those couple of changes. >>Awesome. Mike, this has been fantastic. Last question. I, I can tell, I know a lot of growth for Sigma. I can feel it in your energy alone. What are some of the key priorities that you're gonna be focusing on for the rest of the year? >>Our number one priority, our number two priority and number three priority are always build the best product on the market, right? We, we want customers to increase usage. We want them to be delighted. You know, we want them to be RA. Like we have customers at our booth that walk up and it's like, you're building a great company. We love your product. I, if you want to show up happy at work, have customers come up proactively and tell you how your products changed their life. And that is, that is the absolute, most important thing because the real marathon here is that enablement over the long term, right? It is being a great provider to a bunch of great companies under that. We are growing, you know, we've been tripling the company for the fast few years, every year, that takes a lot of hiring. So I would've alongside product is building a great culture with bringing the best people to the company that I guess have my energy level. >>You know, if you could get paid in energy, we would've more than tripled it, you know, but that's always gonna be number two, where we're focused on the segment side, you know, is really the large enterprise customer. At this point, we are doing a great job in the mid-market. We have customer, we have hundreds of customers in our free trial on a constant basis. I think that without wanting to seem over confident or arrogant, I think our technology speaks for itself and the product experience for those users, making a great ROI case to a large enterprise takes effort. It's a different motion. We're, we're very committed to building that motion. We're very committed to building out the partner ecosystem that has been doing that for years. And that is now coming around to the, the snowflake and all of the ecosystem changes around snowflake because they've learned these customers for decades and now have a new opportunity to bring to them. How do we enable them? That is where you're gonna see Sigma going over the next couple of years. >>Wow, fantastic. Good stuff. And a lot of momentum, Mike, thank you so much for joining Dave and me talking about Sigma, the momentum, the flywheel of what you're doing with snowflake and what you're enabling customers to achieve the massive business outcomes. Really cool stuff. >>Thank you. And thank you for continuing to give us a platform to do this and glad to be back in conferences, doing it face to face. It's fantastic. >>It it's the best. Awesome. Mike, thank you for Mike Palmer and Dave ante. I'm Lisa Martin. You've been watching the cube hopefully all day. We've been here since eight o'clock this morning, Pacific time giving you wall the wall coverage of snowflake summit 22 signing off for today. Dave and I will see you right bright and early tomorrow morning. I will take care guys.
SUMMARY :
And we have an alumni back with us. And I guess again So talk to the audience about Sigma before we get into the snowflake partnership and what you guys are doing from a technical the one that makes the business decision in the marketing department and the finance team, you know, in the works in How are they as an investor? know, all of the rest doesn't matter if the product is not great, we work directly on And the connection So, but if I may just to follow up on that, I mean, you could do that without some of the things that I will, you know, leaning forward that we've heard here that we're very excited about is And we go toward transactional databases. And you're enabling those business users to have access to that data. do I have inventory in the warehouse when the order comes in? Part of that is just the efficiencies. You know, and that's another example, but I'll even pivot to the various REI You know, I think a lot of companies consider Good, good definition. of an enterprise and for me and for the Sigma customer base, there's really not much you And that's the change that we're gonna see in business over the next few years, You know, if there's two people in the phone system, it's not that valuable, right. And that's what we're seeing now applied to data. You know, the ones where, you know, you think you're buying something, Mike, listen to what you just described. And their first reaction is to say, where'd you get that data? you know, throw FUD at it. So going back to the collaboration point. And if, and if everybody then agrees, we can move on and now talk about the really important stuff. Seems to me that Sigma is an enabler of that curiosity that you mentioned that that's been lacking. We can watch what we call our mouse to Dows, you know, which is our monthly average users to our daily we prove that, you know, in, in minutes to hours, if you're working at a large enterprise and you have three or four other So we are, you know, we have a cross departmental sale, but across every department, you know, a lot of private companies being, you know, recapped, et cetera, you guys obviously very You never, never waste a good, you know, in this case recession, And I mentioned earlier, having more people be able to update that data creates more data, What are some of the key priorities that you're gonna be focusing on for the We are growing, you know, we've been tripling the company for the fast few years, You know, if you could get paid in energy, we would've more than tripled it, you know, but that's always gonna And a lot of momentum, Mike, thank you so much for joining Dave and me talking about Sigma, And thank you for continuing to give us a platform to do this and glad to be back in conferences, Dave and I will see you right bright and early tomorrow morning.
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Rinesh Patel, Snowflake & Jack Berkowitz, ADP | Snowflake Summit 2022
(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. I'm Lisa Martin with Dave Vellante. We've got a couple of guests joining us now. We're going to be talking about financial services. Rinesh Patel joins us, the Global Head of Financial Services for Snowflake, and Jack Berkowitz, Chief Data Officer at ADP. Guys, welcome to the program. >> Thanks, thanks for having us. >> Thanks for having us. >> Talk to us about what's going on in the financial services industry as a whole. Obviously, we've seen so much change in the last couple of years. What does the data experience look like for internal folks and of course, for those end user consumers and clients? >> So, one of the big things happening inside of the financial services industry is overcoming the COVID wait, right? A lot of banks, a lot of institutions like ours had a lot of stuff on-prem. And then the move to the Cloud allows us to have that flexibility to deal with it. And out of that is also all these new capabilities. So the machine learning revolution has really hit the services industry, right? And so it's affecting how our IT teams or our data teams are building applications. Also really affecting what the end consumers get out of them. And so there's all sorts of consumerization of the experience over the past couple of years much faster than we ever expected it to happen. >> Right, we have these expectations as consumers that bleed into our business lives that I can do transactions. It's going to be on the swipe in terms of checking authenticity, fraud detection, et cetera. And of course we don't want things to go back in terms of how brands are serving us. Talk about some of the things that you guys have put in place with Snowflake in the last couple of years, particularly at ADP. >> Yeah, so one of the big things that we've done, is, one of the things that we provide is compensation data. So we issue a thing called the National Employment Report that informs the world as to what's happening in the U.S. economy in terms of workers. And then we have compensation data on top of that. So the thing that we've been able to do with Snowflake is to lower the time that it takes us to process that and get that information out into the fingertips of people. And so people can use it to see what's changed in terms of with the worker changes, how much people are making. And they can get it very, very quickly. And we're able to do that with Snowflake now. Used to take us weeks, now it's in a matter of moments we can get that updated information out to people. >> Interesting. It helps with the talent war and- >> Helps in the talent war, helps people adjust, even where they're going to put supply chain in reaction to where people are migrating. We can have all of that inside of the Snowflake system and available almost instantaneously. >> You guys announced the Financial Data Cloud last year. What was that like? 'Cause I know we had Frank on early, he clearly was driving the verticalization of Snowflake if you will, which is kind of rare for a relatively new software company but what's that been like? Give us the update on where you're at and biggest vertical, right? >> Absolutely, it's been an exciting 12 months. We're a platform, but the journey and the vision is more. We're trying to bring together a fragmented ecosystem across financial services. The aim is really to bring together key customers, key data providers, key solution providers all across the different Clouds that exist to allow them to collaborate with data in a seamless way. To solve industry problems. To solve industry problems like ESG, to solve industry problems like quantitative research. And we're seeing a massive groundswell of customers coming to Snowflake, looking at the Financial Services Data Cloud now to actually solve business problems, business critical problems. That's really driving a lot of change in terms of how they operate, in terms of how they win customers, mitigate risk and so forth. >> Jack, I think, I feel like the only industry that's sometimes more complicated than security, is data. Maybe not, security's still maybe more fragmented- >> Well really the intersection of the two is a nightmare. >> And so as you look out on this ecosystem, how do you as the chief data officer, how do you and your organization, what process do you use to decide, okay, which of the, like a chef, which of these ingredients am I going to put together for my business. >> It's a great question, right? There's been explosion of companies. We kind of look at it in two ways. One is we want to make sure that the software and the data can interoperate because we don't want to be in the business of writing bridge code. So first thing is, is having the ecosystem so that the things are tested and can work together. The other area is, and it's important to us is understanding the risk profile of that company. We process about 20% of the U.S. payroll, another 25% of the taxes. And so there's a risk to us that we have an imperative to protect. So we're looking at those companies are they financed, what's their management team. What's the sales experience like, that's important to us. And so technology and the experience of the company coming together are super important to us. >> What's your purview as a chief data officer, I mean, a lot of CDOs that I know came out of the back office and it was a compliance or data quality. You come out of industry from a technology company. So you're sort of the modern... You're like the modern CDO. >> Thanks. Thanks. >> Dave: What's your role? >> I appreciate that. >> You know what I'm saying though? >> And for a while it was like, oh yeah, compliance. >> So I actually- >> And then all of a sudden, boom, big deal. >> Yeah, I really have two jobs. So I have that job with data governance but a lot of data security. But I also have a product development unit, a massive business in monetization of data or people analytics or these compensation benchmarks or helping people get mortgages. So providing that information, so that people can get their mortgage, or their bank loans, or all this other type of transactional data. *So it's both sides of that equation is my reading inside. >> You're responsible for building data products? >> That's right. >> Directly. >> That's right. I've got a massive team that builds data products. >> Okay. That's somewhat unique in your... >> I think it's where CDOs need to be. So we build data products. We build, and we assist as a hub to allow other business units to build analytics that help them either optimize their cost or increase their sales. And then we help with all that governance and communication, we don't want to divide it up. There's a continuum to it. >> And you're a peer of the CIO and the CISO? >> Yeah, exactly. They're my peers. I actually talk to them almost every day. So I've got the CIO as a peer. >> It's a team. >> I've got the security as a peer and we get things done together. >> Talk about the alignment with business. We've been talking a lot about alignment with the data folks, the business folks, the technical folks to identify the right solutions, to be able to govern data, to monetize it, to create data products. What does that... You mentioned a couple of your cohorts, but on the business side, who are some of those key folks? >> So we're like any other big, big organization. We have lots of different business units. So we work directly with either the operational team or the heads of those business units to divine analytic missions that they'll actually execute. And at the same time, we actually have a business unit that's all around data monetization. And so I work with them every single day. And so these business units will come together. I think the big thing for us is to define value and measure that value as we go. As long as we're measuring that value as we go, then we can continue to see improvements. And so, like I said, sometimes it's bottom line, sometimes it's top line, but we're involved. Data is actually a substrate of the company. It's not a side thing to the company. >> Yeah, you are. >> ADP. >> Yeah but if they say data first but you really are data first. >> Yeah. I mean, our CEO says- >> Data's your product. >> Data's our middle name. And it literally is. >> Well, so what do you do in the Snowflake financial services data Cloud? Are you monetizing? >> Yeah. >> What's the plan? >> Yeah, so we have clients. So part of our data monetization is actually providing aggregate and anonymized information that helps other clients make business decisions. So they'll take it into their analytics. So, supply chain optimization, where should we actually put the warehouses based on the population shifts? And so we're actually using the file distribution capabilities or the information distribution, no longer files, where we use Snowflake to actually be that data cloud for those clients. So the data just pops up for our other clients. >> I think the industry's existed a lot with the physical movement of data. When you physically move data, you also physically move the data management challenges. Where do you store it? How do you map it? How do you concord it? And ultimately data sharing is taking away that friction that exists. So it's easier to be able to make informed decisions with the data at hand across two counterparties. >> Yeah, and there's a benefit to us 'cause it lowers our friction. We can have a conversation and somebody can be... Obviously the contracts have to be signed, but once they get done, somebody's up and running on it within minutes. And where it used to be, as you were saying, the movement of data and loss of control, we never actually lose control of it. We know where it is. >> Or yeah, contracts signed, now you got to go through this long process of making sure everything's cool, or a lot of times it could slow down the sale. >> That's right. >> Let's see how that's going to... Let's do a little advanced work. Now you're working without a contract. Here, you can say, "Hey, we're in the Snowflake data cloud. It's governed, you're a part of the ecosystem." >> Yeah, and the ecosystem we announced, oh gee, I think it's probably almost a year and a half ago, a relationship with ICE, Intercontinental Exchange, where they're actually taking our information and their information and creating a new data product that they in turn sell. So you get this sort of combination. >> Absolutely. The ability to form partnerships and monetize data with your partners vastly increases as a consequence. >> Talk to us about the adoption of the financial services data cloud in the last what, maybe nine months or so, since it was announced? And also in terms of the its value proposition, how does the ADP use case articulate that? >> So, very much so. So in terms of momentum, we're a global organization, as you mentioned, we are verticalized. So we have increasingly more expertise and expertise experience now within financial services that allows us to really engage and accelerate our momentum with the top banks, with the biggest asset managers by AUM, insurance companies, sovereign wealth funds on Snowflake. And obviously those data providers and solution providers that we engage with. So the momentum's really there. We're really moving very, very fast in a great market because we've got great opportunity with the capabilities that we have. I mean, ADP is just one of many use cases that we're working with and collaborations that we're taking to market. So yeah, the opportunity to monetize data and help our partners monetize the data has vastly increased within this space. >> When you think about... Oh go ahead, please. >> Yeah I was just going to say, and from our perspective, as we were getting into this, Snowflake was with us on the journey. And that's been a big deal. >> So when you think about data privacy, governance, et cetera, and public policy, it seems like you have, obviously you got things going on in Europe, and you got California, you have other states, there's increasing in complexity. You guys probably love that. (Dave laughs) More data warehouses, but where are we at with that whole? >> It's a great question. Privacy is... We hold some of the most critical information about people because that's our job to help people get paid. And we respect that as sort of our prime agenda. Part of it deals with the technology. How do you monitor, how do you see, make sure that you comply with all these regulations, but a lot of it has to do with the basic ethics of why you're doing and what you're doing. So we have a data and AI ethics board that meets and reviews our use cases. Make sure not only are we doing things properly to the regulation, but are these the types of products, are these the types of opportunities that we as a company want to stand behind on behalf of the consumers? Our company's been around 75 years. We talk about ourselves as a national asset. We have a trust relationship. We want to ensure that that trust relationship is never violated. >> Are you in a position where you can influence public policy and create more standards or framework. >> We actually are, right. We issue something every month called the National Employment Report. It actually tells you what's happening in the U.S. economy. We also issue it in some overseas countries like France. Because of that, we work a lot with various groups. And we can help shape, either data policy, we're involved in understanding although we don't necessarily want to be out in the front, but we want to learn about what's happening with federal trade commission, EOC, because at the end of the day we serve people, I always joke ADP, it's my grandfather's ADP. Well, it was actually my grandfather's ADP. (Dave laughs) He was a small businessman, and he used a ADP all those years ago. So we want to be part of that conversation because we want to continue to earn that trust every day. >> Well, plus your observation space is pretty wide. >> And you've got context and perspective on that that you can bring. >> We move somewhere between two, two and a half trillion dollars a year through our systems. And so we understand what's happening in the economy. >> What are some of the, oh sorry. >> Can your National Employment Report combined with a little Snowflake magic tell us what the hell's going to happen with this economy? >> It's really interesting you say that. Yeah, we actually can. >> Okay. (panelists laugh) >> I think when you think about the amount of data that we are working with, the types of partners that we're working with, the opportunities are infinite. They really, really are. >> So it's either a magic eight ball or it's a crystal ball, but you have it. >> We think- >> We've just uncovered that here on theCUBE. >> We think we have great partners. We have great data. We have a set of industry problems out there that we're working, collaboration with the community to be able to solve. >> What are some of the upcoming use cases Rinesh, that excite you, that are coming up in financial services- >> Great question. >> That snowflake is just going to knock out of the park. >> So look, I think there's a set of here and now problems that the industry faces, ESG's a good one. If you think about ESG, it means many different things from business ethics, to diversity, to your carbon footprint and every asset manager has to make sure they have now some form of green strategy that reflects the values of their investors. And every bank is looking to put in place sustainable lending to help their corporate customers transition. That's a big data problem. And so we're very much at the center of helping those organizations support those informed investors and help those corporates transition to a more sustainable landscape. >> Let me give you an example on Snowflake, we launched capabilities about diversity benchmarks. The first time in the industry companies can understand for their industry, their size, their location what their diversity profile looks like and their org chart profile looks like to differentiate or at least to understand are they doing the right things inside the business. The ability for banks to understand that and everything else, it's a big deal. And that was built on Snowflake. >> I think it's massive, especially in the context of the question around regulation 'cause we're seeing more and more disclosure agreements come out where regulators are making sure that there's no greenwashing taking place. So when you have really strong sources of data that are standardized, that allow that investment process to ingest that data, it does allow for a better outcome for investors. >> Real data, I mean, that diversity example they don't have to rely on a survey. >> It's not a survey. >> Anecdotes. >> It's coming right out of the transactional systems and it's updated, whenever those paychecks are run, whether it's weekly, whether it's biweekly or monthly, all that information gets updated and it's available. >> So it sounds like ADP is a facilitator of a lot of companies ESG initiatives, at least in part? >> Well, we partner with companies all the time. We have over 900,000 clients and all of them are... We've never spoken to a client who's not concerned about their people. And that's just good business. And so, yeah we're involved in that and we'll see where it goes over time now. >> I think there's tremendous opportunity if you think about the data that the ADP have in terms of diversity, in terms of gender pay gap. Huge, huge opportunity to incorporate that, as I said into the ESG principles and criteria. >> Good, 'cause that definitely is what needs to be addressed. (Lisa laughs) Guys thank you so much for joining Dave and me on the program, talking about Snowflake ADP, what you're doing together, and the massive potential that you're helping unlock with the value of data. We appreciate your insights and your time. >> Thank you for having us. >> Dave: Thanks guys. >> Thank you so much. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, live in Las Vegas at Snowflake Summit 22. Dave and I will be right back with our next guest. (upbeat music)
SUMMARY :
the Global Head of Financial in the last couple of years. inside of the financial services industry And of course we don't is, one of the things that we It helps with the talent war and- inside of the Snowflake system You guys announced the We're a platform, but the like the only industry Well really the intersection of the two And so as you look so that the things are I mean, a lot of CDOs that I know Thanks. And for a while it was And then all of a sudden, So I have that job with data governance that builds data products. That's somewhat unique in your... And then we help with all that governance So I've got the CIO I've got the security as a peer Talk about the alignment with business. and measure that value as we go. but you really are data first. I mean, our CEO says- And it literally is. So the data just pops up So it's easier to be able Obviously the contracts have to be signed, could slow down the sale. in the Snowflake data cloud. Yeah, and the ecosystem we announced, and monetize data with your partners and help our partners monetize the data When you think about... as we were getting into this, are we at with that whole? behalf of the consumers? where you can influence public policy the day we serve people, Well, plus your observation that you can bring. happening in the economy. It's really interesting you say that. Okay. about the amount of data or it's a crystal ball, but you have it. that here on theCUBE. We think we have great partners. going to knock out of the park. that the industry faces, ESG's a good one. And that was built on Snowflake. of the question around regulation they don't have to rely on a survey. the transactional systems companies all the time. about the data that the ADP and the massive potential Dave and I will be right
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Daniel Fried, Veeam | VeeamON 2022
(digital music) >> Welcome back to VeeamON 2022. We're in the home stretch, actually, Dave Nicholson and Dave Vellante here. Daniel Fried is the general manager and senior vice president for EMEA and Worldwide Channel. Daniel, welcome to theCUBE. You got a big job. >> No, I don't have a big job. I have a job that I love. (chuckles) >> Yeah, a job you love. But seriously Veeam, all channel. I mean it has been. >> Yeah, I mean, it's something which just, just a few seconds on, on that piece here, the channel piece, it's something that I love because the ecosystem of partners, an ecosystem of partners, is something which is spending its time moving and developing and changing. You've got a lot of partners changing their roles, their missions, the type of services, type of product that they offer. They all adapt to what the market needs and all the markets around the world are very different because of all these different cultures, languages, and everything. So it's very interesting. In the middle of all that, you know, these tens of thousands of partners and you try to create and try to understand how you can organize, how you can make them happy. So this is fantastic. >> So you're a native of the continent in Europe, obviously. We heard Anton, today, who couldn't be here or chose not to be here, cause he's supporting family and friends in Ukraine. What's the climate like now? Can you share with us what's it like Europe? Just the overall climate and obviously the business climate. >> So the overall climate, the way I see it or I feel it, and obviously there may be some different opinions, that I will always appreciate as also very good opinions. My view is that it seems in Europe that there are a distinction between what people do for businesses, Their thinking for the business, which may be impacted by the situations that we know in Europe between, because of obviously the issues between Ukraine, because of Russia, let's put it this way. And then there is the personal view, which is okay. That happens from time to time, but life continues and we just continue pushing things and enjoying life, and getting the families together and so on and so forth. So, this is in most of the countries in Europe. Obviously, there are a number of countries, which are a little bit more sensitive, a little bit more impacted. All the ones who are next to Russia, or Belarus, so on and so forth. From an emotional standpoint, which is totally understandable. But overall, I'm pretty impressed by how the economy, how people, how the businesses are, you know, continue to thrive in Europe. >> Has Brexit had any...? What impact, if any, has it had? >> So for us Veeam, the impact is... So first there is an impact which is on the currencies. So all the European currencies are no, have slowed down and, and the US dollar is becoming much stronger. >> Despite its debt. >> Right. >> Shouldn't be, but yeah. >> But that doesn't impact on the business. I just... >> Yeah. Right. >> So everything which is economical, macroeconomical is impacted. We have the inflation also, which has an impact, which also has increased because of the oil, because of the gas of everything that they have been stuck, to be stuck. But people get used to it. As Veeam from a business standpoint, one of the big things is we stopped sales, selling into Russia and into Belarus and we are giving our technology, our product, our solutions for free to Ukraine. And that was a piece of the business that we were doing, within EMEA, which was non-neglectable. So it's, I would say a business hole, now that we need to try to fill with accelerating the business service in the other countries of Europe. >> I mean, okay. So thank you for that but we really didn't see it in last quarter's numbers that you guys shared with I mean, IBM. Similarly IBM said, it's noticeable, but it's not really a big impact on our business, but given the cultural ties that you had to Russia and the affinity, I mean you knew how to do business in Russia. It's quite remarkable that you're able to sort of power through that. How about privacy in, around data, in Europe, particularly versus the US? it seems like Europe is setting the trend on things like privacy, certainly on things like acquisitions, we saw the arm acquisition fail. >> Yeah. So there is a big difference. Effectively, there is a big difference between, I would say North America and the rest of the world. And I would say that EMEA, and within EMEA would say the EU is leading very much on what we call server sovereign cloud. So data privacy, which in other words, data is to as much as possible is to remain within either the EU or better within each of the countries, which means that there is again... It's I would say for in EMEA it's good, I would say for the business, for the partners, because then they have to develop around the cloud a number of functions to ensure that because of this data privacy, because of this GDPR or rules and things, all the data remains and resides in a given geographical environment. So it's, which is good because it creates a number of opportunities for the partners. It makes obviously the life of customers and their self a bit more difficult. But again, I think it's good. It's good. It's part of all the way we structure and we organize. And I think that it's going to expand because data is becoming so key, a key limit, a key asset of companies that we absolutely need to take care of it. And it is where Veeam plays a big role in that because we help paying companies managing their data and secure the data in sort of way. >> Yeah. Ransomware has been a big topic of conversation this week. Do you sense that the perception of that as a threat is universal? Are there, are there differences between North America and the EU and other parts of the world? Universal? >> Yeah, it is universal. We see that everywhere. And I think this is a good point, a good question too, is that it's very interesting because we need to get acquainted to the fact that we are going to ever. And so we are going to be attacked. No way out, no. There... Anybody the morning, is waking up, is going on emails and click clicking on an email. Too late. Was a run somewhere. What can you do against that? You know, all humans make mistakes. You can't so it'll happen, but where, where it's absolutely very important and where Veeam plays a big role and where our partners are going to play an even bigger role with our technology is that they can educate the customers to understand that, to have run somewhere is not an issue. What has, what happened is not a problem. What they have to do is to organize so that if they have run somewhere, their letter is safe. And this is where our place a big place. A couple hours back, I was, I was doing a kind of bar with something else. It's totally crazy, but that's okay. I'm going to say it. It's about the COVID. What, no, what do we do? Do we have, do we have something against COVID? No. People were going to get COVID, certainly many people still doing it, but what is important is to be capable of not being too sick. So it is the prevention, which is important. It's the same thing here. So there is this mindset we have psychologically with the partners and they have, they have to provide that services to their customers on how to organize their data using the technology of Veeam in order to be safe, if anything happens. >> So another related question, if I may. When Snowden blew the whistle on the NSA and divulged that the NSA was listening to all the phone calls, there was seemed to be at the time, as I recall, a backlash sentiment in Europe, particularly toward big tech and cloud providers and skepticism toward the cloud. Has the pandemic and the reliance on cloud and the rise of ransomware changed that sentiment? Had the sentiment changed before then? Obviously plenty of Cloud going on in Europe. But can you describe that dynamic? >> Yeah, no, I think that's... Yeah. I think that people were too... You know, as usual. It absolutely reminds me when I was at VMware, when we went from the physical boxes to the virtual machines. I remember the IT people in the company said, "No, I want to be capable of touching." Something here. When you talk about cloud, you talk about something which is virtual, but virtual outside, even outside somewhere. So there is a resistance, psychological resistance to where is my data? How do I control my data? And that is, I think that is very human. Then you need to, you know, it takes time. And again, depending on the cultures, you need to get acquainted to it. So that's what happened be before the pandemic, but then the pandemic took place. And then there was a big problem. There was nobody anymore in the data centers because they couldn't work there and then people were starting to, to work remotely. So the IT needed to be organized to compensate for all these different changes. And cloud was one of them where the data could be stored, where the data could reside, where things could happen. And that's how actually it has accelerated at least in a number of countries where people are a bit leg out to accept the adoption of cloud, cloud-based data. >> So is there a difference in terms of the level of domination by a small group of hyperscale clouds versus smaller service providers? You know, in theory, you have EU behaving in a unified way in sort of the same way that the United States behaves in sort of a federated way. Do you have that same level of domination or is there more, is there more market share available for smaller players in cloud? Any regional differences? >> Yeah. There are big differences. There are big differences again, because of this sovereignty, which is absolutely approved very much in Europe. I'm tell you, I'm going... I'm giving you an example that it was in, I think in October last year, somewhere. The French, the French administration said, "We don't want anymore. Any administration investing in Microsoft 365, because the data is in Azure. The data is out in the cloud." That's what they said. So now these last days, this last week that has changed because Microsoft, you know, introduced a number of technologies, data centers in France, and so on and so forth. So things are going to get better. But the sovereignty, the fact that the data, the privacy of data, everything has to remain in the countries is doing something like the technology of the hyperscalers is used locally wrapped by local companies like systematic writers, local systematic writers, to ensure that the sovereign is set and that the privacy of the data is for real and according to GDPR. So again, it's a value add. It makes things more complex. It doesn't mean that the Google, the Google cloud, the Azure, or the AWS are not going to exist in Europe, but there are going to be a number of layers between them and the customers in order to make sure that everything is totally brought up and that it complies with the EU regulations. >> Help us understand the numbers, Daniel. So the number of customers is mind-boggling it's over 400,000 now, is that right? >> Yeah. Correct. >> Yes. Comparable to VMware, which is again, pretty astounding and the partner ecosystem. Can you help us understand the scope of that? Part one. part two is how do you service and provide that partnership love to all those companies? >> The partners. So yeah, we have about 35,000 around the world, 35,000 partners, but again, it's 10 times less than Microsoft, by the way. So, and this is very interesting. I often have the questions, how do we manage? So first of all, we do tiering, like anybody does. >> Sure. >> We have an organization for that. And we have a two chair sales motion. That means that we use the distributors to take care of the mass, the volume of the smaller, smaller partners. We help the distributors, we help. So it's a leverage system. And we take care obviously more directly, of the large partners or the more complex partners or the ones of interest. But we don't want to forget any of those because even the small one is very important to us because he has these customers maybe in the middle of nowhere, but he's got a few of them. And again, to have a few of these customers, when you adapt, you know, it makes.. At the end, it makes a big business. You know, one plus one plus 1 million times makes, you know, makes huge things. And plus we are in the recurring business now, now that we've introduced three, four years ago, our subscription licenses, which means that it's only incremental. So it's just like the know the telephony, know the telephony business, where the number, the cell phone plans, you know, it's always grabbing as many as possible consumers in this case. So it was the same thing or I have the same, the same kind of, I do a parallel with the French, the French bakery, the French Boulangerie where I say they do their business with the baguette. And then from time to time, they sell the patisserie or they sell the cake, cookie or something, but the same of small things makes a big things. So it is important to have all these small partners everywhere that, that have their small customers or big customers, and that can serve them. So that's that's way. We segment by geography and what we do now is, it is something which is new. We segment by competencies. So it's what I call the soft segmentation. Because if not, we will have a lot of these partners competing to each other, just to sell Veeam. Veeam being number one in many countries, that is what is taking place. And we want them to be happy. We want, we don't want them to fight against each other. So what we do is we do soft segmentation and soft segmentation is this partner is competent in this field with that kind of use case doing this or this or this or this. It's just like you, when you go to the restaurant, you want the restaurant next to your place. So you click for the geography and then you want to, to go for Indian food. So you click restaurant Indian food, and then you want something. So we want to give that possibility to the customers to say, "Yeah, I think I know what I want." And then you can just click and get the partners or the list of partners, which are the most suited for, for his needs. So it's what I call the soft segmentation. The other thing which is important is the network. It's very interesting because when we look at a lot of companies, it's not the network. You've got VARs, you've got cloud and service providers. You've got SARs, you've got all the things. But if you take each of those individually, they don't have the competencies to answer all the request of the customer. So the networking is partnering with partner. That means to have the, the connection so that the partner A who has his customer, but these customer's are requests that this partner cannot fulfill because it's not its competency. That it's going to find the partners or the other partners that can feel this competency and work together. And then it's between them to have the model that they want so that together they can please the customer with their requests. >> Do you ever want to have VeeamON... I mean, I'm happy it's in the US and I like going to Europe, but you, have you ever want to have VeeamON in Europe? >> Yeah, we have VeeamON. We have many VeeamONs in Europe. >> Yeah. The mini ones. Okay. >> VeeamON tours. >> Globally. So where do you have them? >> Europe in APJ, that's what we do. Yes. >> Where do you do it in a APJ? In Japan, obviously in... >> Yeah. I don't know all the locations, tens and tens of them. >> A lot of them. Okay. >> The small ones. What we do, replicate what is done here on one day and then it goes. >> And you'll do that in UK. France, Germany. >> Yeah. Yeah. >> Local. >> And also small countries in Saudi, in South Africa, in Israel, in Bulgaria, in all these countries. Because, you know, we can be virtual. That's nice. >> Oh, right. >> But I love to be having a breakfast or a lunch or drink next to a partner or a customer because you learn so much more. The informal information is so important to understand how the business and how the market develops and what the needs are of customers and so on and so forth. >> How was the European attendance this year? It must have been down. It's hard to get into US. It's actually easier to go back to Europe. >> Virtually I, don't have the numbers, but I- >> No. Virtual. I'm sure it was huge. Yeah. But physical. >> Physical here, we've got about 300, 300 Europeans. >> Yeah. Okay. Out of, do we know? What are the numbers here? Do we know? Have we heard numbers? >> I know 45 was supposed to be around 45K combined. >> That's hybrid. >> So, yeah. >> It's hard to get into the US. We're still figuring that out. So I'm not surprised, but now you... >> But it's complimentary. Yeah. >> Do you go to 'em all? >> No >> You can't. >> No. That's not possible. I cannot. I actually, I would love... >> But some, yes. >> I would love to be capable of duplicate myself, but- >> You go to the one. >> I'm unique. >> You go to the one in France, obviously. Yeah? >> Yeah. Usually in France. Well... >> Depends if you're home. >> Yeah. You know, that is interesting is, the way we organize, the way we organize in Europe is I really want the local leaders to be the ones managing the countries. I'm there to support. I'm not there to be, you know? Yeah. The big boss is coming, he showing. No. It is not that. Again, if they request me to come, if they want me to pass a message to certain type of customer partners, I'll do that. But I don't want to run the show. It's not the way I manage that. >> Yeah. I get that. You want to respect that as if you show up in France and that's your home country, it's like rat man showing up here. It's like taking over the stage. You'll be like, you know, it's our turn. >> But it's just like, you know, I give you another example. So obviously we have... It's even the headquarters, the EMEA headquarters is in France. Right? But it is the French office. And I don't go there. I try not to be there because it is the place for the French people taking care of the French market. And for the French manager, if I go there, everybody's going to come and ask me questions and ask me to make decisions and things. No, they have to run their business. >> So where do you spend, where and how do you spend your time? >> In airports and in planes. (indistinct) What are you asking? >> Of course. >> Do you have another question? >> Actually, if we have time really quickly on just on that subject of sovereignty, we are here in Nevada just across the border, California. People in California have no problem at all, replicating things here for disaster recovery, because it's in the US. Now, is there sort of a cultural sense that tearing down those borders from a sovereignty perspective within Europe would fundamentally change the business climate and maybe tilt things in favor of the AWS and GCPs of the world instead of local regional business? The joke that I heard recently from someone, I thought it was funny. I don't know if it would offend either Germans or French, but it was that it was that AWS was confused and they were planning on putting a data center in Strasbourg, because they thought it was in Germany and it was- >> A joke. >> But the point is, the point is it's like, it's a gum bear. >> Is it true? >> No. But it was a dumb American joke. This was told by a French person basically saying... >> But this person was certainly not from- >> Yes. Right. >> Tell you, because I would've been a very bad way. >> But the point is this idea that you have these mega hyper clouds coming in and saying, "Okay, boom, we're putting one here and you're going to use us regardless of the country you're in." How does that, you know... Is there a push within the EU to tear those barriers down? Or are those sovereignty walls enjoyed by the majority because of the way that it changes the business climate? Any thoughts from that perspective? >> Oh yeah. Yeah. To me, it's very simple. It is a hybrid thing. That means that these big hyperscalers are there, not going to be used but what they do is they're going to partition themselves and work with these local people. So that their big thing appears as being independent, smaller data centers. That's the only thing, you know. You build a house and then you put walls between the different, between the different rooms. That's the only thing that happens. So it's not at all, no. At all to Azures or Google cloud. No, it's not that. It just means that there is a structure and organization that has to be put in place in order that the data resides in given geographical locations using their infrastructures, their technologies. That make, does it make sense? >> Yeah. Except that it puts them in the position of having to have a physical presence in each place, which is advantageous in one way and maybe less efficient in another. >> Yeah. But there are some big markets. >> Yeah. And they eventually got to get there. Right. I mean... >> Yeah. >> They started it. One patient in the world where they restarted was in ANZ. And that's what they did. You know, what, 5, 6, 7 years ago. They put their data centers over there because they wanted to gain the Australian market and the New Zealand market. >> So build it and they will come. Daniel, thanks so much for coming to the theCUBE. Very interesting conversation. >> Pleasure. >> Appreciate it. >> Thank you very much. >> All right, we're wrapping up. Day two at VeeamON 2022. Keep it right there. Dave and I will be back right after this break. (vibrant music)
