>> 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)

>>The Cube Presents UI Path Forward five. Brought to you by UI Path, >>Everybody. We're back in Las Vegas. The cube's coverage we're day one at UI Path forward. Five. Pascal Borne is here. He's an expert and bestselling author in the topic of AI and automation and the book Intelligent Automation. Welcome to the world of Hyper Automation, the first book on the topic. And of course, Ray Wong is back on the cube. He's the founder, chairman and principal analyst, Constellation Reese, also bestselling author of Everybody Wants To Rule the World. Guys, thanks so much for coming on The Cubes. Always a pleasure. Ray Pascal, First time on the Cube, I believe. >>Yes, thank you. Thanks for the invitation. Thank you. >>So what is artificial about artificial intelligence, >>For sure, not people. >>So, okay, so you guys are both speaking at the conference, Ray today. I think you're interviewing the co CEOs. What do you make of that? What's, what are you gonna, what are you gonna probe with these guys? Like, how they're gonna divide their divide and conquer, and why do you think the, the company Danielle in particular, decided to bring in Rob Sland? >>Well, you know what I mean, Like, you know, these companies are now at a different stage of growth, right? There's that early battle between RPA vendors. Now we're actually talking something different, right? We're talking about where does automation go? How do we get the decisioning? What's the next best action? That's gonna be the next step. And to take where UI path is today to somewhere else, You really want someone with that enterprise cred and experience the sales motions, the packages, the partnership capabilities, and who else better than Roblin? He, that's, he's done, he can do that in his sleep, but now he's gotta do that in a new space, taking whole category to another level. Now, Daniel on the other hand, right, I mean, he's the visionary founder. He put this thing from nothing to where he is today, right? I mean, at that point you want your founder thinking about the next set of ideas, right? So you get this interesting dynamic that we've seen for a while with co CEOs, those that are doing the operations, getting the stuff out the door, and then letting the founders get a chance to go back and rethink, take a look at the perspective, and hopefully get a chance to build the next idea or take the next idea back into the organization. >>Right? Very well said. Pascal, why did you write your book on intelligent automation and, and hyper automation, and what's changed since you've written that book? >>So, I, I wrote this book, An Intelligent Automation, two years ago. At that time, it was really a new topic. It was really about the key, the, the key, the key content of the, of the book is really about combining different technologies to automate the most complex end to end business processes in companies. And when I say capabilities, it's, we, we hear a lot about up here, especially here, robotic process automation. But up here alone, if you just trying to transform a company with only up here, you just fall short. Okay? A lot of those processes need more than execution. They need language, they need the capacity to view, to see, they need the capacity to understand and to, and to create insights. So by combining process automation with ai, natural language processing, computer vision, you give this capability to create impact by automating end to end processes in companies. >>I, I like the test, what I hear in the keynote with independent experts like yourself. So we're hearing that that intelligent automation or automation is a fundamental component of digital transformation. Is it? Or is it more sort of a back office sort of hidden in inside plumbing Ray? What do you think? >>Well, you start by understanding what's going on in the process phase. And that's where you see discover become very important in that keynote, right? And that's where process mining's playing a role. Then you gotta automate stuff. But when you get to operations, that's really where the change is going to happen, right? We actually think that, you know, when you're doing the digital transformation pieces, right? Analytics, automation and AI are coming together to create a concept we call decision velocity. You and I make a quick decision, boom, how long does it take to get out? Management committee could free forever, right? A week, two months, never. But if you're thinking about competing with the automation, right? These decisions are actually being done a hundred times per second by machine, even a thousand times per second. That asymmetry is really what people are facing at the moment. >>And the companies that are gonna be able to do that and start automating decisions are gonna be operating at another level. Back to what Pascal's book talking about, right? And there are four questions everyone has to ask you, like, when do you fully intelligently automate? And that happens right in the background when you augment the machine with a human. So we can find why did you make an exception? Why did you break a roll? Why didn't you follow this protocol so we can get it down to a higher level confidence? When do you augment the human with the machine so we can give you the information so you can act quickly. And the last one is, when do you wanna insert a human in the process? That's gonna be the biggest question. Order to cash, incident or resolution, Hire to retire, procure to pay. It doesn't matter. When do you want to put a human in the process? When do you want a man in the middle, person in the middle? And more importantly, when do you want insert friction? >>So Pascal, you wrote your book in the middle of the, the pandemic. Yes. And, and so, you know, pre pandemic digital transformation was kind of a buzzword. A lot of people gave it lip service, eh, not on my watch, I don't have to worry about that. But then it became sort of, you're not a digital business, you're out of business. So, so what have you seen as the catalyst for adoption of automation? Was it the, the pandemic? Was it sort of good runway before that? What's changed? You know, pre isolation, post isolation economy. >>You, you make me think about a joke. Who, who did your best digital transformation over the last years? The ceo, C H R O, the Covid. >>It's a big record ball, right? Yeah. >>Right. And that's exactly true. You know, before pandemic digital transformation was a competitive advantage. >>Companies that went into it had an opportunity to get a bit better than their, their competitors during the pandemic. Things have changed completely. Companies that were not digitalized and automated could not survive. And we've seen so many companies just burning out and, and, and those companies that have been able to capitalize on intelligent automation, digital transformations during the pandemic have been able not only to survive, but to, to thrive, to really create their place on the market. So that's, that has been a catalyst, definitely a catalyst for that. That explains the success of the book, basically. Yeah. >>Okay. Okay. >>So you're familiar with the concept of Stew the food, right? So Stew by definition is something that's delicious to eat. Stew isn't simply taking one of every ingredient from the pantry and throwing it in the pot and stirring it around. When we start talking about intelligent automation, artificial intelligence, augmented intelligence, it starts getting a bit overwhelming. My spy sense goes off and I start thinking, this sounds like mush. It doesn't sound like Stew. So I wanna hear from each of you, what is the methodical process that, that people need to go through when they're going through digital trans transmission, digital transformation, so that you get delicious stew instead of a mush that's just confused everything in your business. So you, Ray, you want, you want to, you wanna answer that first? >>Yeah. You know, I mean, we've been talking about digital transformation since 2010, right? And part of it was really getting the business model, right? What are you trying to achieve? Is that a new type of offering? Are you changing the way you monetize something? Are you taking existing process and applying it to a new set of technologies? And what do you wanna accomplish, right? Once you start there, then it becomes a whole lot of operational stuff. And it's more than st right? I mean, it, it could be like, well, I can't use those words there. But the point being is it could be a complete like, operational exercise. It could be a complete revenue exercise, it could be a regulatory exercise, it could be something about where you want to take growth into the next level. And each one of those processes, some of it is automation, right? There's a big component of it today. But most of it is really rethinking about what you want things to do, right? How do you actually make things to be successful, right? Do I reorganize a process? Do I insert a place to do monetization? Where do I put engagement in place? How do I collect data along the way so I can build better feedback loop? What can I do to build the business graph so that I have that knowledge for the future so I can go forward doing that so I can be successful. >>The Pascal should, should, should the directive be first ia, then ai? Or are these, are these things going to happen in parallel naturally? What's your position on that? Is it first, >>So it, so, >>So AI is part of IA because that's, it's, it's part of the big umbrella. And very often I got the question. So how do you differentiate AI in, I a, I like to say that AI is only the brain. So think of ai cuz I'm consider, I consider AI as machine learning, Okay? Think of AI in a, like a brain near jar that only can think, create, insight, learn, but doesn't do anything, doesn't have any arms, doesn't have any eyes, doesn't not have any mouth and ears can't talk, can't understand with ia, you, you give those capabilities to ai. You, you basically, you create a cap, the capability, technological capability that is able to do more than just thinking, learning and, and create insight, but also acting, speaking, understanding the environment, viewing it, interacting with it. So basically performing these, those end to end processes that are performed currently by people in companies. >>Yeah, we're gonna get to a point where we get to what we call a dynamic scenario generation. You're talking to me, you get excited, well, I changed the story because something else shows up, or you're talking to me and you're really upset. We're gonna have to actually ch, you know, address that issue right away. Well, we want the ability to have that sense and respond capability so that the next best action is served. So your data, your process, the journey, all the analytics on the top end, that's all gonna be served up and changed along the way. As we go from 2D journeys to 3D scenarios in the metaverse, if we think about what happens from a decentralized world to decentralized, and we think about what's happening from web two to web three, we're gonna make those types of shifts so that things are moving along. Everything's a choose your end venture journey. >>So I hope I remember this correctly from your book. You talked about disruption scenarios within industries and within companies. And I go back to the early days of, of our industry and East coast Prime, Wang, dg, they're all gone. And then, but, but you look at companies like Microsoft, you know, they were, they were able to, you know, get through that novel. Yeah. Ibm, you know, I call it survived. Intel is now going through their, you know, their challenge. So, so maybe it's inevitable, but how do you see the future in terms of disruption with an industry, Forget our industry for a second, all industry across, whether it's healthcare, financial services, manufacturing, automobiles, et cetera. How do you see the disruption scenario? I'm pretty sure you talked about this in your book, it's been a while since I read it, but I wonder if you could talk about that disruption scenario and, and the role that automation is going to play, either as the disruptor or as the protector of the incumbents. >>Let's take healthcare and auto as an example. Healthcare is a great example. If we think about what's going on, not enough nurses, massive shortage, right? What are we doing at the moment? We're setting five foot nine robots to do non-patient care. We're trying to capture enough information off, you know, patient analytics like this watch is gonna capture vitals from a going forward. We're doing a lot what we can do in the ambient level so that information and data is automatically captured and decisions are being rendered against that. Maybe you're gonna change your diet along the way, maybe you're gonna walk an extra 10 minutes. All those things are gonna be provided in that level of automation. Take the car business. It's not about selling cars. Tesla's a great example. We talk about this all the time. What Tesla's doing, they're basically gonna be an insurance company with all the data they have. They have better data than the insurance companies. They can do better underwriting, they've got better mapping information and insights they can actually suggest next best action do collision avoidance, right? Those are all the things that are actually happening today. And automation plays a big role, not just in the collection of that, that information insight, but also in the ability to make recommendations, to do predictions and to help you prevent things from going wrong. >>So, you know, it's interesting. It's like you talk about Tesla as the, the disrupting the insurance companies. It's almost like the over the top vendors have all the data relative to the telcos and mopped them up for lunch. Pascal, I wanna ask you, you know, the topic of future of work kind of was a bromide before, but, but now I feel like, you know, post pandemic, it, it actually has substance. How do you see the future of work? Can you even summarize what it's gonna look like? It's, it's, Or are we here? >>It's, yeah, it's, and definitely it's, it's more and more important topic currently. And you, you all heard about the great resignation and how employee experience is more and more important for companies according to have a business review. The companies that take care of their employee experience are four times more profitable that those that don't. So it's a, it's a, it's an issue for CEOs and, and shareholders. Now, how do we get there? How, how do we, how do we improve the, the quality of the employee experience, understanding the people, getting information from them, educating them. I'm talking about educating them on those new technologies and how they can benefit from those empowering them. And, and I think we've talked a lot about this, about the democratization local type of, of technologies that democratize the access to those technologies. Everyone can be empowered today to change their work, improve their work, and finally, incentivization. I think it's a very important point where companies that, yeah, I >>Give that. What's gonna be the key message of your talk tomorrow. Give us the bumper sticker, >>If you will. Oh, I'm gonna talk, It's a little bit different. I'm gonna talk for the IT community in this, in the context of the IT summit. And I'm gonna talk about the future of intelligent automation. So basically how new technologies will impact beyond what we see today, The future of work. >>Well, I always love having you on the cube, so articulate and, and and crisp. What's, what's exciting you these days, you know, in your world, I know you're traveling around a lot, but what's, what's hot? >>Yeah, I think one of the coolest thing that's going on right now is the fact that we're trying to figure out do we go to work or do we not go to work? Back to your other point, I mean, I don't know, work, work is, I mean, for me, work has been everywhere, right? And we're starting to figure out what that means. I think the second thing though is this notion around mission and purpose. And everyone's trying to figure out what does that mean for themselves? And that's really, I don't know if it's a great, great resignation. We call it great refactoring, right? Where you work, when you work, how we work, why you work, that's changing. But more importantly, the business models are changing. The monetization models are changing macro dynamics that are happening. Us versus China, G seven versus bricks, right? War on the dollar. All these things are happening around us at this moment and, and I think it's gonna really reshape us the way that we came out of the seventies into the eighties. >>Guys, always a pleasure having folks like yourself on, Thank you, Pascal. Been great to see you again. All right, Dave Nicholson, Dave Ante, keep it right there. Forward five from Las Vegas. You're watching the cue.

>> Narrator: From theCUBE Studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The next 10 years of cloud, they're going to differ dramatically from the past decade. The early days of cloud, deployed virtualization of standard off-the-shelf components, X86 microprocessors, disk drives et cetera, to then scale out and build a large distributed system. The coming decade is going to see a much more data-centric, real-time, intelligent, call it even hyper-decentralized cloud that will comprise on-prem, hybrid, cross-cloud and edge workloads with a services layer that will obstruct the underlying complexity of the infrastructure which will also comprise much more custom and varied components. This was a key takeaway of the guests from theCUBE on Cloud, an event hosted by SiliconANGLE on theCUBE. Welcome to this week's Wikibon CUBE Insights Powered by ETR. In this episode, we'll summarize the findings of our recent event and extract the signal from our great guests with a couple of series and comments and clips from the show. CUBE on Cloud is our very first virtual editorial event. It was designed to bring together our community in an open forum. We ran the day on our 365 software platform and had a great lineup of CEOs, CIOs, data practitioners technologists. We had cloud experts, analysts and many opinion leaders all brought together in a day long series of sessions that we developed in order to unpack the future of cloud computing in the coming decade. Let me briefly frame up the conversation and then turn it over to some of our guests. First, we put forth our view of how modern cloud has evolved and where it's headed. This graphic that we're showing here, talks about the progression of cloud innovation over time. A cloud like many innovations, it started as a novelty. When AWS announced S3 in March of 2006, nobody in the vendor or user communities really even in the trade press really paid too much attention to it. Then later that year, Amazon announced EC2 and people started to think about a new model of computing. But it was largely tire kickers, bleeding-edge developers that took notice and really leaned in. Now the financial crisis of 2007 to 2009, really created what we call a cloud awakening and it put cloud on the radar of many CFOs. Shadow IT emerged within departments that wanted to take IT in bite-sized chunks and along with the CFO wanted to take it as OPEX versus CAPEX. And then I teach transformation that really took hold. We came out of the financial crisis and we've been on an 11-year cloud boom. And it doesn't look like it's going to stop anytime soon, cloud has really disrupted the on-prem model as we've reported and completely transformed IT. Ironically, the pandemic hit at the beginning of this decade, and created a mandate to go digital. And so it accelerated the industry transformation that we're highlighting here, which probably would have taken several more years to mature but overnight the forced March to digital happened. And it looks like it's here to stay. Now the next wave, we think we'll be much more about business or industry transformation. We're seeing the first glimpses of that. Holger Mueller of Constellation Research summed it up at our event very well I thought, he basically said the cloud is the big winner of COVID. Of course we know that now normally we talk about seven-year economic cycles. He said he was talking about for planning and investment cycles. Now we operate in seven-day cycles. The examples he gave where do we open or close the store? How do we pivot to support remote workers without the burden of CAPEX? And we think that the things listed on this chart are going to be front and center in the coming years, data AI, a fully digitized and intelligence stack that will support next gen disruptions in autos, manufacturing, finance, farming and virtually every industry where the system will expand to the edge. And the underlying infrastructure across physical locations will be hidden. Many issues remain, not the least of which is latency which we talked about at the event in quite some detail. So let's talk about how the Big 3 cloud players are going to participate in this next era. Well, in short, the consensus from the event was that the rich get richer. Let's take a look at some data. This chart shows our most recent estimates of IaaS and PaaS spending for the Big 3. And we're going to update this after earning season but there's a couple of points stand out. First, we want to make the point that combined the Big 3 now account for almost $80 billion of infrastructure spend last year. That $80 billion, was not all incremental (laughs) No it's caused consolidation and disruption in the on-prem data center business and within IT shops companies like Dell, HPE, IBM, Oracle many others have felt the heat and have had to respond with hybrid and cross cloud strategies. Second while it's true that Azure and GCP they appear to be growing faster than AWS. We don't know really the exact numbers, of course because only AWS provides a clean view of IaaS and passwords, Microsoft and Google. They kind of hide them all ball on their numbers which by the way, I don't blame them but they do leave breadcrumbs and clues on growth rates. And we have other means of estimating through surveys and the like, but it's undeniable Azure is closing the revenue gap on AWS. The third is that I like the fact that Azure and Google are growing faster than AWS. AWS is the only company by our estimates to grow its business sequentially last quarter. And in and of itself, that's not really enough important. What is significant is that because AWS is so large now at 45 billion, even at their slower growth rates it grows much more in absolute terms than its competitors. So we think AWS is going to keep its lead for some time. We think Microsoft and AWS will continue to lead the pack. You know, they might converge maybe it will be a 200 just race in terms of who's first who's second in terms of cloud revenue and how it's counted depending on what they count in their numbers. And Google look with its balance sheet and global network. It's going to play the long game and virtually everyone else with the exception of perhaps Alibaba is going to be secondary players on these platforms. Now this next graphic underscores that reality and kind of lays out the competitive landscape. What we're showing here is survey data from ETR of more than 1400 CIOs and IT buyers and on the vertical axis is Net Score which measures spending momentum on the horizontal axis is so-called Market Share which is a measure of pervasiveness in the data set. The key points are AWS and Microsoft look at it. They stand alone so far ahead of the pack. I mean, they really literally, it would have to fall down to lose their lead high spending velocity and large share of the market or the hallmarks of these two companies. And we don't think that's going to change anytime soon. Now, Google, even though it's far behind they have the financial strength to continue to position themselves as an alternative to AWS. And of course, an analytics specialist. So it will continue to grow, but it will be challenged. We think to catch up to the leaders. Now take a look at the hybrid zone where the field is playing. These are companies that have a large on-prem presence and have been forced to initiate a coherent cloud strategy. And of course, including multicloud. And we include Google in this so pack because they're behind and they have to take a differentiated approach relative to AWS, and maybe cozy up to some of these traditional enterprise vendors to help Google get to the enterprise. And you can see from the on-prem crowd, VMware Cloud on AWS is stands out as having some, some momentum as does Red Hat OpenShift, which is it's cloudy, but it's really sort of an ingredient it's not really broad IaaS specifically but it's a component of cloud VMware cloud which includes VCF or VMware Cloud Foundation. And even Dell's cloud. We would expect HPE with its GreenLake strategy. Its financials is shoring up, should be picking up momentum in the future in terms of what the customers of this survey consider cloud. And then of course you could see IBM and Oracle you're in the game, but they don't have the spending momentum and they don't have the CAPEX chops to compete with the hyperscalers IBM's cloud revenue actually dropped 7% last quarter. So that highlights the challenges that that company facing Oracle's cloud business is growing in the single digits. It's kind of up and down, but again underscores these two companies are really about migrating their software install basis to their captive clouds and as well for IBM, for example it's launched a financial cloud as a way to differentiate and not take AWS head-on an infrastructure as a service. The bottom line is that other than the Big 3 in Alibaba the rest of the pack will be plugging into hybridizing and cross-clouding those platforms. And there are definitely opportunities there specifically related to creating that abstraction layer that we talked about earlier and hiding that underlying complexity and importantly creating incremental value good examples, snowfallLike what snowflake is doing with its data cloud, what the data protection guys are doing. A company like Loomio is headed in that direction as are others. So, you keep an eye on that and think about where the white space is and where the value can be across-clouds. That's where the opportunity is. So let's see, what is this all going to look like? How does the cube community think it's going to unfold? Let's hear from theCUBE