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We're in the home stretch, actually, I have a job that I love. Yeah, a job you love. and all the markets around obviously the business climate. because of obviously the What impact, if any, has it had? and the US dollar is on the business. because of the gas of everything and the affinity, and secure the data in sort of way. and the EU and other parts of the world? So it is the prevention, and divulged that the NSA was listening So the IT needed to be organized in sort of the same way that and that the privacy So the number of the partner ecosystem. I often have the questions, So it's just like the know the telephony, I mean, I'm happy it's in the Yeah, we have VeeamON. Okay. So where do you have them? Europe in APJ, that's what we do. Where do you do it in a APJ? tens and tens of them. A lot of them. and then it goes. And you'll do that in UK. Because, you know, we can be virtual. how the business and It's hard to get into US. I'm sure it was huge. Physical here, we've got about 300, What are the numbers here? to be around 45K combined. It's hard to get into the US. But it's complimentary. I actually, I would love... You go to the one in the local leaders to be the It's like taking over the stage. But it is the French office. In airports and in planes. and GCPs of the world But the point is, No. But it was a dumb American joke. Tell you, because I that it changes the business climate? in order that the data resides of having to have a physical presence eventually got to get there. and the New Zealand market. for coming to the theCUBE. Dave and I will be back
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Bill Andrews, ExaGrid | VeeamON 2022
(upbeat music) >> We're back at VeeamON 2022. We're here at the Aria in Las Vegas Dave Vellante with Dave Nicholson. Bill Andrews is here. He's the president and CEO of ExaGrid, mass boy. Bill, thanks for coming on theCUBE. >> Thanks for having me. >> So I hear a lot about obviously data protection, cyber resiliency, what's the big picture trends that you're seeing when you talk to customers? >> Well, I think clearly we were talking just a few minutes ago, data's growing like crazy, right This morning, I think they said it was 28% growth a year, right? So data's doubling almost just a little less than every three years. And then you get the attacks on the data which was the keynote speech this morning as well, right. All about the ransomware attacks. So we've got more and more data, and that data is more and more under attack. So I think those are the two big themes. >> So ExaGrid as a company been around for a long time. You've kind of been the steady kind of Eddy, if you will. Tell us about ExaGrid, maybe share with us some of the differentiators that you share with customers. >> Sure, so specifically, let's say in the Veeam world you're backing up your data, and you really only have two choices. You can back that up to disc. So some primary storage disc from a Dell, or a Hewlett Packard, or an NetApp or somebody, or you're going to back it up to what's called an inline deduplication appliance maybe a Dell Data Domain or an HPE StoreOnce, right? So what ExaGrid does is we've taken the best of both those but not the challenges of both those and put 'em together. So with disc, you're going to get fast backups and fast restores, but because in backup you keep weekly's, monthly's, yearly retention, the cost of this becomes exorbitant. If you go to a deduplication appliance, and let's say the Dell or the HPs, the data comes in, has to be deduplicated, compare one backup to the next to reduce that storage, which lowers the cost. So fixes that problem, but the fact that they do it inline slows the backups down dramatically. All the data is deduplicated so the restores are slow, and then the backup window keeps growing as the data grows 'cause they're all scale up technologies. >> And the restores are slow 'cause you got to rehydrate. >> You got to rehydrate every time. So what we did is we said, you got to have both. So our appliances have a front end disc cache landing zone. So you're right directed to the disc., Nothing else happens to it, whatever speed the backup app could write at that's the speed we take it in at. And then we keep the most recent backups in that landing zone ready to go. So you want to boot a VM, it's not an hour like a deduplication appliance it's a minute or two. Secondly, we then deduplicate the data into a second tier which is a repository tier, but we have all the deduplicated data for the long term retention, which gets the cost down. And on top of that, we're scale out. Every appliance has networking processor memory end disc. So if you double, triple, quadruple the data you double, triple, quadruple everything. And if the backup window is six hours at 100 terabyte it's six hours at 200 terabyte, 500 terabyte, a petabyte it doesn't matter. >> 'Cause you scale out. >> Right, and then lastly, our repository tier is non-network facing. We're the only ones in the industry with this. So that under a ransomware attack, if you get hold of a rogue server or you hack the media server, get to the backup storage whether it's disc or deduplication appliance, you can wipe out all the backup data. So you have nothing to recover from. In our case, you wipe it out, our landing zone will be wiped out. We're no different than anything else that's network facing. However, the only thing that talks to our repository tier is our object code. And we've set up security policies as to how long before you want us to delete data, let's say 10 days. So if you have an attack on Monday that data doesn't get deleted till like a week from Thursday, let's say. So you can freeze the system at any time and do restores. And then we have immutable data objects and all the other stuff. But the culmination of a non-network facing tier and the fact that we do the delayed deletes makes us the only one in the industry that can actually truly recover. And that's accelerating our growth, of course. >> Wow, great description. So that disc cache layer is a memory, it's a flash? >> It's disc, it's spinning disc. >> Spinning disc, okay. >> Yeah, no different than any other disc. >> And then the tiered is what, less expensive spinning disc? >> No, it's still the same. It's all SaaS disc 'cause you want the quality, right? So it's all SaaS, and so we use Western Digital or Seagate drives just like everybody else. The difference is that we're not doing any deduplication coming in or out of that landing zone to have fast backups and fast restores. So think of it like this, you've got disc and you say, boy it's too expensive. What I really want to do then is put maybe a deduplication appliance behind it to lower the cost or reverse it. I've got a deduplication appliance, ugh, it's too slow for backups and restores. I really want to throw this in front of it to have fast backups first. Basically, that's what we did. >> So where does the cost savings, Bill come in though, on the tier? >> The cost savings comes in the fact that we got deduplication in that repository. So only the most recent backup >> Ah okay, so I get it. >> are the duplicated data. But let's say you had 40 copies of retention. You know, 10 weekly's, 36 monthly's, a few yearly. All of that's deduplicated >> Okay, so you're deduping the stuff that's not as current. >> Right. >> Okay. >> And only a handful of us deduplicate at the layer we do. In other words, deduplication could be anywhere from two to one, up to 50 to one. I mean it's all over the place depending on the algorithm. Now it's what everybody's algorithms do. Some backup apps do two to one, some do five to one, we do 20 to one as well as much as 50 to one depending on the data types. >> Yeah, so the workload is going to largely determine the combination >> The content type, right. with the algos, right? >> Yeah, the content type. >> So the part of the environment that's behind the illogical air gap, if you will, is deduped data. >> Yes. >> So in this case, is it fair to say that you're trading a positive economic value for a little bit longer restore from that environment? >> No, because if you think about backup 95% of the customers restores are from the most recent data. >> From the disc cache. >> 95% of the time 'cause you think about why do you need fast restores? Somebody deleted a file, somebody overwrote a file. They can't go work, they can't open a file. It's encrypted, it's corrupted. That's what IT people are trying to keep users productive. When do you go for longer-term retention data? It's an SEC audit. It's a HIPAA audit. It's a legal discovery, you don't need that data right away. You have days and weeks to get that ready for that legal discovery or that audit. So we found that boundary where you keep users productive by keeping the most recent data in the disc cache landing zone, but anything that's long term. And by the way, everyone else is long term, at that point. >> Yeah, so the economics are comparable to the dedupe upfront. Are they better, obviously get the performance advance? >> So we would be a lot looped. The thing we replaced the most believe it or not is disc, we're a lot less expensive than the disc. I was meeting with some Veeam folks this morning and we were up against Cisco 3260 disc at a children's hospital. And on our quote was $500,000. The disc was 1.4 million. Just to give you an example of the savings. On a Data Domain we're typically about half the price of a Data Domain. >> Really now? >> The reason why is their front end control are so expensive. They need the fastest trip on the planet 'cause they're trying to do inline deduplication. >> Yeah, so they're chasing >> They need the fastest memory >> on the planet. >> this chips all the time. They need SSD on data to move in and out of the hash table. In order to keep up with inline, they've got to throw so much compute at it that it drives their cost up. >> But now in the case of ransomware attack, are you saying that the landing zone is still available for recovery in some circumstances? Or are you expecting that that disc landing zone would be encrypted by the attacker? >> Those are two different things. One is deletion, one is encryption. So let's do the first scenario. >> I'm talking about malicious encryption. >> Yeah, absolutely. So the first scenario is the threat actor encrypts all your primary data. What's does he go for next? The backup data. 'Cause he knows that's your belt and suspend is to not pay the ransom. If it's disc he's going to go in and put delete commands at the disc, wipe out the disc. If it's a data domain or HPE StoreOnce, it's all going to be gone 'cause it's one tier. He's going to go after our landing zone, it's going to be gone too. It's going to wipe out our landing zone. Except behind that we have the most recent backup deduplicate in the repository as well as all the other backups. So what'll happen is they'll freeze the system 'cause we weren't going to delete anything in the repository for X days 'cause you set up a policy, and then you restore the most recent backup into the landing zone or we can restore it directly to your primary storage area, right? >> Because that tier is not network facing. >> That's right. >> It's fenced off essentially. >> People call us every day of the week saying, you saved me, you saved me again. People are coming up to me here, you saved me, you saved me. >> Tell us a story about that, I mean don't give me the names but how so. >> I'll actually do a funnier story, 'cause these are the ones that our vendors like to tell. 'Cause I'm self-serving as the CEO that's good of course, a little humor. >> It's your 15 minutes of job. >> That is my 15 minutes of fame. So we had one international company who had one ExaGrid at one location, 19 Data Domains at the other locations. Ransomware attack guess what? 19 Data Domains wiped out. The one ExaGrid, the only place they could restore. So now all 20 locations of course are ExaGrids, China, Russia, Mexico, Germany, US, et cetera. They rolled us out worldwide. So it's very common for that to occur. And think about why that is, everyone who's network facing you can get to the storage. You can say all the media servers are buttoned up, but I can find a rogue server and snake my way over the storage, I can. Now, we also of course support the Veeam Data Mover. So let's talk about that since we're at a Veeam conference. We were the first company to ever integrate the Veeam Data Mover. So we were the first actually ever integration with Veeam. And so that Veeam Data Mover is a protocol that goes from Veeam to the ExaGrid, and we run it on both ends. So that's a more secure protocol 'cause it's not an open format protocol like SaaS. So with running the Veeam Data Mover we get about 30% more performance, but you do have a more secure protocol layer. So if you don't get through Veeam but you get through the protocol, boom, we've got a stronger protocol. If you make it through that somehow, or you get to it from a rogue server somewhere else we still have the repository. So we have all these layers so that you can't get at it. >> So you guys have been at this for a while, I mean decade and a half plus. And you've raised a fair amount of money but in today's terms, not really. So you've just had really strong growth, sequential growth. I understand it, and double digit growth year on year. >> Yeah, about 25% a year right now >> 25%, what's your global strategy? >> So we have sales offices in about 30 countries already. So we have three sales teams in Brazil, and three in Germany, and three in the UK, and two in France, and a lot of individual countries, Chile, Argentina, Columbia, Mexico, South Africa, Saudi, Czech Republic, Poland, Dubai, Hong Kong, Australia, Singapore, et cetera. We've just added two sales territories in Japan. We're adding two in India. And we're installed in over 50 countries. So we've been international all along the way. The goal of the company is we're growing nicely. We have not raised money in almost 10 years. >> So you're self-funding. You're cash positive. >> We are cash positive and self-funded and people say, how have you done that for 10 years? >> You know what's interesting is I remember, Dave Scott, Dave Scott was the CEO of 3PAR, and he told me when he came into that job, he told the VCs, they wanted to give him 30 million. He said, I need 80 million. I think he might have raised closer to a hundred which is right around what you guys have raised. But like you said, you haven't raised it in a long time. And in today's terms, that's nothing, right? >> 100 is 500 in today's terms. >> Yeah, right, exactly. And so the thing that really hurt 3PAR, they were public companies so you could see all this stuff is they couldn't expand internationally. It was just too damn expensive to set up the channels, and somehow you guys have figured that out. >> 40% of our business comes out of international. We're growing faster internationally than we are domestically. >> What was the formula there, Bill, was that just slow and steady or? >> It's a great question. >> No, so what we did, we said let's build ExaGrid like a McDonald's franchise, nobody's ever done that before in high tech. So what does that mean? That means you have to have the same product worldwide. You have to have the same spares model worldwide. You have to have the same support model worldwide. So we early on built the installation. So we do 100% of our installs remotely. 100% of our support remotely, yet we're in large enterprises. Customers racks and stacks the appliances we get on with them. We do the entire install on 30 minutes to about three hours. And we've been developing that into the product since day one. So we can remotely install anywhere in the world. We keep spares depots all over the world. We can bring 'em up really quick. Our support model is we have in theater support people. So they're in Europe, they're in APAC, they're in the US, et cetera. And we assign customers to the support people. So they deal with the same support person all the time. So everything is scalable. So right now we're going to open up India. It's the same way we've opened up every other country. Once you've got the McDonald's formula we just stamp it all over the world. >> That's amazing. >> Same pricing, same product same model, same everything. >> So what was the inspiration for that? I mean, you've done this since day one, which is what like 15, 16 years ago. Or just you do engineering or? >> No, so our whole thought was, first of all you can't survive anymore in this world without being an international company. 'Cause if you're going to go after large companies they have offices all over the world. We have companies now that have 17, 18, 20, 30 locations. And there were in every country in the world, you can't go into this business without being able to ship anywhere in the world and support it for a single customer. You're not going into Singapore because of that. You're going to Singapore because some company in Germany has offices in the U.S, Mexico Singapore and Australia. You have to be international. It's a must now. So that was the initial thing is that, our goal is to become a billion dollar company. And we're on path to do that, right. >> You can see a billion. >> Well, I can absolutely see a billion. And we're bigger than everybody thinks. Everybody guesses our revenue always guesses low. So we're bigger than you think. The reason why we don't talk about it is we don't need to. >> That's the headline for our writers, ExaGrid is a billion dollar company and nobody's know about it. >> Million dollar company. >> On its way to a billion. >> That's right. >> You're not disclosing. (Bill laughing) But that's awesome. I mean, that's a great story. I mean, you kind of are a well kept secret, aren't you? >> Well, I dunno if it's a well kept secret. You know, smaller companies never have their awareness of big companies, right? The Dells of the world are a hundred billion. IBM is 70 billion, Cisco is 60 billion. Easy to have awareness, right? If you're under a billion, I got to give a funny story then I think we got to close out here. >> Oh go ahead please. >> So there's one funny story. So I was talking to the CIO of a super large Fortune 500 company. And I said to him, "Just so who do you use?" "I use IBM Db2, and I use, Cisco routers, and I use EMC primary storage, et cetera. And I use all these big." And I said, "Would you ever switch from Db2?" "Oh no, the switching costs would kill me. I could never go to Oracle." So I said to him, "Look would you ever use like a Pure Storage, right. A couple billion dollar company." He says, "Who?" >> Huh, interesting. >> I said to him, all right so skip that. I said, "VMware, would you ever think about going with Nutanix?" "Who?" Those are billion dollar plus companies. And he was saying who? >> Public companies. >> And he was saying who? That's not uncommon when I talk to CIOs. They see the big 30 and that's it. >> Oh, that's interesting. What about your partnership with Veeam? Tell us more about that. >> Yeah, so I would actually, and I'm going to be bold when I say this 'cause I think you can ask anybody here at the conference. We're probably closer first of all, to the Veeam sales force than any company there is. You talk to any Veeam sales rep, they work closer with ExaGrid than any other. Yeah, we are very tight in the field and have been for a long time. We're integrated with the Veeam Data Boomer. We're integrated with SOBR. We're integrated with all the integrations or with the product as well. We have a lot of joint customers. We actually do a lot of selling together, where we go in as Veeam ExaGrid 'cause it's a great end to end story. Especially when we're replacing, let's say a Dell Avamar to Dell Data Domain or a Dell Network with a Dell Data Domain, very commonly Veeam ExaGrid go in together on those types of sales. So we do a lot of co-selling together. We constantly train their systems engineers around the world, every given week we're training either inside sales teams, and we've trained their customer support teams in Columbus and Prague. So we're very tight with 'em we've been tight for over a decade. >> Is your head count public? Can you share that with us? >> So we're just over 300 employees. >> Really, wow. >> We have 70 open positions, so. >> Yeah, what are you looking for? Yeah, everything, right? >> We are looking for engineers. We are looking for customer support people. We're looking for marketing people. We're looking for inside sales people, field people. And we've been hiring, as of late, major account reps that just focus on the Fortune 500. So we've separated that out now. >> When you hire engineers, I mean I think I saw you were long time ago, DG, right? Is that true? >> Yeah, way back in the '80s. >> But systems guy. >> That's how old I am. >> Right, systems guy. I mean, I remember them well Eddie Castro and company. >> Tom West. >> EMV series. >> Tom West was the hero of course. >> The EMV 4000, the EMV 20,000, right? >> When were kids, "The Soul of a New Machine" was the inspirational book but anyway, >> Yeah Tracy Kidder, it was great. >> Are you looking for systems people, what kind of talent are you looking for in engineering? >> So it's a lot of Linux programming type stuff in the product 'cause we run on a Linux space. So it's a lot of Linux programs so its people in those storage. >> Yeah, cool, Bill, hey, thanks for coming on to theCUBE. Well learned a lot, great story. >> It's a pleasure. >> That was fun. >> Congratulations. >> Thanks. >> And good luck. >> All right, thank you. >> All right, and thank you for watching theCUBE's coverage of VeeamON 2022, Dave Vellante for Dave Nicholson. We'll be right back right after this short break, stay with us. (soft beat music)
SUMMARY :
We're here at the Aria in Las Vegas And then you get the attacks on the data You've kind of been the steady and let's say the Dell or And the restores are slow that's the speed we take it in at. and the fact that we So that disc cache layer No, it's still the same. So only the most recent backup are the duplicated data. Okay, so you're deduping the deduplicate at the layer we do. with the algos, right? So the part of the environment 95% of the customers restores 95% of the time 'cause you think about Yeah, so the economics are comparable example of the savings. They need the fastest trip on the planet in and out of the hash table. So let's do the first scenario. So the first scenario is the threat actor Because that tier day of the week saying, I mean don't give me the names but how so. 'Cause I'm self-serving as the CEO So if you don't get through Veeam So you guys have been The goal of the company So you're self-funding. what you guys have raised. And so the thing that really hurt 3PAR, than we are domestically. It's the same way we've Same pricing, same product So what was the inspiration for that? country in the world, So we're bigger than you think. That's the headline for our writers, I mean, you kind of are a The Dells of the world So I said to him, "Look would you ever I said, "VMware, would you ever think They see the big 30 and that's it. Oh, that's interesting. So we do a lot of co-selling together. that just focus on the Fortune 500. Eddie Castro and company. in the product 'cause thanks for coming on to theCUBE. All right, and thank you for watching
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Ajay Mungara, Intel | Red Hat Summit 2022
>>mhm. Welcome back to Boston. This is the cubes coverage of the Red Hat Summit 2022. The first Red Hat Summit we've done face to face in at least two years. 2019 was our last one. We're kind of rounding the far turn, you know, coming up for the home stretch. My name is Dave Valentin here with Paul Gillon. A J monger is here is a senior director of Iot. The Iot group for developer solutions and engineering at Intel. AJ, thanks for coming on the Cube. Thank you so much. We heard your colleague this morning and the keynote talking about the Dev Cloud. I feel like I need a Dev Cloud. What's it all about? >>So, um, we've been, uh, working with developers and the ecosystem for a long time, trying to build edge solutions. A lot of time people think about it. Solutions as, like, just computer the edge. But what really it is is you've got to have some component of the cloud. There is a network, and there is edge and edge is complicated because of the variety of devices that you need. And when you're building a solution, you got to figure out, like, where am I going to push the computer? How much of the computer I'm going to run in the cloud? How much of the computer? I'm gonna push it at the network and how much I need to run it at the edge. A lot of times what happens for developers is they don't have one environment where all of the three come together. And so what we said is, um, today the way it works is you have all these edge devices that customers by the instal, they set it up and they try to do all of that. And then they have a cloud environment they do to their development. And then they figure out how all of this comes together. And all of these things are only when they are integrating it at the customer at the solution space is when they try to do it. So what we did is we took all of these edge devices, put it in the cloud and gave one environment for cloud to the edge. Very good to your complete solution. >>Essentially simulates. >>No, it's not >>simulating span. So the cloud spans the cloud, the centralised cloud out to the edge. You >>know, what we did is we took all of these edge devices that will theoretically get deployed at the edge like we took all these variety of devices and putting it put it in a cloud environment. So these are non rack mountable devices that you can buy in the market today that you just have, like, we have about 500 devices in the cloud that you have from atom to call allusions to F. P. G s to head studio cards to graphics. All of these devices are available to you. So in one environment you have, like, you can connect to any of the cloud the hyper scholars, you could connect to any of these network devices. You can define your network topology. You could bring in any of your sources that is sitting in the gate repository or docker containers that may be sitting somewhere in a cloud environment, or it could be sitting on a docker hub. You can pull all of these things together, and we give you one place where you can build it where you can test it. You can performance benchmark it so you can know when you're actually going to the field to deploy it. What type of sizing you need. So >>let me show you, understand? If I want to test, uh, an actual edge device using 100 gig Ethernet versus an Mpls versus the five G, you can do all that without virtualizing. >>So all the H devices are there today, and the network part of it, we are building with red hat together where we are putting everything on this environment. So the network part of it is not quite yet solved, but that's what we want to solve. But the goal is here is you can let's say you have five cameras or you have 50 cameras with different type of resolutions. You want to do some ai inference type of workloads at the edge. What type of compute you need, what type of memory you need, How many devices do you need and where do you want to push the data? Because security is very important at the edge. So you gotta really figure out like I've got to secure the data on flight. I want to secure the data at Brest, and how do you do the governance of it. How do you kind of do service governance? So that all the services different containers that are running on the edge device, They're behaving well. You don't have one container hogging up all the memory or hogging up all the compute, or you don't have, like, certain points in the day. You might have priority for certain containers. So all of these mortals, where do you run it? So we have an environment that you could run all of that. >>Okay, so take that example of AI influencing at the edge. So I've got an edge device and I've developed an application, and I'm going to say Okay, I want you to do the AI influencing in real time. You got something? They become some kind of streaming data coming in, and I want you to persist, uh, every hour on the hour. I want to save that time stamp. Or if the if some event, if a deer runs across the headlights, I want you to persist that day to send that back to the cloud and you can develop that tested, benchmark >>it right, and then you can say that. Okay, look in this environment I have, like, five cameras, like at different angles, and you want to kind of try it out. And what we have is a product which is into, um, open vino, which is like an open source product, which does all of the optimizations you need for age in France. So you develop the like to recognise the deer in your example. I developed the training model somewhere in the cloud. Okay, so I have, like, I developed with all of the things have annotated the different video streams. And I know that I'm recognising a deer now. Okay, so now you need to figure out Like when the deer is coming and you want to immediately take an action. You don't want to send all of your video streams to the cloud. It's too expensive. Bandwidth costs a lot. So you want to compute that inference at the edge? Okay. In order to do that inference at the edge, you need some environment. You should be able to do it. And to build that solution What type of age device do you really need? What type of compute you need? How many cameras are you computing it? What different things you're not only recognising a deer, probably recognising some other objects could do all of that. In fact, one of the things happened was I took my nephew to San Diego Zoo and he was very disappointed that he couldn't see the chimpanzees. Uh, that was there, right, the gorillas and other things. So he was very sad. So I said, All right, there should be a better way. I saw, like there was a stream of the camera feed that was there. So what we did is we did an edge in friends and we did some logic to say, At this time of the day, the gorillas get fed, so there's likelihood of you actually seeing the gorilla is very high. So you just go at that point and so that you see >>it, you >>capture, That's what you do, and you want to develop that entire solution. It's based on whether, based on other factors, you need to bring all of these services together and build a solution, and we offer an environment that allows you to do it. Will >>you customise the the edge configuration for the for the developer If if they want 50 cameras. That's not You don't have 50 cameras available, right? >>It's all cameras. What we do is we have a streaming capability that we support so you can upload all your videos. And you can say I want to now simulate 50 streams. Want to simulate 30 streams? Or I want to do this right? Or just like two or three videos that you want to just pull in. And you want to be able to do the infant simultaneously, running different algorithms at the edge. All of that is supported, and the bigger challenge at the edge is developing. Solution is fine. And now when you go to actual deployment and post deployment monitoring, maintenance, make sure that you're like managing it. It's very complicated. What we have seen is over 50% 51% to be precise of developers are developed some kind of a cloud native applications recently, right? So that we believe that if you bring that type of a cloud native development model to the edge, then you're scaling problem. Your maintenance problem, you're like, how do you actually deploy it? All of these challenges can be better managed, Um, and if you run all of that is an orchestration later on kubernetes and we run everything on top of open shift, so you have a deployment ready solution already there it's everything is containerised everything. You have it as health charged Dr Composed. You have all their you have tested and in this environment, and now you go take that to the deployment. And if it is there on any standard kubernetes environment or in an open ship, you can just straight away deploy your application. >>What's that edge architecture looked like? What's Intel's and red hats philosophy around? You know what's programmable and it's different. I know you can run a S, a p a data centre. You guys got that covered? What's the edge look like? What's that architecture of silicon middleware? Describe that for us. >>So at the edge, you think about it, right? It can run traditional, Uh, in an industrial PC. You have a lot of Windows environment. You have a lot of the next. They're now in a in an edge environment. Quite a few of these devices. I'm not talking about Farage where there are tiny micro controllers and these devices I'm talking about those devices that connect to these forage devices. Collect the data. Do some analytics do some compute that type of thing. You have foraged devices. Could be a camera. Could be a temperature sensor. Could be like a weighing scale. Could be anything. It could be that forage and then all of that data instead of pushing all the data to the cloud. In order for you to do the analysis, you're going to have some type of an edge set of devices where it is collecting all this data, doing some decisions that's close to the data. You're making some analysis there, all of that stuff, right? So you need some analysis tools, you need certain other things. And let's say that you want to run like, UH, average costs or rail or any of these operating systems at the edge. Then you have an ability for you to manage all of that. Using a control note, the control node can also sit at the edge. In some cases, like in a smart factory, you have a little data centre in a smart factory or even in a retail >>store >>behind a closet. You have, like a bunch of devices that are sitting there, correct. And those devices all can be managed and clustered in an environment. So now the question is, how do you deploy applications to that edge? How do you collect all the data that is sitting through the camera? Other sensors and you're processing it close to where the data is being generated make immediate decisions. So the architecture would look like you have some club which does some management of this age devices management of this application, some type of control. You have some network because you need to connect to that. Then you have the whole plethora of edge, starting from an hybrid environment where you have an entire, like a mini data centre sitting at the edge. Or it could be one or two of these devices that are just collecting data from these sensors and processing it that is the heart of the other challenge. The architecture varies from different verticals, like from smart cities to retail to healthcare to industrial. They have all these different variations. They need to worry about these, uh, different environments they are going to operate under, uh, they have different regulations that they have to look into different security protocols that they need to follow. So your solution? Maybe it is just recognising people and identifying if they are wearing a helmet or a coal mine, right, whether they are wearing a safety gear equipment or not, that solution versus you are like driving in a traffic in a bike, and you, for safety reasons. We want to identify the person is wearing a helmet or not. Very different use cases, very different environments, different ways in which you are operating. But that is where the developer needs to have. Similar algorithms are used, by the way, but how you deploy it very, quite a bit. >>But the Dev Cloud make sure I understand it. You talked about like a retail store, a great example. But that's a general purpose infrastructure that's now customised through software for that retail environment. Same thing with Telco. Same thing with the smart factory, you said, not the far edge, right, but that's coming in the future. Or is that well, that >>extends far edge, putting everything in one cloud environment. We did it right. In fact, I put some cameras on some like ipads and laptops, and we could stream different videos did all of that in a data centre is a boring environment, right? What are you going to see? A bunch of racks and service, So putting far edge devices there didn't make sense. So what we did is you could just have an easy ability for you to stream or connect or a Plourde This far edge data that gets generated at the far edge. Like, say, time series data like you can take some of the time series data. Some of the sensor data are mostly camera data videos. So you upload those videos and that is as good as your streaming those videos. Right? And that means you are generating that data. And then you're developing your solution with the assumption that the camera is observing whatever is going on. And then you do your age inference and you optimise it. You make sure that you size it, and then you have a complete solution. >>Are you supporting all manner of microprocessors at the edge, including non intel? >>Um, today it is all intel, but the plan, because we are really promoting the whole open ecosystem and things like that in the future. Yes, that is really talking about it, so we want to be able to do that in the future. But today it's been like a lot of the we were trying to address the customers that we are serving today. We needed an environment where they could do all of this, for example, and what circumstances would use I five versus i nine versus putting an algorithm on using a graphics integrated graphics versus running it on a CPU or running it on a neural computer stick. It's hard, right? You need to buy all those devices you need to experiment your solutions on all of that. It's hard. So having everything available in one environment, you could compare and contrast to see what type of a vocal or makes best sense. But it's not >>just x 86 x 86 your portfolio >>portfolio of F. P. G s of graphics of like we have all what intel supports today and in future, we would want to open it up. So how >>do developers get access to this cloud? >>It is all free. You just have to go sign up and register and, uh, you get access to it. It is difficult dot intel dot com You go there, and the container playground is all available for free for developers to get access to it. And you can bring in container workloads there, or even bare metal workloads. Um, and, uh, yes, all of it is available for you >>need to reserve the endpoint devices. >>Comment. That is where it is. An interesting technology. >>Govern this. Correct. >>So what we did was we built a kind of a queuing system. Okay, So, schedule, er so you develop your application in a controlled north, and only you need the edge device when you're scheduling that workload. Okay, so we have this scheduling systems, like we use Kafka and other technologies to do the scheduling in the container workload environment, which are all the optimised operators that are available in an open shift, um, environment. So we regard those operators. Were we installed it. So what happens is you take your work, lord, and you run it. Let's say on an I seven device, when you're running that workload and I summon device, that device is dedicated to you. Okay, So and we've instrumented each of these devices with telemetry so we could see at the point your workload is running on that particular device. What is the memory looking like power looking like How hard is the device running? What is a compute looking like? So we capture all that metrics. Then what you do is you take it and run it on a 99 or run it on a graphic, so can't run it on an F p g a. Then you compare and contrast. And you say Huh? Okay for this particular work, Lord, this device makes best sense. In some cases, I'll tell you. Right, Uh, developers have come back and told me I don't need a bigger process that I need bigger memory. >>Yeah, sure, >>right. And some cases they've said, Look, I have I want to prioritise accuracy over performance because if you're in a healthcare setting, accuracy is more important. In some cases, they have optimised it for the size of the device because it needs to fit in the right environment in the right place. So every use case where you optimise is up to the solution up to the developer, and we give you an ability for you to do that kind >>of folks are you seeing? You got hardware developers, you get software developers are right, people coming in. And >>we have a lot of system integrators. We have enterprises that are coming in. We are seeing a lot of, uh, software solution developers, independent software developers. We also have a lot of students are coming in free environment for them to kind of play with in sort of them having to buy all of these devices. We're seeing those people. Um I mean, we are pulling through a lot of developers in this environment currently, and, uh, we're getting, of course, feedback from the developers. We are just getting started here. We are continuing to improve our capabilities. We are adding, like, virtualisation capabilities. We are working very closely with red hat to kind of showcase all the goodness that's coming out of red hat, open shift and other innovations. Right? We heard, uh, like, you know, in one of the open shift sessions, they're talking about micro shifts. They're talking about hyper shift, the talking about a lot of these innovations, operators, everything that is coming together. But where do developers play with all of this? If you spend half your time trying to configure it, instal it and buy the hardware, Trying to figure it out. You lose patience. What we have time, you lose time. What is time and it's complicated, right? How do you set up? Especially when you involve cloud. It has network. It has got the edge. You need all of that right? Set up. So what we have done is we've set up everything for you. You just come in. And by the way, not only just that what we realised is when you go talk to customers, they don't want to listen to all our optimizations processors and all that. They want to say that I am here to solve my retail problem. I want to count the people coming into my store, right. I want to see that if there is any spills that I recognise and I want to go clean it up before a customer complaints about it or I have a brain tumour segmentation where I want to identify if the tumour is malignant or not, right and I want to telehealth solutions. So they're really talking about these use cases that are talking about all these things. So What we did is we build many of these use cases by talking to customers. We open sourced it and made it available on Death Cloud for developers to use as a starting point so that they have this retail starting point or they have this healthcare starting point. All these use cases so that they have all the court we have showed them how to contain arise it. The biggest problem is developers still don't know at the edge how to bring a legacy application and make it cloud native. So they just wrap it all into one doctor and they say, OK, now I'm containerised got a lot more to do. So we tell them how to do it, right? So we train these developers, we give them an opportunity to experiment with all these use cases so that they get closer and closer to what the customer solutions need to be. >>Yeah, we saw that a lot with the early cloud where they wrapped their legacy apps in a container, shove it into the cloud. Say it's really hosting a legacy. Apps is all it was. It wasn't It didn't take advantage of the cloud. Never Now people come around. It sounds like a great developer. Free resource. Take advantage of that. Where do they go? They go. >>So it's def cloud dot intel dot com >>death cloud dot intel dot com. Check it out. It's a great freebie, AJ. Thanks very much. >>Thank you very much. I really appreciate your time. All right, >>keep it right there. This is Dave Volonte for Paul Dillon. We're right back. Covering the cube at Red Hat Summit 2022. >>Mhm. Yeah. Mhm. Mm.