Guests and theCUBE on Cloud speakers and some of those highlights. Now, unfortunately we don't have time to show you clips from every speaker. We are like 10-plus hours of video content but we've tried to pull together some comments that summarize the sentiment from the community. So I'm going to have John Furrier briefly explain what theCUBE on Cloud is all about and then let the guests speak for themselves. After John, Pradeep Sindhu is going to give a nice technical overview of how the cloud was built out and what's changing in the future. I'll give you a hint it has to do with data. And then speaking of data, Mai-Lan Bukovec, who heads up AWS is storage portfolio. She'll explain how she views the coming changes in cloud and how they look at storage. Again, no surprise, it's all about data. Now, one of the themes that you'll hear from guests is the notion of a distributed cloud model. And Zhamak Deghani, he was a data architect. She'll explain her view of the future of data architectures. We also have thoughts from analysts like Zeus Karavalla and Maribel Lopez, and some comments from both Microsoft and Google to compliment AWS's view of the world. In fact, we asked JG Chirapurath from Microsoft to comment on the common narrative that Microsoft products are not best-to-breed. They put out a one dot O and then they get better, or sometimes people say, well, they're just good enough. So we'll see what his response is to that. And Paul Gillin asks, Amit Zavery of Google his thoughts on the cloud leaderboard and how Google thinks about their third-place position. Dheeraj Pandey gives his perspective on how technology has progressed and been miniaturized over time. And what's coming in the future. And then Simon Crosby gives us a framework to think about the edge as the most logical opportunity to process data not necessarily a physical place. And this was echoed by John Roese, and Chris Wolf to experience CTOs who went into some great depth on this topic. Unfortunately, I don't have the clips of those two but their comments can be found on the CTO power panel the technical edge it's called that's the segment at theCUBE on Cloud events site which we'll share the URL later. Now, the highlight reel ends with CEO Joni Klippert she talks about the changes in securing the cloud from a developer angle. And finally, we wrap up with a CIO perspective, Dan Sheehan. He provides some practical advice on building on his experience as a CIO, COO and CTO specifically how do you as a business technology leader deal with the rapid pace of change and still be able to drive business results? Okay, so let's now hear from the community please run the highlights. >> Well, I think one of the things we talked about COVID is the personal impact to me but other people as well one of the things that people are craving right now is information, factual information, truth, textures that we call it. But here this event for us Dave is our first inaugural editorial event. Rob, both Kristen Nicole the entire cube team, SiliconANGLE on theCUBE we're really trying to put together more of a cadence. We're going to do more of these events where we can put out and feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires of people making things happen, but it's often the people under them that are the real Newsmakers. >> If you look at the architecture of cloud data centers the single most important invention was scale-out. Scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPU's with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute but this architecture, Dave is a compute centric architecture. And the reason it's a compute centric architecture is if you open this, is server node. What you see is a connection to the network typically with a simple network interface card. And then you have CPU's which are in the middle of the action. Not only are the CPU's processing the application workload but they're processing all of the IO workload what we call data centric workload. And so when you connect SSDs and hard drives and GPU is everything to the CPU, as well as to the network you can now imagine that the CPU is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go to the CPU and you're executing instructions typically in the operating system. And you're interrupting the CPU many many millions of times a second. Now general purpose CPU and the architecture of the CPU's was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where does a lot of data, a lot of these stress traffic the percentage of workload, which is data centric has gone from maybe one to 2% to 30 to 40%. >> The path to innovation is paved by data. If you don't have data, you don't have machine learning you don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data Lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> We are actually moving towards decentralization if we think today, like if it let's move data aside if we said is the only way web would work the only way we get access to various applications on the web or pages to centralize it We would laugh at that idea. But for some reason we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organizations that are beyond the bounds of organization. And then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in, you know, globally in place then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me that requires a paradigm shift a full stack from how we organize our organizations how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> I actually think we're in the midst of the transition to what's called a distributed cloud, where if you look at modernized cloud apps today they're actually made up of services from different clouds. And also distributed edge locations. And that's going to have a pretty profound impact on the way we go vast. >> We wake up every day, worrying about our customer and worrying about the customer condition and to absolutely make sure we dealt with the best in the first attempt that we do. So when you take the plethora of products we've dealt with in Azure, be it Azure SQL be it Azure cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks Azure machine learning. And recently when we sort of offered the world's first comprehensive data governance solution and Azure overview, I would, I would humbly submit to you that we are leading the way. >> How important are rankings within the Google cloud team or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about we are not focused on ranking or any of that stuff. Typically I think we are worried about making sure customers are satisfied and the adding more and more customers. So if you look at the volume of customers we are signing up a lot of the large deals we did doing. If you look at the announcement we've made over the last year has been tremendous momentum around that. >> The thing that is really interesting about where we have been versus where we're going is we spend a lot of time talking about virtualizing hardware and moving that around. And what does that look like? And creating that as more of a software paradigm. And the thing we're talking about now is what does cloud as an operating model look like? What is the manageability of that? What is the security of that? What, you know, we've talked a lot about containers and moving into different, DevSecOps and all those different trends that we've been talking about. Like now we're doing them. So we've only gotten to the first crank of that. And I think every technology vendor we talked to now has to address how are they are going to do a highly distributed management insecurity landscape? Like, what are they going to layer on top of that? Because it's not just about, oh, I've taken a rack of something, server storage, compute, and virtualized it. I know have to create a new operating model around it in a way we're almost redoing what the OSI stack looks like and what the software and solutions are for that. >> And the whole idea of we in every recession we make things smaller. You know, in 91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year, 2000 windows the next bubble burst and the recession afterwards we moved from Unix servers to Wintel windows and Intel x86 and eventually Linux as well. Again, we made things smaller going from million dollar servers to $5,000 servers, shorter lib servers. And that's what we did in 2008, 2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There's nothing in the physical world that actually even lives but we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade. They're going to make it even smaller not just in space, which is size, with functions and containers and virtual machines, but also in time. >> So I think the right way to think about edges where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have but much data is encrypted between the original device say and the application. And so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze it in the care. >> When I think of Shift-left, I think of that Mobius that we all look at all of the time and how we deliver and like plan, write code, deliver software, and then manage it, monitor it, right like that entire DevOps workflow. And today, when we think about where security lives, it either is a blocker to deploying production or most commonly it lives long after code has been deployed to production. And there's a security team constantly playing catch up trying to ensure that the development team whose job is to deliver value to their customers quickly, right? Deploy as fast as we can as many great customer facing features. They're then looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are and trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as they're writing code or in the CIC CD pipeline long before code has been deployed to production. >> During this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as they have the right requirements. So that goes back to people making sure we have the partnership that goes back to leadership and the people and then the change management aspects right out of the gate, you should be worrying about how this change is going to be how it's going to affect, and then the adoption and an engagement, because adoption is critical because you can go create the best thing you think from a technology perspective. But if it doesn't get used correctly, it's not worth the investment. So I agree, what is a digital transformation or innovation? It still comes down to understand the business model and injecting and utilizing technology to grow our reduce costs, grow the business or reduce costs. >> Okay, so look, there's so much other content on theCUBE on Cloud events site we'll put the link in the description below. We have other CEOs like Kathy Southwick and Ellen Nance. We have the CIO of UI path. Daniel Dienes talks about automation in the cloud and Appenzell from Anaplan. And a plan is not her company. By the way, Dave Humphrey from Bain also talks about his $750 million investment in Nutanix. Interesting, Rachel Stevens from red monk talks about the future of software development in the cloud and CTO, Hillary Hunter talks about the cloud going vertical into financial services. And of course, John Furrier and I along with special guests like Sergeant Joe Hall share our take on key trends, data and perspectives. So right here, you see the coupon cloud. There's a URL, check it out again. We'll, we'll pop this URL in the description of the video. So there's some great content there. I want to thank everybody who participated and thank you for watching this special episode of theCUBE Insights Powered by ETR. This is Dave Vellante and I'd appreciate any feedback you might have on how we can deliver better event content for you in the future. We'll be doing a number of these and we look forward to your participation and feedback. Thank you, all right, take care, we'll see you next time. (upbeat music)

>> Announcer: From around the globe, it's the queue with digital coverage of DockerCon live 2020. Brought to you by Docker and its ecosystem partners. >> Welcome, welcome, welcome to DockerCon 2020. We got over 50,000 people registered so there's clearly a ton of interest in the world of Docker and Eddie's as I like to call it. And we've assembled a power panel of Open Source and cloud native experts to talk about where things stand in 2020 and where we're headed. I'm Shawn Conley, I'll be the moderator for today's panel. I'm also a proud alum of JBoss, Red Hat, SpringSource, VMware and Hortonworks and I'm broadcasting from my hometown of Philly. Our panelists include; Michelle Noorali, Senior Software Engineer at Microsoft, joining us from Atlanta, Georgia. We have Kelsey Hightower, Principal developer advocate at Google Cloud, joining us from Washington State and we have Chris Aniszczyk, CTO CIO at the CNCF, joining us from Austin, Texas. So I think we have the country pretty well covered. Thank you all for spending time with us on this power panel. Chris, I'm going to start with you, let's dive right in. You've been in the middle of the Docker netease wave since the beginning with a clear focus on building a better world through open collaboration. What are your thoughts on how the Open Source landscape has evolved over the past few years? Where are we in 2020? And where are we headed from both community and a tech perspective? Just curious to get things sized up? >> Sure, when CNCF started about roughly four, over four years ago, the technology mostly focused on just the things around Kubernetes, monitoring communities with technology like Prometheus, and I think in 2020 and the future, we definitely want to move up the stack. So there's a lot of tools being built on the periphery now. So there's a lot of tools that handle running different types of workloads on Kubernetes. So things like Uvert and Shay runs VMs on Kubernetes, which is crazy, not just containers. You have folks that, Microsoft experimenting with a project called Kruslet which is trying to run web assembly workloads natively on Kubernetes. So I think what we've seen now is more and more tools built around the periphery, while the core of Kubernetes has stabilized. So different technologies and spaces such as security and different ways to run different types of workloads. And at least that's kind of what I've seen. >> So do you have a fair amount of vendors as well as end users still submitting in projects in, is there still a pretty high volume? >> Yeah, we have 48 total projects in CNCF right now and Michelle could speak a little bit more to this being on the DOC, the pipeline for new projects is quite extensive and it covers all sorts of spaces from two service meshes to security projects and so on. So it's ever so expanding and filling in gaps in that cloud native landscape that we have. >> Awesome. Michelle, Let's head to you. But before we actually dive in, let's talk a little glory days. A rumor has it that you are the Fifth Grade Kickball Championship team captain. (Michelle laughs) Are the rumors true? >> They are, my speech at the end of the year was the first talk I ever gave. But yeah, it was really fun. I wasn't captain 'cause I wasn't really great at anything else apart from constantly cheer on the team. >> A little better than my eighth grade Spelling Champ Award so I think I'd rather have the kickball. But you've definitely, spent a lot of time leading an Open Source, you've been across many projects for many years. So how does the art and science of collaboration, inclusivity and teamwork vary? 'Cause you're involved in a variety of efforts, both in the CNCF and even outside of that. And then what are some tips for expanding the tent of Open Source projects? >> That's a good question. I think it's about transparency. Just come in and tell people what you really need to do and clearly articulate your problem, more clearly articulate your problem and why you can't solve it with any other solution, the more people are going to understand what you're trying to do and be able to collaborate with you better. What I love about Open Source is that where I've seen it succeed is where incentives of different perspectives and parties align and you're just transparent about what you want. So you can collaborate where it makes sense, even if you compete as a company with another company in the same area. So I really like that, but I just feel like transparency and honesty is what it comes down to and clearly communicating those objectives. >> Yeah, and the various foundations, I think one of the things that I've seen, particularly Apache Software Foundation and others is the notion of checking your badge at the door. Because the competition might be between companies, but in many respects, you have engineers across many companies that are just kicking butt with the tech they contribute, claiming victory in one way or the other might make for interesting marketing drama. But, I think that's a little bit of the challenge. In some of the, standards-based work you're doing I know with CNI and some other things, are they similar, are they different? How would you compare and contrast into something a little more structured like CNCF? >> Yeah, so most of what I do is in the CNCF, but there's specs and there's projects. I think what CNCF does a great job at is just iterating to make it an easier place for developers to collaborate. You can ask the CNCF for basically whatever you need, and they'll try their best to figure out how to make it happen. And we just continue to work on making the processes are clearer and more transparent. And I think in terms of specs and projects, those are such different collaboration environments. Because if you're in a project, you have to say, "Okay, I want this feature or I want this bug fixed." But when you're in a spec environment, you have to think a little outside of the box and like, what framework do you want to work in? You have to think a little farther ahead in terms of is this solution or this decision we're going to make going to last for the next how many years? You have to get more of a buy in from all of the key stakeholders and maintainers. So it's a little bit of a longer process, I think. But what's so beautiful is that you have this really solid, standard or interface that opens up an ecosystem and allows people to build things that you could never have even imagined or dreamed of so-- >> Gotcha. So I'm Kelsey, we'll head over to you as your focus is on, developer advocate, you've been in the cloud native front lines for many years. Today developers are faced with a ton of moving parts, spanning containers, functions, Cloud Service primitives, including container services, server-less platforms, lots more, right? I mean, there's just a ton of choice. How do you help developers maintain a minimalist mantra in the face of such a wealth of choice? I think minimalism I hear you talk about that periodically, I know you're a fan of that. How do you pass that on and your developer advocacy in your day to day work? >> Yeah, I think, for most developers, most of this is not really the top of mind for them, is something you may see a post on Hacker News, and you might double click into it. Maybe someone on your team brought one of these tools in and maybe it leaks up into your workflow so you're forced to think about it. But for most developers, they just really want to continue writing code like they've been doing. And the best of these projects they'll never see. They just work, they get out of the way, they help them with log in, they help them run their application. But for most people, this isn't the core idea of the job for them. For people in operations, on the other hand, maybe these components fill a gap. So they look at a lot of this stuff that you see in the CNCF and Open Source space as number one, various companies or teams sharing the way that they do things, right? So these are ideas that are put into the Open Source, some of them will turn into products, some of them will just stay as projects that had mutual benefit for multiple people. But for the most part, it's like walking through an ion like Home Depot. You pick the tools that you need, you can safely ignore the ones you don't need, and maybe something looks interesting and maybe you study it to see if that if you have a problem. And for most people, if you don't have that problem that that tool solves, you should be happy. No one needs every project and I think that's where the foundation for confusion. So my main job is to help people not get stuck and confused in LAN and just be pragmatic and just use the tools that work for 'em. >> Yeah, and you've spent the last little while in the server-less space really diving into that area, compare and contrast, I guess, what you found there, minimalist approach, who are you speaking to from a server-less perspective versus that of the broader CNCF? >> The thing that really pushed me over, I was teaching my daughter how to make a website. So she's on her Chromebook, making a website, and she's hitting 127.0.0.1, and it looks like geo cities from the 90s but look, she's making website. And she wanted her friends to take a look. So she copied and paste from her browser 127.0.0.1 and none of her friends could pull it up. So this is the point where every parent has to cross that line and say, "Hey, do I really need to sit down "and teach my daughter about Linux "and Docker and Kubernetes." That isn't her main goal, her goal was to just launch her website in a way that someone else can see it. So we got Firebase installed on her laptop, she ran one command, Firebase deploy. And our site was up in a few minutes, and she sent it over to her friend and there you go, she was off and running. The whole server-less movement has that philosophy as one of the stated goal that needs to be the workflow. So, I think server-less is starting to get closer and closer, you start to see us talk about and Chris mentioned this earlier, we're moving up the stack. Where we're going to up the stack, the North Star there is feel where you get the focus on what you're doing, and not necessarily how to do it underneath. And I think server-less is not quite there yet but every type of workload, stateless web apps check, event driven workflows check, but not necessarily for things like machine learning and some other workloads that more traditional enterprises want to run so there's still work to do there. So server-less for me, serves as the North Star for why all these Projects exists for people that may have to roll their own platform, to provide the experience. >> So, Chris, on a related note, with what we were just talking about with Kelsey, what's your perspective on the explosion of the cloud native landscape? There's, a ton of individual projects, each can be used separately, but in many cases, they're like Lego blocks and used together. So things like the surface mesh interface, standardizing interfaces, so things can snap together more easily, I think, are some of the approaches but are you doing anything specifically to encourage this cross fertilization and collaboration of bug ability, because there's just a ton of projects, not only at the CNCF but outside the CNCF that need to plug in? >> Yeah, I mean, a lot of this happens organically. CNCF really provides of the neutral home where companies, competitors, could trust each other to build interesting technology. We don't force integration or collaboration, it happens on its own. We essentially allow the market to decide what a successful project is long term or what an integration is. We have a great Technical Oversight Committee that helps shepherd the overall technical vision for the organization and sometimes steps in and tries to do the right thing when it comes to potentially integrating a project. Previously, we had this issue where there was a project called Open Tracing, and an effort called Open Census, which is basically trying to standardize how you're going to deal with metrics, on the tree and so on in a cloud native world that we're essentially competing with each other. The CNCF TC and committee came together and merged those projects into one parent ever called Open Elementary and so that to me is a case study of how our committee helps, bridges things. But we don't force things, we essentially want our community of end users and vendors to decide which technology is best in the long term, and we'll support that. >> Okay, awesome. And, Michelle, you've been focused on making distributed systems digestible, which to me is about simplifying things. And so back when Docker arrived on the scene, some people referred to it as developer dopamine, which I love that term, because it's simplified a bunch of crufty stuff for developers and actually helped them focus on doing their job, writing code, delivering code, what's happening in the community to help developers wire together multi-part modern apps in a way that's elegant, digestible, feels like a dopamine rush? >> Yeah, one of the goals of the(mumbles) project was to make it easier to deploy an application on Kubernetes so that you could see what the finished product looks like. And then dig into all of the things that that application is composed of, all the resources. So we're really passionate about this kind of stuff for a while now. And I love seeing projects that come into the space that have this same goal and just iterate and make things easier. I think we have a ways to go still, I think a lot of the iOS developers and JS developers I get to talk to don't really care that much about Kubernetes. They just want to, like Kelsey said, just focus on their code. So one of the projects that I really like working with is Tilt gives you this dashboard in your CLI, aggregates all your logs from your applications, And it kind of watches your application changes, and reconfigures those changes in Kubernetes so you can see what's going on, it'll catch errors, anything with a dashboard I love these days. So Yali is like a metrics dashboard that's integrated with STL, a service graph of your service mesh, and lets you see the metrics running there. I love that, I love that dashboard so much. Linkerd has some really good service graph images, too. So anything that helps me as an end user, which I'm not technically an end user, but me as a person who's just trying to get stuff up and running and working, see the state of the world easily and digest them has been really exciting to see. And I'm seeing more and more dashboards come to light and I'm very excited about that. >> Yeah, as part of the DockerCon just as a person who will be attending some of the sessions, I'm really looking forward to see where DockerCompose is going, I know they opened up the spec to broader input. I think your point, the good one, is there's a bit more work to really embrace the wealth of application artifacts that compose a larger application. So there's definitely work the broader community needs to lean in on, I think. >> I'm glad you brought that up, actually. Compose is something that I should have mentioned and I'm glad you bring that up. I want to see programming language libraries, integrate with the Compose spec. I really want to see what happens with that I think is great that they open that up and made that a spec because obviously people really like using Compose. >> Excellent. So Kelsey, I'd be remiss if I didn't touch on your January post on changelog entitled, "Monoliths are the Future." Your post actually really resonated with me. My son works for a software company in Austin, Texas. So your hometown there, Chris. >> Yeah. >> Shout out to Will and the chorus team. His development work focuses on adding modern features via micro services as extensions to the core monolith that the company was founded on. So just share some thoughts on monoliths, micro services. And also, what's deliverance dopamine from your perspective more broadly, but people usually phrase as monoliths versus micro services, but I get the sense you don't believe it's either or. >> Yeah, I think most companies from the pragmatic so one of their argument is one of pragmatism. Most companies have trouble designing any app, monolith, deployable or microservices architecture. And then these things evolve over time. Unless you're really careful, it's really hard to know how to slice these things. So taking an idea or a problem and just knowing how to perfectly compartmentalize it into individual deployable component, that's hard for even the best people to do. And double down knowing the actual solution to the particular problem. A lot of problems people are solving they're solving for the first time. It's really interesting, our industry in general, a lot of people who work in it have never solved the particular problem that they're trying to solve for the first time. So that's interesting. The other part there is that most of these tools that are here to help are really only at the infrastructure layer. We're talking freeways and bridges and toll bridges, but there's nothing that happens in the actual developer space right there in memory. So the libraries that interface to the structure logging, the libraries that deal with rate limiting, the libraries that deal with authorization, can this person make this query with this user ID? A lot of those things are still left for developers to figure out on their own. So while we have things like the brunettes and fluid D, we have all of these tools to deploy apps into those target, most developers still have the problem of everything you do above that line. And to be honest, the majority of the complexity has to be resolved right there in the app. That's the thing that's taking requests directly from the user. And this is where maybe as an industry, we're over-correcting. So we had, you said you come from the JBoss world, I started a lot of my Cisco administration, there's where we focus a little bit more on the actual application needs, maybe from a router that as well. But now what we're seeing is things like Spring Boot, start to offer a little bit more integration points in the application space itself. So I think the biggest parts that are missing now are what are the frameworks people will use for authorization? So you have projects like OPA, Open Policy Agent for those that are new to that, it gives you this very low level framework, but you still have to understand the concepts around, what does it mean to allow someone to do something and one missed configuration, all your security goes out of the window. So I think for most developers this is where the next set of challenges lie, if not actually the original challenge. So for some people, they were able to solve most of these problems with virtualization, run some scripts, virtualize everything and be fine. And monoliths were okay for that. For some reason, we've thrown pragmatism out of the window and some people are saying the only way to solve these problems is by breaking the app into 1000 pieces. Forget the fact that you had trouble managing one piece, you're going to somehow find the ability to manage 1000 pieces with these tools underneath but still not solving the actual developer problems. So this is where you've seen it already with a couple of popular blog posts from other companies. They cut too deep. They're going from 2000, 3000 microservices back to maybe 100 or 200. So to my world, it's going to be not just one monolith, but end up maybe having 10 or 20 monoliths that maybe reflect the organization that you have versus the architectural pattern that you're at. >> I view it as like a constellation of stars and planets, et cetera. Where you you might have a star that has a variety of, which is a monolith, and you have a variety of sort of planetary microservices that float around it. But that's reality, that's the reality of modern applications, particularly if you're not starting from a clean slate. I mean your points, a good one is, in many respects, I think the infrastructure is code movement has helped automate a bit of the deployment of the platform. I've been personally focused on app development JBoss as well as springsSource. The Spring team I know that tech pretty well over the years 'cause I was involved with that. So I find that James Governor's discussion of progressive delivery really resonates with me, as a developer, not so much as an infrastructure Deployer. So continuous delivery is more of infrastructure notice notion, progressive delivery, feature flags, those types of things, or app level, concepts, minimizing the blast radius of your, the new features you're deploying, that type of stuff, I think begins to speak to the pain of application delivery. So I'll guess I'll put this up. Michelle, I might aim it to you, and then we'll go around the horn, what are your thoughts on the progressive delivery area? How could that potentially begin to impact cloud native over 2020? I'm looking for some rallying cries that move up the stack and give a set of best practices, if you will. And I think James Governor of RedMonk opened on something that's pretty important. >> Yeah, I think it's all about automating all that stuff that you don't really know about. Like Flagger is an awesome progressive delivery tool, you can just deploy something, and people have been asking for so many years, ever since I've been in this space, it's like, "How do I do AB deployment?" "How do I do Canary?" "How do I execute these different deployment strategies?" And Flagger is a really good example, for example, it's a really good way to execute these deployment strategies but then, make sure that everything's happening correctly via observing metrics, rollback if you need to, so you don't just throw your whole system. I think it solves the problem and allows you to take risks but also keeps you safe in that you can be confident as you roll out your changes that it all works, it's metrics driven. So I'm just really looking forward to seeing more tools like that. And dashboards, enable that kind of functionality. >> Chris, what are your thoughts in that progressive delivery area? >> I mean, CNCF alone has a lot of projects in that space, things like Argo that are tackling it. But I want to go back a little bit to your point around developer dopamine, as someone that probably spent about a decade of his career focused on developer tooling and in fact, if you remember the Eclipse IDE and that whole integrated experience, I was blown away recently by a demo from GitHub. They have something called code spaces, which a long time ago, I was trying to build development environments that essentially if you were an engineer that joined a team recently, you could basically get an environment quickly start it with everything configured, source code checked out, environment properly set up. And that was a very hard problem. This was like before container days and so on and to see something like code spaces where you'd go to a repo or project, open it up, behind the scenes they have a container that is set up for the environment that you need to build and just have a VS code ID integrated experience, to me is completely magical. It hits like developer dopamine immediately for me, 'cause a lot of problems when you're going to work with a project attribute, that whole initial bootstrap of, "Oh you need to make sure you have this library, this install," it's so incredibly painful on top of just setting up your developer environment. So as we continue to move up the stack, I think you're going to see an incredible amount of improvements around the developer tooling and developer experience that people have powered by a lot of this cloud native technology behind the scenes that people may not know about. >> Yeah, 'cause I've been talking with the team over at Docker, the work they're doing with that desktop, enable the aim local environment, make sure it matches as closely as possible as your deployed environments that you might be targeting. These are some of the pains, that I see. It's hard for developers to get bootstrapped up, it might take him a day or two to actually just set up their local laptop and development environment, and particularly if they change teams. So that complexity really corralling that down and not necessarily being overly prescriptive as to what tool you use. So if you're visual code, great, it should feel integrated into that environment, use a different environment or if you feel more comfortable at the command line, you should be able to opt into that. That's some of the stuff I get excited to potentially see over 2020 as things progress up the stack, as you said. So, Michelle, just from an innovation train perspective, and we've covered a little bit, what's the best way for people to get started? I think Kelsey covered a little bit of that, being very pragmatic, but all this innovation is pretty intimidating, you can get mowed over by the train, so to speak. So what's your advice for how people get started, how they get involved, et cetera. >> Yeah, it really depends on what you're looking for and what you want to learn. So, if you're someone who's new to the space, honestly, check out the case studies on cncf.io, those are incredible. You might find environments that are similar to your organization's environments, and read about what worked for them, how they set things up, any hiccups they crossed. It'll give you a broad overview of the challenges that people are trying to solve with the technology in this space. And you can use that drill into the areas that you want to learn more about, just depending on where you're coming from. I find myself watching old KubeCon talks on the cloud native computing foundations YouTube channel, so they have like playlists for all of the conferences and the special interest groups in CNCF. And I really enjoy talking, I really enjoy watching excuse me, older talks, just because they explain why things were done, the way they were done, and that helps me build the tools I built. And if you're looking to get involved, if you're building projects or tools or specs and want to contribute, we have special interest groups in the CNCF. So you can find that in the CNCF Technical Oversight Committee, TOC GitHub repo. And so for that, if you want to get involved there, choose a vertical. Do you want to learn about observability? Do you want to drill into networking? Do you care about how to deliver your app? So we have a cig called app delivery, there's a cig for each major vertical, and you can go there to see what is happening on the edge. Really, these are conversations about, okay, what's working, what's not working and what are the next changes we want to see in the next months. So if you want that kind of granularity and discussion on what's happening like that, then definitely join those those meetings. Check out those meeting notes and recordings. >> Gotcha. So on Kelsey, as you look at 2020 and beyond, I know, you've been really involved in some of the earlier emerging tech spaces, what gets you excited when you look forward? What gets your own level of dopamine up versus the broader community? What do you see coming that we should start thinking about now? >> I don't think any of the raw technology pieces get me super excited anymore. Like, I've seen the circle of around three or four times, in five years, there's going to be a new thing, there might be a new foundation, there'll be a new set of conferences, and we'll all rally up and probably do this again. So what's interesting now is what people are actually using the technology for. Some people are launching new things that maybe weren't possible because infrastructure costs were too high. People able to jump into new business segments. You start to see these channels on YouTube where everyone can buy a mic and a B app and have their own podcasts and be broadcast to the globe, just for a few bucks, if not for free. Those revolutionary things are the big deal and they're hard to come by. So I think we've done a good job democratizing these ideas, distributed systems, one company got really good at packaging applications to share with each other, I think that's great, and never going to reset again. And now what's going to be interesting is, what will people build with this stuff? If we end up building the same things we were building before, and then we're talking about another digital transformation 10 years from now because it's going to be funny but Kubernetes will be the new legacy. It's going to be the things that, "Oh, man, I got stuck in this Kubernetes thing," and there'll be some governor on TV, looking for old school Kubernetes engineers to migrate them to some new thing, that's going to happen. You got to know that. So at some point merry go round will stop. And we're going to be focused on what you do with this. So the internet is there, most people have no idea of the complexities of underwater sea cables. It's beyond one or two people, or even one or two companies to comprehend. You're at the point now, where most people that jump on the internet are talking about what you do with the internet. You can have Netflix, you can do meetings like this one, it's about what you do with it. So that's going to be interesting. And we're just not there yet with tech, tech is so, infrastructure stuff. We're so in the weeds, that most people almost burn out what's just getting to the point where you can start to look at what you do with this stuff. So that's what I keep in my eye on, is when do we get to the point when people just ship things and build things? And I think the closest I've seen so far is in the mobile space. If you're iOS developer, Android developer, you use the SDK that they gave you, every year there's some new device that enables some new things speech to text, VR, AR and you import an STK, and it just worked. And you can put it in one place and 100 million people can download it at the same time with no DevOps team, that's amazing. When can we do that for server side applications? That's going to be something I'm going to find really innovative. >> Excellent. Yeah, I mean, I could definitely relate. I was Hortonworks in 2011, so, Hadoop, in many respects, was sort of the precursor to the Kubernetes area, in that it was, as I like to refer to, it was a bunch of animals in the zoo, wasn't just the yellow elephant. And when things mature beyond it's basically talking about what kind of analytics are driving, what type of machine learning algorithms and applications are they delivering? You know that's when things tip over into a real solution space. So I definitely see that. I think the other cool thing even just outside of the container and container space, is there's just such a wealth of data related services. And I think how those two worlds come together, you brought up the fact that, in many respects, server-less is great, it's stateless, but there's just a ton of stateful patterns out there that I think also need to be addressed as these richer applications to be from a data processing and actionable insights perspective. >> I also want to be clear on one thing. So some people confuse two things here, what Michelle said earlier about, for the first time, a whole group of people get to learn about distributed systems and things that were reserved to white papers, PhDs, CF site, this stuff is now super accessible. You go to the CNCF site, all the things that you read about or we used to read about, you can actually download, see how it's implemented and actually change how it work. That is something we should never say is a waste of time. Learning is always good because someone has to build these type of systems and whether they sell it under the guise of server-less or not, this will always be important. Now the other side of this is, that there are people who are not looking to learn that stuff, the majority of the world isn't looking. And in parallel, we should also make this accessible, which should enable people that don't need to learn all of that before they can be productive. So that's two sides of the argument that can be true at the same time, a lot of people get caught up. And everything should just be server-less and everyone learning about distributed systems, and contributing and collaborating is wasting time. We can't have a world where there's only one or two companies providing all infrastructure for everyone else, and then it's a black box. We don't need that. So we need to do both of these things in parallel so I just want to make sure I'm clear that it's not one of these or the other. >> Yeah, makes sense, makes sense. So we'll just hit the final topic. Chris, I think I'll ask you to help close this out. COVID-19 clearly has changed how people work and collaborate. I figured we'd end on how do you see, so DockerCon is going to virtual events, inherently the Open Source community is distributed and is used to not face to face collaboration. But there's a lot of value that comes together by assembling a tent where people can meet, what's the best way? How do you see things playing out? What's the best way for this to evolve in the face of the new normal? >> I think in the short term, you're definitely going to see a lot of virtual events cropping up all over the place. Different themes, verticals, I've already attended a handful of virtual events the last few weeks from Red Hat summit to Open Compute summit to Cloud Native summit, you'll see more and more of these. I think, in the long term, once the world either get past COVID or there's a vaccine or something, I think the innate nature for people to want to get together and meet face to face and deal with all the serendipitous activities you would see in a conference will come back, but I think virtual events will augment these things in the short term. One benefit we've seen, like you mentioned before, DockerCon, can have 50,000 people at it. I don't remember what the last physical DockerCon had but that's definitely an order of magnitude more. So being able to do these virtual events to augment potential of physical events in the future so you can build a more inclusive community so people who cannot travel to your event or weren't lucky enough to win a scholarship could still somehow interact during the course of event to me is awesome and I hope something that we take away when we start all doing these virtual events when we get back to physical events, we find a way to ensure that these things are inclusive for everyone and not just folks that can physically make it there. So those are my thoughts on on the topic. And I wish you the best of luck planning of DockerCon and so on. So I'm excited to see how it turns out. 50,000 is a lot of people and that just terrifies me from a cloud native coupon point of view, because we'll probably be somewhere. >> Yeah, get ready. Excellent, all right. So that is a wrap on the DockerCon 2020 Open Source Power Panel. I think we covered a ton of ground. I'd like to thank Chris, Kelsey and Michelle, for sharing their perspectives on this continuing wave of Docker and cloud native innovation. I'd like to thank the DockerCon attendees for tuning in. And I hope everybody enjoys the rest of the conference. (upbeat music)

hello everybody and thank you for joining us today for the virtual verdict of BBC 2020 today's breakout session is entitled the shortest path to Vertica best practices for data warehouse migration ETL I'm Jeff Healey I'll leave verdict and marketing I'll be your host for this breakout session joining me today are Marco guesser and Mauricio lychee vertical product engineer is joining us from yume region but before we begin I encourage you to submit questions or comments or in the virtual session don't have to wait just type question in a comment in the question box below the slides that click Submit as always there will be a Q&A session the end of the presentation will answer as many questions were able to during that time any questions we don't address we'll do our best to answer them offline alternatively visit Vertica forums that formed at vertical comm to post your questions there after the session our engineering team is planning to join the forums to keep the conversation going also reminder that you can maximize your screen by clicking the double arrow button and lower right corner of the sides and yes this virtual session is being recorded be available to view on demand this week send you a notification as soon as it's ready now let's get started over to you mark marco andretti oh hello everybody this is Marco speaking a sales engineer from Amir said I'll just get going ah this is the agenda part one will be done by me part two will be done by Mauricio the agenda is as you can see big bang or piece by piece and the migration of the DTL migration of the physical data model migration of et I saw VTL + bi functionality what to do with store procedures what to do with any possible existing user defined functions and migration of the data doctor will be by Maurice it you want to talk about emeritus Rider yeah hello everybody my name is Mauricio Felicia and I'm a birth record pre-sales like Marco I'm going to talk about how to optimize that were always using some specific vertical techniques like table flattening live aggregated projections so let me start with be a quick overview of the data browser migration process we are going to talk about today and normally we often suggest to start migrating the current that allows the older disease with limited or minimal changes in the overall architecture and yeah clearly we will have to port the DDL or to redirect the data access tool and we will platform but we should minimizing the initial phase the amount of changes in order to go go live as soon as possible this is something that we also suggest in the second phase we can start optimizing Bill arouse and which again with no or minimal changes in the architecture as such and during this optimization phase we can create for example dog projections or for some specific query or optimize encoding or change some of the visual spools this is something that we normally do if and when needed and finally and again if and when needed we go through the architectural design for these operations using full vertical techniques in order to take advantage of all the features we have in vertical and this is normally an iterative approach so we go back to name some of the specific feature before moving back to the architecture and science we are going through this process in the next few slides ok instead in order to encourage everyone to keep using their common sense when migrating to a new database management system people are you often afraid of it it's just often useful to use the analogy of how smooth in your old home you might have developed solutions for your everyday life that make perfect sense there for example if your old cent burner dog can't walk anymore you might be using a fork lifter to heap in through your window in the old home well in the new home consider the elevator and don't complain that the window is too small to fit the dog through this is very much in the same way as Narita but starting to make the transition gentle again I love to remain in my analogy with the house move picture your new house as your new holiday home begin to install everything you miss and everything you like from your old home once you have everything you need in your new house you can shut down themselves the old one so move each by feet and go for quick wins to make your audience happy you do bigbang only if they are going to retire the platform you are sitting on where you're really on a sinking ship otherwise again identify quick wings implement published and quickly in Vertica reap the benefits enjoy the applause use the gained reputation for further funding and if you find that nobody's using the old platform anymore you can shut it down if you really have to migrate you can still go to really go to big battle in one go only if you absolutely have to otherwise migrate by subject area use the group all similar clear divisions right having said that ah you start off by migrating objects objects in the database that's one of the very first steps it consists of migrating verbs the places where you can put the other objects into that is owners locations which is usually schemers then what do you have that you extract tables news then you convert the object definition deploy them to Vertica and think that you shouldn't do it manually never type what you can generate ultimate whatever you can use it enrolls usually there is a system tables in the old database that contains all the roads you can export those to a file reformat them and then you have a create role and create user scripts that you can apply to Vertica if LDAP Active Directory was used for the authentication the old database vertical supports anything within the l dubs standard catalogued schemas should be relatively straightforward with maybe sometimes the difference Vertica does not restrict you by defining a schema as a collection of all objects owned by a user but it supports it emulates it