SUMMARY :
We're kind of rounding the far turn, you know, coming up for the home stretch. devices that you need. So the cloud spans the cloud, the centralised You can pull all of these things together, and we give you one place where you can build it where gig Ethernet versus an Mpls versus the five G, you can do all that So all of these mortals, where do you run it? and I've developed an application, and I'm going to say Okay, I want you to do the AI influencing So you develop the like to recognise the deer in your example. and we offer an environment that allows you to do it. you customise the the edge configuration for the for the developer So that we believe that if you bring that type of a cloud native I know you can run a S, a p a data So at the edge, you think about it, right? So now the question is, how do you deploy applications to that edge? Same thing with the smart factory, you said, So what we did is you could just have an easy ability for you to stream or connect You need to buy all those devices you need to experiment your solutions on all of that. portfolio of F. P. G s of graphics of like we have all what intel And you can bring in container workloads there, or even bare metal workloads. That is where it is. So what happens is you take your work, So every use case where you optimise is up to the You got hardware developers, you get software developers are What we have time, you lose time. container, shove it into the cloud. Check it out. Thank you very much. Covering the cube at Red Hat Summit 2022.
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2022 007 Ren Besnard and Jeremiah Owyang
>>Hello, and welcome to the cube unstoppable Doneen showcase. I'm John furrier, host of the cube. We got a great discussion here called the influencers around what's going on in web three and also this new sea change cultural change around this next generation, internet web cloud, all happening, Jeremiah yang industry analyst, and founding part of the cleaner insights. Share my great to see you. Thanks for coming on. Appreciate it. Uh, registered vice-president of marketing at unstoppable domains in the middle of all the actions. Gentlemen, thanks for coming on on the cube for this showcase. >>My pleasure. So I think it was done >>At Jeremy. I want to start with you. You've seen many ways, but fallen all of your work for over a decade now. Um, you've seen the web 2.0 wave. Now the web three's here. Um, and it's not, I wouldn't say hyped up. It's really just ramping up and you're seeing real practical examples. Uh, you're in the middle of all the action. What is this web three? Can you frame for us that mean you've seen many waves? What is web three mean? What is it? What is it all about? >>Well, John, you and I worked in the web to space and essentially that enabled peer to peer media where people could, could upload their thoughts and ideas and videos, um, without having to rely on centralized media. And unfortunately that distributed and decentralized movement actually became centralized on the platforms or the big social networks and big tech companies. And this has caused an uproar because the people who are creating the content did not have control, could not control their identities and could not really monetize or make decisions. So web three is what is, which is a moniker of a lot of different trends, including crypto blockchain. And sometimes the metaverse is to undo the controlling that has become centralized. And the power is now shifting back into the hands of the participants again, and then this movement, they want to have more control over their identities, their governance, the content that they're creating, how they're actually building it and then how they're monetizing it. So in many ways, it's, it's changing the power and it's a new economic model. So that's web three without really even mentioning the technologies. Is that helpful? >>Yeah, that's great. And ran. We were talking about, on the cute many times and one notable stat, I don't think it's been reported, but it's been more kind of a rumor. I hear that 30% of the, um, Berkeley computer science students are dropping out and going into crypto or blockchain or decentralized startups, which means that this there's a big wave coming in of talent. You seeing startups, you're seeing a lot more formation. You're seeing a lot more, I would say, kind of ramping up of real people, not just, you know, people with a dream it's actual builders out here doing stuff. What's your take on the web three, moving with all this kind of change happening, uh, from people and also the new ideas being refactored. >>I think that the competition for talent is extremely real. And we start looking at the stats. We see that there is an draft of people that are moving into this space. People that are fascinated by technology and are embracing the ethos of web three. And at this stage, I think it's not only engineers and developers, but we have moved into a second phase where we see that a lot of supporting functions know marketing, being one of them, sales, business development, uh, are being built up quite rapidly. It's not without actually reminding me of the mid two thousands. You know, when I started, uh, working with Google at that point in time, the walled gardens rightly absorbing vast, vast cohorts of young graduates and more experienced professionals that are passionate and moving into the web environment. And I think we are seeing a movement right now, which is not entirely dissimilar, except >>Yeah, Jeremiah. You've seen the conversations over the cloud. I call the cloud kind of revolution. You had mobile in 2007, but then you got Amazon web services changed the application space on how people developed in the cloud. And again, that created a lot of value. Now you're seeing the role of data as a huge part of how people are scaling and the decentralized movement. So you've got cloud, which is kind of classic today. State-of-the-art, you know, enterprise and or app developers and you've got now decentralized wave coming. Okay. You're seeing apps being developed on that, that architecture data is central in all of this, right. So how do you view this? As, as someone who's watching the landscape, you know, these walled gardens are hoarding all the data. I mean, LinkedIn Facebook, they're not sharing that data with anyone they're using it for themselves. So as they can control back, comes to the forefront, how do you see this market with the applications and what comes out of that? >>So the thing that we've seen and out of the five things that I had mentioned that are decentralizing, the ones that have been easier to move across have been the ability to monetize and to build. But the data aspect has actually stayed pretty much central. Frankly. What has decentralized is that the contracts to block blockchain ledgers to those of decentralized. But the funny thing is often a big portion of these blockchain networks are on Amazon 63 to 70%, same thing with Stelara. So they're still using the web 2.0 architectures. However, we're also seeing other farms like IPFS, where the data could be to spread it across a wider range of folks. But right now we're still dependent on what we're to point out. So the vision and the problem with 3.0, when it comes to full de-centralization is not here by any means. I'd say we're at a web 2.2, five, >>Pre-web 3m, no actions there. What do you guys, how do you guys see the, um, the dangers? Cause there's a lot of negative press, but also is a lot of positive press. You seeing, you know, a lot of fraud, we've seen a lot of the crypto fraud over the past years. You've seen a lot of now positives, it's almost a self-governance thing and environment, the way the culture is, but what are the dangers? How do you guys educate people? What should people pay attention to? What should people look for to understand, you know, where to position themselves? >>Yes. So we've learned a lot from web one, we to the sharing economy and we are walking into two and three with eyes wide open. So people have rightfully put forth a number of challenges, the sustainability issues with excess using of computing and mining, the, um, the excessive amount of scams that are happening in part due to unknown identities. Um, also the architecture breaks down in certain periods and there's a lack of regulation. Um, this, this is something different though in the last, uh, uh, periods that we've gone through, we didn't really know what was gonna happen. And we walked in big, this is going to be great. The sharing economy, the gig economy, the social media is going to change the world. Hurrah is very different. Now people are a little bit jaded. So I think that's the big change. And so I think we're going to see that, uh, you know, soar it out and suss out just like we've seen with other prints. It's still very much in the early years, >>Right. I got to get your take on this whole, uh, should influencers and should people be anonymous or should they be doxed out there? You saw the board eight guys that did, that were kind of docs a little bit there and that went, went viral. Um, this is an issue, right? Because we, we just had a problem of fake news, uh, fake people, fake information, and now you have a much more secure environment. Immutability is a wonderful thing. It's, it's a feature, not a bug, right. So how is this all coming down? And I know you guys are in the middle of it with, uh, NFTs as, as authentication tickets. What's your take on this because this is a big issue. >>Look, I think first I am extremely optimistic about technology in general. Uh, so I'm super, super bullish about this. And yet, you know, I think that while crypto has so many upsides, it's important to be super conscious and aware of the downsides that come with it too. You know, if you think about every fortune 500 company, there is always training required by all employees on internet safety reporting of potential attacks. And so on in web three, we don't have that kind of standard reporting mechanisms yet, uh, for bad actors in that space. And so when you think about influencers in particular, they do have a responsibility to educate people about, uh, the potential, but also the dangers of the technology of web three, uh, of crypto basically, uh, whether you're talking about hacks online safety, the need for hardware impersonators on discord, uh, security, uh, storing your, your seed phrase. >>So every actor in France or ELs has got a role to play. I think that, uh, in that context, to your point, it's very hard to tell whether influencers should be, uh, anonymous, opposite inverse or footy dogs. The decentralized nature of web three will probably lead us to see a combination of those anonymity levels, um, so to speak, um, and the, uh, movements that we've seen around some influencers, identities becoming public are particularly interesting. I think there's probably a convergence of web two and web three at play here. You know, maybe a on the notion of 2.5 for, I think in way to all business founders and employees are known and they're held accountable for their public comments and actions. Um, if web three enables us to be anonymous, if dials have 14 control, you know, what happens if people make comments and there is no way to know who they are basically, uh, what if the dowel doesn't take appropriate action? I think eventually there will be an element of community self-regulation where influencers will be, uh, acting in the best interest of their reputation. And I believe that the communities will self regulate themselves and we'll create natural boundaries around what can be said or not. >>I think that's a really good point about, um, influencers and reputation because Jeremiah doesn't matter that you're anonymous. I have an icon that could be a NFT or a picture, but if I have an ongoing reputation, I have trust there's trust there. It's not like a, you know, just a bot that was created just to spam someone. It was just, you know what I'm saying? They getting into you getting into this new way. >>You're right. And that, that word you said, trust, that's what really, this is about. But we've seen that public docks people with their full identities have made mistakes. They have pulled the hood over people's faces in and really scammed them out of a lot of money. We've seen that in it that doesn't change anything in human behavior. So I think over time that we will see a new form of a reputation system emerged even for pseudonyms and perhaps for people that are just anonymous that only show their a potential, a wallet address, a series of numbers and letters. Um, that form might take a new form of a web 3.0 FICO score, and you can look at their behaviors. Did they transact? You know, how do they behave? Do they, were they involved in projects that were not healthy? And because all of that information is public on the chain and you can go back in time and see that we might see a new form of, of, of a scoring emerge. >>Of course, who controls that scoring that's a whole nother topic, gong on control and trust. So right now, John, we do see that there's a number of projects, new NFG projects, where the founders will claim and use this as a point of differentiation that they are fully docs. So you know who they are and their names. Secondly, we're seeing a number of, um, uh, products or platforms that require KYC, know your customer so that self-identification often with a government ID or a credit card in order to bridge out your, your coins and turn that into a Fiat. In some cases that's required in some of these marketplaces. So we're seeing a coalition here between, uh, full names and pseudonyms and being anonymous. >>That's awesome. And that, and I think this is the new, again, a whole new form of governance ran. You mentioned some comments about Dow. So I want to get your thoughts again, you know, Jeremiah, we become historians over the years. We're getting old, I'm a little bit older than you, but we've seen the movie war. You know, I remember breaking in the business when the computer standards bodies were built to be more organic, and then they became much more of a kind of an anti-innovation environment where people, the companies would get involved the standards organization just to slow things down and muck things up a little bit. Um, so you know, you look at Dallas like, Hmm, is a Dal, a good thing, or a bad thing that the answer is from people I talked to, is it depends. So I'd love to get your thoughts on getting momentum and becoming defacto with value, a value proposition. Vis-a-vis just adapt for the sake of having a doubt. This has been a conversation that's been kind of in the inside the baseball here, inside the ropes of the industry, but there's trade-offs, can you guys share your thoughts on when to do a Dow and when not to do a Dow and the benefits and trade-offs of that? >>Sure. Maybe I'll start off with a definition and then we'll go to rent. So a Dao, a decentralized autonomous organization, the best way to think about this. It's a digital cooperative and we've heard of worker cooperatives before the differences that they're using blockchain technologies in order to do the three things, identity governance, and rewards and mechanisms. They're relying on web 2.0 tools and technologies like discord and telegram and social networks to communicate. And there's a cooperative they're trying to come up with a common goal, um, Ren, but what's your take, that's the setup? >>So, you know, for me, when I started my journey into crypto and web tree, I had no idea about, you know, what that actually meant and, uh, an easy way for me to think of it and to grasp the nature of it was about the comparison between a dowel and perhaps a more traditional company structure. Um, you know, in a traditional company structure, you have a Yorkie, the company is led by a CEO and other executives, uh, that that was a flat structure. And it's very much led by a group of core contributors. So, uh, to Jeremiah's point, you know, you get that notion of a co-operative, uh, type of structure. The decision-making is very different. You know, we're talking about a hot, super high level of transparency proposals getting submitted and, and voting systems, using applications, as opposed to, you know, management, making decisions behind closed doors. >>I think that speaks to a totally new form of governance. And I think we have hardly, hardly scratched the surface. We have seen recently, uh, very interesting moments in web tree culture. And we have seen how that was suddenly have to make certain decisions and then come to moments of claiming responsibility, uh, in order to, uh, put his behavior, uh, of some of the members. I think that's important. I think it's going to redefine how we're thinking about that, particularly new governance models. And I think he's going to pave the way for a lot of super interesting structure in the near future. >>That's a great point, ran around the transparency for governance. So John, you posed the question, does this make things faster or slower? And right now most dowels are actually pretty slow because they're set up as a flat organization. So as a response to that, they're actually shifting to become representative democracies. Does that sound familiar where you can appoint a delegates and use tokens to vote for them? And they have a decision power, almost like a committee and they can function. And so we've seen actually there are some times our hierarchies, except the person at the top is voted by those that have the tokens. In some cases, the people at the top had the most tokens, but that's a whole nother topic. So we're seeing a wide variety of governance structures, >>You know, rent. I was talking with Matt G the founder of, and I was telling him about the domain name system. And one little trivia note that many people don't know about is that the U S government cause unit it was started by the U S the department of commerce kept that on tight leash because the international telecommunications union wanted to get their hands on it because of ccTLDs and other things. So at that time, because the innovation yet wasn't yet baked out. It was organically growing the governance, the rules of the road, keeping it very stable versus meddling with it. So there's certain technologies that require Jeremiah that let's keep an eye on as a community. Let's not formalize anything like the government did with the domain name system. Let's keep it tight. And then finally released it, I think multiple years after 2004, I think it went over to the, to the ITU, but this is a big point. I mean, if you get too structured, organic innovation, can't go, what you guys' reaction to that. >>So I think to take a stab at it, um, we have as a business, you know, thinking of unstoppable domains, a strong incentive to innovate, uh, and this is what is going to be determining longterm value growth for the organization for, uh, partners, for users, for customers. So, you know, that degree of formalization actually gives us a sense of purpose and a sense of action. And if you compare that to Dows, for instance, you can see how some of the upsides and downsides can pan out either way. It's not to say that there is a perfect solution. I think one of the advantages of the Dow is that you can let more people contribute. You can probably remove bias quite effectively, and you can have a high level of participation and involvement in decisions and all the upside in many ways. Um, you know, as a company, it's a slightly different setup. We have the opportunity to coordinate a very, uh, diverse and part-time workforce in a very, uh, you know, different way. Um, and we do not have to deal with the inefficiencies that might be, you never run to some form of extreme decentralization so that those are balanced from an organizational structure, uh, that comes, uh, either side >>Sharon. I want to get your thoughts on, on, on a trend that you've been involved in. We both been involved in, and you're seeing it now with the kind of social media world, the world of a role of an influencer it's kind of moved from what was open source and influencer was a connect to someone who shared graded content, um, enabled things to much more of a vanity that the photo on Instagram and having a large audience. Um, so is there a new influencer model with web three or is it, is it the, I control the audience I'm making money that way. Is there a shift in the influencer role or, or ideas that you see that should be in place for what is the role of an influencer? Because as web three comes, you're going to see that role become instrumental. We've seen it in open source projects, influences, you know, the people who write code or ship code. So what's your take on that because there's been a conversation with people who have been having the word influencer and redefining and reframing it. >>Sure. The influence model really hasn't changed that much, but the way that they're behaving has when it comes to at three, this market, I mean, there's a couple of things. Some of the influencers are in investors. And so when you see their name on a project or a new startup, that's an indicator, there's a higher level of success. You might want to pay more attention to it or not. Secondly, influencers themselves are launching their own NFC projects. Gary Vaynerchuk, a number of celebrities, Paris Hilton is involved and they are also doing this as well. Steve Aoki, a famous DJ launched his as well. So they're going head first and participating in building in this model. And there are communities are coming around them and they're building economies. Now the difference is it's not, I speak as an influencer to the fans. The difference is that the fans are now part of the community and they hold, they literally holding own some of the economic value, whether it's tokens or the NFTs. So it's a collaborative economy, if you will, where they're all benefiting together. And that's a, that's a big difference as well. Lastly, there's, there's one little tactic we're seeing where marketers are airdropping in FTS, branded NFTs influencers with wallet. So you can see it in there. So there's new tactics that are forming as well. Yes. >>Super exciting. Ren, what's your reaction to that? Because he just hit on a whole new way of, of how engagement's happening, how people are closed, looping their, their votes, their, their votes of confidence or votes with their wallet. Um, and some brands which are artists now, influencers. I mean, this is a whole game-changing instrumentation level. >>I think that's what we are seeing right now is super re invigorating as a marketeer who has been around for a few years, basically. Um, I think that the shift in the web brands are going to communicate and engage with our audiences is profound. It's probably as revolutionary and even more revolutionary than the movement for, uh, brands in getting into digital. And you have that sentiment of a gold rush right now with a lot of brands that are trying to understand NFTs and, and how to actually engage with those communities and those audiences, um, dominate levels in which brands and influencers are going to engage. There are many influencers that actually advanced the message and the mission because the explosion of content on web tree has been crazy. Part of that is due to the network effect nature of crypto, because as Jeremiah mentioned, people are incentivized to promote projects, holders of an NFTA, also incentivized to promote it. So you end up with a flywheel, which is pretty unique of people that are hyping the project, and that are educating other people about it and commenting on the ecosystem, uh, with IP rights, being given to NFT holders, you're going to see people pull a brand since then of the brands actually having to. And so the notion of brands, again, judging and delivering, you know, elements of the value to their fans is something that's super attractive, extremely interesting. And I think, again, we've hardly scratched the surface of all that is possible in that. >>It's interesting. You guys are bringing some great insight here, Jeremiah, the old days, the word authentic was a kind of a cliche and brands like tried to be authentic and they didn't really know what to do. They called it organic, right? And now you have the trust concept with aura authenticity and environment like web three, where you can actually measure it and monetize it and capture it if you're actually authentic and trustworthy. >>That's right. And because it's on blockchain, you can see how somebody is behave with their economic behavior. In the past, of course, big corporations. Aren't going to have that type of trail on blockchain just yet. But the individuals and executives who participate in this market might be, and we'll also see a new types of affinity. Do you executives, do they participate in these NFT communities? Do they purchase them? We're seeing numerous brands like Adidas to acquire, uh, you know, different MTV projects to participate. And of course the big brands are grabbing their domains. Of course, you can talk to rant about that because it's owning your own name as a part of this trust and being >>That's awesome. Great insight guys. Closing comments, takeaways for the audience here. Each of you take a minute to give, share your thoughts on what you think is happening now, where it goes. All right, where's it going to go, Jeremy, we'll start with you. >>Sure. Um, I think the vision of web three, where full decentralization happens, where the power is completely shifted to the edges. I don't think it's going to happen. I think we will reach web 2.5 and I've been through so many tech trends where we said that the power is going to shift completely to the end. It just doesn't, there's two reasons. One is the venture capital are the ones who tend to own the pro programs in the first place. And secondly, the, the startups themselves end up becoming the one percenters. We see Airbnb and Uber are one-percenters now. So that trend happens over and over and over. Now with that said, the world will be in a better place. We will have more transparency. We will see economic power shifted to the people, the participants. And so they will have more control over the internet that they are building. >>Right. And final, final comments, >>Um, fully aligned with Jeremiah on the notions of control, being returned to users, the notion of ownership and the notion of redistribution of the economic value that is created across all the different chains, uh, uh, that we are going to see. And, and all those ecosystems. I believe that we are going to witness to palliate movements of expansion, one that is going to be very lateral. When you think of crypto and web three, essentially you think of a few hundred tribes. Uh, and I think that more projects are going to appear more, uh, coalitions of individuals and entities, and those are going to exist around those projects. So you're going to see an increase in the number of tribes that one might join. And I also think that we're going to progress rapidly from the low hundred millions of people and an FTE holders into the billions perfectly. Uh, and that's going to be extremely interesting. I think that the next wave of crypto users and Ft fans are going to look very different from the early adopters that we had witnessed in the very early days. So it's not going to be your traditional model of technology, adoption curves. I think the demographics going to shift and the motivations are going to be different as well, which is going to be a wonderful time to educate and engage with new community members. >>All right, Ron, Jeremy, thank you both for that great insight, great segment, uh, breaking down web three or web 2.5 as Jeremiah says, but we're in a better place. This is a segment with the influencers as part of the cubes and the unstoppable domain showcase. Um, John for your hosts. Thanks for watching.
SUMMARY :
I'm John furrier, host of the cube. So I think it was done Now the web three's here. And sometimes the metaverse is to undo the controlling that has become centralized. you know, people with a dream it's actual builders out here doing stuff. And I think we are seeing a movement right now, which is not entirely dissimilar, back, comes to the forefront, how do you see this market with the applications and what comes is that the contracts to block blockchain ledgers to those of decentralized. What should people look for to understand, you know, a number of challenges, the sustainability issues with excess using of computing and mining, And I know you guys are in the middle of it with, uh, NFTs as, as authentication tickets. And yet, you know, I think that while crypto has so many And I believe that the communities will self regulate themselves and we'll create natural It's not like a, you know, just a bot that was created just to spam someone. And because all of that information is public on the chain and you can go back in time and see that we might see a new So you know who they are and their names. Um, so you know, you look at Dallas like, And there's a cooperative they're trying to come up with a common goal, um, Ren, I had no idea about, you know, what that actually meant and, uh, an easy way for me to think of it And I think he's going to pave the way for a lot of super interesting structure in the near future. Does that sound familiar where you can appoint a delegates Let's not formalize anything like the government did with the domain name system. So I think to take a stab at it, um, we have as a business, role or, or ideas that you see that should be in place for what is the role of an influencer? And so when you see their name on a project or a new startup, that's an indicator, there's a higher level of success. I mean, this is a whole game-changing instrumentation And you have that sentiment of a gold rush right now with a lot And now you have the trust concept with aura authenticity and environment We're seeing numerous brands like Adidas to acquire, uh, you know, different MTV projects Each of you take a minute to give, share your thoughts on what you think is happening now, I don't think it's going to happen. And final, final comments, and the motivations are going to be different as well, which is going to be a wonderful time to educate of the cubes and the unstoppable domain showcase.