for old times sake Vertica does not need the catalog or if you absolutely need the catalog from the old tools that you use it it usually said it is always set to the name of the database in case of vertical having had now the schemas the catalogs the users and roles in place move the take the definition language of Jesus thought if you are allowed to it's best to use a tool that translates to date types in the PTL generated you might see as a mention of old idea to listen by memory to by the way several times in this presentation we are very happy to have it it actually can export the old database table definition because they got it works with the odbc it gets what the old database ODBC driver translates to ODBC and then it has internal translation tables to several target schema to several target DBMS flavors the most important which is obviously vertical if they force you to use something else there are always tubes like sequel plots in Oracle the show table command in Tara data etc H each DBMS should have a set of tools to extract the object definitions to be deployed in the other instance of the same DBMS ah if I talk about youth views usually a very new definition also in the old database catalog one thing that you might you you use special a bit of special care synonyms is something that were to get emulated different ways depending on the specific needs I said I stop you on the view or table to be referred to or something that is really neat but other databases don't have the search path in particular that works that works very much like the path environment variable in Windows or Linux where you specify in a table an object name without the schema name and then it searched it first in the first entry of the search path then in a second then in third which makes synonym hugely completely unneeded when you generate uvl we remained in the analogy of moving house dust and clean your stuff before placing it in the new house if you see a table like the one here at the bottom this is usually corpse of a bad migration in the past already an ID is usually an integer and not an almost floating-point data type a first name hardly ever has 256 characters and that if it's called higher DT it's not necessarily needed to store the second when somebody was hired so take good care in using while you are moving dust off your stuff and use better data types the same applies especially could string how many bytes does a string container contains for eurozone's it's not for it's actually 12 euros in utf-8 in the way that Vertica encodes strings and ASCII characters one died but the Euro sign thinks three that means that you have to very often you have when you have a single byte character set up a source you have to pay attention oversize it first because otherwise it gets rejected or truncated and then you you will have to very carefully check what their best science is the best promising is the most promising approach is to initially dimension strings in multiples of very initial length and again ODP with the command you see there would be - I you 2 comma 4 will double the lengths of what otherwise will single byte character and multiply that for the length of characters that are wide characters in traditional databases and then load the representative sample of your cells data and profile using the tools that we personally use to find the actually longest datatype and then make them shorter notice you might be talking about the issues of having too long and too big data types on projection design are we live and die with our projects you might know remember the rules on how default projects has come to exist the way that we do initially would be just like for the profiling load a representative sample of the data collector representative set of already known queries from the Vertica database designer and you don't have to decide immediately you can always amend things and otherwise follow the laws of physics avoid moving data back and forth across nodes avoid heavy iOS if you can design your your projections initially by hand encoding matters you know that the database designer is a very tight fisted thing it would optimize to use as little space as possible you will have to think of the fact that if you compress very well you might end up using more time in reading it this is the testimony to run once using several encoding types and you see that they are l e is the wrong length encoded if sorted is not even visible while the others are considerably slower you can get those nights and look it in look at them in detail I will go in detail you now hear about it VI migrations move usually you can expect 80% of everything to work to be able to live to be lifted and shifted you don't need most of the pre aggregated tables because we have live like regain projections many BI tools have specialized query objects for the dimensions and the facts and we have the possibility to use flatten tables that are going to be talked about later you might have to ride those by hand you will be able to switch off casting because vertical speeds of everything with laps Lyle aggregate projections and you have worked with molap cubes before you very probably won't meet them at all ETL tools what you will have to do is if you do it row by row in the old database consider changing everything to very big transactions and if you use in search statements with parameter markers consider writing to make pipes and using verticals copy command mouse inserts yeah copy c'mon that's what I have here ask you custom functionality you can see on this slide the verticals the biggest number of functions in the database we compare them regularly by far compared to any other database you might find that many of them that you have written won't be needed on the new database so look at the vertical catalog instead of trying to look to migrate a function that you don't need stored procedures are very often used in the old database to overcome their shortcomings that Vertica doesn't have very rarely you will have to actually write a procedure that involves a loop but it's really in our experience very very rarely usually you can just switch to standard scripting and this is basically repeating what Mauricio said in the interest of time I will skip this look at this one here the most of the database data warehouse migration talks should be automatic you can use you can automate GDL migration using ODB which is crucial data profiling it's not crucial but game-changing the encoding is the same thing you can automate at you using our database designer the physical data model optimization in general is game-changing you have the database designer use the provisioning use the old platforms tools to generate the SQL you have no objects without their onus is crucial and asking functions and procedures they are only crucial if they depict the company's intellectual property otherwise you can almost always replace them with something else that's it from me for now Thank You Marco Thank You Marco so we will now point our presentation talking about some of the Vertica that overall the presentation techniques that we can implement in order to improve the general efficiency of the dot arouse and let me start with a few simple messages well the first one is that you are supposed to optimize only if and when this is needed in most of the cases just a little shift from the old that allows to birth will provide you exhaust the person as if you were looking for or even better so in this case probably is not really needed to to optimize anything in case you want optimize or you need to optimize then keep in mind some of the vertical peculiarities for example implement delete and updates in the vertical way use live aggregate projections in order to avoid or better in order to limit the goodbye executions at one time used for flattening in order to avoid or limit joint and and then you can also implement invert have some specific birth extensions life for example time series analysis or machine learning on top of your data we will now start by reviewing the first of these ballots optimize if and when needed well if this is okay I mean if you get when you migrate from the old data where else to birth without any optimization if the first four month level is okay then probably you only took my jacketing but this is not the case one very easier to dispute in session technique that you can ask is to ask basket cells to optimize the physical data model using the birth ticket of a designer how well DB deal which is the vertical database designer has several interfaces here I'm going to use what we call the DB DB programmatic API so basically sequel functions and using other databases you might need to hire experts looking at your data your data browser your table definition creating indexes or whatever in vertical all you need is to run something like these are simple as six single sequel statement to get a very well optimized physical base model you see that we start creating a new design then we had to be redesigned tables and queries the queries that we want to optimize we set our target in this case we are tuning the physical data model in order to maximize query performances this is why we are using my design query and in our statement another possible journal tip would be to tune in order to reduce storage or a mix between during storage and cheering queries and finally we asked Vertica to produce and deploy these optimized design in a matter of literally it's a matter of minutes and in a few minutes what you can get is a fully optimized fiscal data model okay this is something very very easy to implement keep in mind some of the vertical peculiarities Vaska is very well tuned for load and query operations aunt Berta bright rose container to biscuits hi the Pharos container is a group of files we will never ever change the content of this file the fact that the Rose containers files are never modified is one of the political peculiarities and these approach led us to use minimal locks we can add multiple load operations in parallel against the very same table assuming we don't have a primary or unique constraint on the target table in parallel as a sage because they will end up in two different growth containers salad in read committed requires in not rocket fuel and can run concurrently with insert selected because the Select will work on a snapshot of the catalog when the transaction start this is what we call snapshot isolation the kappa recovery because we never change our rows files are very simple and robust so we have a huge amount of bandages due to the fact that we never change the content of B rows files contain indiarose containers but on the other side believes and updates require a little attention so what about delete first when you believe in the ethica you basically create a new object able it back so it appeared a bit later in the Rose or in memory and this vector will point to the data being deleted so that when the feed is executed Vertica will just ignore the rules listed in B delete records and it's not just about the leak and updating vertical consists of two operations delete and insert merge consists of either insert or update which interim is made of the little insert so basically if we tuned how the delete work we will also have tune the update in the merge so what should we do in order to optimize delete well remember what we said that every time we please actually we create a new object a delete vector so avoid committing believe and update too often we reduce work the work for the merge out for the removal method out activities that are run afterwards and be sure that all the interested projections will contain the column views in the dedicate this will let workers directly after access the projection without having to go through the super projection in order to create the vector and the delete will be much much faster and finally another very interesting optimization technique is trying to segregate the update and delete operation from Pyrenean third workload in order to reduce lock contention beliefs something we are going to discuss and these contain using partition partition operation this is exactly what I want to talk about now here you have a typical that arouse architecture so we have data arriving in a landing zone where the data is loaded that is from the data sources then we have a transformation a year writing into a staging area that in turn will feed the partitions block of data in the green data structure we have at the end those green data structure we have at the end are the ones used by the data access tools when they run their queries sometimes we might need to change old data for example because we have late records or maybe because we want to fix some errors that have been originated in the facilities so what we do in this case is we just copied back the partition we want to change or we want to adjust from the green interior a the end to the stage in the area we have a very fast operation which is Tokyo Station then we run our updates or our adjustment procedure or whatever we need in order to fix the errors in the data in the staging area and at the very same time people continues to you with green data structures that are at the end so we will never have contention between the two operations when we updating the staging area is completed what we have to do is just to run a swap partition between tables in order to swap the data that we just finished to adjust in be staging zone to the query area that is the green one at the end this swap partition is very fast is an atomic operation and basically what will happens is just that well exchange the pointer to the data this is a very very effective techniques and lot of customer useless so why flops on table and live aggregate for injections well basically we use slot in table and live aggregate objection to minimize or avoid joint this is what flatten table are used for or goodbye and this is what live aggregate projections are used for now compared to traditional data warehouses better can store and process and aggregate and join order of magnitudes more data that is a true columnar database joint and goodbye normally are not a problem at all they run faster than any traditional data browse that page there are still scenarios were deficits are so big and we are talking about petabytes of data and so quickly going that would mean be something in order to boost drop by and join performances and this is why you can't reduce live aggregate projections to perform aggregations hard loading time and limit the need for global appear on time and flux and tables to combine information from different entity uploading time and again avoid running joint has query undefined okay so live aggregate projections at this point in time we can use live aggregate projections using for built in aggregate functions which are some min Max and count okay let's see how this works suppose that you have a normal table in this case we have a table unit sold with three columns PIB their time and quantity which has been segmented in a given way and on top of this base table we call it uncle table we create a projection you see that we create the projection using the salad that will aggregate the data we get the PID we get the date portion of the time and we get the sum of quantity from from the base table grouping on the first two columns so PID and the date portion of day time okay what happens in this case when we load data into the base table all we have to do with load data into the base table when we load data into the base table we will feel of course big injections that assuming we are running with k61 we will have to projection to projections and we will know the data in those two projection with all the detail in data we are going to load into the table so PAB playtime and quantity but at the very same time at the very same time and without having to do nothing any any particular operation or without having to run any any ETL procedure we will also get automatically in the live aggregate projection for the data pre aggregated with be a big day portion of day time and the sum of quantity into the table name total quantity you see is something that we get for free without having to run any specific procedure and this is very very efficient so the key concept is that during the loading operation from VDL point of view is executed again the base table we do not explicitly aggregate data or we don't have any any plc do the aggregation is automatic and we'll bring the pizza to be live aggregate projection every time we go into the base table you see the two selection that we have we have on in this line on the left side and you see that those two selects will produce exactly the same result so running select PA did they trying some quantity from the base table or running the select star from the live aggregate projection will result exactly in the same data you know this is of course very useful but is much more useful result that if we and we can observe this if we run an explained if we run the select against the base table asking for this group data what happens behind the scene is that basically vertical itself that is a live aggregate projection with the data that has been already aggregating loading phase and rewrite your query using polite aggregate projection this happens automatically you see this is a query that ran a group by against unit sold and vertical decided to rewrite this clearly as something that has to be collected against the light aggregates projection because if I decrease this will save a huge amount of time and effort during the ETL cycle okay and is not just limited to be information you want to aggregate for example another query like select count this thing you might note that can't be seen better basically our goodbyes will also take advantage of the live aggregate injection and again this is something that happens automatically you don't have to do anything to get this okay one thing that we have to keep very very clear in mind Brassica what what we store in the live aggregate for injection are basically partially aggregated beta so in this example we have two inserts okay you see that we have the first insert that is entered in four volts and the second insert which is inserting five rules well in for each of these insert we will have a partial aggregation you will never know that after the first insert you will have a second one so better will calculate the aggregation of the data every time irin be insert it is a key concept and be also means that you can imagine lies the effectiveness of bees technique by inserting large chunk of data ok if you insert data row by row this technique live aggregate rejection is not very useful because for every goal that you insert you will have an aggregation so basically they'll live aggregate injection will end up containing the same number of rows that you have in the base table but if you everytime insert a large chunk of data the number of the aggregations that you will have in the library get from structure is much less than B base data so this is this is a key concept you can see how these works by counting the number of rows that you have in alive aggregate injection you see that if you run the select count star from the solved live aggregate rejection the query on the left side you will get four rules but actually if you explain this query you will see that he was reading six rows so this was because every of those two inserts that we're actively interested a few rows in three rows in India in the live aggregate projection so this is a key concept live aggregate projection keep partially aggregated data this final aggregation will always happen at runtime okay another which is very similar to be live aggregate projection or what we call top K projection we actually do not aggregate anything in the top case injection we just keep the last or limit the amount of rows that we collect using the limit over partition by all the by clothes and this again in this case we create on top of the base stable to top gay projection want to keep the last quantity that has been sold and the other one to keep the max quantity in both cases is just a matter of ordering the data in the first case using the B time column in the second page using quantity in both cases we fill projection with just the last roof and again this is something that we do when we insert data into the base table and this is something that happens automatically okay if we now run after the insert our select against either the max quantity okay or be lost wanted it okay we will get the very last you see that we have much less rows in the top k projections okay we told at the beginning that basically we can use for built-in function you might remember me max sum and count what if I want to create my own specific aggregation on top of the lid and customer sum up because our customers have very specific needs in terms of live aggregate projections well in this case you can code your own live aggregate production user-defined functions so you can create the user-defined transport function to implement any sort of complex aggregation while loading data basically after you implemented miss VPS you can deploy using a be pre pass approach that basically means the data is aggregated as loading time during the data ingestion or the batch approach that means that the data is when that woman is running on top which things to remember on live a granade projections they are limited to be built in function again some max min and count but you can call your own you DTF so you can do whatever you want they can reference only one table and for bass cab version before 9.3 it was impossible to update or delete on the uncle table this limit has been removed in 9.3 so you now can update and delete data from the uncle table okay live aggregate projection will follow the segmentation of the group by expression and in some cases the best optimizer can decide to pick the live aggregates objection or not depending on if depending on the fact that the aggregation is a consistent or not remember that if we insert and commit every single role to be uncoachable then we will end up with a live aggregate indirection that contains exactly the same number of rows in this case living block or using the base table it would be the same okay so this is one of the two fantastic techniques that we can implement in Burtka this live aggregate projection is basically to avoid or limit goodbyes the other which we are going to talk about is cutting table and be reused in order to avoid the means for joins remember that K is very fast running joints but when we scale up to petabytes of beta we need to boost and this is what we have in order to have is problem fixed regardless the amount of data we are dealing with so how what about suction table let me start with normalized schemas everybody knows what is a normalized scheme under is no but related stuff in this slide the main scope of an normalized schema is to reduce data redundancies so and the fact that we reduce data analysis is a good thing because we will obtain fast and more brides we will have to write into a database small chunks of data into the right table the problem with these normalized schemas is that when you run your queries you have to put together the information that arrives from different table and be required to run joint again jointly that again normally is very good to run joint but sometimes the amount of data makes not easy to deal with joints and joints sometimes are not easy to tune what happens in in the normal let's say traditional data browser is that we D normalize the schemas normally either manually or using an ETL so basically we have on one side in this light on the left side the normalized schemas where we can get very fast right on the other side on the left we have the wider table where we run all the three joints and pre aggregation in order to prepare the data for the queries and so we will have fast bribes on the left fast reads on the Left sorry fast bra on the right and fast read on the left side of these slides the probability in the middle because we will push all the complexity in the middle in the ETL that will have to transform be normalized schema into the water table and the way we normally implement these either manually using procedures that we call the door using ETL this is what happens in traditional data warehouse is that we will have to coach in ETL layer in order to round the insert select that will feed from the normalized schema and right into the widest table at the end the one that is used by the data access tools we we are going to to view store to run our theories so this approach is costly because of course someone will have to code this ETL and is slow because someone will have to execute those batches normally overnight after loading the data and maybe someone will have to check the following morning that everything was ok with the batch and is resource intensive of course and is also human being intensive because of the people that will have to code and check the results it ever thrown because it can fail and introduce a latency because there is a get in the time axis between the time t0 when you load the data into be normalized schema and the time t1 when we get the data finally ready to be to be queried so what would be inverter to facilitate this process is to create this flatten table with the flattened T work first you avoid data redundancy because you don't need the wide table on the normalized schema on the left side second is fully automatic you don't have to do anything you just have to insert the data into the water table and the ETL that you have coded is transformed into an insert select by vatika automatically you don't have to do anything it's robust and this Latin c0 is a single fast as soon as you load the data into the water table you will get all the joints executed for you so let's have a look on how it works in this case we have the table we are going to flatten and basically we have to focus on two different clauses the first one is you see that there is one table here I mentioned value 1 which can be defined as default and then the Select or set using okay the difference between the fold and set using is when the data is populated if we use default data is populated as soon as we know the data into the base table if we use set using Google Earth to refresh but everything is there I mean you don't need them ETL you don't need to code any transformation because everything is in the table definition itself and it's for free and of course is in latency zero so as soon as you load the other columns you will have the dimension value valued as well okay let's see an example here suppose here we have a dimension table customer dimension that is on the left side and we have a fact table on on the right you see that the fact table uses columns like o underscore name or Oh the score city which are basically the result of the salad on top of the customer dimension so Beezus were the join is executed as soon as a remote data into the fact table directly into the fact table without of course loading data that arise from the dimension all the data from the dimension will be populated automatically so let's have an example here suppose that we are running this insert as you can see we are running be inserted directly into the fact table and we are loading o ID customer ID and total we are not loading made a major name no city those name and city will be automatically populated by Vertica for you because of the definition of the flood table okay you see behave well all you need in order to have your widest tables built for you your flattened table and this means that at runtime you won't need any join between base fuck table and the customer dimension that we have used in order to calculate name and city because the data is already there this was using default the other option was is using set using the concept is absolutely the same you see that in this case on the on the right side we have we have basically replaced this all on the school name default with all underscore name set using and same is true for city the concept that I said is the same but in this case which we set using then we will have to refresh you see that we have to run these select trash columns and then the name of the table in this case all columns will be fresh or you can specify only certain columns and this will bring the values for name and city reading from the customer dimension so this technique this technique is extremely useful the difference between default and said choosing just to summarize the most important differences remember you just have to remember that default will relate your target when you load set using when you refresh end and in some cases you might need to use them both so in some cases you might want to use both default end set using in this example here we'll see that we define the underscore name using both default and securing and this means that we love the data populated either when we load the data into the base table or when we run the Refresh this is summary of the technique that we can implement in birth in order to make our and other browsers even more efficient and well basically this is the end of our presentation thank you for listening and now we are ready for the Q&A session you