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Steve George, Weaveworks & Steve Waterworth, Weaveworks | AWS Startup Showcase S2 E1
(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase Open Cloud Innovations. This is season two of the ongoing series. We're covering exciting start startups in the AWS ecosystem to talk about open source community stuff. I'm your host, Dave Nicholson. And I'm delighted today to have two guests from Weaveworks. Steve George, COO of Weaveworks, and Steve Waterworth, technical marketing engineer from Weaveworks. Welcome, gentlemen, how are you? >> Very well, thanks. >> Very well, thanks very much. >> So, Steve G., what's the relationship with AWS? This is the AWS Startup Showcase. How do Weaveworks and AWS interact? >> Yeah sure. So, AWS is a investor in Weaveworks. And we, actually, collaborate really closely around EKS and some specific EKS tooling. So, in the early days of Kubernetes when AWS was working on EKS, the Elastic Kubernetes Service, we started working on the command line interface for EKS itself. And due to that partnership, we've been working closely with the EKS team for a long period of time, helping them to build the CLI and make sure that users in the community find EKS really easy to use. And so that brought us together with the AWS team, working on GitOps and thinking about how to deploy applications and clusters using this GitOps approach. And we've built that into the EKS CLI, which is an open source tool, is a project on GitHub. So, everybody can get involved with that, use it, contribute to it. We love hearing user feedback about how to help teams take advantage of the elastic nature of Kubernetes as simply and easily as possible. >> Well, it's great to have you. Before we get into the specifics around what Weaveworks is doing in this area that we're about to discuss, let's talk about this concept of GitOps. Some of us may have gotten too deep into a Netflix series, and we didn't realize that we've moved on from the world of DevOps or DevSecOps and the like. Explain where GitOps fits into this evolution. >> Yeah, sure. So, really GitOps is an instantiation, a version of DevOps. And it fits within the idea that, particularly in the Kubernetes world, we have a model in Kubernetes, which tells us exactly what we want to deploy. And so what we're talking about is using Git as a way of recording what we want to be in the runtime environment, and then telling Kubernetes from the configuration that is stored in Git exactly what we want to deploy. So, in a sense, it's very much aligned with DevOps, because we know we want to bring teams together, help them to deploy their applications, their clusters, their environments. And really with GitOps, we have a specific set of tools that we can use. And obviously what's nice about Git is it's a very developer tool, or lots and lots of developers use it, the vast majority. And so what we're trying to do is bring those operational processes into the way that developers work. So, really bringing DevOps to that generation through that specific tooling. >> So Steve G., let's continue down this thread a little bit. Why is it necessary then this sort of added wrinkle? If right now in my organization we have developers, who consider themselves to be DevOps folks, and we give them Amazon gift cards each month. And we say, "Hey, it's a world of serverless, "no code, low code lights out data centers. "Go out and deploy your code. "Everything should be fine." What's the problem with that model, and how does GitOps come in and address that? >> Right. I think there's a couple of things. So, for individual developers, one of the big challenges is that, when you watch development teams, like deploying applications and running them, you watch them switching between all those different tabs, and services, and systems that they're using. So, GitOps has a real advantage to developers, because they're already sat in Git, they're already using their familiar tooling. And so by bringing operations within that developer tooling, you're giving them that familiarity. So, it's one advantage for developers. And then for operations staff, one of the things that it does is it centralizes where all of this configuration is kept. And then you can use things like templating and some other things that we're going to be talking about today to make sure that you automate and go quickly, but you also do that in a way which is reliable, and secure, and stable. So, it's really helping to bring that run fast, but don't break things kind of ethos to how we can deploy and run applications in the cloud. >> So, Steve W., let's start talking about where Weaveworks comes into the picture, and what's your perspective. >> So, yeah, Weaveworks has an engine, a set of software, that enables this to happen. So, think of it as a constant reconciliation engine. So, you've got your declared state, your desired state is declared in Git. So, this is where all your YAML for all your Kubernetes hangs out. And then you have an agent that's running inside Kubernetes, that's the Weaveworks GitOps agent. And it's constantly comparing the desired state in Git with the actual state, which is what's running in Kubernetes. So, then as a developer, you want to make a change, or an operator, you want to make a change. You push a change into Git. The reconciliation loop runs and says, "All right, what we've got in Git does not match "what we've got in Kubernetes. "Therefore, I will create story resource, whatever." But it also works the other way. So, if someone does directly access Kubernetes and make a change, then the next time that reconciliation loop runs, it's automatically reverted back to that single source of truth in Git. So, your Kubernetes cluster, you don't get any configuration drift. It's always configured as you desire it to be configured. And as Steve George has already said, from a developer or engineer point of view, it's easy to use. They're just using Git just as they always have done and continue to do. There's nothing new to learn. No change to working practices. I just push code into Git, magic happens. >> So, Steve W., little deeper dive on that. When we hear Ops, a lot of us start thinking about, specifically in terms of infrastructure, and especially since infrastructure when deployed and left out there, even though it's really idle, you're paying for it. So, anytime there's an Ops component to the discussion, cost and resource management come into play. You mentioned this idea of not letting things drift from a template. What are those templates based on? Are they based on... Is this primarily an infrastructure discussion, or are we talking about the code itself that is outside of the infrastructure discussion? >> It's predominantly around the infrastructure. So, what you're managing in Git, as far as Kubernetes is concerned, is always deployment files, and services, and horizontal pod autoscalers, all those Kubernetes entities. Typically, the source code for your application, be it in Java, Node.js, whatever it is you happen to be writing it in, that's, typically, in a separate repository. You, typically, don't combine the two. So, you've got one set of repository, basically, for building your containers, and your CLI will run off that, and ultimately push a container into a registry somewhere. Then you have a separate repo, which is your config. repo, which declares what version of the containers you're going to run, how many you're going to run, how the services are bound to those containers, et cetera. >> Yeah, that makes sense. Steve G., talk to us about this concept of trusted application delivery with GitOps, and frankly, it's what led to the sort of prior question. When you think about trusted application delivery, where is that intertwinement between what we think of as the application code versus the code that is creating the infrastructure? So, what is trusted application delivery? >> Sure, so, with GitOps, we have the ability to deploy the infrastructure components. And then we also define what the application containers are, that would go to be deployed into that environment. And so, this is a really interesting question, because some teams will associate all of the services that an application needs within an application team. And sometimes teams will deploy sort of horizontal infrastructure, which then all application teams services take advantage of. Either way, you can define that within your configuration, within your GitOps configuration. Now, when you start deploying speed, particularly when you have multiple different teams doing these sorts of deployments, one of the questions that starts to come up will be from the security team, or someone who's thinking about, well, what happens if we make a deployment, which is accidentally incorrect, or if there is a security issue in one of those dependencies, and we need to get a new version deployed as quickly as possible? And so, in the GitOps pipeline, one of the things that we can do is to put in various checkpoints to check that the policy is being followed correctly. So, are we deploying the right number of applications, the right configuration of an application? Does that application follow certain standards that the enterprise has set down? And that's what we talk about when we talk about trusted policy and trusted delivery. Because really what we're thinking about here is enabling the development teams to go as quickly as possible with their new deployments, but protecting them with automated guard rails. So, making sure that they can go fast, but they are not going to do anything which destroys the reliability of the application platform. >> Yeah, you've mentioned reliability and kind of alluded to scalability in the application environment. What about looking at this from the security perspective? There've been some recently, pretty well publicized breaches. Not a lot of senior executives in enterprises understand that a very high percentage of code that their businesses are running on is coming out of the open source community, where developers and maintainers are, to a certain degree, what they would consider to be volunteers. That can be a scary thing. So, talk about why an enterprise struggles today with security, policy, and governance. And I toss this out to Steve W. Or Steve George. Answer appropriately. >> I'll try that in a high level, and Steve W. can give more of the technical detail. I mean, I'll say that when I talk to enterprise customers, there's two areas of concern. One area of concern is that, we're in an environment with DevOps where we started this conversation of trying to help teams to go as quickly as possible. But there's many instances where teams accidentally do things, but, nonetheless, that is a security issue. They deploy something manually into an environment, they forget about it, and that's something which is wrong. So, helping with this kind of policy as code pipeline, ensuring that everything goes through a set of standards could really help teams. And that's why we call it developer guard rails, because this is about helping the development team by providing automation around the outside, that helps them to go faster and relieves them from that mental concern of have they made any mistakes or errors. So, that's one form. And then the other form is the form, where you are going, David, which is really around security dependencies within software, a whole supply chain of concern. And what we can do there, by, again, having a set of standard scanners and policy checking, which ensures that everything is checked before it goes into the environment. That really helps to make sure that there are no security issues in the runtime deployment. Steve W., anything that I missed there? >> Yeah, well, I'll just say, I'll just go a little deeper on the technology bit. So, essentially, we have a library of policies, which get you started. Of course, you can modify those policies, write your own. The library is there just to get you going. So, as a change is made, typically, via, say, a GitHub action, the policy engine then kicks in and checks all those deployment files, all those YAML for Kubernetes, and looks for things that then are outside of policy. And if that's the case, then the action will fail, and that'll show up on the pull request. So, things like, are your containers coming from trusted sources? You're not just pulling in some random container from a public registry. You're actually using a trusted registry. Things like, are containers running as route, or are they running in privileged mode, which, again, it could be a security? But it's not just about security, it can also be about coding standards. Are the containers correctly annotated? Is the deployment correctly annotated? Does it have the annotation fields that we require for our coding standards? And it can also be about reliability. Does the deployment script have the health checks defined? Does it have a suitable replica account? So, a rolling update. We'll actually do a rolling update. You can't do a rolling update with only one replica. So, you can have all these sorts of checks and guards in there. And then finally, there's an admission controller that runs inside Kubernetes. So, if someone does try and squeeze through, and do something a little naughty, and go directly to the cluster, it's not going to happen, 'cause that admission controller is going to say, "Hey, no, that's a policy violation. "I'm not letting that in." So, it really just stops. It stops developers making mistakes. I know, I know, I've done development, and I've deployed things into Kubernetes, and haven't got the conflict quite right, and then it falls flat on its face. And you're sitting there scratching your head. And with the policy checks, then that wouldn't happen. 'Cause you would try and put something in that has a slightly iffy configuration, and it would spit it straight back out at you. >> So, obviously you have some sort of policy engine that you're you're relying on. But what is the user experience like? I mean, is this a screen that is reminiscent of the matrix with non-readable characters streaming down that only another machine can understand? What does this look like to the operator? >> Yeah, sure, so, we have a console, a web console, where developers and operators can use a set of predefined policies. And so that's the starting point. And we have a set of recommendations there and policies that you can just attach to your deployments. So, set of recommendations about different AWS resources, deployment types, EKS deployment types, different sets of standards that your enterprise might be following along with. So, that's one way of doing it. And then you can take those policies and start customizing them to your needs. And by using GitOps, what we're aiming for here is to bring both the application configuration, the environment configuration. We talked about this earlier, all of this being within Git. We're adding these policies within Git as well. So, for advanced users, they'll have everything that they need together in a single unit of change, your application, your definitions of how you want to run this application service, and the policies that you want it to follow, all together in Git. And then when there is some sort of policy violation on the other end of the pipeline, people can see where this policy is being violated, how it was violated. And then for a set of those, we try and automate by showing a pull request for the user about how they can fix this policy violation. So, try and make it as simple as possible. Because in many of these sorts of violations, if you're a busy developer, there'll be minor configuration details going against the configuration, and you just want to fix those really quickly. >> So Steve W., is that what the Mega Leaks policy engine is? >> Yes, that's the Mega Leaks policy engine. So, yes, it's a SaaS-based service that holds the actual policy engine and your library of policies. So, when your GitHub action runs, it goes and essentially makes a call across with the configuration and does the check and spits out any violation errors, if there are any. >> So, folks in this community really like to try things before they deploy them. Is there an opportunity for people to get a demo of this, get their hands on it? what's the best way to do that? >> The best way to do it is have a play with it. As an engineer, I just love getting my hands dirty with these sorts of things. So, yeah, you can go to the Mega Leaks website and get a 30-day free trial. You can spin yourself up a little, test cluster, and have a play. >> So, what's coming next? We had DevOps, and then DevSecOps, and now GitOps. What's next? Are we going to go back to all infrastructure on premises all the time, back to waterfall? Back to waterfall, "Hot Tub Time Machine?" What's the prediction? >> Well, I think the thing that you set out right at the start, actually, is the prediction. The difference between infrastructure and applications is steadily going away, as we try and be more dynamic in the way that we deploy. And for us with GitOps, I think we're... When we talk about operations, there's a lots of depth to what we mean about operations. So, I think there's lots of areas to explore how to bring operations into developer tooling with GitOps. So, that's, I think, certainly where Weaveworks will be focusing. >> Well, as an old infrastructure guy myself, I see this as vindication. Because infrastructure still matters, kids. And we need sophisticated ways to make sure that the proper infrastructure is applied. People are shocked to learn that even serverless application environments involve servers. So, I tell my 14-year-old son this regularly, he doesn't believe it, but it is what it is. Steve W., any final thoughts on this whole move towards GitOps and, specifically, the Weaveworks secret sauce and superpower. >> Yeah. It's all about (indistinct)... It's all about going as quickly as possible, but without tripping up. Being able to run fast, but without tripping over your shoe laces, which you forgot to tie up. And that's what the automation brings. It allows you to go quickly, does lots of things for you, and yeah, we try and stop you shooting yourself in the foot as you're going. >> Well, it's been fantastic talking to both of you today. For the audience's sake, I'm in California, and we have a gentleman in France, and a gentlemen in the UK. It's just the wonders of modern technology never cease. Thanks, again, Steve Waterworth, Steve George from Weaveworks. Thanks for coming on theCUBE for the AWS Startup Showcase. And to the rest of us, keep it right here for more action on theCUBE, your leader in tech coverage. (upbeat music)
SUMMARY :
of the AWS Startup Showcase This is the AWS Startup Showcase. So, in the early days of Kubernetes from the world of DevOps from the configuration What's the problem with that model, to make sure that you and what's your perspective. that enables this to happen. that is outside of the how the services are bound to that is creating the infrastructure? one of the things that we can do and kind of alluded to scalability that helps them to go And if that's the case, is reminiscent of the matrix and start customizing them to your needs. So Steve W., is that what that holds the actual policy engine So, folks in this community So, yeah, you can go to on premises all the in the way that we deploy. that the proper infrastructure is applied. and yeah, we try and stop you and a gentlemen in the UK.
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Tanuja Randery, AWS | AWS re:Invent 2021
>>Hey, welcome back everyone to the cubes coverage of eaters reinvent 2021. So our third day wall-to-wall coverage. I'm my coach, Dave Alonzo. He we're getting all the action two sets in person. It's also a virtual hybrid events with a lot of great content online, bringing you all the fresh voices, all the knowledge, all the news and all the action and got great guests here today. As your renderer, managing director of AWS is Europe, middle east, and Africa also known as EMIA. Welcome to the cube. Welcome, >>Welcome. Thanks for coming on. Lovely to be here. >>So Europe is really hot. Middle east Africa. Great growth. The VC culture in Europe specifically has been booming this year. A lot of great action. We've done many cube gigs out there talking to folks, uh, entrepreneurship, cloud, native growth, and then for us it's global. It's awesome. So first question got to ask you is, is you're new to AWS? What brought you here? >>Yeah, no, John, thank you so much. I've been here about three and a half months now, actually. Um, so what brought me here? Um, I have been in and around the tech world since I was a baby. Um, my father was an entrepreneur. I sold fax machines and microfilm equipment in my early days. And then my career has spanned technology in some form or the other. I was at EMC when we bought VMware. Uh, I was a Colt when we did a FinTech startup joined Schneider in my background, which is industrial tech. So I guess I'm a bit of a tech nerd, although I'm not an engineer, that's for sure. The other thing is I've spent a huge part of my career advising clients. And so while I was at McKinsey on business transformation and cloud keeps coming up, especially post pandemic, huge, huge, huge enabler, right of transformation. So when I got the call from AWS, I thought here's my opportunity to finally take what companies are wrestling with, bring together a pioneer in cloud with our enterprise and start-up and SMB clients connect those dots between business and technology and make things happen. So it real magic. So that's what brought me here. And I guess the only other thing to say is I'd heard a lot of other culture, customer mash, obsession, and leadership principles. >>That's why I'm here. It's been a great success. I got to ask you too, now that your new ostium McKinsey, even seeing the front lines, all the transformation, the pandemic has really forced everybody globally to move faster. Uh, things like connect were popular in EMEA. How, how is that going out? There's at the same kind of global pressure on the digital transformation with cloud? What are you seeing out there? >>I've been traveling since I joined, uh, around 10 of the countries already. So Ben planes, trains, automobiles, and what you definitely see is massive acceleration. And I think it's around reinvention of the business. So people are adopting cloud because it's obviously there's cost reasons. There's MNA reasons. There's really increasingly more about innovating. How do I innovate my business? How do I reinvent my business? So you see that constantly. Um, and whether you're a enterprise company or you're a startup, they're all adopting cloud in different, different ways. Um, I mean, I want to tell a core to stack because it's really interesting. And Adam mentioned this in his keynote five to 15% only of workloads have moved to the cloud. So there's a tremendous runway ahead of us. Um, and the three big things on people's minds helped me become a tech company. So it doesn't matter who you are, you're retail, whether you're life sciences or healthcare. You've probably heard about the Roche, uh, work that we're doing with Roche around accelerating R and D with data, or if you're a shoes Addie desk, how do you accelerate again, your personalized experiences? So it doesn't matter who you have helped me become a tech company, give me skills, digital skills, and then help me become a more sustainable company. Those are the three big things I'm thinking of. >>So a couple of things to unpack there. So think about it. Transformation. We still have a long way to go to your point, whatever 10, 15%, depending on which numbers you look at. We've been talking a lot in the cube about the next decade around business transformation, deeper business integration, and the four smarts to digital. And the woke us up to that, accelerated that as you say, so as you travel around to customers in AMEA, what are you hearing with regard to that? I mean, many customers maybe didn't have time to plan. Now they can sit back and take what they've learned. What are you hearing? >>Yeah. And it's, it's a little bit different in different places, right? So, I mean, if you start, if you look at, uh, you know, our businesses, for example, in France, if you look at our businesses in Iberia or Italy, a lot of them are now starting they're on the, at least on the enterprise front, they are now starting to adopt cloud. So they stepping back and thinking about their overall strategy, right? And then the way that they're doing it is actually they're using data as the first trigger point. And I think that makes it easier to migrate because if you, if you look at large enterprises and if you think of the big processes that they've got and all the mainframes and everything that they need to do, if you S if you look at it as one big block, it's too difficult. But when you think about data, you can actually start to aggregate all of your data into one area and then start to analyze and unpack that. >>So I think what I'm seeing for sure is in those countries, data is the first trigger. If you go out to Israel, well that you've got all, it's really start up nation as you know, right. And then we've got more of the digital natives and they want to, you know, absorb all of the innovation that we're throwing at them. And you've heard a lot here at reinvent on some of the things, whether it's digital twins or robotics, or frankly, even using 5g private network, we've just announcement. They are adopting innovation and really taking that in. So it really does differ, but I think the one big message I would leave you with is bringing industry solutions to business is critical. So rather than just talking it and technology, we've got to be able to bring some of what we've done. So for example, the Goldman Sachs financial cloud, bring that to the rest of financial services companies and the media, or if you take the work we're doing on industrials and IOT. So it's really about connecting what industry use cases with. >>What's interesting about the Goldman Dave and I were commenting. I think we coined the term, the story we wrote on Thursday last week, and then PIP was Sunday superclouds because you look at the rise of snowflake and Databricks and Goldman Sachs. You're going to start to see people building on AWS and building these super clouds because they are taking unique platform features of AWS and then sacrificing it for their needs, and then offering that as a service. So there's kind of a whole nother tier developing in the natural evolution of clouds. So the partners are on fire right now because the creativity, the market opportunities are there to be captured. So you're seeing this opportunity recognition, opportunity, capture vibe going on. And it's interesting. I'd love to get your thoughts on how you see that, because certainly the VCs are here in force. I did when I saw all the top Silicon valley VCs here, um, and some European VCs are all here. They're all seeing this. >>So pick up on two things you mentioned that I think absolutely spot on. We're absolutely seeing with our partners, this integration on our platform is so important. So we talk about the power of three, which is you bring a JSI partner, you bring an ISV partner, you bring AWS, you create that power of three and you take it to our customers. And it doesn't matter which industry we are. Our partner ecosystem is so rich. The Adam mentioned, we have a hundred thousand partners around the world, and then you integrate that with marketplace. Um, and the AWS marketplace just opens the world. We have about 325,000 active customers on marketplace. So sassiphy cation integration with our platform, bringing in the GSI and the NSIs. I think that's the real power to, to, to coming back to your point on transformation on the second one, the unicorns, you know, it's interesting. >>So UK France, um, Israel, Mia, I spent a lot of time, uh, recently in Dubai and you can see it happening there. Uh, Africa, Nigeria, South Africa, I mean all across those countries, you're saying huge amount of VC funding going in towards developers, towards startups to at scale-ups more and more of a, um, our startup clients, by the way, uh, are actually going IPO. You know, initially it used to be a lot of M and a and strategic acquisitions, but they have actually bigger aspirations and they're going IPO and we've seen them through from when they were seed or pre-seed all the way to now that they are unicorns. Right? So that there's just a tremendous amount happening in EMEA. Um, and we're fueling that, you know, you know, I mean, born in the cloud is easy, right? In terms of what AWS brings to the table. >>Well, I've been sacred for years. I always talked to Andy Jassy about this. Cause he's a big sports nut. When you bring like these stadiums to certain cities that rejuvenates and Amazon regions are bringing local rejuvenation around the digital economies. And what you see with the startup culture is the ecosystems around it. And Silicon valley thrives because you have all the service providers, you have all the fear of failure goes away. There's support systems. You start to see now with AWS as ecosystem, that same ecosystem support the robustness of it. So, you know, it's classic, rising tide floats all boats kind of vibe. So, I mean, we don't really have our narrative get down on this, but we're seeing this ecosystem kind of play going on. Yeah. >>And actually it's a real virtuous circle, or we call flywheel right within AWS because a startup wants to connect to an enterprise. An enterprise wants to connect to a startup, right? A lot of our ISV partners, by the way, were startups. Now they've graduated and they're like very large. So what we are, I see our role. And by the way, this is one of the other reasons I came here is I see our role to be able to be real facilitators of these ecosystems. Right. And, you know, we've got something that we kicked off in EMEA, which I'm really proud of called our EMEA startup loft accelerator. And we launched that a web summit. And the idea is to bring startups into our space virtually and physically and help them build and help them make those connections. So I think really, I really do think, and I enterprise clients are asking us all the time, right? Who do I need to involve if I'm thinking IOT, who do I need to involve if I want to do something with data. And that's what we do. Super connectors, >>John, you mentioned the, the Goldman deal. And I think it was Adam in his keynote was talking about our customers are asking us to teach them how to essentially build a Supercloud. I mean, our words. But so with your McKinsey background, I would imagine there's real opportunities there, especially as you, I hear you talk about IMIA going around to see customers. There must be a lot of, sort of non-digital businesses that are now transforming to digital. A lot of capital needs there, but maybe you could talk about sort of how you see that playing out over the next several years in your role and AWS's role in affecting that transfer. >>Yeah, no, absolutely. I mean, you're right actually. And I, you know, maybe I will, from my past experience pick up on something, you know, I was in the world of industry, uh, with Schneider as an example. And, you know, we did business through the channel. Um, and a lot of our channel was not digitized. You know, you had point of sale, electrical distributors, wholesalers, et cetera. I think all of those businesses during the pandemic realized that they had to go digital and online. Right. And so they started from having one fax machine in a store. Real literally I'm not kidding nothing else to actually having to go online and be able to do click and collect and various other things. And we were able with AWS, you can spin up in minutes, right. That sort of service, right. I love the fact that you have a credit card you can get onto our cloud. >>Right. That's the whole thing. And it's about instances. John Adam talked about instances, which I think is great. How do businesses transform? And again, I think it's about unpacking the problem, right? So what we do a lot is we sit down with our customers and we actually map a migration journey with them, right? We look across their core infrastructure. We look at their SAP systems. For example, we look at what's happening in the various businesses, their e-commerce systems, that customer life cycle value management systems. I think you've got to go business by business by business use case by use case, by use case, and then help our technology enable that use case to actually digitize. And whether it's front office or back office. I think the advantages are pretty clear. It's more, I think the difficulty is not technology anymore. The difficulty is mindset, leadership, commitment, the operating model, the organizational model and skills. And so what we have to do is AWS is bringing not only our technology, but our culture of innovation and our digital innovation teams to help our clients on that journey >>Technology. Well, we really appreciate you taking the time coming on the cube. We have a couple more minutes. I do want to get into what's your agenda. Now that you're got you're in charge, got the landscape and the 20 mile stare in front of you. Cloud's booming. You got some personal passion projects. Tell us what your plans are. >>So, um, three or four things, right? Three or four, really big takeaways for me is one. I, I came here to help make sure our customers could leverage the power of the cloud. So I will not feel like my job's been done if I haven't been able to do that. So, you know, that five to 15% we talked about, we've got to go 50, 60, 70%. That that's, that's the goal, right? And why not a hundred percent at some point, right? So I think over the next few years, that's the acceleration we need to help bring in AMEA Americas already started to get there as you know, much more, and we need to drive that into me. And then eventually our APJ colleagues are going to do the same. So that's one thing. The other is we talked about partners. I really want to accelerate and expand our partner ecosystem. >>Um, we have actually a huge growth by the way, in the number of partners signing up the number of certifications they're taking, I really, really want to double down on our partners and actually do what they ask us for, which is join. Co-sell joined marketing globalization. So that's two, I think the third big thing is when you mentioned industry industry industry, we've got to bring real use cases and solutions to our customers and not only talk technology got to connect those two dots. And we have lots of examples to bring by the way. Um, and then for hire and develop the best, you know, we've got a new LP as you know, to strive to be at its best employer. I want to do that in a Mia. I want to make sure we can actually do that. We attract, we retain and we grow and we develop that. >>And the diversity has been a huge theme of this event. It's front and center in virtually every company. >>I am. I'm usually passionate about diversity. I'm proud actually that when I was back at Schneider, I launched something called the power women network. We're a network of a hundred senior women and we meet every month. I've also got a podcast out there. So if anyone's listening, it's called power. Women's speak. It is, I've done 16 over the pandemic with CEOs of women podcast, our women speak >>Or women speak oh, >>And Spotify and >>Everything else. >>And, um, you know, what I love about what we're doing is AWS on diversity and you heard Adam onstage, uh, talk to this. We've got our restock program where we really help under employed and unemployed to get a 12 week intensive course and get trained up on thought skills. And the other thing is, get it helping young girls, 12 to 15, get into stem. So lots of different things on the whole, but we need to do a lot more of course, on diversity. And I look forward to helping our clients through that as well. >>Well, we had, we had the training VP on yesterday. It's all free trainings free. >>We've got such a digital skills issue that I love that we've said 29 million people around the world, free cloud training. >>Literally the th the, the gap there between earnings with cloud certification, you can be making six figures like with cloud training. So, I mean, it's really easy. It's free. It's like, it's such a great thing. >>Have you seen the YouTube video on Charlotte Wilkins? Donald's fast food. She changed her mind. She wanted to take Korea. She now has a tech career as a result of being part of restock. Awesome. >>Oh, really appreciate. You got a lot of energy and love, love the podcast. I'm subscribing. I'm going to listen. We love doing the podcast as well. So thanks for coming on the >>Queue. Thank you so much for having me >>Good luck on anemia and your plans. Thank you. Okay. Cube. You're watching the cube, the leader in global tech coverage. We go to the events and extract the signal from the noise. I'm John furrier with Dave, a lot to here at re-invent physical event in person hybrid event as well. Thanks for watching.
SUMMARY :
It's also a virtual hybrid events with a lot of great content online, bringing you all the fresh voices, Lovely to be here. So first question got to ask you is, is you're new to AWS? And I guess the only other thing to say is I'd heard a lot of other culture, I got to ask you too, now that your new ostium McKinsey, even seeing the front So Ben planes, trains, automobiles, and what you definitely see is massive And the woke us up to that, accelerated that as you say, so as you travel around to customers in AMEA, and all the mainframes and everything that they need to do, if you S if you look at it as one big block, it's too difficult. So for example, the Goldman Sachs financial cloud, bring that to the rest of because the creativity, the market opportunities are there to be captured. second one, the unicorns, you know, it's interesting. and we're fueling that, you know, you know, I mean, born in the cloud is easy, right? all the service providers, you have all the fear of failure goes away. And the idea is to bring A lot of capital needs there, but maybe you could talk about sort of how you see that playing I love the fact that you have a credit card you can get onto our cloud. So what we do a lot is we sit down with our customers and we actually map Well, we really appreciate you taking the time coming on the cube. in AMEA Americas already started to get there as you know, much more, and we need to drive that into So that's two, I think the third big thing is when you mentioned industry industry And the diversity has been a huge theme of this event. back at Schneider, I launched something called the power women network. And I look forward to helping our clients through that as well. Well, we had, we had the training VP on yesterday. around the world, free cloud training. Literally the th the, the gap there between earnings with cloud certification, Have you seen the YouTube video on Charlotte Wilkins? So thanks for coming on the Thank you so much for having me We go to the events and extract the signal from the noise.
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Ian Buck, NVIDIA | AWS re:Invent 2021
>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.
SUMMARY :
knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the AI. Uh, people are applying AI to things like, um, meeting transcriptions, I mean, you mentioned some of those apps, the new enablers, Yeah, it's the innovations on two fronts. technologies, along with the, you know, the AI training capabilities and different capabilities in I mean, I think one of the things you mentioned about the neural networks, You have points that are connected to each Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads And we're excited to see the advancements that Amazon is making and AWS is making with arm and interfaces and the new servers, new technology that you guys are doing, you're enabling applications. Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, I mean, a lot of people are really going in jumping in the big time into this. So the customer is going to have a new kind of experience with a computer And then you had, do have hardware. not just the Silicon and the GPU, but the server designs themselves, we actually do entire server I want to get that plug for, I think it's worth noting that you guys are, that that's the cost, it's the opportunity that AI can provide your business and many, Can you share some of the use cases, examples with customers, notable customers? research that, uh, it's just so exciting to see what people are doing with our technology together with, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up Uh, the new instances with G one's going to complain with more computing know, thanks for coming on.
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Simon Guest, Generali Vitality & Nils Müller-Sheffer, Accenture | AWS Executive Summit 2021
welcome back to the cube's presentation of the aws executive summit at re invent 2021 made possible by accenture my name is dave vellante we're going to look at how digital infrastructure is helping to transform consumer experiences specifically how an insurance company is changing its industry by incentivizing and rewarding consumers who change their behavior to live healthier lives a real passion of of mine and getting to the really root cause of health with me now are simon guest who's the chief executive officer of generality vitality gmbh and niels mueller who's the managing director at the cloud first application engineering lead for the european market at accenture gentlemen welcome to the cube thanks for having us you're very welcome simon generally vitality it's a really interesting concept that you guys have envisioned and now put into practice tell us how does it all work sure no problem and thanks for for having us on dave it's a pleasure to be here so look uh generally vitality is in its uh it's core pretty simple concepts so it's uh it's a program that you have on your phone and the idea of this program is that it's a it's a wellness coach for you as an individual and it's going to help you to understand your health and where you are in terms of the state of your health at the moment and it's going to take you on a journey to improve your your lifestyle and your wellness and hopefully help you to lead a healthier and a more sort of mindful life i guess is is the best way of summarizing it from um from our point of view with insurance company of course you know our historical role has always been to uh be the company that's there if something goes wrong you know so if unfortunately you pass away or you have sickness in your in your life or in your family's life that's that's historically been our role but what we see with generality vitality is something a little bit different so it's a program that really is uh supposed to be with you every day of your life to help you to live a healthier life it's something that we already have in in four european markets in fact in five from this week i'm a little bit behind the time so we're live already in in germany in france in austria and italy and in spain and fundamentally what we what we do dave is too is to say to customers look if you want to understand your health if you want to improve it by moving a little bit more by visiting the doctor more by eating healthier by healthy choices on a daily basis we're going to help you to do that and we're going to incentivize you for going on this journey and making healthy choices and we're going to reward you for for doing the same so you know we partner up with with great companies like garmin like adidas like big brands that are let's say invested in this health and wellness space so that we can produce really an ecosystem for customers that's all about live well make good choices be healthy have an insurance company that partners you along that journey and if you do that we're going to reward you for for that so you know we're here not just in the difficult times which of course is one of our main roles but we're here as a partner as a lifetime partner to you too to help you feel better and live a better life i love it i mean it sounds so simple but but it's i'm sure it's very complicated to to make the technology simple for the user you've got mobile involved you've got the back end and we're going to get into some of the tech but first i want to understand the member engagement and some of the lifestyle changes simon that you've analyzed what's the feedback that you're getting from your customers what does the data tell you how do the incentives work as well what what is the incentive for the the member to actually do the right thing sure look i think actually the the covered uh situation that we've had in the last sort of two years has really crystallized the fact that this is something that we really ought to be doing and something that our customers really value so i mean look just to give you a bit of a sort of information about how it works for for customers so what we try to do with them is is to get customers to understand uh their current health situation you know using their phone so uh you know we ask our customers to go through a sort of health assessment around how they live what they eat how they sleep you know and to go through that sort of process uh and to give them what a vitality age which is a sort of uh you know sort of actuarial comparison with their real age so i'm i'm 45 but unfortunately my my vitality age is 49 and it means i have some work to do to bring that back together uh and what we see is that you know two-thirds of our customers take this test every year because they want to see how they are progressing on an annual basis in terms of living a healthier life and if what if what they are doing is having an impact on their life expectancy and their lifespan and their health span so how long are they going to live healthier for so you see them really engaging in this in this approach of understanding their current situation then what we know actually because the program is built around this model that uh really activity and moving and exercise is the biggest contributor to living a healthier life we know that the majority of deaths are caused by lifestyle illness is like you know poor nutrition and smoking and drinking alcohol and not exercising and so a lot of the program is really built around getting people to move more and it's not about being an athlete it's about you know getting off the the underground one station earlier walking home or making sure you do your 10 000 steps a day and what we see is that that sort of 40 of our customers are on a regularly basis linking either their phone or their their exercise device to our program and downloading that data so that they can see how how much they are exercising and at the same time what we do is we set we set our customers weekly challenges to say look if you can move a little bit more than last week we are going to to reward you for that and we see that you know almost half of our customers are achieving this weekly goal every week and it's really a fantastic level of engagement that normally is an insurer uh we don't see the way the rewards work is is pretty simple it's similar in a way to an airline program so every good choice you make every activity you do every piece of good food that you eat when you check your on your health situation we'll give you points and the more points you get you go through through a sort of status approach of starting off at the bottom status and ending up at a gold and then a platinum status and the the higher up you get in the status that the higher the value of the rewards that we give you so almost a quarter of our customers now and this is accelerated through provide they've reached that platinum status so they are the most engaged customers that we we have and those ones who are really engaging in the in the program and what we really try to create is this sort of virtuous circle that says if you live well you make good choices you improve your health you you progress through the program and we give you better and stronger and more uh valuable rewards for for doing that and some of those rewards are are around health and wellness so it might be that you get you get a discount on on gym gear from adidas it might be that you get a discount on a uh on a device from garmin or it might be actually on other things so we also give people amazon vouchers we also give people uh discounts on holidays and another thing that we we did actually in the last year which we found really powerful is that we've given the opportunity for our customers to convert those rewards into charitable donations because we we work in generality with a with a sort of um campaign called the human safety net which is helping out the poorest people in society and some what our customers do a lot of the time is instead of taking those financial rewards for themselves they convert it into a charitable donation so we're actually also thinking wellness and feeling good and insurance and some societal good so we're really trying to create a virtuous circle of uh of engagement with our customers i mean that's a powerful cocktail i love it you got the the data because if i see the data then i can change my behavior you got the gamification piece you actually have you know hard dollar rewards you could give those to charities and and you've got the the most important which is priceless can't put a value on good health i got one more question for simon and niels i'd love you to chime in as well on this question how did you guys decide simon to engage with accenture and aws and the cloud to build out this platform what's the story behind that collaboration was there unique value that you saw that that you wanted to tap that you feel like they bring to the table what was your experience yeah look i mean we worked at accenture as well because the the the sort of construct of this vitality proposition is a pretty a pretty complex one so you mentioned that the idea is simple but the the build is not so uh is not so simple and that that's the case so accenture's been part of that journey uh from the beginning they're one of the partners that we work with but specifically around the topic of rewards uh you know we're we're a primarily european focused organization but when you take those countries that i mentioned even though we're next to each other geographically we're quite diverse and what we wanted to create was really a sustainable and reusable and consistent customer experience that allowed us to go and get to market with an increasing amount of efficiency and and to do that we needed to work with somebody who understood our business has this historical let's say investment in in the vitality concept so so knows how to bring it to life but that what then could really support us in making uh what can be a complex piece of work as simple and as as replicable as possible across multiple markets because we don't want to go reinventing the wheel every time we do we move to a new market so we need to find a balance between having a consistent product a consistent technology offer a consistent customer experience with the fact that we we operate in quite diverse markets so this was let's say the the reason for more deeply engaging with accenture on this journey thank you very much niels why don't you comment on on that as well i'd love to to get your thoughts and and really really it's kind of your role here i mean accenture global si deep expertise in industry but also technology what are your thoughts on this topic yeah i'd love to love to comment so when we started the journey it was pretty clear from the outset that we would need to build this on cloud in order to get this scalability and this ability to roll out to different markets have a central solution that can act as a template for the different markets but then also have the opportunity to localize different languages different partners for the rewards there's different reward partners in the different markets so we needed to build in an asset basically that could work as a tempos centrally standardizing things but also leaving enough flexibility to to then localize in the individual markets and if we talk about some of the more specific requirements so one one thing that gave us headaches in the beginning was the authentication of the users because each of the markets has their own systems of record where the basically the authentication needs to happen and we somehow needed to still find a holistic solution that comes through the central platform and we were able to do that at the end through the aws cognito service sort of wrapping the individual markets uh local idp systems and by now we've even extended that solution to have a standalone cloud native kind of idp solution in place for markets that do not have a local idp solution in place or don't want to use it for for this purpose yeah so you had you had data you have you had the integration you've got local laws you mentioned the flexibility you're building ecosystems that are unique to the to the local uh both language and and cultures uh please you had another comment i interrupted you yeah i know i just wanted to expand basically on the on the requirements so that was the central one being able to roll this out in a standardized way across the markets but then there were further requirements for example like being able to operate that platform with very low operations overhead there is no large i.t team behind generally vitality that you know works to serve us or can can act as this itis backbone support so we needed to have basically a solution that runs itself that runs on autopilot and that was another big big driver for first of all going to cloud but second of all making specific choices within cloud so we specifically chose to build this as a cloud native solution using for example manage database services you know with automatic backup with automatic ability to restore data that scales automatically that you know has all this built in which usually maybe a database administrator would take care of and we applied that concept basically to every component to everything we looked at we we applied this requirement of how can this run on autopilot how can we make this as much managed by itself within the cloud as possible and then land it on these services and for example we also used the the api gateway from from aws for our api services that also came in handy when for example we had some response time issues with the third party we needed to call and then we could just with a flick of a button basically introduce caching on the level of the api gateway and really improve the user experience because the data you know wasn't updated so much so it was easier to cache so these are all experiences i think that that proved in the end that we made the right choices here and the requirements that that drove that to to have a good user experience niels would you say that the architecture is is a sort of a data architecture specifically is it a decentralized data architecture with sort of federated you know centralized governance or is it more of a centralized view what if you could talk about that yeah it's it's actually a centralized platform basically so the core product is the same for all the markets and we run them as different tenants basically on top of that infrastructure so the data is separated in a way obviously by the different tenants but it's in a central place and we can analyze it in a central fashion if if the need arises from from the business and the reason i ask that simon is because essentially i look at this as a as largely a data offering for your customers and so niels you were talking about the local language and simon as well i would imagine that that the local business lines have specific requirements and specific data requirements and so you've got to build an architecture that is flexible enough to meet those needs yet at the same time can ensure data quality and governance and security that's not a trivial challenge i wonder if you both could comment on that yeah maybe maybe i'll give a start and then simon can chime in so um what we're specifically doing is managing the rewards experience right so so our solution will take care of tracking what rewards have been earned for what customer what rewards have been redeemed what rewards can be unlocked on the next level and we we foreshadow a little bit to to motivate to incentivize the customer and as that data sits in an aws database in a tenant by tenant fashion and you can run analysis on top of that maybe what you're getting into is also the let's say the exercise data the fitness device tracking data that is not specifically part of what my team has built but i'm sure simon can comment a little bit on that angle as well yeah please yeah sure sure yeah sure so look i think them the topic of data and how we use it uh in our business is a very is very interesting one because it's um it's not historically being seen let's say as the remit of insurers to go beyond the you know the the data that you need to underwrite policies or process claims or whatever it might be but actually we see that this is a whole point around being able to create some shared value in in this kind of product and and what i mean by that is uh look if you are a customer and you're buying an insurance policy it might be a life insurance or health insurance policy from from generali and we are giving you access to this uh to this program and through that program you are living a healthier life and that might have a you know a positive impact on generali in terms of you know maybe we're going to increase our market share or maybe we're going to lower claims or we're going to generate value out of that then one of the points of this program is that we then share that value back with customers through the rewards on the platform that we that we've built here and of course being able to understand that data and to quantify it and to value that data is an important part of the of the the different stages of how you of how much value you are creating and it's also interesting to know that you know in a couple of our markets we we operate in the corporate space so not with retail customers but with with organizations and one of the reasons that those companies give vitality to their employees is that they want to see things like the improved health of a workforce they want to see higher presenteeism lower absenteeism of employees and of course being able to demonstrate that there's a sort of correlation between participation in the vitality program and things like that is also is also important and as we've said the markets are very different so we need to be able to to take the data uh that we have out of the vitality program uh and be able in in the company that that i'm managing to to interpret that data so that in our insurance businesses we are able to make good decisions about the kind of insurance products we i think what's interesting to uh to make clear is that actually that the kind of health data that we generate stays purely within the vitality business itself and what we do inside the vitality business is to analyze that data and say okay is this is this also helping our insurance businesses to to drive uh yeah you know better top line and bottom line in the in the relevant business lines and this is different per company and per mark so yeah being able to interrogate that data understand it apply it in different markets and different uh distribution systems and different kinds of approaches to insurance is an is an important one yes it's an excellent example of a digital business in in you know we talk about digital transformation what does that mean this is what it means i i'd love i mean it must be really interesting board discussions because you're transforming an industry you're lowering overall cost i mean if people are getting less sick that's more profit for your company and you can choose to invest that in new products you can give back some to your corporate clients you can play that balancing act you can gain market share and and you've got some knobs to turn some levers uh for your stakeholders which is which is awesome neil something that i'm interested in i mean it must have been really important for you to figure out how to determine and measure success i mean you're obviously removed it's up it's up to generality vitality to get adoption for for their customers but at the same time the efficacy of your solution is going to determine you know the ease of of of delivery and consumption so so how did you map to the specific goals what were some of the key kpis in terms of mapping to their you know aggressive goals besides the things we already touched on i think one thing i would mention is the timeline right so we we started the team ramping in january or february and then within six months basically we had the solution built and then we went through a extensive test phase and within the next six months we had the product rolled out to three markets so this speed to value speed to market that we were able to achieve i think is one of the key um key criteria that also simon and team gave to us right there was a timeline and that timeline was not going to move so we needed to make a plan adjust to that timeline and i think it's both a testament to to the team's work that they did that we made this timeline but it also is enabled by technologies like cloud i have to say if i go back five years ten years if if you had to build in a solution like this on a corporate data center across so many different markets and each managed locally there would have been no way to do this in 12 months right that's for sure yeah i mean simon you're a technology company i mean insurance has always been a tech heavy company but but as niels just mentioned if you had to do that with it departments in each region so my question is is now you've got this it's almost like non-recurring engineering costs you've got that it took one year to actually get the first one done how fast are you able to launch into new markets just from a technology perspective not withstanding any you know local regulations and figuring out to go to market is that compressed yeah so if you are specifically technology-wise i think we would be able to set up a new market including localizations that often involves translation of because in europe you have all the different languages and so on at i would say four to six weeks we probably could stand up a localized solution in reality it takes more like six to nine months to get it rolled out because there's many other things involved obviously but just our piece of the solution we can pretty quickly localize it to a new market but but simon that means that you can spend time on those other factors you don't have to really worry so much about the technology and so you've launched in multiple european markets what do you see for the future of this program come to america you know you can fight you can find that this program in america dave but with one of our competitors we're not we're not operating so much in uh but you can find it if you want to become a customer for sure but yes you're right so look i think from from our perspective uh you know to put this kind of business into a new market it's not it's not an easy thing because what we're doing is not offering it just as a as a service on a standalone basis to customers we want to link it with with insurance business in the end we are an insurance business and we want to to see the value that comes from that so there's you know there's a lot of effort that has to go into making sure that we land it in the right way also from a customer publishing point of view with our distribution and they are they are quite different so so yeah look coming to the question of what's next i mean it comes in three stages for me so as i mentioned we are uh in five markets already uh in next in the first half of 2022 we'll also come to to the czech republic and poland uh which we're excited to to do and that will that will basically mean that we we have this business in in the seven main uh general markets in europe related to life and health business which is the most natural uh let's say fit for something like vitality then you know the next the sort of second part of that is to say okay look we have a program that's very heavily focused around uh activity and rewards and that that's a good place to start but you know wellness these days is not just about you know can you move a bit more than you did historically it's also about mental well-being it's about sleeping good it's about mindfulness it's about being able to have a more holistic approach to well-being and and covert has taught us and customer feedback has taught us actually that this is something where we need to to go and here we need to have the technology to move there as well so to be able to work with partners that are not just based on on on physical activity but also also on mindfulness so this is how one other way we'll develop the proposition and i think the third one which is more strategic and and we are you know really looking into is there's clearly something in the whole uh perception of incentives and rewards which drives a level of engagement between an insurer like generali and its customers that it hasn't had historically so i think we need to learn you know forget you know forgetting about the specific one of vitality being a wellness program but if there's an insurer there's a role for us to play where we offer incentives to customers to do something in a specific way and reward them for doing that and it creates value for us as an insurer then then this is probably you know a place we want to investigate more and to be able to do that in in other areas means we need to have the technology available that is as i said before replicable faster market can adapt quickly to to other ideas that we have so we can go and test those in in different markets so yes we have to we have to complete our scope on vitality we have to get that to scale and be able to manage all of this data at scale all of those rewards at real scale and uh to have the technology that allows us to do that without without thinking about it too much and then to say okay how do we widen the proposition and how do we take the concept of vitality that sits behind vitality to see if we can apply it to other areas of our business and that's really what the future is is going to look like for us you know the the isolation era really taught us that if you're not a digital business you're out of business and pre-kov a lot of these stories were kind of buried uh but the companies that have invested in digital are now thriving and this is an awesome example jeff another point is that jeff amebacher one of the founders of cloudera early facebook employee famously said about 10 12 years ago the best and greatest engineering minds of our my generation are trying to figure out how to get people to click on ads and this is a wonderful example of how to use data to change people's lives so guys congratulations best of luck really awesome example of applying technology to create an important societal outcome really appreciate you your time on the cube thank you thanks bye-bye all right and thanks for watching this segment of thecube's presentation of the aws executive summit at reinvent 2021 made possible by accenture keep it right there for more deep dives [Music] you
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Simon Guest Nil V2 | AWS Executive Summit 2021
(upbeat music) >> Welcome back to theCUBE's presentation of the AWS Executive Summit at re:Invent 2021 made possible by Accenture. My name is Dave Vellante. We're going to look at how digital infrastructure is helping to transform consumer experiences, specifically how an insurance company is changing its industry by incentivizing and rewarding consumers who changed their behavior to live healthier lives, a real passion of mine, and getting to the really root cause of health. With me now are Simon Guest, who's the Chief Executive Officer of Generali Vitality, GmbH, and Nils Muller-Sheffer, who's the Managing Director at the Cloud First Application Engineering Lead for the European market at Accenture. Gentlemen, welcome to theCUBE. >> Thanks for having us. >> You're very welcome Simon. Simon, Generali Vitality is a really interesting concept that you guys have envisioned and now put it into practice. Tell us how does it all work? >> Sure. No problem. And thanks for having us on David, pleasure to be here. So look, Generali Vitality is in its core a pretty simple concept. It's a program that you have on your phone. And the idea of this program is that it's a wellness coach for you as an individual, and it's going to help you to understand your health and where you are in terms of the state of your health at the moment, and it's going to take you on a journey to improve your lifestyle and your wellness, and hopefully help you to live a healthier and a more sort of mindful life, I guess, is the best way of summarizing it. From our point of view as an insurance company, of course, our historical role has always been to be the company that's there if something goes wrong. So if unfortunately you pass away or you have sickness in your life or your family's life, that's historically been our role. But what we see with Generali Vitality is something a little bit different. So it's a program that really is supposed to be with you every day of your life to help you to live a healthier life. It's something that we already have in for European markets and in fact, in five from this week, I'm a little bit behind the times. So we're live already in Germany, in France, in Austria, in Italy and in Spain. And fundamentally what we do Dave, is to say to customers, "Look, if you want to understand your health, if you want to improve it by moving a little bit more, or by visiting the doctor more, by eating healthier, by healthy choices on a daily basis, we're going to help you to do that. And we're going to incentivize you for going on this journey and making healthy choices. And we're going to reward you for doing the same." So, we partner up with great companies like Garmin, like Adidas, like big brands that are, let's say, invested in this health and wellness space so that we can produce really an ecosystem for customers that's all about live well, make good choices, be healthy, have an insurance company that partners you along that journey. And if you do that, we've going to reward you for that. So, we're here not just in a difficult times, which of course is one of our main roles, but we're here as a partner, as a lifetime partner to you to help you feel better and live a better life. >> I love it, I mean, it sounds so simple, but I'm sure it's very complicated to make the technology simple for the user. You've got mobile involved, you've got the back end and we're going to get into some of the tech, but first I want to understand the member engagement and some of the lifestyle changes Simon that you've analyzed. What's the feedback that you're getting from your customers? What does the data tell you? How do the incentives work as well? What is the incentive for the member to actually do the right thing? >> Sure, I think actually that the COVID situation that we've had in the last sort of two years is really crystallized the fact that this is something that we really ought to be doing and something that our customers really value. Just to give you a bit of a sort of information about how it works for our customers. So what we try to do with them, is to get customers to understand their current health situation, using their phone. So, we asked our customers to go through a sort of health assessments around how they live, what they eat, how they sleep, and to go through that sort of process and to give them all the Vitality age, which is a sort of actuarial comparison with their real age. So I'm 45, but unfortunately my Vitality age is 49 and it means I have some work to do to bring that back together. And what we see is that, two thirds of our customers take this test every year because they want to see how they are progressing on an annual basis in terms of living a healthier life. And if what they are doing is having an impact on their life expectancy and their lifespan and their health span. So how long are they going to live healthier for? So you see them really engaging in this approach of understanding that current situation. Then what we know actually, because the program is built around this model that's really activity and moving, and exercise is the biggest contributors to living a healthier life. We know that the majority of deaths are caused by lifestyle illnesses like poor nutrition and smoking and drinking alcohol and not exercising. And so a lot of the program is really built around getting people to move more. And it's not about being an athlete. It's about, getting off the underground one station earlier and walking home or making sure you do your 10,000 steps a day. And what we see is that that sort of 40% of our customers are on a regularly basis linking either their phone or their exercise device to our program and downloading that data so that they can see how much they are exercising. And at the same time, what we do is we set our customers weekly challenges to say, look, if you can move a little bit more than last week, we are go into to reward you for that. And we see that almost half of our customers are achieving this weekly goal every week. And it's really a level of engagement that normally as an insurer, we don't see. The way that rewards work is pretty simple. It's similar in a way to an airline program. So every good choice you make every activity to every piece of good food that you eat. When you check your on your health situation, we'll give you points. And the more points you get, you go through through a sort of status approach of starting off at the bottom status and ending up at a golden and a platinum status. And the higher up you get in the status, the higher the value of the rewards that we give you. So almost a quarter of our customers now, and this has accelerated through COVID have reached that platinum status. So they are the most engaged customers that we have and those ones who are really engaging in the program. And what we really tried to create is this sort of virtuous circle that says If you live well, you make good choices, you improve your health, you progress through the program and we give you better and stronger and more valuable rewards for doing that. And some of those rewards are around health and wellness. So it might be that you get a discounts on gym gear from Adidas, it might be that you get a discount on a device from Garmin, or it might be actually on other things. We also give people Amazon vouchers. We also give people discounts on holidays. And another thing that we did actually in the last year, which we found really powerful is that we've given the opportunity for our customers to convert those rewards into charitable donations. Because we work in generosity with a sort of campaign called The Human Safety Net, which is helping out the poorest people in society. And so what our customers do a lot of the time is instead of taking those financial rewards for themselves, they convert it into a charitable donation. So we're actually also linking wellness and feeling good and insurance and some societal goods. So we're really trying to create a virtuous circle of engagement with our customers. >> That's a powerful cocktail. I love it. You've got the data, because if I see the data, then I can change my behavior. You've got the gamification piece. You actually have hard dollar rewards. You could give those to charities and you've got the most important, which is priceless, you can't put a value on good health. I got one more question for Simon and Nils I'd love for you to chime in as well on this question. How did you guys decide, Simon, to engage with Accenture and AWS and the cloud to build out this platform? What's the story behind that collaboration? Was there unique value that you saw that you wanted to tap, that you feel like they bring to the table? What was your experience? >> Yeah, we work with Accenture as well because the sort of constructs of this Vitality proposition is a pretty complex one. So you mentioned that the idea is simple, but the build is not so simple and that's the case. So Accenture has been part of that journey from the beginning. They are one of the partners that we work with, but specifically around the topic of rewards, we're primarily European focused organization, but when you take those countries that I mentioned, even though we're next to each other geographically, we're quite diverse. And what we wanted to create was really a sustainable and reusable and consistent customer experience that allowed us to go get to market with an increasing amounts of efficiency. And to do that, we needed to work with somebody who understood our business, has this historical, let's say investment in the Vitality concepts and so knows how to bring it to life, but then could really support us in making what can be a complex piece of work, as simple, as replicable as possible across multiple markets, because we don't want to go reinventing the wheel every time we knew we moved to a new market. So we need to find a balance between having a consistent product, a consistent technology offer, a consistent customer experience with the fact that we operate in quite diverse markets. So this was, let's say the reason for more deeply engaging with Accenture on this journey. >> Thank you very much, Nils, why don't you comment on that as well? I'd love to get your thoughts and really is kind of your role here, an Accenture global SI, deep expertise in industry, but also technology, what are your thoughts on this topic? >> Yeah, I'd love to love to comment. So when we started the journey, it was pretty clear from the outset that we would need to build this on cloud in order to get this scalability and this ability to roll out to different markets, have a central solution that can act as a template for the different markets, but then also have the opportunity to localize different languages, different partners for the rewards, there's different reward partners in the different markets. So we needed to build an asset basically that could work as a template, centrally standardizing things, but also leaving enough flexibility to then localize in the individual markets. And if we talk about some of the most specific requirements, so one thing that gave us headaches in the beginning was the authentication of the users because each of the markets has their own systems of record where the, basically the authentication needs to happen. And if we somehow needed to still find a holistic solution that comes through the central platform, and we were able to do that at the end through the AWS cognitive service, sort of wrapping the individual markets, local IDP systems. And by now we've even extended that solution to have a standalone cloud native kind of IDP solution in place for markets that do not have a local IDP solution in place, or don't want to use it for this purpose. >> So you had data, you had the integration, you've got local laws, you mentioned the flexibility, you're building ecosystems that are unique to the local, both language and cultures. Please, you had another comment, I interrupted you. >> No, I just wanted to expand basically on the requirements. So that was the central one being able to roll this out in a standardized way across the markets, but then there were further requirements. For example, like being able to operate the platform with very low operations overhead. There is no large IT team behind Generali Vitality that, works disservice or can act as this backbone support. So we needed to have basically a solution that runs itself that runs on autopilot. And that was another big, big driver for first of all, going to cloud, but second of all, making specific choices within cloud. So we specifically chose to build this as a cloud native solution using for example, managed database services, with automatic backup, with automatic ability to restore data that scales automatically that has all this built in which usually maybe in a database administrator would take care of. And we applied that concept basically to every component, to everything we looked at, we applied this requirement of how can this run on autopilot? How can we make this as much managed by itself within the cloud as possible, and then lend it on these services. For example, we also use the API gateway from AWS for our API services that also came in handy when, for example, we had some response time issues with the third party we needed to call. And then we could just with a flick of a button basically, introduced caching on the level of the API gateway and really improve the user experience because the data wasn't updated so much, so it was easier to cache. So these are all experiences I think that that proved in the end that we made the right choices here and the requirements that drove that to have a good user experience. >> Would you say that the architecture is a sort of a, data architecture specifically, is it a decentralized data architecture with sort of federated, centralized governance? Or is it more of a centralized view, wonder if you could talk about that? >> Yeah, it's actually a centralized platform basically. So the core product is the same for all the markets and we run them as different tenants basically on top of the infrastructure. So the data is separated in a way, obviously by the different tenants, but it's in a central place and we can analyze it in a central fashion if the need arises from the business. >> And the reason I asked that Simon is because essentially I look at this as largely a data offering for your customers. And so Nils, you were talking about the local language and Simon as well. I would imagine that the local business lines have specific requirements and specific data requirements. And so you've got to build an architecture that is flexible enough to meet those needs yet at the same time can ensure data quality and governance and security. And that's not a trivial challenge. I wonder if you both could comment on that. >> Yeah, maybe I'll give a start and then Simon can chime in. So what we're specifically doing is managing the rewards experience, so our solution will take care of tracking what rewards have been earned for what customer, what rewards have been redeemed, what rewards can be unlocked on the next level, and we foreshadow a little bit to motivate incentivize the customer and asset that data sits in an AWS database by tenant fashion. And you can run analysis on top of that. Maybe what you're getting into is also the, let's say the exercise data, the fitness device tracking data that is not specifically part of what my team has built, but I'm sure Simon can comment a little bit on that angle as well. >> Yeah, please. >> Yeah, sure. I think the topic of data and how we use it in our business is a very interesting one because it's not historically been seen, let's say as the remit of insurance to go beyond the data that you need to underwrite policies or process claims or whatever it might be. But actually we see that this is a whole point around being able to create some shared value in this kind of products. And what I mean by that is, if you are a customer and you're buying an insurance policy, it might be a life insurance or health insurance policy from Generali, and we're not giving you access to this program. And through that program, you are living a healthier life and that might have a positive impact on generosity in terms of, maybe we're going to increase our market share, or maybe we are going through lower claims, or we're going to generate value of that then. One of the points of this program is we then share that value back with customers, through the rewards on the platform that we've built here. And of course, being able to understand that data and to quantify it and to value that data is an important part of the different stages of how much value you are creating. And it's also interesting to know that, in a couple of our markets, we operate in the corporate space. So not with retail customers, but with organizations. And one of the reasons that those companies give Vitality to their employees is that they want to see things like the improved health of a workforce. They want to see higher presenteeism, lower absenteeism of employees, and of course, being able to demonstrate that there's a sort of correlation between participation in the Vitality program and things like that is also important. And as we've said, the markets are very different. So we need to be able to take the data that we have out of the Vitality Program and be able in the company that I'm managing to interpret that data so that in our insurance businesses, we are able to make good decisions about kind of insurance product we have. I think what's interesting to make clear is that actually that the kind of health data that we generate states purely within the Vitality business itself and what we do inside the Vitality business is to analyze that data and say, is this also helping our insurance businesses to drive better top line and bottom line in the relevant business lines? And this is different per company. Being able to interrogate that data, understand it, apply it in different markets, in different distribution systems and different kinds of approaches to insurance is an important one, yes. >> It's an excellent example of a digital business and we talked about digital transformation. What does that mean? This is what it means. It must be really interesting board discussions because you're transforming an industry, you're lowering overall costs. I mean, if people are getting less sick, that's more profit for your company and you can choose to invest that in new products, you can give back some to your corporate clients, you can play that balancing act, you can gain market share. And you've got some knobs to turn, some levers, for your stakeholders, which is awesome. Nils, something that I'm interested in, it must've been really important for you to figure out how to determine and measure success. Obviously it's up to Generali Vitality to get adoption for their customers, but at the same time, the efficacy of your solution is going to determine, the ease of delivery and consumption. So, how did you map to the specific goals? What were some of the key KPIs in terms of mapping to their aggressive goals. >> Besides the things we already touched on, I think one thing I would mention is the timeline. So, we started the team ramping in January, February, and then within six months basically, we had the solution built and then we went through a extensive test phase. And within the next six months we had the product rolled out to three markets. So this speed to value, speed to market that we were able to achieve, I think is one of the key criteria that also Simon and team gave to us. There was a timeline and that time I was not going to move. So we needed to make a plan, adjust to that timeline. And I think it's both a testament to the team's work that we met this timeline, but it also is enabled by a technology stack cloud. I have to say, if I go back five years, 10 years, if you had to build in a solution like this on a corporate data center across so many different markets and each managed locally, there would've been no way to do this in 12 months, that's for sure. >> Yeah, Simon, you're a technology company. I mean, insurance has always been a tech heavy company, but as Nils just mentioned, if you had to do that with IT departments in each region. So my question is now you've got this, it's almost like nonrecurring engineering costs, it took one year to actually get the first one done, how fast are you able to launch into new markets just from a technology perspective, not withstanding local regulations and figuring out the go to market? Is that compressed? >> So you asked specifically technology-wise I think we would be able to set up a new market, including localizations that often involves translation of, because in Europe you have all the different languages and so on, I would say four to six weeks, we probably could stand up a localized solution. In reality, it takes more like six to nine months to get it rolled out because there's many other things involved, obviously, but just our piece of the solution, we can pretty quickly localize it to a new market. >> But Simon, that means that you can spend time on those other factors, you don't have to really worry so much about the technology. And so you've launched in multiple European markets, what do you see for the future of this program? Come to America. >> You can find that this program in America Dave, but with one of our competitors, we're not operating so much in the US, but you can find it if you want to become a customer for sure. But yes, you're right. I think from our perspective, to put this kind of business into a new market is not an easy thing because what we're doing is not offering it just as a service on a standalone basis to customers, we want to link it with insurance business. In the end, we are an insurance business, and we want to see the value that comes from that. So there's a lot of effort that has to go into making sure that we land it in the right way, also from a customer proposition points of view with our distribution, they are all quite different. Coming to the question of what's next? It comes in three stages for me. So as I mentioned, we are in five markets already. In the first half of 2022, we'll also come to the Czech Republic and Poland, which we're excited to do. And that will basically mean that we have this business in the seven main Generali markets in Europe related to life and health business, which is the most natural at let's say fit for something like Vitality. Then, the sort of second part of that is to say, we have a program that is very heavily focused around activity and rewards, and that's a good place to start, but, wellness these days is not just about, can you move a bit more than you did historically, it's also about mental wellbeing, it's about sleeping good, it's about mindfulness, it's about being able to have a more holistic approach to wellbeing and COVID has taught us, and customer feedback has taught is actually that this is something where we need to go. And here we need to have the technology to move there as well. So to be able to work with partners that are not just based on physical activity, but also on mindfulness. So this is how one other way we will develop the proposition. And I think the third one, which is more strategic and we are really looking into is, there's clearly something in the whole perception of incentives and rewards, which drives a level of engagement between an insurer like Generali and its customers that it hasn't had historically. So I think we need to learn, forgetting about the specific one or Vitality being a wellness program, but if there's an insurer, there's a role for us to play where we offer incentives to customers to do something in a specific way and reward them for doing that. And it creates value for us as an insurer, then this is probably a place that we'd want to investigate more. And to be able to do that in other areas means we need to have the technology available, that is, as I said before, replicable faster market can adapt quickly to other ideas that we have, so we can go and test those in different markets. So yes, we have to, we have to complete our scope on Vitality, We have to get that to scale and be able to manage all of this data at scale, all of those rewards that real scale, and to have the technology that allows us to do that without thinking about it too much. And then to say, okay, how do we widen the proposition? And how do we take the concept that sits behind Vitality to see if we can apply it to other areas of our business. And that's really what the future is going to look like for us. >> The isolation era really taught us that if you're not a digital business, you're out of business, and pre COVID, a lot of these stories were kind of buried, but the companies that have invested in digital are now thriving. And this is an awesome example, and another point is that Jeff Hammerbacher, one of the founders of Cloudera, early Facebook employee, famously said about 10, 12 years ago, "The best and greatest engineering minds of my generation are trying to figure out how to get people to click on ads." And this is a wonderful example of how to use data to change people's lives. So guys, congratulations, best of luck, really awesome example of applying technology to create an important societal outcome. Really appreciate your time on theCUBE. Thank you. >> Bye-bye. >> All right, and thanks for watching this segment of theCUBE's presentation of the AWS Executive Summit at re:Invent 2021 made possible by Accenture. Keep it right there for more deep dives. (upbeat music)
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Sanzio Bassini, Cineca | CUBE Conversation, October 2021
(upbeat music) >> Welcome to this Cube Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the head of High Performance Computing at Cineca a Dell Technologies customer. Sanzio, welcome to theCUBE. >> Thank you, it's a pleasure. >> Lisa Martin: Likewise. Nice to see you. So tell us a little bit about Cineca, this is a large computing center, but a very large Italian non-profit consortium. Tell us about it. >> Yes, Cineca has been founded 50 years ago, from the university systems in Italy to support the scientific discovery and the industry innovations using the high-performance computing, and the correlated mythologies like intelligence together with the big data processing, and the simulations. We are a corsortium, which means that is a private not-for-profit organization. Currently our member of the consortium, almost all the universities in Italy and also all the national agencies. >> Lisa Martin: And I also read that you are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our statutory visions in the last 10 to 15 years , we have been to say, frequent buyers in the top 10. The idea is that we're enabling the scientific discovery by mean of the providing the most advanced systems, and the co-designing the the most advanced HPC systems to promote to support the accents in science. And being part of the European high-performance computing ecosystems. >> Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the human brain project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> The human brain project is one of the flagship projects that has been co-funded by the European Commission and the participating member states that are two different, right now , flagships together with another that is just in progress, which is the the quantum of flagship we are participating indirectly together with the National Disaster Council. And we are core partners of the HPC constructors , that is the human brain project. One billion euro of investment, co-founded by the participating states and the European Commissions. it's a project that would combine both the technology issues and the designing of a high-performance computing systems that would meet the requirements of the community. And the big scientific challenges, correlated to the physiological functions of the human brains, including different related to the behavior of the, of the human brain, either from the pathological point of view either from the physiological point of view. In order to better understand the aging user, that it would impact the, the health the public health systems, some other that are correlating with what would be the support for the physiological knowledge of the human brains. And finally computational performance, the human brain is more than Exascale systems, but with a average consumption, which is very low. We are talking about some hundred of wards of energy would provide a, an extreme and computational performance. So if we put the organizing the technology high-performance computing in terms of interconnections now we're morphing the computing systems that would represent a tremendous step in order to facing the big challenges of our base and energies, personalized medicine, climate change, food for all those kinds of big social economic challenge that we are facing. >> Which reading them, besides the human brain project, there are other projects going on, such as that you mentioned. I'd like to understand how Cineca is working with Dell Technologies. You have to translate, as you mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell Technologies. >> In our computing architectures. We had the need to address the capability to facing the big data processing involved with respect of the Human Brain Project and generally speaking that evolved with the respect of the science-driven that would provide cloud access to the systems by means of containers technologies. And the capability also to address what will be the creation of a Federation for high performance computing facility in Europe. So at the end we manage a competitive dialogue procurement the processor, that in a certain sense would share together with the different potential technology providers, what would be the visions and also the constraints with respect to the divisions including budget constraints and at the end Dell had shown the characteristics of the solution, that it will be more, let's say compliant. And at the same time, flexible with respect of the combinations of very different constraints and requirements. >> Dell Technologies has been sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> The Italian commitment together with the European member states is that by mean of scientific methods and peer review process roughly speaking of the production capacity, would be shared at the European level, that it's a commitment that has been shared together with the France, Germany, Spain, and Switzerland. Where also of course, the Italian scientists, can apply and participate, but in a sort of emulations and the advanced competition for addressing what will be the excellence in science. The remaining 50% of our production capacity is for, for the national community and in somehow to support the Italian community to be competitive on the worldwide scenario that setting up would lead also to the agreement after the international level, with respect of some of the actions that are promoted in progress in the US and in Japan also that means the sharing options with the US researchers or Japanese researchers in an open space. >> It sounds like the human brain project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the human brain project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be lead by Euro SPC, which is called the phoenix that stands for Federation of a high-performance computing system in Europe. That provide open service based on two concepts One is the sharing of the ID at the European level. So it means that open the access to the Cineca system to the system in France , to UNIX system in Germany, to fifth system in Switzerland, and to the diocese the marine ocean system in Spain that is federated, ID management, others, et cetera, related to what will be the Federation of data access. The scientific community may share their data in a seamless mode, the actions is being supported by genetic, has to do with the two specific target. One is the elaborations of the data that are provided by the lens, laser, laboratory facility in Florence, that is one of the core parts of garnering the data that would come from the mouse brains, the time user for caviar. And the second part is for the meso scale studies of the cortex of the brain. In some situations they combinations of performance capability of the Federation systems for addressing what would be the simulations of the overall being of the human brain would take a lot of performance that are challenging simulation periodically that they would happen combining that they HPC facility as at European level. >> Right. So I was reading there's a case study by the way, on Cineca that Dell Technologies has published. And some of the results you talked about those at the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prosthesis for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the human brain project. One last question for you. What advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> There is a continuous sharing, of knowledge, experience, best practices, where the situation is different in the sense that there are, what would we be the integration of the high-performance computing technology into their production workflow. That is the sharing of the experience in order to provide a spreads and amplifications of the opportunity for supporting the innovation. That is part of our social mission in Italy, but it's also the objective. that is supported by the European Commission. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio, thank you so much for sharing with us, what Cineca is doing and the great research that's going on there. And across a lot of disciplines. We appreciate you joining the program today. Thank you. >> Thank you. Thank you very much. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this Cube Conversation. (upbeat music)
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the head of High Performance Nice to see you. and also all the national agencies. of the world's fastest super computers. in the last 10 to 15 years , the human brain project. that is the human brain project. the human brain project, And the capability also to address what will be the creation of a Talk to me about how you that means the sharing options of the results that the So it means that open the access And some of the results of the high-performance fundamental and the environment Thank you very much. for Sanzio Bassini.