>> Narrator: From Seattle, Washington it's the CUBE covering AWS Imagine non-profit. Brought to you by Amazon web services. >> Hey welcome back everybody, Jeff Frick here with the CUBE. We're on the waterfront in Seattle, it's an absolutely gorgeous couple of days here at the AWS Imagine Nonprofit Conference. We went to the AWS Imagine Education Conference, this is really all about nonprofits and we're hearing all kinds of interesting stories about how these people are using AWS to help conquer really big problems. We're going to shift gears a little bit from the two footed problems to the four footed problems and that's animals and everybody likes animals but nobody likes animal shelters and nobody likes the ultimate solution that many animal shelters used to use to take care of problems. But thank you to our next guest, that is not quite the case so much anymore. So we're really happy to have Angie Embree on. She is the CIO of Best Friends Animal Society, Angie great to see you. >> It's great to see you as well and thank you for having me. >> Oh absolutely! So before we got on I just heard this crazy, crazy statistic that when your organization started in 1984 approximately 17 million animals were killed in US shelters per year. That number is now down to 700 thousand, that is a giant, giant reduction. And yet you, with big audacious goals really are looking to get that to zero. So, that's a giant goal, give us a little bit of background on the organization and how you decided to go after a goal like that and some of the ways you are actually going to achieve it. >> Well, the organization started in 1984 and it started with a group of friends in Southern Utah who decided that, you know the killing in America's shelters just had to go. So really the Best Friends founders started the no-kill movement along with a gentleman in San Francisco by the name of Rich Avanzino. And as you said, they took you know the killing down from 17 million in 1984 to approximately 733 thousand now. The organization started as just the sanctuary, we have the largest no-kill companion animal sanctuary in the country where we hold about 17 hundred animals every day. And we also have, you know, knowing that we needed to help out the rest of the country we have built life saving centers in Houston, Texas. Or we're working on Houston, Texas but Los Angels, California, New York City, Salt Lake City, Atlanta, Georgia, it seems like I've left somebody out but, >> Probably, but that's okay. >> We have life saving centers all over the country. So it was really, you know, when they realized what was going on in America's shelters it was really the idea that we should not be killing animals for space. So, just recently in fact, I will say recently but in the last few years, Julie Castle our CEO put kind of, did our moon shot, put that stake in the ground and said we're going to take this country no-kill by the year 2025. >> Right. >> So it's super exciting. >> So it's really interesting because you guys are trying to execute your vision, and it's easy to execute your own vision, but it's a whole different thing when you're trying to execute your vision through this huge infrastructure of shelters that have been around forever. So, I wonder if you can explain kind of what's your relationship with shelters that you don't own. I guess, I think you said before we turned on the cameras there are affiliates, so how does that relationship work? How do you help them achieve your goal which is no-kill. >> Yeah, so we have over 27 hundred network partners around the country. And what we do is we help to educate them on, you know we understand their problems, we have creative programs to solve those problems. So we help to educate them on, you know, how they can implement these programs within their shelters. We provide them grant funding, we have an annual conference every year where they can come and learn. But they're really our partners and you know we know we can't do it alone. It's going to take us, it's going to take them and it's going to take everybody in every community to really step up and help solve the problem. >> Right, and what was the biggest thing that changed in terms of kind of attitude in terms of the way they operate the shelter because I think you said before that a lot of the killing was done to make room. >> Right, killing is done usually for space. >> So what do they do know? Clearly the space demands probably haven't changed so what are they doing alternatively where before they would put the animal down? >> Well alternatively we're doing transport programs. So there are areas in the country that actually have a demand for animals. So instead of killing the animals, we put them on some sort of transport vehicle and we take them to the areas that are in demand. We also do what's called a trap-neuter-return program. So one of the biggest problems across the country are community cats so those, a lot of people call them feral cats but they're community cats and usually have a caretaker. But what we do is we trap those cats, we take them into the shelter, we neuter them and vaccinate them and then return them to their home. That keeps them from making a lot of other little cats. >> Making babies (laughs) >> So yeah, cat's are one of the biggest problems in shelters today because of the community cats, they're feral cats and they're not adoptable. So if we can, we don't have to kill them. We can, you know, we can keep them from reproducing as I said and then we can put them back in their habitat where they live a long healthy life, happy life. >> Right, so you said you've joined the organization 5 years ago, 5 and 1/2 years ago and you're the CIO, first ever CIO. >> I am (laughs) >> What brought you here and then now that you're here with kind of a CIO hat, what are some of the new perspective that you can bring to the organization that didn't necessarily, that they had had before from kind of a technical perspective? >> Well, what brought me here was, I never expected to be here, if you would have told me I would be the CIO at Best Friends Animal Society you know 10 years ago I would have said you're kidding because I didn't really realize that there were professional positions in organizations like Best Friends. But I, you know, my journey begins the same as, began the same as a lot of peoples did. I was that little kid always bringing home animals and you know my mother hated it. You know it was always something showing up at our doorstep with me, you know. And I just loved animals all my life and as I went through college and got my degree and started my professional career, then I thought well I'm going to of course have animals because I can have as many as I want now, right! (laughs) So I started adopting, and I didn't even realize until I was in my 30s that they were killing in shelters and I learned that in Houston, Texas when I lived there. I was working for IBM at the time, and one day a lady came on the television and she said they were doing a new segment and she said we're a no-kill shelter and I thought oh my god if there are no-kill shelters then there are kill shelters, right? >> There must be the other. >> Yeah so, to make a long story short then I started not working in animal welfare but doing more to support the movement and donating. Adopting from shelters and fostering animals and then one day I had been to Best Friends as a visitor vacationing in this beautiful part of Utah. But I saw the CIO ... >> Position. >> position open and I said I'm going for it. >> Good for you. >> Yeah. >> Good for you, so now you're there so what are some of the things you've implemented from kind of a techy, you know kind of data perspective that they didn't have before? >> Well, they didn't have a lot. >> They probably didn't have a lot, besides email and the obvious things. >> Being the first CIO I don't know that I knew what I was walking into at the time because I got to Kanab, and Kanab Utah where the sanctuary is, is the headquarters. And Kanab is very infrastructure challenged. >> (laughs) Infrastructure challenged, I like that. >> There is one ISP in Kanab and there is no redundancy in networks so we really don't have, you know, you come from the city and you think, you take these things for granted and you find out oh my god, what am I going to do? And Kanab is you know the hub of our network, so if Kanab goes down, you know the whole organization is down so one of the first decisions I made was that we were going to the cloud. >> Right, right. >> Because we had to get Kanab out of that position and that was one of our, one of the first major decisions I made and we chose AWS as our partner to do that so that was very very exciting. We knew that they had infrastructure we couldn't dream of providing. >> Right, right. >> And, you know we could really make our whole network more robust, our applications would be available and we could really do some great things. >> You're not worried about the one ISP provider in Kanab because of an accident that knocks a phone pole down. >> Yeah, yeah. >> All right but then you're talking about some new things that you're working on and a new thing you talked about before we turned the cameras on community lifesaving dashboards, what is that all about? >> Okay, so a couple of years ago the community lifesaving dashboard is the culmination of two years of work. From all across the Best Friends organization not just the IT department, in fact it was the brainchild of our Chief Mission Officer Holly Sizemore. But it's really, in animal welfare there's never been a national picture of what the problem really is regarding killing animals in shelters. So we did this big. >> Because they're all regional right? They're all regional shelters, very local. >> They're all local community shelters, yes. And transparency isn't forced, so you know some states force transparency, they reinforce in the report numbers but a lot of states don't. >> At the state level. >> Yeah, a lot of states don't, so. You know when you're killing animals in shelters you really don't want people to know that. >> Yeah, yeah it's not something you want to advertise. >> Because the American public doesn't believe in it. So anyway we worked really hard to collect all this data from across the country and we put it all into this dashboard and it is now a tool where anybody in the public, it's on our website, can look at it and they can see that where we're at from a national level. They can see where they're at from a state level, they can drill down into their community and they can drill down to an individual shelter. >> Wow. >> And the idea behind the dashboard is to really, is to get communities behind helping their shelters. Because as I said earlier, it's going to take us all. >> Right. >> And not only Best Friends and our partners but the public plays a big part of this. >> Right, and so when did that roll out? Do you have any kind of feedback, how's it working? >> It's working wonderfully, we rolled it out at our conference in July. >> So recently, so it's a pretty new initiative. >> Yeah it's just a few weeks old. >> Okay. >> We rolled it out at our national conference and we were all a bit nervous about it, you know especially from a technology perspective. >> Right, right. >> We knew that being the first of it's kind ever in animal welfare that you know it was going to get a lot of publicity both inside and outside the movement. >> (laughs) How you want to say both pro and con. >> Yeah, and it's sitting on our website, well really pro and con. >> Right, right. >> But it's sitting on our website and we're like okay, we don't know what kind of traffic we're going to get, you know what are we going to do about this? So we spent a lot of time with Amazon prior to the launch, you know having them look at our environment and getting advice, discussing it with them. >> Not going to bring down that ISP in Utah. >> No, thank god! (laughs) >> (laughs) >> No it wasn't, thank god we were in the cloud. So Amazon really helped us prepare and then the day of the launch, we knew the time of the launch. So we actually had a war room set up, a virtual war room and we had Amazon employees participating in our war room. We watched the traffic and we did get huge spikes in traffic at all times through the day when certain things were happening. And I'm happy to say from a technology perspective it was a non-event because we did not crash we stayed up, we handled all the traffic, we scaled when we needed to, and we did it you know, virtually at the press of a button. >> Awesome. >> Or the flick of a switch, whatever you want to say. >> That's what you want right? >> Yeah, exactly. >> You just don't want anyone to know, I was like give a good ref, nobody's talking about you you probably did a good job. >> Yeah, exactly yeah. >> Good, so before I let you go so what are some of your initiatives now looking forward. You've got this great partner in AWS, you have basically as much horsepower as you need to get done what you need to get done. What are some of the things that you see, you know kind of next for your roadmap? >> Well, we have a lot. >> Don't give me the whole list (laughs) >> No I'm just going to hit on a few key points. I think, you know we used Amazon initially as our cloud infrastructure but I think the biggest thing we're looking at is platform as a service. There is so much capability out there with predictive analytics, machine learning, artificial intelligence, ARVR, you name it facial recognitions, so we're really investigating those technologies because we think they have you know they could have a huge impact on our movement and really help us achieve life saving. >> Right, right. >> And, I think that, you know we're starting we have our fledgling data science program. We're using the Amazon data lake technology, Athena, Glue, they were just telling me about data lake formation which I just a few minutes ago emailed my data guy and said start looking at data lake formation. >> Right, right. >> So, I mean we're really investing in the platform as a service. The other thing I see is that we're, animal welfare is sort of broken from a technology perspective and a data perspective. In that we have no interoperability and you know we don't have the data available. So lets say you want to adopt a 5-year old animal. Well, you go to a shelter you can't get 5 years of history on a 5 year old animal. So it's really starting to fix the foundation for the movement as a whole, not just Best Friends. So, making sure that you know the veterinary data is there, all the data from the pet ecosystem is there. So we're investigating with AWS they're actually coming to our sanctuary in a couple of months, we're going to do a workshop to figure out how we do this, how we really fix it so that we have interoperability between every shelter when an animal moves from shelter to rescue or whatever so that their data follows them wherever they go. So adopters are fully informed when adopting an animal. >> Because you're in a pretty interesting position, because you're not with any one particular shelter you kind of cross many many boundaries. So you're in a good position to be that aggregator of that data. >> Yeah, I don't know that we want to be the aggregator but we want to lead the movement towards doing that. Just getting the technology players, the shelter management systems, the other people who play a role in technology for animal welfare, getting them in a room and talking and figuring out this problem is huge. >> Right. >> And with a partner like Amazon we feel it can be solved. >> Right. Well Angie thank you for taking a few minutes and sharing your story, really really enjoyed hearing it. >> All right thank you so much. >> All right, she's Angie, I'm Jeff you're watching the CUBE we're at AWS Imagine in Seattle, thanks for watching we'll see you next time. (upbeat music)

(upbeat music) >> Live, from Miami Beach, Florida, it's theCUBE, covering UiPath Forward Americas. Brought to you by UiPath. >> Welcome back to the former home of Lebron James, I'm Dave Vellante, this is two minimum, we are here at South Beach at the hotel Fontainebleau. This is UiPath Forward Americas, and this is theCUBE, the leader in live tech coverage Leo Da Silva is here, he is the process excellent leader for Best Day Travel and Arnold Schiemann who's Vice President of Latin America and Spain. You get to go to all the fun places for Symphony. Welcome to theCUBE >> Thank you, thank you guys for your invitation >> You're very welcome, Leo let's start with you Best Day Travel, travel site, specializing in Mexico and other parts of the region tell us about the company >> Well, we have a leadership in Mexico we are, the last year we have five point four million travelers, okay? And there's a lot of people, okay? We've been in the business for 35 years, 34 years actually, okay? So, we're pretty solid, okay? While 75% of the all the transactions we have online, okay? And 25% we have offline, and that's why we're doing, all the transformation that we're doing is under this 25%, alright? Like, just to get the additional transformation and everything. >> So 35 years, so you started before the internet (Leo laughing) >> So I guess you should be 100% offline you obviously successfully made that transition. >> That's correct, that's correct. >> Okay, and Arnold, Symphony is the solution provider right? the implementation partner in this case, right? tell us about symphony and your role. >> Well, Symphony is probably the is particularly, suddenly concentrated on our PA management and our PA design, and our PA process rewardization. We were invited by Best Day Travel Group to look at the process, to look at the project and we embark in a very interesting transformation for them, so that they could move into their PA arena with a clear road map. >> So you guys are both process experts I mean that's, >> Yes >> You've got process in your title talk more about your role, if you would. >> Yeah, well, I'm a green belt, okay? And at least six sigma, and we use this methodology actually, and we are like, two years ago we implemented like a BPM, the department, you know inside the company, just to lead this transformation, okay? So that's what we're seeking right now to lead this transformation and, it's a very good challenge, you know? It's not easy, but we are trying to do our best. >> With your six sigma background, I think it would really tie right into what RPA is, 'cause you can really understand what has variance, and what is pretty standardized and that would seem is that the direct correlation with thing that you can have, the robot and the automation based on, really, the variance piece? >> Yes, totally, you know, well, when you start, all the implementation was right before where start you like to do a benchmark and you're able to see which technology we wanted to use and well, we found UiPath, alright? In which we found Symphony, and but it's not exactly, I think the technology is the last thing, right? So, the technology is the enabling alright? To do all those thing happening but if you don't have, like process management, you know, if you don't have that, it's kind of difficult to reach the target, okay? So, yeah, it's pretty much, I think it's when you, I think the most challenging is let people know what they're doing wrong you know, what they're doing repeating tasks, right so, when you do, like, the process walk through, people just get amazed, you know, like, what? Are you serious, we're doing that? >> When did you start? >> We started in February >> This year? >> Yeah >> Okay, so, take us back to February or January whatever, December, when you were maybe even before that, thinking about the business case. How did it come about, and how'd you guys meet? Take us through the sort of initiative. >> Yeah, well, right before, it was six months before I think it was, on July of last year, we started a conversation, right? And when I found that, within like six months of benchmarking and, we reached that like UiPath, and we start to ... trying to get something different, you know? To do something different enterprise and we had this need, okay? From inside, you know, from back office to tranformate because it's operation sometimes it costs a lot, alright? The first step that we did was like a future of work accelerator, okay? Which is, it's this scan, it's a total scan of the area, okay? And to see how how big are the opportunities, okay? To transformate things, right, so was the first step and after we had the pilot, we have three or four projects ongoing. >> And you were involved from the beginning Arnold, last July? >> Yes, yes >> One thing which was really very interesting about the project is that the client was the C.E.O and the C.F.O was totally the C-suite involvement So, and we believe that our PA is about the business, is about the process, it was ideal. So, we had really I believe it was really not work but, really a good time that we spent together integrating very closely with the team from Best Day Travel Group, to the point that you couldn't tell who was from Best Day and who was from Symphony, and then we were able to present to the C-suite, the result of the road map to move forward with a very clear business case, the process that was going to be robotized. Simultaneously, Best Day wanted approved inside, saying lets develop robotized version of one of the processes, and we did one which had been quite successful, we were just talking that the amount of work that that robot is handling today life, is such that either robot doesn't operate, he wouldn't know what to do because there is so much work to do behind in the past, and he doesn't know what he did, but today, it is almost impossible to recreate that. >> Yeah, that's correct, singularity is here >> One of the things that maybe you can help me understand, 'cause I'm a little bit new to this technology, how do you figure out, how do you size this, like how do you know how many things a robot can do, we heard one of the customers has a thousand robots, how does this scale, and how does this build out inside of a customer? >> Two thing that we do is that we look at the company, we identify those process, with heavy like, say, head count with lots of repetitive tasks that can be partially or totally robotized, and then we present it as a road map because the first question they have is "how do we start?" I mean, this is a company, 3000 people 4 million passengers, where do we start? How we get good advantage of the robots and that's how we did it, and then it's going on, the project we just did the first part, we continue now with the second part which is going to be even more interesting. >> What'd the business case look like? I mean, was it a saving money, making presumably some of this was cost reduction right off the bat, right? >> Yes, yes >> Lets talk about that business case what's that framework look like? >> Well, the action will have a pilot, that we just did, we launched already, alright? The business case was like, to to reduce cost, alright? The operational cost is very high, okay? So, now, we have like, just to have an idea the situation before would have, like six person working, you know, like the eight hour shift, okay? And doing like issuing tickets and you know and right now we have, like, just one robot and we built a capability of, 126% okay? On this, just with one robot, alright, and yeah, it's amazing, its amazing and 24/7, you know, right now it working pretty fine. >> Specifically, where do the cost savings come from? >> Well, the cost savings is not exactly that ease, but it's a customer's experience, okay? And also the capability that you can build alright? To get more sales, okay? And there's another project that, before that we had the first one, we have to to reduce the cost of the operation you, know, for 65 people, alright? And ... the transactions cost a lot of money for us, okay? So that's how we're trying to we're trying to understand that and we're trying to eliminate those costs or reduce, you know like, as much as we can. >> Its a part of that, you redeploy people, you put 'em on other tasks, is that what you're doing? >> Yes, yes, we free them up, you put another, you add value task, right? >> So the C.F.O is one of the stakeholders here, >> It was >> So many C.F.Os might say "okay, well, we're "not going to cut head count, so where do I "get my savings?" so the answer, if I'm hearing it is well we're going to increase revenue because these people are going to be on other tasks, and >> That's it, yes >> And, do you have visibility in line of sight as to how fast that can happen, whether, is it already starting to happen? >> Yeah, it already start to happen, already start to happen, like in, you know, this project was we have the roll back in 15 days >> I was going to ask you what the break even was it was inside of a month? >> You know, its already paid, it all 15 days, it's already paid, right so, yeah, the C.F.O is pretty happy with that. >> The first project was relatively small right? >> Yeah, yeah yeah. >> You proved it out and now you're going to throw gasoline on the fire >> That's it, that's it. >> That's great, so what's next for you guys? >> Well, next, we are go to the customer service you know, like ano-traceability, there's a traceability project that we have to do, alright? Just to ... To have the client in front of everything, you know? So that's our strategy right now and we're going to do, well Symphony is going to help us out with our PA and with implementation and the process, because its going to be a new process, it doesn't exist, alright? So there's going to be a brand new one so we have to create from scratch. >> Arnold, I wonder if you can go a little broader for us on this, it sounds like you've got a perfect partner inside the company with, you know, process in his title you've got the C-suite engaged, is that a typical deployment, what are you finding? >> Is not typical but it is, that is something that we look for all the time. 