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James Hodge
>> Well, hello everybody, John Walls here on theCUBE and continuing our coverage. So splunk.com for 21, you know, we talk about big data these days, you realize the importance of speed, right? We all get that, but certainly Formula One Racing understands speed and big data, a really neat marriage there. And with us to talk about that is James Hodge, who was the global vice president and chief strategy officer international at Splunk. James, good to see it today. Thanks for joining us here on theCUBE. >> Thank you, John. Thank you for having me and yeah, the speed of McLaren. Like I'm, I'm all for it today. >> Absolutely. And I find it interesting too, that, that you were telling me before we started the interview that you've been in Splunk going on nine years now. And you remember being at splunk.com, you know, back in the past other years and watching theCUBE and here you are! you made it. >> I know, I think it's incredible. I love watching you guys every single year and kind of the talk that guests. And then more importantly, like it reminds me of conf for every time we see theCUBE, no matter where you are, it reminds me of like this magical week there's dot com for us. >> Well, excellent. I'm glad that we could be a part of it at once again and glad you're a part of it here on theCUBE. Let's talk about McLaren now and the partnership, obviously on the racing side and the e-sports side, which is certainly growing in popularity and in demand. So just first off characterize for our audience, that relationship between Splunk and McLaren. >> Well, so we started the relationship almost two years ago. And for us it was McLaren as a brand. If you think about where they were, they recently, I think it's September a Monza. They got a victory P1 and P2. It was over 3200 days since their last victory. So that's a long time to wait. I think of that. There's 3000 days of continual business transformation, trying to get them back up to the grid. And what we found was that ethos, the drive to digital the, the way they're completely changing things, bringing in kind of fluid dynamics, getting people behind the common purpose that really seem to fit the Splunk culture, what we're trying to do and putting data at the heart of things. So kind of Formula One and McLaren, it felt a really natural place to be. And we haven't really looked back since we started at that partnership. It's been a really exciting last kind of 18 months, two years. >> Well, talk a little bit about, about the application here a little bit in terms of data cars, the, the Formula One cars, the F1 cars, they've got hundreds of sensors on them. They're getting, you know, hundreds of thousands or a hundred thousand data points almost instantly, right? I mean, there's this constant processing. So what are those inputs basically? And then how has McLaren putting them to use, and then ultimately, how is Splunk delivering on that from McLaren? >> So I learned quite a lot, you know, I'm, I'm, I been a childhood Formula One fan, and I've learned so much more about F1 over the last kind of couple of years. So it actually starts with the car going out on the track, but anyone that works in the IT function, the car can not go out on track and less monitoring from the car actually is being received by the garage. It's seen as mission critical safety critical. So IT, when you see a car out and you see the race engineer, but that thumbs up the mechanical, the thumbs up IT, get their vote and get to put the thumbs up before the car goes out on track there around about 300 sensors on the car in practice. And there were two sites that run about 120 on race day that gets streamed on a two by two megabits per second, back to the FIA, the regulating body, and then gets streams to the, the garage where they have a 32 unit rack near two of them that have all of their it equipment take that data. They then stream it over the internet over the cloud, back to the technology center in working where 32 race engineers sit in calm conditions to be able to go and start to make decisions on when the car should pit what their strategy should be like to then relate that back to the track side. So you think about that data journey alone, that is way more complicated and what you see on TV, you know, the, the race energy on the pit wall and the driver going around at 300 kilometers an hour. When we look at what Splunk is doing is making sure that is resilient. You know, is the data coming off the car? Is it actually starting to hit the garage when it hits that rack into the garage, other than streaming that back with the right latency back to the working technology center, they're making sure that all of the support decision-making tools there are available, and that's just what we do for them on race weekend. And I'll give you one kind of the more facts about the car. So you start the beginning of the season, they launched the car. The 80% of that car will be different by the end of the season. And so they're in a continual state of development, like constantly developing to do that. So they're moving much more to things like computational fluid dynamics applications before the move to wind tunnel that relies on digital infrastructure to be able to go and accelerate that journey and be able to go make those assumptions. That's a Splunk is becoming the kind of underpinning of to making sure those mission critical applications and systems are online. And that's kind of just scratching the surface of kind of the journey with McLaren. >> Yeah. So, so what would be an example then maybe on race day, what's a stake race day of an input that comes in and then mission control, which I find fascinating, right? You've got 32 different individuals processing this input and then feeding their, their insights back. Right. And so adjustments are being made on the fly very much all data-driven what would be an example of, of an actual application of some information that came in that was quickly, you know, recorded, noted, and then acted upon that then resulted in an improved performance? >> Well, the most important one is pit stop strategy. It can be very difficult to overtake on track. So starting to look at when other teams go into the pit lane and when they come out of the, the pit lane is incredibly important because it gives you a choice. Do you stay also in your current set of tires and hope to kind of get through that team and kind of overtake them, or do you start to go into the pits and get your fresh sets of tires to try and take a different strategy? There are three people in mission control that have full authority to go and make a Pit lane call. And I think like the thing that really resonated for me from learning about McLaren, the technology is amazing, but it's the organizational constructs on how they turn data into an action is really important. People with the right knowledge and access to the data, have the authority to make a call. It's not the team principle, it's not the person on the pit wall is the person with the most amount of knowledge is authorized and kind of, it's an open kind of forum to go and make those decisions. If you see something wrong, you are just as likely to be able to put your hand up and say, something's wrong here. This is my, my decision than anyone else. And so when we think about all these organizations that are trying to transform the business, we can learn a lot from Formula One on how we delegate authority and just think of like technology and data as the beginning of that journey. It's the people in process that F1 is so well. >> We're talking a lot about racing, but of course, McLaren is also getting involved in e-sports. And so people like you like me, we can have that simulated experience to gaming. And I know that Splunk has, is migrating with McLaren in that regard. Right. You know, you're partnering up. So maybe if you could share a little bit more about that, about how you're teaming up with McLaren on the e-sports side, which I'm sure anybody watching this realizes there's a, quite a big market opportunity there right now. >> It's a huge market opportunity is we got McLaren racing has, you know, Formula One, IndyCar and now extreme E and then they have the other branch, which is e-sports so gaming. And one of the things that, you know, you look at gaming, you know, we were talking earlier about Ted Lasso and, you know, the go to the amazing game of football or soccer, depending on kind of what side of the Atlantic you're on. I can go and play something like FIFA, you know, the football game. I can be amazing at that. I have in reality, you know, in real life I have two left feet. I am never going to be good at football however, what we find with e-sports is it makes gaming and racing accessible. I can go and drive the same circuits as Lando Norris and Daniel Ricardo, and I can improve. And I can learn like use data to start to discover different ways. And it's an incredibly expanding exploding industry. And what McLaren have done is they've said, actually, we're going to make a professional racing team, an e-sports team called the McLaren Shadow team. They have this huge competition called the Logitech KeyShot challenge. And when we looked at that, we sort of lost the similarities in what we're trying to achieve. We are quite often starting to merge the physical world and the digital world with our customers. And this was an amazing opportunity to start to do that with the McLaren team. >> So you're creating this really dynamic racing experience, right? That, that, that gives people like me, or like our viewers, the opportunity to get even a better feel for, for the decision-making and the responsiveness of the cars and all that. So again, data, where does that come into play there? Now, What, what kind of inputs are you getting from me as a driver then as an amateur driver? And, and how has that then I guess, how does it express in the game or expressed in, in terms of what's ahead of me to come in a game? >> So actually there are more data points that come out of the F1 2021 Codemasters game than there are in Formula One car, you get a constant stream. So the, the game will actually stream out real telemetry. So I can actually tell your tire pressures from all of your tires. I can see the lateral G-Force longitudinal. G-Force more importantly for probably amateur drivers like you and I, we can see is the tire on asphalt, or is it maybe on graphs? We can actually look at your exact position on track, how much accelerator, you know, steering lock. So we can see everything about that. And that gets pumped out in real time, up to 60 Hertz. So a phenomenal amount of information, what we, when we started the relationship with McLaren, Formula One super excited or about to go racing. And then at Melbourne, there's that iconic moment where one of the McLaren team tested positive and they withdrew from the race. And what we found was, you know, COVID was starting and the Formula One season was put on hold. The FIA created this season and called i can't remember the exact name of it, but basically a replica e-sports gaming F1 series. We're using the game. Some of the real drivers like Lando, heavy gamer was playing in the game and they'd run that the same as race weekends. They brought celebrity drivers in there. And I think my most surreal zoom call I ever was on was with Lando Norris and Pierre Patrick Aubameyang, who was who's the arsenal football captain, who was the guest driver in the series to drive around Monaco and Randy, the head of race strategy as McLaren, trying to coach him on how to go drive the car, what we ended up with data telemetry coming from Splunk. And so Randy could look out here when he pressing the accelerator and the brake pedal. And what was really interesting was Lando was watching how he was entering corners on the video feed and intuitively kind of coming to the same conclusions as Randy. So kind of, you could see that race to intuition versus the real stats, and it was just incredible experience. And it really shows you, you know, racing, you've got that blurring of the physical and the virtual that it's going to be bigger and bigger and bigger. >> So to hear it here, as I understand what you were just saying now, the e-sports racing team actually has more data to adjust its performance and to modify its behaviors, then the real racing team does. Yep. >> Yeah, it completely does. So what we want to be able to do is turn that into action. So how do you do the right car setup? How do you go and do the right practice laps actually have really good practice driver selection. And I think we're just starting to scratch the surface of what really could be done. And the amazing part about this is now think of it more like a digital twin, what we learn on e-sports we can actually say we've learned something really interesting here, and then maybe a low, you know, if we get something wrong, it may be doesn't matter quite as much as maybe getting an analytics wrong on race weekend. >> Right. >> So we can actually start to look and improve through digital and then start to move that support. That's over to kind of race weekend analytics and supporting the team. >> If I could, you know, maybe pun intended here, shift gears a little bit before we run out of time. I mean, you're, you're involved on the business side, you know, you've got, you know, you're in the middle east Africa, right? You've got, you know, quite an international portfolio on your plate. Now let's talk about just some of the data trends there for our viewers here in the U S who maybe aren't as familiar with what's going on overseas, just in terms of, especially post COVID, you know, what, what concerns there are, or, or what direction you're trying to get your clients to, to be taking in terms of getting back to work in terms of, you know, looking at their workforce opportunities and strengths and all those kinds of things. >> I think we've seen a massive shift. I think we've seen that people it's not good enough just to be storing data its how do you go and utilize that data to go and drive your business forwards I think a couple of key terms we're going to see more and more over the next few years is operational resilience and business agility. And I'd make the assertion that operational resilience is the foundation for the business agility. And we can dive into that in a second, but what we're seeing take the Netherlands. For example, we run a survey last year and we found that 87% of the respondents had created new functions to do with data machine learning and AI, as all they're trying to do is go and get more timely data to front line staff to go. And next that the transformation, because what we've really seen through COVID is everything is possible to be digitized and we can experiment and get to market faster. And I think we've just seen in European markets, definitely in Asia Pacific is that the kind of brand loyalty is potentially waning, but what's the kind of loyalty is just to an experience, you know, take a ride hailing app. You know, I get to an airport, I try one ride hailing app. It tells me it's going to be 20 minutes before a taxi arrives. I'm going to go straight to the next app to go and stare. They can do it faster. I want the experience. I don't necessarily want the brand. And we're find that the digital experience by putting data, the forefront of that is really accelerating and actually really encouraging, you know, France, Germany are actually ahead of UK. Let's look, listen, their attitudes and adoption to data. And for our American audience and America, America is more likely, I think it's 72% more likely to have a chief innovation officer than the rest of the world. I think I'm about 64% in EMEA. So America, you are still slightly ahead of us in terms of kind of bringing some of that innovation that. >> I imagine that gap is going to be shrinking though I would think. >> It is massively shrinking. >> So before we, we, we, we are just a little tight on time, but I want to hear about operational resilience and, and just your, your thought that definition, you know, define that for me a little bit, you know, put a little more meat on that bone, if you would, and talk about why, you know, what that is in, in your thinking today and then why that is so important. >> So I think inputting in, in racing, you know, operational resilience is being able to send some response to what is happening around you with people processing technology, to be able to baseline what your processes are and the services you're providing, and be able to understand when something is not performing as it should be, what we're seeing. Things like European Union, in financial services, or at the digital operational resilience act is starting to mandate that businesses have to be operational in resilient service, monitoring fraud, cyber security, and customer experience. And what we see is really operational resilience is the amount of change that can be absorbed before opportunities become risk. So having a stable foundation of operational resilience allows me to become a more agile business because I know my foundation and people can then move and adjust quickly because I have the awareness of my environment and I have the ability to appropriately react to my environment because I've thought about becoming a resilient business with my digital infrastructure is a theme. I think we're going to see in supply chain coming very soon and across all other industries, as we realize digital is our business. Nowadays. >> What's an exciting world. Isn't it, James? That you're, that you're working in right now. >> Oh, I, I love it. You know, you said, you know, eight and an eight and a half years, nine years at Splunk, I'm still smiling. You know, it is like being at the forefront of this diesel wave and being able to help people make action from that. It's an incredible place to be. I, is liberating and yeah, I can't even begin to imagine what's, you know, the opportunities are over the next few years as the world continually evolves. >> Well, every day is a school day, right? >> It is my favorite phrase >> I knew that. >> And it is, James Hodge. Thanks for joining us on theCUBE. Glad to have you on finally, after being on the other side of the camera, it's great to have you on this side. So thanks for making that transition for us. >> Thank you, John. You bet James Hodge joining us here on the cube coverage of splunk.com 21, talking about McLaren racing team speed and Splunk.
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2021 095 VMware Vijay Ramachandran
>>Welcome to the cubes coverage of VMworld 2021. I'm Lisa Martin VJ ramen. Shannon joins me next VP of product management at VMware VJ. Welcome back to the program. >>Thank you. So >>We're going to be talking about disaster recovery, VMware cloud. Dr. We've had a lot of challenges with respect to cybersecurity, but the world has in the last 18 months, I'd like to get your, your thoughts on the disaster recovery as a service, the dearest market. What are some of the key trends? Anything that you've noticed have particular interest in the last year and a half? >>Yeah, actually you're right. I mean that the last one year, since the pandemic, you know, the whole, um, lot of industries want to, uh, deploy DLR systems and want to protect themselves in France, somewhere and other, uh, other areas of the Amazon predicting that the disaster service market is going to reach about $10 billion by 2025. And so we, uh, we introduced bandwidth disaster recovery, you know, the last beam work with an acquisition of a company called atrium. And since then we've had tremendous success and it was really largely driven by two key trends that we seen in the market. One is that a lot of our customers have regulatory and mandates to do have a PR plan in place. And second is ransomware and ransomware a lot more in this interview, but ransomware is top of mind for a lot of customers. So those, these two combined together is really making a huge push to, uh, to protect all the data against, uh, disasters. >>What type of customers and any particular industries that you see that are really keenly adopting VMware cloud and D anything that you think is interesting. >>Yeah, it's actually interesting that you say it's actually not a single vertical or a size of the customer. What we have again, what we're finding is that a lot of the regulated industries, I, you know, having 92 to do the art, but the existing VR and data production systems are extremely complex and not cost effective. So, you know, customers are asked to do more with less. And so a lot of our customers, a lot of those customers are asking for, uh, looking for a cost-effective way to protect all the data. And, you know, and ransomware is not something that, that impacts, you know, any single vertical or, or any single size of customer. It impacts everyone. So we're seeing interest from all different verticals, different sizes of customers, uh, across, uh, the, you know, the B cell this, >>Yeah, you're right. The ransomware is a universal problem. And as we saw in the last few months, a problem that is really one of national public health and safety and security concerns. So you mentioned that customers from a regulatory perspective, those that need to implement Dr. Ransomware, as we talked about, are there, and then you also mentioned legacy solutions are kind of costly complex. Talk to me about some of the challenges with respect to those legacy solutions that you're helping customers to address with VMware cloud disaster recovery. >>Yeah. There are a few traits of chains that are, uh, that are emerging and then the whole data production space. One is, uh, customers want to do more with the data. And so with legacy systems, what they're finding is that customers are, you know, are able to replicate the data, but the data is sitting idle and not being used. And so, um, you know, and that's extremely, very expensive for our customers on the line. And secondly, from an outpatient standpoint, backup and Dr, as kind of merging into a single single solution and ransomware protection is becoming a critical use case as we spoke about at the talk about for that. So, uh, customers are not looking to deploy different systems for different types of production. They're looking for a similar solution that, that the lowest cost and gives them enough production across all these different use cases. >>And so where the NFL disaster recovery comes into play is that, is that we are able to use the data that we protect for other uses such as, uh, such as ransomware recovery, such as data protection, such as disaster recovery. So single copy of data that's being could be used in multiple use cases. Number one. And secondly, uh, it's a very expensive, uh, proposition to have, um, you know, on-prem to on-prem, you know, having to, you know, people who shouldn't capacity just sitting idle. And so where Vizio comes into play is that they're able to use, uh, protect the data into cloud, store it in a cost effective manner, and then just use the data when it's acquired either fatal or during disasters in ransomware. And that's where you're able to in, in, in, in the market today, >>Dig through some of those differentiators, if you will, one by one, because there's so much choice out there, there's a lot of backup solutions. Some that are providing backup only some that are doing also Dr. Depending on how customers have deployed and how they're using the technology. But when you're in customer conversations, what are the three things that you articulate about VMware cloud DVR that really help it stand out above the pack? >>Yeah, number one is the cost, right? Um, we, you know, we're able to bring down the cost of, uh, of a disaster protection, uh, by 65, by 65%. And, uh, and, you know, um, that's one big value proposition that we, uh, that we know highlight in our solution. Number two, a lot of our customers also becoming environmentally friendly and, you know, and I'm in a conscious, I should say. And so, because we're able to store the data in a more cost-effective manner, in a more efficient manner in the cloud, they're able to bring down the carbon footprint by 80% compared to regular, you know, your legacy, uh, disaster recovery and data protection solution. And the third, you know, sort of major value proposition from, from, uh, from the BMS is that, you know, we're able to integrate the, uh, uh, BCDR solution, the disaster coriander data protection solution. So well into our, um, you know, into, into the ecosystem, uh, can easily operationally easily recover data into a BM ware cloud. And so for, for the BMA ecosystem, it just becomes a natural logical extension of their, uh, their, uh, toolset. >>That's huge having a console that you're familiar with, you know, the whole point of, of backing up data and the need to recover from a disaster is to be able to restore the data in a timely fashion. I talked with a lot of customers who were using legacy technologies, and that was one of the biggest challenges backup windows weren't completing, or they simply couldn't recover data that was either, um, lost in an, in a ransomware attack or accidentally lost that recovery is what it's all about. Right. >>That's it, that's exactly right. And so at this rainbow ledger using a key enhancements and features that specifically speak to that, uh, you know, to that pain point that you just mentioned, you know, uh, we are bringing down, uh, the, uh, you know, the replication time, uh, to 30 to 30 minutes. So in other words, your Delta is, is, is, uh, is at a 300 interval now compared to all us in a traditional backup system. And number two, um, we are extending, uh, you know, be in love with a copy of it regardless it's always had with single file recovery. And so, especially for the, for the ransomware, uh, use case customers are quickly able to figure out which file leads to the restore, and they're able to restore those files individually rather than restoring their entire VM for the entire data center. And so it becomes a critical, uh, use case for, uh, critical functionality, I should say, for a ransomware recovery. And the other huge announcement of a major announcement media announcement had been made, uh, uh, others be involved is the integration into the VMware cloud in such a way that customers who move are migrating data into the BMR, the cloud on AWS can, uh, have the opportunity to, um, uh, protect the data, um, you know, uh, you know, easily BCDR and >>Got it. I'd love to get an example of a customer that you helped to recover from ransomware. As we mentioned, it's on the rise. In fact, I was looking at some cybersecurity data in the last few weeks, and it's the first half of 2021 calendar. It was up nearly 11 ax. And obviously the, the, the hockey stick lists looking like it's going to continue to go up into the right. So give me an example of a customer that you helped recover after they were hit with ransomware. >>Yeah. Yeah, I lose. And in fact, before I give you one set, one statistic that I just saw recently, um, it is, um, every Lennon are going to be across the board. There's some ransomware attack and in the world. And so, uh, you know, it is a big, you know, it is a huge, huge top of mind for a lot of, uh, the CEO's across and I, you know, across the globe now, uh, we, I just give you an example of one customer that we helped, um, you know, protect the data against ransomware. Merrick is the customer name, uh, it's a public reference. It can, um, you know, it's, it's in the BMI website and they had legacy systems, just like we talked about before they had legacy systems for protecting the data and they had, you know, backup systems and they had disaster recovery systems. >>And the big pain point was that, you know, they knew that they are, you know, they needed to protect against ransomware and, but they had two different systems backup and disaster recovery, and their cost was high because they were replicating the light data or production data, uh, you know, across different sites. And so they were looking for a, uh, to lower the cost of disaster recovery, but more importantly, they're looking to, uh, to protect themselves against potential ransomware threats and, um, and they were able to deploy VCR. And how does multiple points in time? Um, you know, I, in, in, um, in the, in the cloud that are, that allows them to go to any point, uh, you know, uh, after a ransomware attack and record from it. And as I said, the single file recovery was a huge benefit for them because they can then figure out exactly which, you know, which of those files, uh, you know, required, um, recovery. And so, um, they're able to lower the cost and protect, uh, and at the same time, uh, you know, meet the regulatory requirements and mandates to have a production in place so that the women all up there in all over the place, >>As you said, there, the data show one ransomware attack occurs every 11 seconds. And of course we only hear about the ones that make the news, right, for the most part, our customers talk about, Hey, we've had this problem. So it is no longer a, if we get hit with ransomware for every industry, like you were saying before, no industry is blind to this. It's when we get hit, we've gotta be able to recover the data. It sounds like what you're talking about from a recovery perspective is it's, it's very granular. So folks can go in and find exactly what they're looking for. Like, they don't have to restore entire VM. They can go down to the file level. >>That's exactly right. And, and you need the grant of the recovery because you want to be able to quickly restore, you know, your data, uh, and get back on, uh, you know, get back in the business. And so, uh, we provide that granular, granular recovery at the file level so that you can quickly scan your data, figure out which file needs to be at least a bit of cover and recollect just those files. Of course, you can also the color. We also provide authorization for the whole data center for the whole, uh, you know, BM and all the beings in the data center, but customers when they hit the trends and where they want to be able to quickly get back, get back into production, to those flights that, you know, that they critically need. And so that's, um, yeah, that's, it's a critical functionality. >>So is this whole entire solution in the cloud, or is there anything that the customer needs to have on premise? >>So this is, uh, all the data is go to the cloud in an efficient day, in an efficient way. Again, uh, you know, this is another sort of, um, like be that behalf, which is it's easy to just store data in the cloud in a debate, but what we do is be efficiently store the data so that, you know, you, uh, you know, you can know what the cost of your storage and, uh, uh, in the cloud. And so, you know, we used to be at BCDR, we'll be in the cloud disaster recovery. Those data in the cloud is, uh, and, and, and the data repository is in the cloud. And, uh, you can either recover data back to where you need to recover, or we allow filo or orchestrate automatically feel or of, uh, workloads into VMware on AWS, again, operational consistent, because it's a BMI software that's running on ground BMI software, that's running on data and you can, um, you know, fail a lot and bring the data onto the in-vitro Needham, VSO. It's, uh, uh, it's, uh, you know, and it's all there to look for SAS customer customer doesn't have to really manage anything on prem fuel, >>Which must've been a huge advantage in the last year and a half when it was so hard to get to the on-prem locations. Right. >>That's exactly right. And this is one of the clear differentiators, you know, against, uh, you know, with, um, uh, compared to the legacy systems, because in legacy backup and disaster recovery systems, you need to manage your, not just your target tourists, but also, you know, the Asians and, you know, all the stuff that, uh, uh, all the software that goes along with that, uh, data production and, uh, and the disaster recovery solution. And so by T and Matt upgrades and patches and so on. And so what we do with, with a SAS based approach is take away that burden away from customer. So we deliver this entire service as a SAS first as a cloud service first, um, uh, delivery mechanisms of customers are don't have water. You don't have to whatever any of those things. >>And that's critical, especially as we've seen in the last 18 months with what's been going on the challenge of getting to locations, but also what's been happening as we talked about in the cybersecurity space, on the increase, the massive increase in ransomware. Talk to me a little bit about, I want to dig in before we go about some of the ways that you've simplified and integrated the way to backup VMware cloud on AWS. Talk to me a little bit more about some of those enhancements specifically. Yeah, >>Yeah. So, um, a lot of the customers, customers, as you know, are, uh, you know, have a dual pronged approach where they have, you know, some workloads running on prem and they have some workloads running and the VMware cloud on AWS and for BNB, uh, for VMs that are running on VMware cloud on AWS. Um, you know, now they have a choice of, uh, of protecting, protecting the data and the VM very simply, uh, using the McLaurin disaster cloud disaster recovery. And what that means is that they don't need to have the full band BR solution, but they can simply protect the data and automatically restore and recover of data. If they, you know, if there's a corruption or something goes wrong with their, uh, you know, the beans, they can simply restore the data without going through an entire field processes. So we provide a simplified way for customers to automatically protect data, and then that are running on VMware cloud on AWS. And that's a, and it's fully integrated with our cloud on AWS, you know, workflows. And, um, and so that's a great win for anyone who's, who's migrating data man workloads into BMC >>Is the primary objective of that to deliver a business resiliency. Dr. >>Both actually that's, that's, that's, that's a great part about that. You know, that's a bit part of the solution is that customers don't have to choose between Dr and business resiliency. They get both with a single solution. They can start off, it's a specific business resiliency and protecting the data, but if they choose to, they can them, uh, you know, add BR as well to that, to those workflows. And so it's not either, or it's both. >>Excellent. Got it. Any other enhancements that you guys are announcing at the Emerald this year? >>Yeah. I just want to reiterate the announcements and the key enhancements and the making, making, uh, you know, the balancing beam. Well, um, the first one, as I said is, uh, uh, is 30 minutes RPO. So customers that are business critical workloads can now pro protect the data and be guaranteed that they're, you know, the, the, you know, the demo data, the data that they, um, you know, they lag behind it's, it's in the 30 minute range and not in the other screens, like with other legacy backup solutions. That's one. The second is the integration, uh, as all enhancements that, you know, that I just talked about for ransom recovery, single file, thin file restore. Um, they always had, you know, number of snapshots and, you know, failure was and so on, but silverish was a key and that's what they've been making for a ransomware recovery. And the third one is the integration with BNB coordinator. So the fully integrated solution and provides a simple, you know, sort of plug and play solution for any workload that's funding in being AWS. Those are the three Tiki announcements. There's a lot more in, um, in the world. So you'll see that in the coming weeks and months, but these are the three on to get the input, >>A lot of enhancements to a solution that was launched just about a year ago. VJ, thank you for sharing with us. What's new with VMware cloud DVR, the enhancements, what you're doing, and also how it's enabling customers to recover from that ever pressing, increasing threat of ransomware. We appreciate your thoughts and likewise for VJ Ramachandra and I'm Lisa Martin, you're watching the cubes coverage of VMworld 2021.