'cause it's, if the client is not engaged, we can do nothing, if the C-suite is not engaged, there is very little process people can do and by being engaged the C-suite, we're driving the cost reductions, but there is another point besides cost, consistency, and also we are eliminating side loss that had existed for long time, 'cause the companies are starting with one organization then another one, another one and all of them touch the customer what the probably will be doing to them hopefully before the end of the year, early next year, to be able to see the transverse of the customer, one and a half million passengers arriving to Cancún and they are passengers. But you don't know how many people will come back so you better know that these guys came here they like to go to the scuba diving next time he's around, we can offer him a scuba diving, we can pick him up from the airport, we can offer other services and then, the company is structured to be exponentially, so that you can grow from 4 million to 8 million passengers without adding head count, adding, that is the future of Best Day Travel Group and that's why we have engaged the management. >> Okay, so you're looking at the moon shot double the number of passengers served with the same head count, that's a huge productivity boost, so I'm hearing 15 day break even, some of that was hard cost reduction, its revenue increased, its proven, now you're going to invest more consistency, better customer service, cross selling, hey they like to scuba dive, maybe we can make an offer here, and better data allows you to do that that's going to summarizes the the business case and we're talking I mean, I don't want to, you know, squeeze the M.P.V at it, but we're talking millions? Hundreds of thousands? >> Millions >> Hundreds of millions? >> Millions right? >> Yeah, yeah, pretty much, it's a huge number you, know, its a huge number and, we have a lot of opportunities and, I think it's going to be a success, you know? >> And presumably the employees want to be part of this ride, right? They want to get, whether it's re-trained, or become R.P.A experts, deploy this technology, drive their digital automation and service those 8 million customers with the same resources you know, or invest in other resources. >> yes >> New growth areas. >> Yes, yes. >> Great story >> Yeah, it is, it is, >> we're working hard >> (laughs) figuring it out >> We're privileged to have been work with them because they are, I say unique but it was done for us from day one everything was put in place, engagement, people, and then the company itself is very easy to manipulate and transform because of the way that it was structured 30 years ago. >> And why UiPath? I mean, you said I chose them last summer why, why'd they win? >> Well, because of, well during a benchmarking, I can see a lot of difference between them, you know? And we have concluded that, well they actually Symphony recommend us, alright? So, you want this, you want that for this situation, it's going to be the best solution, right? And after that, we're pretty sure that it's it's the best it's the best choice, right? Because of the personalities, because a lot of stuffs that they have they can bring to us, you know? >> Do you worry about, do you worry about shadow R.P.A, like (laughter) >> The divisions going off and doing their own robots, or have you guys got a handle on that? >> Yeah, you know (laughing) no, not worried about that, you know, but yeah it's going to happen. >> It's a good thing. >> Alright, gentlemen, thanks so much for coming on theCUBE it was great to have you. >> Thank you for inviting us >> Alright keep it right there everybody, Stu and I will be back at UiPath Forward Americas right after this short break, you're watching theCube, we'll be right back. (closing music)

>> Voiceover: Live from San Jose, California, in the heart of Silicon Valley, it's the Cube. Covering QuickBooks Connect 2016, sponsored by Intuit QuickBooks. Now, here are your hosts, Jeff Frick and John Walls. >> Welcome back here on the Cube as we continue our coverage here at Quickbooks Connect 2016 live from San Jose at the Convention Center. 5,000 attendees, the third year of this event, more than ever, and certainly that explosive growth is personified in what's happening here. On this floor and the key note station, and of course at home, if you're a small business owner you know exactly what we're talking about. Along with Jeff Frick, I'm John Walls and we're joined now by probably one of the most popular authors, most widely read authors in America today. Malcolm Gladwell, five times New York Times Bestseller Author. Congratulations on that. And the Revisionist History Podcast, which we love. I love the Wilt Chamberlain podcast, Big Man Can't Shoot. Thanks for joining us. Great to have you. >> Delighted to be here. >> So, first off, tell us about, and the whole spirit of this show is about the entrepreneurial capabilities of so many people in the workplace today. What's your thought about entrepreneurism if you will, and what does it take to be a good outside the box thinker? Like so many of these folks are. >> Well there ... The explosion ... Here we are in the middle of Silicon Valley and what this part of the country has done to change the culture of the entire world's economy in the last 20 years, 25 years is nothing short of incredible. Entrepreneurship has gone from something that people thought of as the province of wackos and weirdos and strange people to a kind of thing that kids aspire to do and be. That's an amazing transformation. And I think when we ... What's happened over the course of that transformation is we've discovered that the definition of what it takes to be good is a lot broader than we thought. That many different kinds of people using many different kinds of strategies can be effective at starting businesses and achieving. I think that's been the great take home lesson of this entrepreneurial explosion of the last generation. >> I think probably in all of your works, there are pieces of it that you could extract and apply to this world, but what really struck me I think about David and Goliath, about advantages, disadvantages and making the most of your strengths basically, how do you see that translating or how would you want to communicate that to somebody, a small business owner, who thinks "Man, I'm up against the wall"? "How am I going to cut through the clutter?" "How am I going to get there?" All this sweat equity. But yet, there are advantages that they have. >> Yeah. Yeah, because this goes to this issue of learning strategies that there's a kind of learning called compensation learning, where you are learning out of weakness, not out of strength. You're learning from your failures and that kind of learning is a lot harder to do, but it's a lot more powerful. So the task of the small business owner, who is facing a whole series of disadvantages and weaknesses relative to much larger competitors, there's no question, it's a harder way to go. But, if you can pull it off, you'll end up in a much stronger position. If you can be one of those people who can do compensation learning, and in that book I talk, for example, about how many entrepreneurs are dyslexic, and that's a beautiful example of that. Some portion of people who suffer from quite a serious learning disorder, not all of them, some portion of them manage to turn that around into an advantage. To take something, to take a basic inability to read, and turn that into developing skills or delegation and leadership and problem solving and developing an incredible resilience, the ability to cope with failure. They turn a weakness into a strength and they end up being far more powerful than they would be as a result. And when I interviewed all these successful, dyslexic entrepreneurs for that book, what was amazing was that all of them said, "I did not succeed despite my disability, I succeeded because of it." And that's the crux of it. And so I think there is a silver lining to many of the clouds that small business owners face. >> It's a really powerful statement because so often, people are using drugs and medication and other things to kind of normalize people that are maybe not in the mean, that are on the fringe. But in fact, it's their ability to put a different lens, and see things differently that opens up an opportunity that the regular person just trucking down the road didn't see right in front of them. >> That's what I meant when I said earlier, talking about how our kind of definition of what it takes to be a successful entrepreneur is expanding. I think we're beginning to understand that lots of traits that we once thought of as just problematic have unexpected benefits. Like I remember once reading someone who was putting out that basically, most of the great research scientists in the world have OCD. And you kind of have to have OCD if you want to be ... 'Cause what are you doing? You're spending hours and hours in the lab doing the same incredibly precise experiment over and over and over again, and measuring your results to the slightest. That's OCD behavior that has found a beautiful home. Right? Has found a world where you need to be that way, right? And I read that as like, "That's lovely." These are people who we drugged up and pushed off to the fringes two generations ago, and now we've found a home for them in labs where they're doing incredibly productive and satisfying work. >> Yeah, I think you profiled in one of the podcasts, a cancer researcher who you said nobody really likes the guy, he's kind of an ordinary guy, but he was just so laser focused on the very specific problem that he was trying to solve. He didn't really care. That's what he was all about. >> Yeah, no, this has been a lovely development in our understanding of human capacity. >> So where do the ideas come from? I'm one of the many fans and I've read, and every time I read one of your books, it never ceases to amaze me how much you make me think. Which is, I think, why we're all so attracted to it. Because it seems so obvious, right? After you present this beautiful, elegant case, like "I never thought of that." Where do those ideas come from? What motivates you to say "I'm going to write blank. I'm going to do tipping point." >> I wish I had a system, 'cause right now I'm planning the next season of my podcast, so I need 10 more ideas for that, and I'm starting to write a new book so I need 80,000 words for that. And I'm wondering, I wish I had a big bucket full of ideas. (laughter) So I'm running around with my head cut off talking to people, but I spent the summer ... I probably read 40 books this summer to do with ... Apart from, I'm not talking about novels and fillers, and serious books that I'm trying to get. And I've been going around talking to people, just talking to interesting people trying to work out what I'm interested in. And trying to just uncover interesting things that will prompt me to go in cool new directions. There is a kind of, you have to let your mind ... It's like, the farmer lets his field go fallow for a while. You've got to have a fallow period where you just let everything regenerate and then you plant the crop again. >> But somehow reading 40 books doesn't sound like, to me, you're letting your mind go fallow. >> Well I didn't have a ... I was literally just lying around reading books. It seemed pretty fallow to me. >> What was your favorite one out of that read? Or the most enlightening one out of that read? >> I got on these weird side tracks this summer. I became obsessed with Churchill's Best Friend. Churchill had a best friend who betrays him. And it's this incredibly moving story. And I don't know how it fits in what I want to do, but I want to try and make it fit, 'cause it's such a weird and troubling story about this, I mean a truly transcendent figure in history who has a best friend who stabs him in the back with consequences for the world. Anyway, so I read like seven bizarre, weird, obscure books about this guy. And I was like "There's something there I think." >> He's out there, yeah. >> Alright, so we'll pick something that was a little more topical. Last night, they had a drink making robot machine over in the corner making drinks. And it just brings up, as we get into more automation, more connected systems. We had the huge knockout of the web last week from the East coast. As you look at the future, there's the happy future, where the machines do all the hard work and we get to sit around and read books like you did, which is fantastic. And then there's the darker potential future, where the machines take everyone's jobs. What are people going to do? And if it can make drinks and it can diagnose disease and read every manual that came out. How do people fit? And then there's the middle ground, right? The best chess player is the best chess player and a machine, not either or. So I'm just curious to get your thoughts as we look to the next big wave of AI and machine learning and automation, how you see that shaking out. >> I think it's important not to overstate how much of our lives we will be willing to let machines take over. So it's been very interesting for me as a writer, to observe, for example, what happened with eBooks over the last 10 years. So eBooks come along and everyone says, "The printed book is over. It's going to all going to be on ... Why would you go and lug around a big, heavy book when you can get for a fraction of the cost something that'll be ..." And so there were all these gloom and doom, and expectations, and what happens? Well, it turns out that eBooks are still a fairly sizeable portion of the market place. But it turns out that most people actually want to read a book, a physical object, that that's more pleasurable somehow, that the interaction with this thing, this pages and paper, is pleasing. It's part of the experience. And I think that's a useful ... No, that's not a robot and that's not AI, but it's an important reminder that the interactions and the activities that make up our lives are not just functional activities. They are opportunities for enjoyment and engagement, and part of the reason you go to a restaurant is not just to eat the food, but to engage with the people in the restaurant. Part of the pleasure is the person who brings you the wine bottle and gives you a little spiel. Now, I can replace that person with a robot, but the question is do you want to? Now, you can do it. And I can imagine a future where the robot brings you the best wine in the world and does some algorithm and gives you the finest wine. But I don't know, if I'm having a nice night out and I'm paying 60 dollars a plate for my dinner, I kind of want the human interaction. I mean, it's part of the pleasure. Same thing with self-driving cars. It baffles me as a kind of car guy how everyone assumes that "Oh, well, by 2020, it'll all be self-driving cars." Wait a minute, what if I enjoy driving a car? We've forgotten this. It's actually quite a pleasant thing to go and to make decisions unconsciously and consciously and drive down the road. And I like a manual transmission, I like the feel of driving a car. I don't want to give that up. Why should I have to give that up? So it's like, we can't get ahead of ourselves. You mentioned the chess thing, which is a great example of this. Can you make a machine that will beat a person at chess? Yes, you can. But it's not chess. Chess is a gameplay between two people. That's why it's interesting. If it's played between two machines no one will watch it! So it's this absurd thing. I can also make a machine that can run faster than Usain Bolt. It's called a car. Do I want to watch a race between a car and Usain Bolt? No. Why? Because what's pleasurable is watching human beings race. >> But Jeff hit on something, and then you touched on it with the car, and I think about GPS. And how it wasn't that long ago, and I kind of sound like my grandfather now or my father, that we just drove around, right? And if you came to the traffic, "Oh God, I've hit traffic." But now we use applications that take us, and they're using their intelligence. Is it possible, can you see with this generation of kids coming up now, that artificial intelligence kind of makes our personal thinking obsolete? And we don't process like we do, we don't evaluate, we don't analyze, and so we're raising a whole different kind of human, because of the interaction with technology or what we can sign to technology, because we give up on it. >> Well it'd be different. I think that, so let's stick with cars for a moment. I think now we have a world where a whole class of people drive their car to work in the morning. And when they're driving their car, the number of things they can do with their imagination and mind is limited. They can listen to music or the news or a podcast, or they can just sit there, but they can't ... They can maybe talk on a phone even though they shouldn't, but they can't do work and they can't lie in the back and take a nap, and they can't daydream, and they can't have a meaningful interaction with more than one person. What we're going to move to is a world where some people will give up whatever kind of pleasure or interaction that came from driving a car, and replace it with another kind of interaction. So driving a car becomes ... The time that you're in a car becomes a place where an infinite number of things can happen, as opposed to five things can happen. And I sort of think that's what the world looks like, is we get this incredibly complicated mix. Medicine becomes some mixture of the computer is going to do all the easy stuff, but half of medicine is about being reassured. It's about your personal fears. It's not about the diagnosis, or which drug you take. And for that stuff, I imagine that we're going to have much longer, deeper, more meaningful conversations with our doctors 15 years from now, when the computer has taken all the easy stuff off the table, or the AI, the robot. So in many ways, that world allows for much richer, personal interactions than the one we're in now. The doctor really will have ... My doctor has no time for me now. He's like "I got to move around." >> "Got to go." >> In ten years, it's possible my doctor will be able to sit down with me for half an hour or 45 minutes twice a year and really talk about what's going on with me and that's the promise of the future. I don't think we're going to have a situation where everything's done by the robot. >> Well this is one of those occasions where I truly wish we had tons of more time, but you have a busy schedule and so we're going to allow you to go on, but thank you so much ... >> Thank you. It was super fun. >> John: For sharing this time with us. We've thoroughly enjoyed it. >> Jeff: Look forward to the KeyNote later this afternoon as well. >> And we look forward to the next 80,000 words, so good luck with that too! >> Thank you. >> Malcolm Gladwell, joining us here on the Cube. Back with more from San Jose right after this. (upbeat music)

>> TheCUBE presents Ignite 22 brought to you by Palo Alto Networks. >> Welcome back to Las Vegas, everyone. We're glad you're with us. This is theCUBE live at Palo Alto Ignite 22 at the MGM Grant in Las Vegas. Lisa Martin here with Dave Vellante, day one of our coverage. We've had great conversations. The cybersecurity landscape is so interesting Dave, it's such a challenging problem to solve but it's so diverse and dynamic at the same time. >> You know, Lisa theCUBE started in May of 2010 in Boston. We called it the chowder event, chowder and Lobster. It was a EMC world, 2010. BJ Jenkins, who's here, of course, was a longtime friend of theCUBE and made the, made the transition into from, well, it's still data, data to, to cyber. So >> True. And BJ is back with us. BJ Jenkins, president Palo Alto Networks great to have you back on theCUBE. >> It is great to be here in person on theCube >> Isn't it great? >> In Vegas. It's awesome. >> And we can tell by your voice will be, will be gentle. You, you've been in Vegas typical Vegas occupational hazard of losing the voice. >> Yeah. It was one of the benefits of Covid. I didn't lose my voice at home sitting talking to a TV. You lose it when you come to Vegas. >> Exactly. >> But it's a small price to pay. >> So things kick off yesterday with the partner summit. You had a keynote then, you had a customer, a CISO on stage. You had a keynote today, which we didn't get to see. But talk to us a little bit about the lay of the land. What are you hearing from CISOs, from CIOs as we know security is a board level conversation. >> Yeah, I, you know it's been an interesting three or four months here. Let me start with that. I think, cybersecurity in general is still front and center on CIOs and CISO's minds. It has to be, if you saw Wendy's presentation today and the threats out there companies have to have it front and center. I do think it's been interesting though with the macro uncertainty. We've taken to calling this year the revenge of the CFO and you know these deals in cybersecurity are still a top priority but they're getting finance and procurements, scrutiny which I think in this environment is a necessity but it's still a, you know, number one number two imperative no matter who you talked to, in my mind >> It was interesting what Nikesh was saying in the last conference call that, hey we just have to get more approvals. We know this. We're, we're bringing more go-to-market people on board. We, we have, we're filling the pipeline 'cause we know they're going to split up deals big deals go into smaller chunks. So the question I have for you is is how are you able to successfully integrate those people so that you can get ahead of that sort of macro transition? >> Yeah I, you know, I think there's two things I'd say about uncertain macro situations and Dave, you know how old I am. I'm pretty old. I've been through a lot of cycles. And in those cycles I've always found stronger companies with stronger value proposition separate themselves actually in uncertain, economic times. And so I think there's actually an opportunity here. The message tilts a little bit though where it's been about innovation and new threat vectors to one of you have 20, 30, 40 vendors you can consolidate become more effective in your security posture and save money on your TCOs. So one of the things as we bring people on board it's training them on that business value proposition. How do you take a customer who's got 20 or 30 tools take 'em down to 5 or 10 where Palo is more central and strategic and be able to demonstrate that value. So we do that through, we're making a huge investment in our people but macroeconomic times also puts some stronger people back on the market and we're able to incorporate them into the business. >> What are the conditions that are necessary for that consolidation? Like I would imagine if you're, if you're a big customer of a big, you know, competitor of yours that that migration is going to be harder than if you're dealing with lots of little point tools. Do those, do those point tools, are they sort of is it the end of the subscription? Is it just stuff that's off the books now? What's, the condition that is ripe for that kind of consolidation? >> Look, I think the challenge coming into this year was skills. And so customers had all of these point products. It required a lot more human intervention as Nikesh was talking about to integrate them or make them work. And as all of us know finding people with cybersecurity skills over the last 12 months has been incredibly hard. That drove, if you know, if you think about that a CIO and a CISO sitting there going, I have all all this investment in tools. I don't have the people to operate 'em. What do I need to do? What we tried to do is elevate that conversation because in a customer, everybody who's bought one of those, they they bought it to solve a problem. And there's people with affinity for that tool. They're not just going to say I want to get consolidated and give up my tool. They're going to wrap their arms around it. And so