SUMMARY :
Welcome to the cubes coverage of VMworld 2021. So What are some of the key trends? uh, we introduced bandwidth disaster recovery, you know, the last beam work with adopting VMware cloud and D anything that you think is interesting. uh, across, uh, the, you know, the B cell this, those that need to implement Dr. Ransomware, as we talked about, are there, and then you also mentioned And so, um, you know, and that's extremely, you know, on-prem to on-prem, you know, having to, you know, people who shouldn't capacity Dig through some of those differentiators, if you will, one by one, because there's so much choice out there, And the third, you know, sort of major value proposition from, from, uh, from the BMS is that, and the need to recover from a disaster is to be able to restore the data in a timely and features that specifically speak to that, uh, you know, to that pain point that you just mentioned, So give me an example of a customer that you helped recover after they were hit with ransomware. And so, uh, you know, it is a big, in the cloud that are, that allows them to go to any point, uh, you know, uh, if we get hit with ransomware for every industry, like you were saying before, uh, you know, BM and all the beings in the data center, but customers when they hit the trends It's, uh, uh, it's, uh, you know, and it's all there to look for SAS customer customer doesn't have Which must've been a huge advantage in the last year and a half when it was so hard to get to the on-prem locations. And this is one of the clear differentiators, you know, against, uh, on the challenge of getting to locations, but also what's been happening as we talked about in the cybersecurity And that's a, and it's fully integrated with our cloud on AWS, you know, Is the primary objective of that to deliver a business resiliency. they can them, uh, you know, add BR as well to that, to those workflows. Any other enhancements that you guys are announcing at the Emerald this year? is the integration, uh, as all enhancements that, you know, that I just talked about for ransom VJ, thank you for sharing
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Sanzio Bassini, Cineca | CUBE Conversation, July 2021
(upbeat music) >> Welcome to the CUBE Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the Head of High Performance Computing at Cineca, at DELL technologies customer. Sanzio, welcome to the CUBE. >> Thank you, it's a pleasure, it's a pleasure. >> Likewise, nice to see you. So tell us a little bit about Cineca. This is a large computing center, but a very large Italian nonprofit consortium. Tell us about it. >> Yes, Cineca been founded 50 years ago, from the university systems in Italy. For a statutory mission, which is to support, the scientific discovery, and the industry innovations, using the High Performance Computing and the correlated methodologies like a, Artificial Intelligence, which is one of the, you see the more, in a, in a adopted in those days, but together with the big data processing and and simulation. Yes, we are a consortium, which means that this is a private not-for-profit organizations. Currently, our member of the consortium, almost all the universities in Italy and also all the national agencies for those selected structures. Uh. The main quarter of Cineca is in Bologna, which is in the heart Nation, with the bunch of presence in Milan, in Rome and in Naples, so we are a consultation organization. >> And I also read that you were, are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our institutional missions, the last 10 to 15 years we have been to say, frequent flyers in the top 10. There been at least two, three systems that have been ranked at the top 10. Apart, the.., whatever would be the meaning of such an advance market, there's a lot of its criticalities. We are well aware. The idea is that we're enabling the scientific discovery, by means of providing the most advanced systems and the co-designing, the most advanced HPC systems to promote and support the, what is the, excellence in science. And that being part of European high-performance computing IT system. That is the case. >> Excellent. Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the Human Brain Project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> Um, The Human Brain Project is one of the flagship project that has been co-founded by the European commission and that the participating member states. Is not as another situations that are undertaking, it's definitely a joint collaboration between members states and the European commission. There are two different right now, flagships together with another, that is in progress, which is that the quantum of flagship, the first two flagship abroad that that has been lost. The commission for operation with the participating states has been one on the digraph vein on which also we are participating in directly together with the CNR, is the national business counselor. And the second for which we are core partners of the HPC that is, the Human Brain Project. That, that is a big flagship, one million offer, of newer investment, co-founded by the participating states and that the European commission. The project it's going to set up, in what to do be the, third strategic grant agreement that they will go over the next three years, the good, the complete that the, the whole process. Then we see what is going to happen at Africa. We thought that their would be some others progress offer these big projects. It's project that would combine both the technology issues, like the designing the off high-performance computing systems that meet the requirements of the community and the big challenge, scientific challenges correlated to the physiological functions of the human brain center, including the different farm show survey to do with the behavior of the human brain. A from the pathological point of view, from the physiological point of view, that better understand the could be the way for, for a facing that. Let's say the pathology, some of those are very much correlated with respect to aging, and that it would impact the, the health, the public health systems. Some other that are correlating with what would be the support for the physiological knowledge of the, of the human brains. And finally that they, let me say, technological transfer stuff that represented the knowing off at the physiological, behavior of the human brain. Just to use a sort of metaphor to have happen from the point of view of there computational performance, the human brain is a, a, a, more than Exoscale systems, but with a energy consumption, which is very low, we are talking about some hundreds of Watts. So some hundreds of watts of energy, would provide a an extreme and computational performance. So if would could organized the technology of the high-performance computing in terms of interconnections now we're morphing the computing systems and exploitations of that kind of technologies, in I build a system that it might provide the computational power that would represent a tremendous and tremendous step ahead, in order to facing the big challenges of our base, like energies, personalized medicine, try not to change food for all those kinds of big socioeconomic challenges that we are facing. >> Yes I was reading that besides, sorry Sanzio I was reading that besides the Human Brain Project, there are other projects going on, such as that you mentioned, I'd like to understand how Cineca is working with Dell technologies. You have to translate, as you've mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell technologies. >> Yes, in particularly in our computing architectures, we had the need to address the capability to facing the data processing involved with backed off the Human Brain Project and general speaking that is backed off the science vendor, that would combine the capability also to provide the cloud access to the system. So by main soft containers technologies and the capability also, to address what would be the creation of a Federation. So Piper problems with people proceeded in a new world. So at the end that the requirements and the terms of reference of the would matter will decline and the terms of a system that would be capable to manage, let's say, in a holistic approach, the data processing, the cloud computing services and the opportunity before for being integrated that in a Federation of HSBC system in Europe's, and with this backed off, that kind of thing, we manage a competitive dialogue procurement processor, I think I the sentence would share together with the different potential technology providers, what would be the visuals and those are the constraints (inaudible) and those other kinds of constraints like, I don't want to say, I mean, environmental kind of constraints and uh, sharing with this back of the technology provider what would it be the vision for this solution, in a very, let's say hard, the competitive dialogue, and at the end, results in a sort of, I don't want to say Darwinian processes, okay. So I mean, the survivors in terms of the different technology providers being Dell that shown the characteristics of the solution that it will be more, let's say compliant. And at the same time are flexible with respect of the combinations of very different constraints and requirements that has been the, the process that has been the outcomes of such a process. >> I like that you mentioned that Darwinian survival of the fittest and that Dell technologies has been, it sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> Definitely the volume commitment that together with the, European member states is that by means of scientific merits and the peer review process, roughly speaking the arc of the production capacity, would be shared at the European level. That it's a commitment that, that there's been, that there's been a shared of that together with France, Germany, Spain, and, and with the London. So, I mean, our, half of our production capacity, it's a share of that at the European level, where also of course the Italian scientist can apply in the participates, but in a sort of offer emulations and the advanced competition for addressing what it would be the excellence in science. The remaining 50% of our production capacity is for, for the national community and, somehow to prepare and support the Italian community to be competitive on the worldwide scenario on the European and international scenario, uh that setting up would lead also to the agreement at the international level, with respect of some of the options that, that are promoted the progress in a US and in Japan also. So from this point of view, that mean that in some cases also the, access that it would come from researchers the best collaborations and the sharing options with the US researchers their or Japanese researchers in an open space. >> Open space for, it sounds like the Human Brain Project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the Human Brain Project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be need the by rural SPC that is the Jared undertaking um entities, that has been created for consolidating and for progressing the high-performance computing ecosystem in Europe. It represented by the Federations of high-performance computing systems at European level, there is a, a, an option that, that has been encapsulated and the elaborated inside the human brain flagship project which is called the FEHIPCSE that stand for Federation of a High-Performance Computing System in Europe. That uh provide the open service based on the two concepts on one, one is the sharing of the Heidi at a European level, so it means that the, the high demand of the users or researchers more properly. It's unique and Universal at the European level. That didn't mean better the same, we see identity management, education management with the open, and the access to the Cineca system, to the SARS system in France, to the unique system in, uh Germany to the, Diocese system in a Switzerland, to the Morocco System in a Spain. That is the part related to what will be the federated, the ID management, the others, et cetera, related to what will be the Federation off the data access. So from the point of view, again, the scientific community, mostly the community of Human Brain Project, but that will be open at other domains and other community, make sure that data in a seamless mode after European language, from the technological point of view, or let's say from the infrastructure point of view, very strong up, from the scientific point of view, uh what they think they may not, will be the most of the options is being supported by Cineca has to do with the two specific target. One is the elaboration of the data that are provided by the lands. The laws are a laboratory facility in that Florence. That is one of the four parts, and from the bottom view of the provisions of the data that is for the scattering, the, the data that would come from the mouse brains, that are use for, for (inaudible) And then the second part is for the Mayor scale studies of the cortex of the of the human brain, and that got add-on by a couple of groups that are believing that action from a European level their group of the National Researcher Counsel the CNR, that are the two main outcome on which we are in some out reference high-performance computing facilities for supporting that kind of research. Then their is in some situations they combinations of the performance a, capability of the Federation systems for addressing what will be the simulations of the overall human brain would take a lot of performance challenge simulation with bacteria that they would happen combining that they SPC facility as at European level. >> Right! So I was reading there's a case study by the way, on Cynic that Dell technologies has published. And some of the results you talked about, those that the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prostheses for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the Human Brain Project. One last question Sanzio, for you, what advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> (coughs laughs) I think that at, at a certain point, that specific know how would became sort of a know how that is been, I mean, accumulated and then by some facilities and institutions around the world. There are the, the federal labs in US, the main nation model centers in Europe, the big facilities in Japan. And of course the, the big university facilities in China that are becoming, how do you say, evident and our progressively occupied increasing the space, that to say that that is somehow it, that, that, that the, those institutions would continues collaborate and sharing that there are periods I would expect off what to do, be the top level systems. Then there is a continuous sharing of uh knowledge, the experience best practices with respect off, let's say the technologies transfers towards productions and services and boosterism. Where the situation is big parenta, in the sense that, their are focused what it would be, uh the integration of the high-performance computing technology into their production workflow. And from the point of view, there is the sharing of the experience in order to provide the, a sort of, let's say, spreads and amplifications of the opportunity for supporting innovation. That is part of are solution means, in a Italy but it also, eh, er sort of um, see objective, that is addressed by the European options er supported by the European commission. I think that that sort of (inaudible) supply that in US, the, that will be coming there, sort of you see the max practice for the technology transfer to support the innovation. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio thank you so much for sharing with us, what Cineca is doing and the great research that's going on there, and across a lot of disciplines, we appreciate you joining the program today. Thank you. >> Thank you, it's been a pleasure, thank you very much for the opportunity. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this cube conversation. (calming music)
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the Head of High Performance Thank you, it's a Likewise, nice to see you. and also all the national agencies are the top 10 out of the that have been ranked at the top 10. the Human Brain Project. and that the European commission. the Human Brain Project, that is backed off the the fittest and that Dell the Italian community to be competitive of the results that the that is for the scattering, the, And some of the results you talked about, that is addressed by the European options and the environment that you've built, thank you very much for the opportunity. for Sanzio Bassini.
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Monica Kumar & Tarkan Maner, Nutanix | CUBEconversation
(upbeat music) >> The cloud is evolving. You know, it's no longer a set of remote services somewhere off in the cloud, in the distance. It's expanding. It's moving to on-prem. On-prem workloads are connecting to the cloud. They're spanning clouds in a way that hides the plumbing and simplifies deployment, management, security, and governance. So hybrid multicloud is the next big thing in infrastructure, and at the recent Nutanix .NEXT conference, we got a major dose of that theme, and with me to talk about what we heard at that event, what we learned, why it matters, and what it means to customers are Monica Kumar, who's the senior vice president of marketing and cloud go-to-market at Nutanix, and Tarkan Maner, who's the chief commercial officer at Nutanix. Guys, great to see you again. Welcome to the theCUBE. >> Great to be back here. >> Great to see you, Dave. >> Okay, so you just completed another .NEXT. As an analyst, I like to evaluate the messaging at an event like this, drill into the technical details to try to understand if you're actually investing in the things that you're promoting in your keynotes, and then talk to customers to see how real it is. So with that as a warning, you guys are all in on hybrid multicloud, and I have my takeaways that I'd be happy to share, but, Tarkan, what were your impressions, coming out of the event? >> Look, you had a great entry. Our goal, as Monica is going to outline, too, cloud is not a destination. It's an operating model. Our customers are basically using cloud as a business model, as an operating model. It's not just a bunch of techno mumbo-jumbo, as, kind of, you outlined. We want to make sure we make cloud invisible to the customer so they can focus on what they need to focus on as a business. So as part of that, we want to make sure the workloads, the apps, they can run anywhere the way the customer wants. So in that context, you know, our entire story was bringing customer workloads, use-cases, partner ecosystem with ISVs and cloud providers and service providers and ISPs we're working with like Citrix on end user computing, like Red Hat on cloud native, and also bringing the right products, both in terms of infrastructure capability and management capability for both operators and application developers. So bringing all these pieces together and make it simple for the customer to use the cloud as an operating model. That was the biggest goal here. >> Great, thank you. Monica, anything you'd add in terms of your takeaways? >> Well, I think Tarkan said it right. We are here to make cloud complexity invisible. This was our big event to get thousands of our customers, partners, our supporters together and unveil our product portfolio, which is much more simplified, now. It's a cloud platform. And really have a chance to show them how we are building an ecosystem around it, and really bringing to life the whole notion of hybrid multicloud computing. >> So, Monica, could you just, for our audience, just summarize the big news that came out of .NEXT? >> Yeah, we actually made four different announcements, and most of them were focused around, obviously, our product portfolio. So the first one was around enhancements to our cloud platform to help customers build modern, software-defined data centers to speed their hybrid multicloud deployments while supporting their business-critical applications, and that was really about the next version of our flagship, AOS six, availability. We announced the general availability of that, and key features really included things like built-in virtual networking, disaster recovery enhancements, security enhancements that otherwise would need a lot of specialized hardware, software, and skills are now built into our platform. And, most importantly, all of this functionality being managed through a single interface, right? Which significantly decreases the operational overhead. So that was one announcement. The second announcement was focused around data services and really making it easy for customers to simplify data management, also optimize big data and database workloads. We announced capability that now improves performances of database workloads by 2x, big data workloads by 3x, so lots of great stuff there. We also announced a new service called Nutanix Data Lens, which is a new unstructured data governance service. So, again, I don't want to go into a lot of details here. Maybe we can do it later. That was our second big announcement. The third announcement, which is really around partnerships, and we'll talk more about that, is with Microsoft. We announced the preview of Nutanix Clusters and Azure, and that's really taking our entire flagship Nutanix platform and running it on Azure. And so, now, we are in preview on that one, and we're super excited about that. And then, last but not least, and I know Tarkan is going to go into a lot more detail, is we announced a strategic partnership with Citrix around the whole future of hybrid work. So lots of big news coming out of it. I just gave you a quick summary. There's a lot more around this, as well. >> Okay. Now, I'd like to give you my honest take, if you guys don't mind, and, Tarkan, I'll steal one of your lines. Don't hate me, okay? So the first thing I'm going to say is I think, Nutanix, you have the absolute right vision. There's no question in my mind. But what you're doing is not trivial, and I think it's going to play out. It's going to take a number of years. To actually build an abstraction layer, which is where you're going, as I take it, as a platform that can exploit all the respective cloud native primitives and run virtually any workload in any cloud. And then what you're doing, as I see it, is abstracting that underlying technology complexity and bringing that same experience on-prem, across clouds, and as I say, that's hard. I will say this: the deep dives that I got at the analyst event, it convinced me that you're committed to this vision. You're spending real dollars on focused research and development on this effort, and, very importantly, you're sticking to your true heritage of making this simple. Now, you're not alone. All the non-hyperscalers are going after the multicloud opportunity, which, again, is really challenging, but my assessment is you're ahead of the game. You're certainly focused on your markets, but, from what I've seen, I believe it's one of the best examples of a true hybrid multicloud-- you're on that journey-- that I've seen to date. So I would give you high marks there. And I like the ecosystem-building piece of it. So, Tarkan, you could course-correct anything that I've said, and I'd love for you to pick up on your comments. It takes a village, you know, you're sort of invoking Hillary Clinton, to bring the right solution to customers. So maybe you could talk about some of that, as well. >> Look, actually, you hit all the right points, and I don't hate you for that. I love you for that, as you know. Look, at the end of the day, we started this journey about 10 years ago. The last two years with Monica, with the great executive team, and overall team as a whole, big push to what you just suggested. We're not necessarily, you know, passionate about cloud. Again, it's a business model. We're passionate about customer outcomes, and some of those outcomes sometimes are going to also be on-prem. That's why we focus on this terminology, hybrid multicloud. It is not multicloud, it's not just private cloud or on-prem and non-cloud. We want to make sure customers have the right outcomes. So based on that, whether those are cloud partners or platform partners like HPE, Dell, Supermicro. We just announced a partnership with Supermicro, now, we're selling our software. HPE, we run on GreenLake. Lenovo, we run on TruScale. Big support for Lenovo. Dell's still a great partner to us. On cloud partnerships, as Monica mentioned, obviously Azure. We had a big session with AWS. Lots of new work going on with Red Hat as an ISV partner. Tying that also to IBM Cloud, as we move forward, as Red Hat and IBM Cloud go hand in hand, and also tons of workarounds, as Monica mentioned. So it takes a village. We want to make sure customer outcomes deliver value. So anywhere, for any app, on any infrastructure, any cloud, regardless standards or protocols, we want to make sure we have an open system coverage, not only for operators, but also for application developers, develop those applications securely and for operators, run and manage those applications securely anywhere. So from that perspective, tons of interest, obviously, on the Citrix or the UC side, as Monica mentioned earlier, we also just announced the Red Hat partnership for cloud services. Right before that, next we highlighted that, and we are super excited about those two partnerships. >> Yeah, so, when I talked to some of your product folks and got into the technology a little bit, it's clear to me you're not wrapping your stack in containers and shoving it into the cloud and hosting it like some do. You're actually going much deeper. And, again, that's why it's hard. You could take advantage of those things, but-- So, Monica, you were on the stage at .NEXT with Eric Lockhart of Microsoft. Maybe you can share some details around the focus on Azure and what it means for customers. >> Absolutely. First of all, I'm so grateful that Eric actually flew out to the Bay Area to be live on stage with us. So very super grateful for Eric and Azure partnership there. As I said earlier, we announced the preview of Nutanix Clusters and Azure. It's a big deal. We've been working on it for a while. What this means is that a select few organizations will have an opportunity to get early access and also help shape the roadmap of our offering. And, obviously, we're looking forward to then announcing general availability soon after that. So that's number one. We're already seeing tremendous interest. We have a large number of customers who want to get their hands on early access. We are already working with them to get them set up. The second piece that Eric and I talked about really was, you know, the reason why the work that we're doing together is so important is because we do know that hybrid cloud is the preferred IT model. You know, we've heard that in spades from all different industries' research, by talking to customers, by talking to people like yourselves. However, when customers actually start deploying it, there's lots of issues that come up. There's limited skill sets, resources, and, most importantly, there's a disparity between the on-premises networking security management and the cloud networking security management. And that's what we are focused on, together as partners, is removing that barrier, the friction between on-prem and Azure cloud. So our customers can easily migrate their workloads in Azure cloud, do cloud disaster recovery, create a burst into cloud for elasticity if they need to, or even use Azure as an on-ramp to modernize applications by using the Azure cloud services. So that's one big piece. The second piece is our partnership around Kubernetes and cloud native, and that's something we've already provided to the market. It's GA with Azure and Nutanix cloud platform working together to build Kubernetes-based applications, container-based applications, and run them and manage them. So there's a lot more information on nutanix.com/azure. And I would say, for those of our listeners who want to give it a try and who want their hands on it, we also have a test drive available. You can actually experience the product by going to nutanix.com/azure and taking the test drive. >> Excellent. Now, Tarkan, we saw recently that you announced services. You've got HPE GreenLake, Lenovo, their Azure service, which is called TruScale. We saw you with Keith White at HPE Discover. I was just with Keith White this week, by the way, face to face. Awesome guy. So that's exciting. You got some investments going on there. What can you tell us about those partnerships? >> So, look, as we talked through this a little bit, the HPE relationship is a very critical relationship. One of our fastest growing partnerships. You know, our customers now can run a Nutanix software on any HPE platform. We call it DX, is the platform. But beyond that, now, if the customers want to use HPE service as-a-service, now, Nutanix software, the entire stack, it's not only hybrid multicloud platform, the database capability, EUC capability, storage capability, can run on HPE's service, GreenLake service. Same thing, by the way, same way available on Lenovo. Again, we're doing similar work with Dell and Supermicro, again, giving our customers choice. If they want to go to a public club partner like Azure, AWS, they have that choice. And also, as you know, I know Monica, you're going to talk about this, with our GSI partnerships and new service provider program, we're giving options to customers because, in some other regions, HPE might not be their choice or Azure not be choice, and a local telco might the choice in some country like Japan or India. So we give options and capability to the customers to run Nutanix software anywhere they like. >> I think that's a really important point you're making because, as I see all these infrastructure providers, who are traditionally on-prem players, introduce as-a-service, one of the things I'm looking for is, sure, they've got to have their own services, their own products available, but what other ecosystem partners are they offering? Are they truly giving the customers choice? Because that's, really, that's the hallmark of a cloud provider. You know, if we think about Amazon, you don't always have to use the Amazon product. You can use actually a competitive product, and that's the way it is. They let the customers choose. Of course, they want to sell their own, but, if you innovate fast enough, which, of course, Nutanix is all about innovation, a lot of customers are going to choose you. So that's key to these as-a-service models. So, Monica, Tarkan mentioned the GSIs. What can you tell us about the big partners there? >> Yeah, definitely. Actually, before I talk about GSIs, I do want to make sure our listeners understand we already support AWS in a public cloud, right? So Nutanix totally is available in general, generally available on AWS to use and build a hybrid cloud offering. And the reason I say that is because our philosophy from day one, even on the infrastructure side, has been freedom of choice for our customers and supporting as large a number of platforms and substrates as we can. And that's the notion that we are continuing, here, forward with. So to talk about GSIs a bit more, obviously, when you say one platform, any app, any cloud, any cloud includes on-prem, it includes hyperscalers, it includes the regional service providers, as well. So as an example, TCS is a really great partner of ours. We have a long history of working together with TCS, in global 2000 accounts across many different industries, retail, financial services, energy, and we are really focused, for example, with them, on expanding our joint business around mission critical applications deployment in our customer accounts, and specifically our databases with Nutanix Era, for example. Another great partner for us is HCL. In fact, HCL's solution SKALE DB, we showcased at .NEXT just yesterday. And SKALE DB is a fully managed database service that HCL offers which includes a Nutanix platform, including Nutanix Era, which is our database service, along with HCL services, as well as the hardware/software that customers need to actually run their business applications on it. And then, moving on to service providers, you know, we have great partnerships like with Cyxtera, who, in fact, was the service provider partner of the year. That's the award they just got. And many other service providers, including working with, you know, all of the edge cloud, Equinix. So, I can go on. We have a long list of partnerships, but what I want to say is that these are very important partnerships to us. All the way from, as Tarkan said, OEMs, hyperscalers, ISVs, you know, like Red Hat, Citrix, and, of course, our service provider, GSI partnerships. And then, last but not least, I think, Tarkan, I'd love for you to maybe comment on our channel partnerships as well, right? That's a very important part of our ecosystem. >> No, absolutely. You're absolutely right. Monica. As you suggested, our GSI program is one of the best programs in the industry in number of GSIs we support, new SP program, enterprise solution providers, service provider program, covering telcos and regional service providers, like you suggested, OVH in France, NTT in Japan, Yotta group in India, Cyxtera in the US. We have over 50 new service providers signed up in the last few months since the announcement, but tying all these things, obviously, to our overall channel ecosystem with our distributors and resellers, which is moving very nicely. We have Christian Alvarez, who is running our channel programs globally. And one last piece, Dave, I think this was important point that Monica brought up. Again, give choice to our customers. It's not about cloud by itself. It's outcomes, but cloud is an enabler to get there, especially in a hybrid multicloud fashion. And last point I would add to this is help customers regardless of the stage they're in in their cloud migration. From rehosting to replatforming, repurchasing or refactoring, rearchitecting applications or retaining applications or retiring applications, they will have different needs. And what we're trying to do, with Monica's help, with the entire team: choice. Choice in stage, choice in maturity to migrate to cloud, and choice on platform. >> So I want to close. First of all, I want to give some of my impressions. So we've been watching Nutanix since the early days. I remember vividly standing around the conference call with my colleague at the time, Stu Miniman. The state-of-the-art was converged infrastructure, at the time, bolting together storage, networking, and compute, very hardware centric. And the founding team at Nutanix told us, "We're going to have a software-led version of that." And you popularized, you kind of created the hyperconverged infrastructure market. You created what we called at the time true private cloud, scaled up as a company, and now you're really going after that multicloud, hybrid cloud opportunity. Jerry Chen and Greylock, they just wrote a piece called Castles on the Cloud, and the whole concept was, and I say this all the time, the hyperscalers, last year, just spent a hundred billion dollars on CapEx. That's a gift to companies that can add value on top of that. And that's exactly the strategy that you're taking, so I like it. You've got to move fast, and you are. So, guys, thanks for coming on, but I want you to both-- maybe, Tarkan, you can start, and Monica, you can bring us home. Give us your wrap up, your summary, and any final thoughts. >> All right, look, I'm going to go back to where I started this. Again, I know I go back. This is like a broken record, but it's so important we hear from the customers. Again, cloud is not a destination. It's a business model. We are here to support those outcomes, regardless of platform, regardless of hypervisor, cloud type or app, making sure from legacy apps to cloud native apps, we are there for the customers regardless of their stage in their migration. >> Dave: Right, thank you. Monica? >> Yeah. And I, again, you know, just the whole conversation we've been having is around this but I'll remind everybody that why we started out. Our journey was to make infrastructure invisible. We are now very well poised to helping our customers, making the cloud complexity invisible. So our customers can focus on business outcomes and innovation. And, as you can see, coming out of .NEXT, we've been firing on all cylinders to deliver this differentiated, unified hybrid multicloud platform so our customers can really run any app, anywhere, on any cloud. And with the simplicity that we are known for because, you know, our customers love us. NPS 90 plus seven years in a row. But, again, the guiding principle is simplicity, portability, choice. And, really, our compass is our customers. So that's what we are focused on. >> Well, I love not having to get on planes every Sunday and coming back every Friday, but I do miss going to events like .NEXT, where I meet a lot of those customers. And I, again, we've been following you guys since the early days. I can attest to the customer delight. I've spent a lot of time with them, driven in taxis, hung out at parties, on buses. And so, guys, listen, good luck in the next chapter of Nutanix. We'll be there reporting and really appreciate your time. >> Thank you so much. >> Thank you so much, Dave. >> All right, and thank you for watching, everybody. This is Dave Vellante for theCUBE, and, as always, we'll see you next time. (light music)
SUMMARY :
and at the recent and then talk to customers and also bringing the right products, terms of your takeaways? and really bringing to just summarize the big news So the first one was around enhancements So the first thing I'm going to say is big push to what you just suggested. and got into the technology a little bit, and also help shape the face to face. and a local telco might the choice and that's the way it is. And that's the notion but cloud is an enabler to get there, and the whole concept was, We are here to support those outcomes, Dave: Right, thank you. just the whole conversation in the next chapter of Nutanix. and, as always, we'll see you next time.