what we needed to do and this changed our ecosystem strategy too how we leverage partners. We needed to get into the CIO and CISO and say look at this chaos you have here and the challenges around people that it's, it's presenting you. We can help solve that by, by standardizing, consolidating taking that integration away from you as Nikesh talked about, and making it easier for your your high skill people to work on high skill, you know high challenges in there. >> Let chaos reign, and then reign in the chaos. >> Yes. >> Andy Grove. >> I was looking at some stats that there's 26 million developers but less than 3 million cybersecurity professionals. >> Talked about that skills gap and what CISOs and CIOs are facing is do you consider from a value prop perspective Palo Alto Networks to be a, a facilitator of helping organizations deal with that skills gap? >> I think there's a short term and a long term. I think Nikesh today talked about the long term that we'll never win this battle with human beings. We're going to have to win it with automation. That, that's the long term the short term right here and now is that people need people with cybersecurity skills. Now what we're trying to do, you know, is multifaceted. We work with universities to standardize programs to develop skills that people can come into the marketplace with. We run our own programs inside the company. We have a cloud academy program now where we take people high aptitude for sales and technical aptitude and we will put them through a six month boot camp on cloud and they'll come out of that ready to really work with the leading experts in cloud security. The third angle is partners, right, there are partners in the marketplace who want to drive their business into high services areas. They have people, they know how to train. We give them, we partner with them to give them training. Hopefully that helps solve some of the short-term gaps that are out there today. >> So you made the jump from data storage to security and >> Yeah. >> You know, network security, all kinds of security. What was that like? What you must have learned a lot in the last better part of a decade? >> Yeah. >> Take us through that. >> You know, so the first jump was from EMC. I was 15 years there to be CEO of Barracuda. And you know, it was interesting because EMC was, you know large enterprise for the most part. At Barracuda we had, you know 250,000 small and mid-size enterprises. And it was, it's interesting to get into security in small and mid-size businesses because, you know Wendy today was talking about nation states. For small and mid-size business, it's common thievery right? It's ransomware, it's, and, those customers don't have, you know, the human and financial resources to keep up with the threat factor. So, you know, Nikesh talked about how it's taken 'em four and a half years to get into cybersecurity. I remember my first week at Barracuda, I was talking with a customer who had, you know, breached data shut down. There wasn't much bitcoin back then so it was just a pure ransom. And I'm like, wow, this is, you know, incredible industry. So it's been a good, you know, transition for me. I still think data is at the heart of all of this. Right? And I have always believed there's a strong connection between the things I learned growing up at EMC and what I put into practice today at Palo Alto Networks. >> And how about a culture because I, you know I know have observed the EMC culture >> Yeah. >> And you were there in really the heyday. >> Yeah. >> Right? Which was an awesome place. And it seems like Palo Alto obviously, different times but you know, similar like laser focus on solving problems, you know, obviously great, you know value sellers, you know, you guys aren't the commodity >> Yeah. For Product. But there seemed to be some similarities from afar. I don't know Palo Alto as well as I know EMC. >> I think there's a lot. When I joined EMC, it was about, it was 2 billion in in revenue and I think when I left it was over 20, 20, 21. And, you know, we're at, you know hopefully 5, 5 5 in revenue. I feel like it's this very similar, there's a sense of urgency, there's an incredible focus on the customer. you know, Near and Moche are definitely different individuals but the both same kind of disruptive, Israeli force out there driving the business. There are a lot of similarities. I, you know, the passion, I feel privileged as a, you know go to market person that I have this incredible portfolio to go, you know, work with customers on. It's a lucky position to be in, but very I feel like it is a movie I've seen before. >> Yeah. And but, and the course, the challenges from the, the target that you're disrupting is different. It was, you know, EMC had a lot of big, you know IBM obviously was, you know, bigger target whereas you got thousands of, you know, smaller companies. >> Yes. >> And, and so that's a different dynamic but that's why the consolidation play is so important. >> Look at, that's why I joined Palo Alto Networks when I was at Barracuda for nine years. It just fascinated me, that there was 3000 plus players in security and why didn't security evolve like the storage market did or the server market or network where working >> Yeah, right. >> You know, two or three big gorillas came to, to dominate those markets. And it's, I think it's what Nikesh talked about today. There was a new problem in best of breed. It was always best of breed. You can never in security go in and, you know, say, Hey it's good I saved us some money but I got the third best product in the marketplace. And there was that kind of gap between products. I, believe in why I joined here I think this is my last gig is we have a chance to change that. And this is the first company as I look from the outside in that had best of breed as, you know Nikesh said 13 categories. >> Yeah. >> And you know, we're in the leaders quadrant and it's a conversation I have with customers. You don't have to sacrifice best of breed but get the benefits of a platform. And I, think that resonates today. I think we have a chance to change the industry from that viewpoint. >> Give us a little view of the voice of the customer. You had, was it Sabre? >> Yeah. >> That was on >> Scott Moser, The CISO from Sabre. >> Give us a view, what are you hearing from the voice of the customer? Obviously they're quite a successful customer but challenges, concerns, the partnership. >> Yeah. Look, I think security is similar to industries where we come up with magic marketing phrases and, you know, things to you know, make you want to procure our solutions. You know, zero trust is one. And you know, you'll talk to customers and they're like, okay, yes. And you know, the government, right? Joe, Joe Biden's putting out zero trust executive orders. And the, the problem is if you talk to customers, it's a journey. They have legacy infrastructure they have business drivers that you know they just don't deal with us. They've got to deal with the business side who's trying to make the money that keeps the, the company going. it's really helped them draw a map from where they're at today to zero trust or to a better security architecture. Or, you know, they're moving their apps into the cloud. How am I going to migrate? Right? Again, that discussion three years ago was around lift and shift, right? Today it's about, well, no I need cloud native developed apps to service the business the way I want to, I want to service it. How do I, so I, I think there's this element of a trusted partner and relationship. And again, I think this is why you can't have 40 or 50 of those. You got to start narrowing it down if you want to be able to meet and beat the threats that are out there for you. So I, you know, the customers, I see a lot of 'em. It's, here's where I'm at help me get here to a better position. And they know it's, you know Scott said in our keynote today, you don't just, you know have layer three firewall policies and decide, okay tomorrow I'm going to go to layer seven. That, that's not how it works. Right? There's, and, and by the way these things are a mission critical type areas. So there's got to be a game plan that you help customers go through to get there. >> Definitely. Last question, my last question for you is, is security being a board level conversation I was reading some stats from a survey I think it was the what's new in Cypress survey that that Palo Alto released today that showed that while significant numbers of organizations think they've got a cyber resiliency playbook, there's a lot of disconnect or lack of alignment at the boardroom. Are you in those conversations? How can you help facilitate that alignment between the executive team and the board when it comes to security being so foundational to any business? >> Yeah, it's, I've been on three, four public company boards. I'm on, I'm on two today. I would say four years ago, this was a almost a taboo topic. It was a, put your head in the sand and pray to God nothing happened. And you know, the world has changed significantly. And because of the number of breaches the impact it's had on brand, boards have to think about this in duty of care and their fiduciary duty. Okay. So then you start with a board that may not have the technical skills. The first problem the security industry had is how do I explain your risk profile in a way you can understand it. I'm, I'm on the board of Generac that makes home generators. It's a manufacturing, you know, company but they put Wifi modules in their boxes so that the dealers could help do the maintenance on 'em. And all of a sudden these things were getting attacked. Right? And they're being used for bot attacks. >> Yeah. >> Everybody on their board had a manufacturing background. >> Ah. >> So how do you help that board understand the risk they have that's what's changed over the last four years. It's a constant discussion. It's one I have with CISOs where they're like help us put it in layman's terms so they understand they know what we're doing and they feel confident but at the same time understand the marketplace better. And that's a journey for us. >> That Generac example is a great one because, you know, think about IOT Technologies. They've historically been air gaped >> Yes. >> By design. And all of a sudden the business comes in and says, "Hey we can put wifi in there", you know >> Connect it to a home Wifi system that >> Make our lives so much easier. Next thing you know, it's being used to attack. >> Yeah. >> So that's why, as you go around the world are you discerning, I know you were just in Japan are you discerning significant differences in sort of attitudes toward, towards cyber? Whether it's public policy, you know things like regulation where you, they don't want you sharing data, but as as a cyber company, you want to share that data with you know, public and private? >> Look it, I, I think around the world we see incredible government activity first of all. And I think given the position we're in we get to have some unique conversations there. I would say worldwide security is an imperative. I, no matter where I go, you know it's in front of everybody's mind. The, on the, the governance side, it's really what do we need to adapt to make sure we meet local regulations. And I, and I would just tell you Dave there's ways when you do that, and we talk with governments that because of how they want to do it reduce our ability to give them full insight into all the threats and how we can help them. And I do think over time governments understand that we can anonymize the data. There's, but that, that's a work in process. Definitely there is a balance. We need to have privacy, we need to have, you know personal security for people. But there's ways to collect that data in an anonymous way and give better security insight back into the architectures that are out there. >> All right. A little shift the gears here. A little sports question. We've had some great Boston's sports guests on theCUBE right? I mean, Randy Seidel, we were talking about him. Peter McKay, Snyk, I guess he's a competitor now but you know, there's no question got >> He got a little funding today. I saw that. >> Down round. But they still got a lot of money. Not of a down round, but they were, but yeah, but actually, you know, he was on several years ago and it was around the time they were talking about trading Brady. He said Never trade Brady. And he got that right. We, I think we can agree Brady's the goat. >> Yes. >> The big question I have for you is, Belichick. Do you ever question Has your belief in him as the greatest coach of all time wavered, you know, now that- No. Okay. >> Never. >> Weigh in on that. >> Never, he says >> Still the Goat. >> I'll give you my best. You know, never In Bill we trust. >> Okay. Still. >> All right >> I, you know, the NFL is a unique property that's designed for parody and is designed, I mean actively designed to not let Mr. Craft and Bill Belichick do what they do every year. I feel privileged as a Boston sports fan that in our worst years we're in the seventh playoff spot. And I have a lot of family in Chicago who would kill for that position, by the way. And you know, they're in perpetual rebuilding. And so look, and I think he, you know the way he's been able to manage the cap and the skill levels, I think we have a top five defense. There's different ways to win titles. And if I, you know, remember in Brady's last title with Boston, the defense won us that Super Bowl. >> Well thanks for weighing in on that because there's a lot of crazy talk going on. Like, 'Hey, if he doesn't beat Arizona, he's got to go.' I'm like, what? So, okay, I'm sometimes it takes a good good loyal fan who's maybe, you know, has >> The good news in Boston is we're emotional fans too so I understand you got to keep the long term long term in mind. And we're, we're in a privileged position in Boston. We've got Celtics, we've got Bruins we've got the Patriots right on the edge of the playoffs and we need the Red Sox to get to work. >> Yeah, no, you know they were last, last year so maybe they're going to win it all like they usually do. So >> Fingers crossed. >> Crazy worst to first. >> Exactly. Well you said, in Bill we trust it sounds like from our conversation in BJ we trust from the customers, the partners. >> I hope so. >> Thank you so much BJ, for coming back on theCUBE giving us the lay of the land, what's new, the voice of the customer and how Palo Alto was really differentiated in the market. We always appreciate your, coming on the show you >> Honor and privilege seeing you here. Thanks. >> You may be thinking that you were watching ESPN just now but you know, we call ourselves the ESPN at Tech News. This is Lisa Martin for Dave Vellante and our guest. You're watching theCUBE, the Leader and live emerging in enterprise tech coverage. (upbeat music)

foreign continued coverage of AWS re invent my name is Savannah Peterson and I am very excited to be joined by two brilliant blokes in the space of efficiency and performance whether you're on Prem or in the cloud today's discussion is going to be fascinating please welcome Brad and Simon to the show how are you Simon coming in from the UK how you feeling well thank you excellent and Brad we have you coming in from Seattle how are you this morning doing fine thank you excellent and feeling bookish given your background love that I know that you both really care about efficiency and performance it's a very hot topic both of the show and in the industry right now I'm curious I'm going to open it up with you Simon what challenges and I think you've actually continued to tackle these throughout the course of your career what challenges were you facing and wanting to solve when you started yellow dog um really we're just looking at cloud and coming from an on-premise environment really wanted to be able to make accessing Cloud particularly a volume to be simple and straightforward um if you look at today at the number of instance types available from the major Cloud providers there's more than seven thousand different instance types whereas on-prem you go along you select your processes you select your systems it's already be really easy when you hit the cloud you've just got this amazing amount of choice so really it was all about how can you make Intelligent Decisions for you know are you going to run your workload how to match it with what you've got on premise and that was really the inspiration for Rafael so staying there for just a second what does yellow dog provide customers is a SAS system so um you get to it by accessing through the yellow platform and what it allows people to do is to be able to make Intelligent Decisions about where to run their workload would that be on premise or in the cloud it has a wealth of information it understands the costs the performance the latency and the availability of every different instance type in all different clouds it really allows people to uh to be able to make use of that information provision exactly what they need and to be able to run their workloads yeah it also includes a provisioner and it also includes a scheduler as well which is a cloud native scheduler so it's designed to be able to cope with um with cloud in terms of things like spots and interruptions and be able to uh to reschedule and fail over between clouds if there's ever need to do so yeah that sounds incredible and I know this means a lot for partners like AMD Brad talk to me about the partnership and what this means for AMD for your customers yeah absolutely it you know we're excited to be aligned with the uh with a company like yellow dog it's it's um you know the the importance of compute is becoming more and more prevalent every day and it's it's always been top of mind but especially now when you think about what the uh what the economy and the rest of the world is kind of facing over the next you know probably a year or longer it's so important that um that you're able to maximize your dollars and your spend and doing away with uh with uh with absolute certainty that you've got the right type of people behind you uh ensuring that you're your dollars are being spent very wisely and the great thing about yell dogs that they have tremendous insight into uh into cost optimization computer optimization across the entire Globe their their indexes is quite remarkable and what it does is it allows uh customers to actually see just how performant and cost efficient AMD is so it allows us to really put our best foot forward and and gives customers a chance to understand something that they probably weren't uh more familiar with the fact that uh that AMD uh is a tremendous a tremendous value in the marketplace yeah and and uh Simon can you tell us a little bit more about the yellow dog index I'm glad you brought that up Brad yes the yellow index is uh is essentially it's live it's available for anyone to access you can just go to index.yam.tech and you'll be able to see pretty much every single instance type that's available from all the major Cloud providers and be able to make your selection are you looking for GPU type nodes are you looking for AMD processors are you looking just for performance essentially what you're able to do is create a live view of effectively what's available in different data centers around the world and the price at this moment in time also just uh as Brad mentioned in terms of you know cost efficiency and uh and being taking green values seriously as we should we should do the yellow index also has the ability to be able to see at that point in time where the best place to be at a runner job is based upon the lowest carbon impact of running at this moment in time and that for many organizations gives an amazing Insight in not just about being able to find the the understand fishing processes but being able to ensure the greenest energy possible is powering that process when you want to be able to run your workload it's so powerful what you just said and I think when we exactly it's not just about it's not just about power but it's about place when we are are looking at Global Computing at scale what I know that there's ESG advantages in and ESG being a very hot topic when we're talking about AMW on AWS and and and leveraging tools like yellow dog what other sorts of advantages Beyond being least carbon impactful can your Mutual customers benefit from so it's not like I say there's many other features you know a very important thing when you're running a high performance Computing workload is being able to match the instruction set that you're running on premise and then being able to use that in the cloud as well and also to be able to make Intelligent Decisions of where should something run should would something be more efficient um to build on premise should we always try and maximize our on-premise resources before going into the cloud there's a lot about being able to just be able to make decisions and yellow itself it makes thousands of decisions per second to be in a workout where the the best and most optimized places to to run your workload yeah so Brad you work with a lot of companies at scale what type of scale is possible when leveraging Technologies like AMD and yellow dog combined well you know I love the fact that you mentioned uh you know HPC and it's one of the areas that actually is most exciting for for me personally and for and for AMD with the combination of yellow dog and AWS and AWS launched the very first HPC uh instance type last year and you know we're we're we haven't even begun to answer a question we haven't gotten to see um the full-scale capability in the cloud when it comes to these uh these very coordinated and very refined workloads that are running at massive scale and and uh you know we've got some some products we'll be launched in the near future as well that are incredibly performant and you know to be honest I don't think I don't think we have even come close to seeing the scale relative to somebody's very optimized workloads in HPC uh that that we're capable of so um we're excited we're excited for the next few years to see how how we can wrap in um some of the tremendous success that AMD has had on-prem in these these these massive compute centers and replicating that same success inside AWS with companies like yellow dog it's uh it we're excited to see what uh what's what's going to come forward can you give us a preview of anything on the record that gets you really excited about the future I was going to ask you what what had you looking forward to 2023 and Beyond but nothing well not nothing official of course uh but um I will say this you know AMD has recently successful had the launch for Genoa uh it's our next next-gen release and it is um it is proving to be it absolutely is the dominant compute engine it at this point that exists and you know when you start to couple that with the the prowess of AWS you know you could see that over time becoming something potentially that um you know um can really start to change the compute landscape quite a bit so we're hopeful that you know in the future we'll have something along those lines uh with AWS and others and um we're very uh we're very bullish in that area love it uh Simon what about you you've been passionate about low carbon I.T for a long time is carbon neutral Tech in our future what I realize is a bold and lofty claim for you but feel free to give us any of your future predictions um yeah so well I started here trying to build solutions for you know many years ago so 2006 um I was part of a team that launched the the world's lowest powered Windows PC that was actually based on the AMD technology back then so uh you can tell that AMD have been working on a low power for us for a long time in terms of carbon neutral yes I think um certainly there's a there's a few data centers around the world now that are getting very close to uh to carbon neutral some of which may have already achieved it so that's really interesting but so you know the the second part of that is really the the manufacturer of everything that goes into those Services systems and being able to to get to uh you know a net zero on those over a period of time and when we do that which is yeah not without challenges but but certainly possible then we really will have carbon neutral I.T which will be uh a benefit to everyone you know mankind itself yeah casual statement and I have to say that I wholeheartedly agree I think that it's one of the greater challenges of Our Generation especially as what we're able to do in HPC in particular since we're talking about it is only going to grow and scale and magnitude and the amount of data that we have to organize certain process is is wild even today so I love that I'm curious is there anything that you can share with us that's in the pipeline for Yellow Dog anything coming up in the future that's very exciting um so we're coming up very soon um we're going to release something called um version 4 again log which contains um what we call a resource framework which is all about making sure you've got everything you need before you run a job either on-prem or in the cloud so that might be anything from making sure you've got the right licenses making sure that your data is all in the right location making sure you've got all aspects of your workflow ready before you start launching compute and start really but you know burning through dollars with computer could potentially sat there uh not not doing anything until other tasks keep catch up so we're really excited about this new V4 release which will uh which will come out very soon awesome we can't wait to learn more about that hopefully here again on the cube Brad what do Partnerships with companies like yellowdog meme for you and for the customers that you're able to serve yeah it's it's incredibly important I it's you know there's one of the difficulties in in compute that we have today especially in Cloud compute there's there's so much available at this point I mean there was a point in time it was very simple and straightforward it's not even close to being that anymore green so you know one of the things I love about yellow dog itself is actually it does a great they do a great job of making very complex situations and environments fairly simple to understand especially from a business perspective and so one of the things that we love about it is it actually helps our customers you know the AMD direct customers better understand how to properly use our technology and to get the most out of it and so it's difficult for us to articulate that message because you know we are a Semiconductor Company so sometimes it's a little tough to be able to articulate workloads and applications in the way that our customer base will be able to understand but you know it's it's so critical to have companies like yellow dog in the middle that can actually you know make that translation for us directly to the customer um you know and and especially too when you start thinking about ESG and environmental relationships and I'd like to make a comment and one of the things that is fantastic about AMD AWS and yellow we all share the same Mission and we're very public about those missions about just being better to the to the planet and um you know AMD has taken some very aggressive uh targets through 2025 much beyond anything that the industry has expected and you know because of that we are you know we are the most um we are the most power efficient xa6 product on the marketplace and it's not even close and you know I look forward to the day when uh you know you start looking at instance types inside these public Cloud providers in conjunction with the old dog and you can actually even start to see maybe potentially what that carbon footprint is based on those decisions you make on compute and um you know considering that more than half to spend for everybody is generally compute in these environments it's critical to really know what your true impact in the world is and um it's just one of the best parts about a partnership like this oh what a wonderful note to close on and I love both the Synergy between all the partners on a technology level but most importantly on a mission level because none of it matters if we don't have a planet that we can continue to innovate on so I'm I'm really grateful that you're both here fighting a good fight working together and also making a lot of information available for companies of all different sizes as they're navigating very complex decision trees in and operating their stack so thank you both Simon and Brad I really appreciate your time it's been incredibly insightful and thank you to our audience for tuning in to our continuing coverage of AWS re invent here on thecube my name is Savannah Peterson and I look forward to learning more with you soon foreign [Music]