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MANUFACTURING Reduce Costs
>>Hey, we're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, it's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great >>To see you take it away. >>All right, guys. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing and flute and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution, things got interesting, right? You started to see automation, but that automation was done essentially programmed your robot to do something and did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different, right? >>Cause now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue, there we'll issue that, but it's important. Not for technology's sake, right? It's important because it actually drives very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, uh, companies and manufacturers moving to improve while its quality prompts still accounts for 20% of sales, right? So every fifth of what you meant are manufactured from a revenue perspective, do back quality issues that are costing you a lot planned downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of new spaces, we're not doing it just merely to implement technology. We're doing it to move these from members, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life with what like, right, but this is actually the business. The cloud area is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I say, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things are taking about time, but this, the ability to take these real-time actions or, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into an enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you could start to think about, you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we can put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one history sets data, you can build out those machine learning models. >>I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. Once you understand that you can actually then build out the smiles, you could deploy the models after the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, but schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. >>So, >>You know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is for SIA for ECA is the, um, is the, was, is the, um, the, uh, a supplier associated with Pooja central line out of France. They are huge, right? This is a multinational automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, um, they connected 2000 machines, right. Um, and they once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor the data firms coming in, you know, monitor the process. >>That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, fibrations pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision, wilding inspection. So let's take pictures of parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections beer. And so they both have those machine learning models. So they took that data. All this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case, a great example of how you can start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you wanted to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turn in the morning sessions and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're gonna, they're gonna hit? >>You know, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right. So, and it's unsafe, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. >>Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world for a long time, the silos, um, uh, you know, the silos, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid theme and you've kind of got this world, that's going toward an equilibrium. You've got the OT side, you know, pretty hardcore engineers. And we know, we know it. Uh, a lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space. And when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to it earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims, kick kickoff. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots by about warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning where simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start something with monitoring, get a lot of value, start, then bring together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases there's value to be had throughout. I >>Remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question that it kind of, um, goes back to one of the things I alluded earlier, we've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they've built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Patera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of industry 4.0, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to lead this discussion on the technology advances. I'd love to talk tech here, uh, are the key technology enablers, and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space, sorry, manufacturing in >>A factory space. Yeah. I knew what you meant in know in the manufacturing space. There's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can w we're finally being able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got back way capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, very much more quickly. Yep. We got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, for everybody who joined us. Thanks. Thanks for joining.
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When you do the math, it's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom Thank you so much. So every fifth of what you meant are manufactured from a revenue perspective, So suddenly we can collect all this data from your, I want to walk you through this, You process that you align your time series data I talked to you about earlier. And as you can see, they operate in 300 sites Uh, and you know, 2000 machines, example of how you can start with monitoring, move to machine learning, but at the end of the day, I think the, the second thing that struck me is, you know, the costs, you know, 20% of sales And then I think the third point, which we turn in the morning sessions and the main stage is really this, And what we mean by the it, you know, your, it systems are the ones, for a long time, the silos, um, uh, you know, So Michael, let's talk about some of the use cases you touched on, on some, And you just make sure that you're staying within the bounds of your control plan, And now on this one, you say, imagine I've got warranty plans in the, in the field, And you can identify those factors that Remember when the, you know, the it industry really started to think about, or in the early days, So now, you know, we're really good at ingesting it if you will, that are going to move connected manufacturing and machine learning forward in that starts to blur at least from a latency perspective where you do your computer, and they believed the book to build a GP, you know, GPU level machine learning, Thank you so much. And thanks.
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MANUFACTURING V1b | CLOUDERA
>>Welcome to our industry. Drill-downs from manufacturing. I'm here with Michael Gerber, who is the managing director for automotive and manufacturing solutions at cloud era. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data connected trucks are fundamental to optimizing fleet performance costs and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're gonna come back and have a little conversation about what we just heard. Michael, great to see you over to you. >>Oh, thank you, Dave. And I appreciate having this conversation today. Hey, um, you know, this is actually an area connected trucks. You know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because, you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022. Um, and the reasons, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made truck makers providing benefits to fleet operators. And if you look at the F the top fleet operator, uh, the top benefits that fleet operators expect, you see this in the graph over here. >>Now almost 80% of them expect improved productivity, things like improved routing rates. So route efficiencies and improve customer service decrease in fuel consumption, but better technology. This isn't technology for technology sake, these connected trucks are coming onto the marketplace because Hey, it can provide for Mendez value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable this right, um, trucks are becoming connected because at the end of the day, um, we want to be able to provide fleet deficiencies through connected truck, um, analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle analytics machine learning life cycle, and to do that, you need to do a few different things, right? >>You start off of course, with connected trucks in the field. And, you know, you can have many of these trucks cause typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first you need to be able to do is connect to those products, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you conducted the, um, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you the ability to take this real-time action is actually the result of your machine learning license. Let me explain to you what I mean by that. >>So we have this trucks, we start to collect data from it right at the end of the day. Well we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected collected from those trucks. And you want to augment that with your dealership, say service information. Now you have, you know, you have sensor data and there was salting repair orders. You're now equipped to do things like predict one day maintenance will work correctly for all the data sets that you need to be able to do that. >>So what do you do here? Like I said, you adjusted your storage, you're enriching it with data, right? You're processing that data. You're aligning say the sensor data to that transactional system data from your, uh, from your, your pair maintenance systems, you know, you're bringing it together so that you can do two things you can do. First of all, you could do self-service BI on that date, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor right values and the need, for example, for, for a dealership repair, or as you could start to correlate, which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time action. >>I mentioned earlier as that data that then comes through in real-time, you're running it against that model, and you can take some real time actions. This is what we are, this, this, this, this analytics and machine learning model, um, machine learning life cycle is exactly what Cloudera enables this end-to-end ability to ingest, um, stroke, you know, store it, um, put a query, lay over it, um, machine learning models, and then run those machine learning models. Real-time now that's what we, that's what we do as a business. Now when such customer, and I just wanted to give you one example, um, a customer that we have worked with to provide these types of results is Navistar and Navistar was kind of an early, early adopter of connected truck analytics. And they provided these capabilities to their fleet operators, right? And they started off, uh, by, um, by, you know, connecting 475,000 trucks to up to well over a million now. >>And you know, the point here is with that, they were centralizing data from their telematics service providers, from their trucks, from telematics service providers. They're bringing in things like weather data and all those types of things. Um, and what they started to do was to build out machine learning models, aimed at predictive maintenance. And what's really interesting is that you see that Navistar, um, made tremendous strides in reducing the need or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring and service it before it broke down so that you could do that in a much more cost-effective manner. And if you see the benefits, right, they, they reduced maintenance costs 3 cents a mile, um, from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. >>So this was a tremendous success for Navistar. And we're seeing this across many of our, um, um, you know, um, uh, truck manufacturers. We were working with many of the truck OEMs and they are all working to achieve, um, you know, very, very similar types of, um, benefits to their customers. So just a little bit about Navistar. Um, now we're gonna turn to Q and a, Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our lives or to our website, what you see up, uh, up on the screen, there's the URLs cloudera.com for slash solutions for slash manufacturing. And you'll see a whole slew of, um, um, lateral and information, uh, in much more detail in terms of how we connect, um, trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >>Thank you. Uh, Michael, that's a great example. You've got, I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that lifecycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into. >>Yeah, that's really, that's a great question. They, you know, cause you know, everybody always thinks about machine learning. Like this is the first thing you go, well, actually it's not right for the first thing you really want to be able to go around. Many of our customers are doing slow. Let's simply connect our trucks or our vehicles or whatever our IOT asset is. And then you can do very simple things like just performance monitoring of the, of the piece of equipment in the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how has the, how has the driver performing? Is there a lot of idle time spent, um, you know, what's, what's route efficiencies looking like, you know, by connecting the vehicles, right? You get insights, as I said into the truck and into the driver and that's not machine learning. >>Right. But that, that, that monitoring piece is really, really important. The first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, uh, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like, uh, predictive maintenance happening, um, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making, but let's not, let's not minimize the value of good old fashioned monitoring that site to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >>You know, it's interesting. I'm, I'm envisioning when you talked about the monitoring, I'm envisioning a, you see the bumper sticker, you know, how am I driving this all the time? If somebody ever probably causes when they get cut off it's snow and you know, many people might think, oh, it's about big brother, but it's not. I mean, that's yeah. Okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization, I mean, that's, that's bottom line business value. So, so that's, I love those, uh, those examples. Um, I wonder, I mean, one of the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into, uh, you know, the blind spots they're, they're going to, they're going to get hit with, >>There's a few different things, right? So first of all, a lot of times your it folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? That's very specialized hardware in the car and things like that. And protocols that's number one, that that's the classic, it OT kind of conundrum that, um, you know, uh, many of our customers struggle with, but then more fundamentally is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. And as you move towards, um, more commercial solutions, you had what I call the silo, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor, you get the idea, you know, one of the things that we really think that we need with that, that needs to be brought to the table is first of all, having an end to end data management platform, that's kind of integrated, it's all tested together. >>You have the data lineage across the entire stack, but then also importantly, to be realistic, we have to be able to integrate to, um, industry kind of best practices as well in terms of, um, solution components in the car, how the hardware and all those types things. So I think there's, you know, it's just stepping back for a second. I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of art, um, offerings. Um, our job as a software maker is to make that easier and connect those dots. So customers don't have to do it all on all on their own. >>And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about, you know, new types of hardware coming in, you guys are optimizing for that. We see the it and the OT worlds blending together, no question. And then that end to end management piece, you know, this is different from your right, from it, normally everything's controlled or the data center, and this is a metadata, you know, rethinking kind of how you manage metadata. Um, so in the spirit of, of what we talked about earlier today, uh, uh, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >>Yeah, I'm really glad you're asking that because we actually embarked on a product on a project called project fusion, which really was about integrating with, you know, when you look at that connected vehicle life cycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Cloudera is Peter piece of this was ingesting data and all the things I talked about being storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners and the partners that we did with, um, integrate with or NXP NXP provides the service oriented gateways in the car. So that's a hardware in the car when river provides an in-car operating system, that's Linux, right? >>That's hardened and tested. We then ran ours, our, uh, Apache magnify, which is part of flood era data flow in the vehicle, right on that operating system. On that hardware, we pump the data over into the cloud where we did them, all the data analytics and machine learning and, and builds out these very specialized models. And then we used a company called Arabic equity. Once we both those models to do, you know, they specialize in automotive over the air updates, right? So they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, um, you know, uh, ecosystem, if you will, of leaders in this space, what we wanted to do is make sure that our, there was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working closely with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some further acceleration in the machine learning side of things. So, uh, yeah, you know, one of the things I always say about this types of use cases, it does take a village. And what we've really tried to do is build out that, that, uh, an ecosystem that provides that village so that we can speed that analytics and machine learning, um, lifecycle just as fast as it can be. This >>Is again another great example of, of data intensive workloads. It's not your, it's not your grandfather's ERP. That's running on, you know, traditional, you know, systems it's, these are really purpose-built, maybe they're customizable for certain edge use cases. They're low cost, low, low power. They can't be bloated, uh, ended you're right. It does take an ecosystem. You've got to have, you know, API APIs that connect and, and that's that, that takes a lot of work and a lot of thoughts. So that, that leads me to the technologies that are sort of underpinning this we've talked we've we talked a lot in the cube about semiconductor technology, and now that's changing and the advancements we're seeing there, what do you see as the, some of the key technical technology areas that are advancing this connected vehicle machine learning? >>You know, it's interesting, I'm seeing it in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about that used to be kind of a, a dumb gateway. That was really all it was doing was pushing data up and down and provided isolation, um, as a gateway down to the, uh, down from the lower level subsistence. So it was really security and just basic, um, you know, basic communication that gateway now is becoming what they call a service oriented gate. So it can run. It's not that it's bad desk. It's got memories that always, so now you could run serious compute in the car, right? So now all of these things like running machine learning, inference models, you have a lot more power in the corner at the same time. >>5g is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now you now you've got credible compute both at the edge in the vehicle and on the cloud. Right. And, um, you know, and then on the, you know, on the cloud, you've got partners like Nvidia who are accelerating, it's still further through better GPU based compute. So I mean the whole stack, if you look at it, that that machine learning life cycle we talked about, no, David seems like there's improvements and EV every step along the way, we're starting to see technology, um, optimum optimization, um, just pervasive throughout the cycle. >>And then real quick, it's not a quick topic, but you mentioned security. If it was seeing a whole new security model emerge, there is no perimeter anymore in this use case like this is there. >>No there isn't. And one of the things that we're, you know, remember where the data management platform platform and the thing we have to provide is provide end-to-end link, you know, end end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into from the beginning of when that data is ingested through, when it's stored through, when it's kind of processed and people are doing machine learning, we provide, we will provide that lineage so that, um, you know, that security and governance is a short throughout the, throughout the data learning life cycle, it >>Federated across in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it, >>Dave. Thank you. And thank you. Thanks for the audience for listening in today. Yes. Thank you for watching. >>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime. And look, when you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces loss opportunities. Michael. Great to see you >>Take it away. All right. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right. And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, massive assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done, essentially programmed a robot to do something. It did the same thing over and over and over irrespective about it, of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfast. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adaptive right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives and very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, of, uh, companies, um, and manufacturers moving to improve while its quality promise still accounted to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. >>Plant downtime, cost companies, $50 billion a year. So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just merely to implement technology. We're doing it to move these from drivers, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle, what like, right, because this is actually the business that cloud era is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI, this, this analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors have connected over the internet. So suddenly we can collect all this data from your, um, ma manufacturing plants. What do we want to be able to do? >>You know, we want to be able to collect it. We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking the time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You've got, you're going to ingest that data. >>You're going to store it. You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. >>But as I mentioned, you, and what's really important here is the fact that once you've stored long histories that say that you can build out those machine learning models I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need, a correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for Maples. Once you understand that you can actually then build out those models for deploy the models out the edge, where they will then work in that inference mode that we talked about, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that PR that predicted the need for maintenance? If so, let's take real-time action, right? >>Let's schedule a work order or an equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connecting connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is bought for Russia, for SIA, for ACA is the, um, is the, was, is the, um, the, uh, a supplier associated with Peugeot central line out of France. They are huge, right? This is a multi-national automotive parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. >>Um, and then once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor data firms coming in, you know, monitor the process. That was the first step, right. Uh, and, you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models or compute. And what they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad Bali outcome. Then you teach the machine to make that decision on its own. >>So now, now the machine, the camera is doing the inspections. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you can start with monitoring, moved to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing, a lot more detail, and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the cost, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit, >>You know, there's, there's, there, there's a few of the, but I think, you know, one of the ones, uh, w one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant, are running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietorial pro protocols. That information can be very, very difficult to get to. Right. So, and it's, it's a much more unstructured than from your OT. So th the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world. And for a long time, the silos, um, uh, the silos a, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge, >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So, Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right. And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, but just talking about simple monitoring next level down, and we're seeing is something we would call quality event forensic analysis. >>And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims kick up. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots. What about warranty issues? What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning, we're simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole slew of machine learning, use dates, you know, and that ranges from things like Wally or say yield optimization. >>We start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. And you're certain start to say, which, um, you know, which on a sensor values or factors drove good or bad yield outcomes, and you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with, with monitoring, get a lot of value, start then bringing together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases, there's this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and the new player would come in and he'd be perfectly white uniform, and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so I question it relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. And it kind of goes back to one of the things I alluded to alluded upon earlier. We've had some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of, um, industry for porno, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and, and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to li lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry, manufacturing. Yeah. >>Yeah. I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can, we've finally been able to get to the OT data, right? That's that's number one, you know, numb number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, the super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed a book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to, to your equipment. All of those things are making this, um, there's, you know, the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, uh, very much more quickly. Yeah, we got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined us. Thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.
SUMMARY :
Michael, great to see you over to you. And if you look at the F the top fleet operator, uh, the top benefits that So, you know, one of the things that's really important to be able to enable this right, And by the way, once you conducted the, um, this information from the trucks, you want to be able to analyze And you want to augment that with your dealership, say service information. So what do you do here? And they started off, uh, by, um, by, you know, connecting 475,000 And you know, the point here is with that, they were centralizing data from their telematics service providers, many of our, um, um, you know, um, uh, truck manufacturers. And you can push that back to the edge. And then you can do very simple things like just performance monitoring And then you start to see things like, uh, predictive maintenance happening, uh, you know, the blind spots they're, they're going to, they're going to get hit with, it OT kind of conundrum that, um, you know, So I think there's, you know, it's just stepping back for a second. the data center, and this is a metadata, you know, rethinking kind of how you manage metadata. with, you know, when you look at that connected vehicle life cycle, there are some core vendors And by the way, you mentioned Nvidia as well. and now that's changing and the advancements we're seeing there, what do you see as the, um, you know, basic communication that gateway now is becoming um, you know, and then on the, you know, on the cloud, you've got partners like Nvidia who are accelerating, And then real quick, it's not a quick topic, but you mentioned security. And one of the things that we're, you know, remember where the data management Thank you so much for that great information. Thank you for watching. And look, when you do the math, that's really quite obvious when the system is down, productivity is lost and it hits Thank you so much. So every fifth of what you meant or manufactured from a revenue So we call this manufacturing edge to AI, I want to walk you through this, um, you know, from your enterprise systems that your maintenance management system, And you can build out those models and say, Hey, here are the sensor values of the conditions And as you can see, they operate in 300 sites in They started off very well with, um, you know, great example of how you can start with monitoring, moved to machine learning, I think the, the second thing that struck me is, you know, the cost, you know, 20% of, And then I think the third point, which we turned in the morning sessions, and the main stage is really this, And what we mean by the it, you know, your, it systems are the ones, You've got the OT side and, you know, pretty hardcore engineers. And you just make sure that you're staying within the bounds of your control plan, And now on this one, you say, imagine I've got warranty plans in the, in the field, look, there's a huge, you know, depending on a customer's maturity around big data, I remember when the, you know, the it industry really started to think about, or in the early days, you know, uh, a barrier that we've always had and, if you will, that are going to move connected manufacturing and machine learning forward that starts to blur at least from a latency perspective where you do your computer, and they believed a book to build a GP, you know, GPU level machine learning, Thank you so much. Thank you for watching.
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Manufacturing Reduce Costs and Improve Quality with IoT Analytics
>>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great to see you, >>Dave. All right, guys. Thank you so much. So I'll tell you, we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done essentially programmed a robot to do something. It did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfasts. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives very important business outcomes. First of all, falling, right? If you look at the cost of quality, even despite decades of, of, uh, companies and manufacturers moving to improve while its quality prompts still account to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. Plant downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just narrowly to implement technology. We're doing it to move these from adverse, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle with what like, right. But so this is actually the business that cloud areas is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics, life something, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking that time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right? And that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you bring these datasets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one histories that say that you can build out those machine learning models I talked to you about earlier. >>So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And then you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. And once you understand that you can actually then build out those models, you deploy the models out to the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, right? Let's schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that a piece of equipment fails and allows us to be very, very proactive. >>So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different, um, manufacturers around the world. I want to just highlight one of them. Cause I thought it's really interesting. This company is bought for Russia. And for SIA for ACA is the, um, is the, is the, um, the, uh, a supplier associated with out of France. They are huge, right? This is a multi-national automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. Um, I mean at once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? >>To be able to just monitor the data from coming in, you know, monitor the process. That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things, just start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections for you. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go, then you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of where the data is, you've got to be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit? >>No, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES system, Freightos your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right? So, and it's uncertain, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own. And for a long time, the silos, the silos, a bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. This is Chris now is getting, you know, instrumented and captured. Uh, and so you've got that, that cultural challenge and, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a great, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about this, a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards the internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I'm got warranty plans in the, in the field, right? So I'm starting to see warranty claims kicked off on them. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots I've got, I've got warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of a car. So, and that, again, also not machine learning is simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day so that you could take corrective actions, but then you get into a whole slew of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with monitoring, get a lot of value, start, then bring together more diverse datasets to do things like connect the.analytics then all and all the way then to, to, to the more advanced machine learning use cases this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. I kind of, um, goes back to one of the things I alluded a little bit about earlier. We've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to get to practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to implement those types of, um, industry 4.0, uh, analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, uh, barrier that we've always had and, and bring together those data sets that really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to Lee lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry. Manufacturing in >>Factor space. Yeah, I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and it had become ubiquitous that number one, we can w we're finally been able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, um, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book, bullet, uh, GP, you know, GPU level, machine learning, all that, those models, and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processes are getting much smarter, uh, very much more quickly. >>Yep. We've got a lot of data and we have way lower costs, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined. Uh, thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.
SUMMARY :
When you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom Thank you so much. So every fifth of what you meant or manufactured from a revenue perspective, And those sensors are connected over the internet. I want to walk you through those machine learning models I talked to you about earlier. And then you can build out those models and say, Hey, here are the sensor values of the conditions And as you can see, they operate in 300 sites To be able to just monitor the data from coming in, you know, monitor the process. And that is the goal of most manufacturers. I think the, the second thing that struck me is, you know, the costs, you know, 20% of, And then I think the third point, which we turned in the morning sessions, and the main stage is really this, And what we mean by the it, you know, your, it systems are the ones, So Michael, let's talk about some of the use cases you touched on, on some, And you just make sure that you're staying within the bounds of your control plan, And now on this one, you say, imagine I'm got warranty plans in the, in the field, And you can identify those factors that I remember when the, you know, the it industry really started to think about, or in the early days, litmus that can open the flood gates of that OT data, making it much easier to if you will, that are going to move connected manufacturing and machine learning forward that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, And at the end of the day, to your point, Dave, that equipment and processes are getting much smarter, Thank you so much. Thank you for watching.
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Dr. Eng Lim Goh, HPE | HPE Discover 2021
>>Please >>welcome back to HPD discovered 2021. The cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover and you talked about thriving in the age of insights and how to craft a data centric strategy. And you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, Right? Yeah, data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight, right? Uh and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new asia. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave You know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is no barriers to insight. You know when we are awash with data. So that's a statement. Right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier. We spoke about barriers to incite when we are washed with data. The second barrier is uh that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of minutes. You know, we we all know those who are financially attuned with know about this uh incident, But this is not the only incident. There are many of them out there and for for that particular May six event, uh you know, it took a long time to get insight months. Yeah, before we for months we had no insight as to what happened, why it happened, right. Um, and and there were many other incidences like this and the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um, one of our customers decided to take the hard road to go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information, took the took the tough took the tough road and analyze that data took a long time to assemble. And they discovered that there was quote stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well, the reason is the process reports that everybody sees the rule in there that says all trades, less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones to get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? By not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with a corresponding other hundreds of millions of them. They say, well I'm going to get all these word pairs. Someone creative thought of a willing source. And you thought it was the United Nations, you see. So sometimes you think you don't have the right data with you, but there might be another source. And the willing one that could give you that data Right? The 2nd 1 has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right? Massive amounts of data, loss of sensors, collect loss of data. And uh, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So, these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data silo the Federated it various associated with data. That's tough to get that. They just took the hard road, right? And, and sometimes, thirdly, you just have to be creative to get the right data. You need, >>wow, I I'll tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months. Nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends, right? Like, uh, you know, we if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees. And in this case uh before the change in the rules. Right? But by the way, after the discovery, uh authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such there are times you just need to go back to the raw data. >>I want to ask, >>it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data but but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing will a two part question will the consumer market as has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge you can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now. What you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model that you have trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swamp they made >>is that learning from the edge? You're learning at the edge? In other words? >>Yes. >>Yeah, I understand the question. Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different Edge devices, assemble it back to the cloud side to to do the learning right. With someone you can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front. And then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again, you know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of insight? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and where will your data be, you know, going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um Let me give you the, you use the autonomous car example, let me use that. And We have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars. Right? Um, so this data collection cars they collect as a fleet of them collect 10 petabytes a day and when it came to us uh building a storage system yeah, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? Should what should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 16 seventies we call a large part of our D. N. A junk DNA. Today we realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know? So, so what's jumped in the yesterday could be valuable today or what's junk today could be valuable tomorrow. Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here. Right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore they did not just need to collect data that is uh that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for. Right? So they want it all. So I tell us them. So what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find out. Of course they also come back to us rightfully that, you know, we have to then work out a way to help them build that system, you know, so that health care, right? And and if you go to other industries like banking, they say they can't afford to keep them on, but they are regulated. Seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can stall right on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future right? Don't worry. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know. Well notwithstanding supply constraints on things like nand, we know the prices of storage is gonna continue to decline. We also know and not a lot of people are really talking about this but the processing power but he says moore's law is dead. Okay, it's waning. But the processing power when you combine the Cpus and N. P. U. S. And Gpus and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're going to have cheaper storage and it's going to be less expensive processing. And so as an ai practitioner, what can you do with that? >>So the amount of data that's gonna come in, it's gonna we exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right? So so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices data back to a central as a bunch of central. Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there, Were you given storage costs dropping? You'll still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data, you want rich data to train with. Right? Some sometimes you wanna mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge today, we don't have enough power. Right? The edge typically is characterized by a lower uh energy capability and therefore lower compute power. But soon, you know, even with lower energy they can do more with compute power, improving in energy efficiency, Right? Uh So learning at the edge today we do influence at the edge. So we data model deploy and you do in France at the age, that's what we do today. But more and more I believe given a massive amount of data at the edge, you, you have to have to start doing machine learning at the edge and, and if when you don't have enough power then you aggregate multiple devices, compute power into a swamp and learn as a swan. >>Oh, interesting. So now of course, if, if I were sitting and fly, fly on the wall in hp board meeting, I said okay. HB is as a leading provider of compute how do you take advantage of that? I mean we're going, we're, I know its future, but you must be thinking about that and participating in those markets. I know today you are, you have, you know, edge line and other products. But there's, it seems to me that it's, it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for the customers? The world will have to have a balance right? Where today the default? Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right? And it has to be hybrid and then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different. They are on a lot of times. On the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery power. Right? Um, so you have to build systems that adapt to it. But at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018 Uh the next four years from 2018, right $4 billion dollars invested to strengthen our edge portfolio. Edge product lines, Right. Edge solutions. >>I can doctor go, I could go on for hours with you. You're you're just such a great guest. Let's close. What are you most excited about in the future? Of of of it. Certainly H. P. E. But the industry in general. >>Yeah. I think the excitement is uh the customers, right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy, right? Just like you know uh you know, the the statement made was was so was profound, right? Um And Antonio said we are in the age of insight powered by data. That's the first line, right. Uh The line that comes after that is as such were becoming more and more data centric with data, the currency. Now the next step is even more profound. That is um You know, we are going as far as saying that you know um data should not be treated as cost anymore. No. Right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right? In thinking that is going to change the way we look at data, the way we value it. So that's a statement that this is the exciting thing because because for for me, a city of Ai right uh machine is only as intelligent as the data you feed it with data is a source of the machine learning to be intelligent. So, so that's that's why when when people start to value data, right? And and and say that it is an investment when we collect it, it is very positive for AI because an AI system gets intelligent, get more intelligence because it has a huge amounts of data and the diversity of data. So it would be great if the community values values data. Well, >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know, data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know, Doug Laney, when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr >>yeah. Question is is the process and methods evaluation right. But I believe we'll get there, we need to get started and then we'll get there. Believe >>doctor goes on >>pleasure. And yeah. And then the Yeah, I will well benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews from HP discover 21 this is Dave Volonte for the cube. The leader in enterprise tech coverage right back
SUMMARY :
Hewlett Packard enterprise Doctor go great to see you again. And you addressed you That's also part of the reason why that's the main reason why you know Antonio on day one So maybe we could talk a little bit about some of the things that you The first one is is the current challenge and that current challenge is uh you know stated So that's and they, and they chalked it up to a glitch like you said, is is that humans put in the rules to decide what goes into So it seems that most of the Ai going on in the enterprise is modeling It starts to evolve right to the point that using a test set of data that you have Yeah. The goal is to learn at the edge so that you don't have to move And then maybe only selectively send the autonomous vehicle example you gave us great because But on the other hand, you you know, you worry you you you But the processing power when you combine the Cpus and N. that there might need to be a balance between you needing to bring all that data from the I know today you are, you have, you know, edge line and other products. Um, so you have to build systems that adapt to it. What are you most excited about in the future? machine is only as intelligent as the data you feed it with data Um and I think increasingly you see it on the income statement, you know, data products and people Question is is the process and methods evaluation right. And then the Yeah, I will well benefit greatly from it. Doctor goes great to see you again. It's only as smart as the data you feed it with. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews
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