welcome back to thecube's live coverage coming to you from the big apple in new york city we're talking all things aws summit but right now i've got two powerhouses you know them you love them john furrier dave vellante going to be talking about super cloud guys we've been talking a lot about this there's a big event coming up on the cube august 9th and i gotta start dave with you because we talk about it pretty much in every interview where it's relevant why super cloud yeah so john furrier years ago started a tradition lisa prior to aws which was to lay down the expectation for our audiences what they should be looking for at aws reinvent okay john when did that start 2012 2013. actually 2013 was our first but 2015 was the first time when we get access to andy jassy who wasn't doing any briefings and we realized that the whole industry started looking at amazon web services as a structural forcing function of massive change uh some say inflection point we were saying complete redefinition so you wrote the trillion dollar baby yeah right which actually turns into probably multi-trillion dollars we got it right on that one surprisingly it was pretty obvious so every year since then john has published the seminal article prior to reinvent so this year we were talking we're coming out of the isolation economy and john hedwig also also adam silevski was the new ceo so we had a one-on-one with adam that's right and then that's where the convergence between andy jassy and adam celebski kicked in which is essentially those guys work together even though they he went off and boomerang back in as they say in aws but what's interesting was is that adam zluski's point of view piggyback jassy but he had a different twist yeah some so you know low you know people who didn't have really a lot of thought into it said oh he's copying microsoft moving up the stack we're like no no no no no something structural is happening again and so john wrote the piece and he started sharing it we're collaborating he said hey dave take a take a look add your perspectives and then jerry chen had just written castles in the cloud and he talked about sub-markets and we were sort of noodling and one of the other things was in 2018 2019 around that time at aws re invent there was this friction between like snowflake and aws because redshift separated compute from storage which was snowflake's whole thing now fast forward to 2021 after we're leaving you know the covert economy by the way everyone was complaining they are asking jassy are you competing with your ecosystem the classic right trope and then in in remember jason used to use cloudera as the example i would like to maybe pick a better example snowflake became that example and what the transition was it went from hey we're kind of competitive for sure there's a lot of examples but it went from we're competitive they're stealing our stuff to you know what we're making so much money building on top of aws specifically but also the clouds and cross clouds so we said there's something new happening in the ecosystem and then just it popped up this term super cloud came up to connote a layer that floats above the hyperscale capex not is it's not pass it's not sas it's the combination of the of those things on top of a new digital infrastructure and we chose the term super cloud we liked it better than multi-cloud because multiplayer at least one other point too i think four or five years earlier dave and i across not just aws reinvent all of our other events we were speculating that there might be a tier two cloud service provider models and we've talked with intel about this and others just kind of like evaluating it staring at it and we met by tier two like maybe competing against amazon but what happened was it wasn't a tier two cloud it was a super cloud built on the capex of aws which means initially was a company didn't have to build aws to be like aws and everybody wanted to be like aws so we saw the emergence of the smart companies saying hey let's refactor our business model in the category or industry scope and to dominate with cloud scale and they did it that then continued that was the premise of chen's post which was kind of rift on the cube initially which is you can have a moat in a castle in the cloud and have a competitive advantage and a sustainable differentiation model and that's exactly what's happening and then you introduce the edge and hybrid you now have a cloud operating model that that super cloud extends as a substrate across all environments so it's not multi-cloud which sounds broken and like put it distance jointed joint barriers hybrid cloud which is the hybrid operating model at scale and you don't have to be amazon to take advantage of all the value creation since they took care of the capex now they win too on the other side because because they're selling ec2 and storage and ml and ai and this is new and this is information that people don't might not know about internally at aws there was a debate dave okay i heard this from sources do we go all in and compete and just own the whole category or open the ecosystem and coexist with [ __ ] why do we have these other companies or snowflake and guess what the decision was let's make it open ecosystem and let's have our own offerings as well and let the winner take off smart because they can't hire enough people and we just had aws and snowflake on the cube a few weeks ago talking about the partnership the co-op petition the value in it but what's been driving it is the voice of the customer but i want to ask you paint the picture for the audience of the critical key components of super cloud what are those yeah so i think first and foremost super cloud as john was saying it's not multi-cloud chuck whitten had a great phrase at dell tech world he said multi-cloud by default right versus multi-cloud by design and multi-cloud has been by default it's been this sort of i run in aws and i run my stack in azure or i run my stack in gcp and it works or i wrap my stack in a container and host it in the cloud that's what multi-cloud has been so the first sort of concept is it's a layer that that abstracts the underlying complexity of all the clouds all the primitives uh it takes advantage of maybe graviton or microsoft tooling hides all that and builds new value on top of that the other piece of of super cloud is it's ecosystem driven really interesting story you just told because literally amazon can't hire everybody right so they have to rely on the ecosystem for feature acceleration so it's it also includes a path layer a super pass layer we call it because you need to develop applications that are specific to the problem that the super cloud is solving so it's not a generic path like openshift it's specific to whether it's snowflake or [ __ ] or aviatrix so that developers can actually build on top of and not have to worry about that underlying and also there's some people that are criticizing um what we're doing in a good way because we want to have an open concept sure but here's the thing that a lot of people don't understand they're criticizing or trying to kind of shoot holes in our new structural change that we're identifying to comparing it to old that's like saying mainframe and mini computers it's like saying well the mainframe does it this way therefore there's no way that's going to be legitimate so the old thinking dave is from people that have no real foresight in the new model right and so they don't really get it right so what i'm saying is that we look at structural change structural change is structural change it either happens or it doesn't so what we're observing is the fact that a snowflake didn't design their solution to be multi-cloud they did it all on aws and then said hey why would we why are we going to stop there let's go to azure because microsoft's got a boatload of customers because they have a vertically stacking integration for their install base so if i'm snowflake why wouldn't i be on azure and the same for gcp and the same for other things so this idea that you can get the value of an amp what amazon did leverage and all that value without paying for it up front is a huge dynamic and that's not just saying oh that's cloud that's saying i have a cloud-like scale cloud-like value proposition which which will look like an ecosystem so to me the acid test is if i build on top of say [ __ ] or say snowflake or super cloud by default i'm either a category leader i own the data at scale or i'm sharing data at scale and i have an ecosystem people are building on top of me so that's a platform so that's really difficult so what's happening is these ecosystem partners are taking advantage as john said of all the hyperscale capex and they're building out their version of a distributed global system and then the other attribute of super cloud is it's got metadata management capability in other words it knows if i'm optimizing for latency where in the super cloud to get the data or how to protect privacy or sovereignty or how many copies to make to have the proper data protection or where the air gap should be for ransomware so these are examples of very specific purpose-built super clouds that are filling gaps that the hyperscalers aren't going after what's a good example of a specific super cloud that you think really articulates what you guys are talking about i think there are a lot of them i think snowflake is a really good example i think vmware is building a multi-cloud management system i think aviatrix and virtual you know private cloud networking and for high performance networking i think to a certain extent what oracle is doing with azure is is is definitely looks like a super cloud i think what capital one is doing by building on to taking their own tools and and and moving that to snowflake now that they're not cross-cloud yet but i predict that they will be of i think uh what veeam is doing in data protection uh dell what they showed at dell tech world with project alpine these are all early examples of super well here's an indicator here's how you look at the example so to me if you're just lifting and shifting that was the first gen cloud that's not changing the business model so i think the number one thing to look at is is the company whether they're in a vertical like insurance or fintech or financial are they refactoring their spend not as an i.t cost but as a refactoring of their business model yes like what snowflake did dave or they say okay i'm gonna change how i operate not change my business model per se or not my business identity if i'm gonna provide financial services i don't have to spend capex it's operating expenses i get the capex leverage i redefine i get the data at scale and now i become a service provider to everybody else because scale will determine the power law of who wins in the verticals and in the industry so we believe that snowflake is a data warehouse in the cloud they call it a data cloud now i don't think snowflake would like that dave i call them a data warehouse no a super data cloud but but so the other key here is you know the old saying that andreessen came up with i guess with every company's a software company well what does that mean it means every company software company every company is going digital well how are they going to do that they're going to do that by taking their business their data their tooling their proprietary you know moat and moving that to the cloud so they can compete at scale every company should be if they're not thinking about doing a super cloud well walmart i think i think andreessen's wrong i think i would revise and say that andreessen and the brain trust at andreas and horowitz is that that's no longer irrelevant every company isn't a software company the software industry is called open source everybody is an open source company and every company will be at super cloud that survives yeah to me to me if you're not looking at super cloud as a strategy to get value and refactor your business model take advantage of what you're paying it for but you're paying now in a new way you're building out value so that's you're either going to be a super cloud or get services from a super cloud so if you're not it's like the old joke dave if you're at the table and you don't know who the sucker is it's probably you right so if you're looking at the marketplace you're saying if i'm not a super cloud i'm probably gonna have to work with one because they're gonna have the data they're gonna have the insights they're gonna have the scale they're going to have the castle in the cloud and they will be called a super cloud so in customer conversations helping customers identify workloads to move to the cloud what are the ideal workloads and services to run in super cloud so i honestly think virtually any workload could be a candidate and i think that it's really the business that they're in that's going to define the workload i'll say what i mean so there's certain businesses where low latency high performance transactions are going to matter that's you know kind of the oracle's business there's certain businesses like snowflake where data sharing is the objective how do i share data in a governed way in a secure way in any location across the world that i can monetize so that's their objective you take a data protection company like veeam their objective is to protect data so they have very specific objectives that ultimately dictate what the workload looks like couchbase is another one they they in my opinion are doing some of the most interesting things at the edge because this is where when you when you really push companies in the cloud including the hyperscalers when they get out to the far edge it starts to get a little squishy couchbase actually is developing capabilities to do that and that's to me that's the big wild card john i think you described it accurately the cloud is expanding you've got public clouds no longer just remote services you're including on-prem and now expanding out to the near edge and the deep what do you call it deep edge or far edge lower sousa called the tiny edge right deep edge well i mean look at look at amazon's outpost announcement to me hp e is opportunity dell has opportunities the hardware box guys companies they have an opportunity to be that gear to be an outpost to be their own output they get better stacks they have better gear they just got to run cloud on it yeah right that's an edge node right so so that's that would be part of the super cloud so this is where i think people that are looking at the old models like operating systems or systems mindsets from the 80s they look they're not understanding the new architecture what i would say to them is yeah i hear what you're saying but the structural change is the nodes on the network distributed computing if you will is going to run hybrid cloud all the way across the fact that it's multiple clouds is just coincidence on who's got the best capex value that people build on for their super cloud capability so why wouldn't i be on azure if microsoft's going to give me all their customers that are running office 365 and teams great if i want to be on amazon's kind of sweet which is their ecosystem why wouldn't i want to tap into that so again you can patch it all together in the super cloud so i think the future will be distributed computing cloud architecture end to end and and we felt that was different from multi-cloud you know if you want to call it multi-cloud 2.0 that's fine but you know frankly you know sometimes we get criticized for not defining it tightly enough but we continue to evolve that definition i've never really seen a great definition from multi-cloud i think multi-cloud by default was the definition i run in multiple clouds you know it works in azure it's not a strategy it's a broken name it's a symptom right it's a symptom of multi-vendor is really what multi-cloud has been and so we felt like it was a new term of examples look what we're talking about snowflake data bricks databricks another good one these are these are examples goldman sachs and we felt like the term immediately connotes something bigger something that sits above the clouds and is part of a digital platform you know the people poo poo the metaverse because it's really you know not well defined but every 15 or 20 years this industry goes through dave let me ask you a question so uh lisa you too if i'm in the insurance vertical uh and i'm a i'm an insurance company i have competitors my customers can go there and and do business with that company and you know and they all know that they go to the same conferences but in that sector now you have new dynamics your i.t spend isn't going to keep the lights on and make your apps work your back-end systems and your mobile app to get your whatever now it's like i have cloud scale so what if i refactored my business model become a super cloud and become the major primary service provider to all the competitors and the people that are the the the channel partners of the of the ecosystem that means that company could change the category totally okay and become the dominant category leader literally in two three years if i'm geico okay i i got business in the cloud because i got the app and i'm doing transactions on geico but with all the data that they're collecting there's adjacent businesses that they can get into maybe they're in the safety business maybe they can sell data to governments maybe they can inform logistics and highway you know patterns roll up all the people that don't have the same scale they have and service them with that data and they get subscription revenue and they can build on top of the geico super insurance cloud right yes it's it's unlimited opportunity that's why it's but the multi-trillion dollar baby so talk to us you've done an amazing job of talking which i know you would of why super cloud what it is the critical components the key workloads great examples talk to us in our last few minutes about the event the cube on super cloud august 9th what's the audience going to who are they going to hear from what are they going to learn yeah so august 9th live out of our palo alto studio we're going to have a program that's going to run from 9 a.m to 1 p.m and we're going to have a number of industry luminaries in there uh kit colbert from from vmware is going to talk about you know their strategy uh benoit de javille uh from snowflake is going to is going to be there of g written house of sky-high security um i i i don't want to give it away but i think steve mullaney is going to come on adrian uh cockroft is coming on the panel keith townsend sanjeev mohan will be on so we'll be running that live and also we'll be bringing in pre-recorded interviews that we'll have prior to the show that will run post the live event it's really a pilot virtual event we want to do a physical event we're thinking but the pilot is to bring our trusted friends together they're credible that have industry experience to try to understand the scope of what we're talking about and open it up and help flesh out the definition make it an open model where we can it's not just our opinion we're observing identifying the structural changes but bringing in smart people our smart friends and companies are saying yeah we get behind this because it has it has legs for a reason so we're gonna zoom out and let people participate and let the conversation and the community drive the content and that is super important to the cube as you know dave but i think that's what's going on lisa is that it's a pilot if it has legs we'll do a physical event certainly we're getting phones to bring it off the hook for sponsors so we don't want to go and go all in on sponsorships right now because it's not about money making it's about getting that super cloud clarity around to help companies yeah we want to evolve the concept and and bring in outside perspectives well the community is one of the best places to do that absolutely organic it's an organic community where i mean people want to find out what's going on with the best practices of how to transform a business and right now digital transformation is not just getting digitized it's taking advantage of the technology to leapfrog the competition so all the successful people we talked to at least have the same common theme i'm changing my game but not changing my game to the customer i'm just going to do it differently better faster cheaper more efficient and have higher margins and beat the competition that's the company doesn't want to beat the competition go to thecube.net if you're not all they're all ready to register for the cube on supercloud august 9th 9am pacific you won't want to miss it for john furrier and dave vellante i'm lisa martin we're all coming at you from new york city at aws summit 22. i'll be right back with our next guest [Music] you

>> Instructor: "theCUBE" presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome to Valencia, Spain and we're at KubeCon, CloudNativeCon Europe 2022. I'm Keith Townsend, and my co-host, Enrico Signoretti. Enrico's really proud of me. I've called him Enrico instead of Enrique every session. >> Every day. >> Senior IT analyst at GigaOm. We're talking to fantastic builders at KubeCon, CloudNativeCon Europe 2022 about the projects and their efforts. Enrico, up to this point, it's been all about provisioning, insecurity, what conversation have we been missing? >> Well, I mean, I think that we passed the point of having the conversation of deployment, of provisioning. Everybody's very skilled, actually everything is done at day two. They are discovering that, well, there is a security problem. There is an observability problem a and in fact, we are meeting with a lot of people and there are a lot of conversation with people really needing to understand what is happening. I mean, in their cluster work, why it is happening and all the questions that come with it. And the more I talk with people in the show floor here or even in the various sessions is about, we are growing so that our clusters are becoming bigger and bigger, applications are becoming bigger as well. So we need to now understand better what is happening. As it's not only about cost, it's about everything at the end. >> So I think that's a great set up for our guests, Matt Provo, founder and CEO of StormForge and Patrick Brixton? >> Bergstrom. >> Bergstrom. >> Yeah. >> I spelled it right, I didn't say it right, Bergstrom, CTO. We're at KubeCon, CloudNativeCon where projects are discussed, built and StormForge, I've heard the pitch before, so forgive me. And I'm kind of torn. I have service mesh. What do I need more, like what problem is StormForge solving? >> You want to take it? >> Sure, absolutely. So it's interesting because, my background is in the enterprise, right? I was an executive at UnitedHealth Group before that I worked at Best Buy and one of the issues that we always had was, especially as you migrate to the cloud, it seems like the CPU dial or the memory dial is your reliability dial. So it's like, oh, I just turned that all the way to the right and everything's hunky-dory, right? But then we run into the issue like you and I were just talking about, where it gets very very expensive very quickly. And so my first conversations with Matt and the StormForge group, and they were telling me about the product and what we're dealing with. I said, that is the problem statement that I have always struggled with and I wish this existed 10 years ago when I was dealing with EC2 costs, right? And now with Kubernetes, it's the same thing. It's so easy to provision. So realistically what it is, is we take your raw telemetry data and we essentially monitor the performance of your application, and then we can tell you using our machine learning algorithms, the exact configuration that you should be using for your application to achieve the results that you're looking for without over-provisioning. So we reduce your consumption of CPU, of memory and production which ultimately nine times out of 10, actually I would say 10 out of 10, reduces your cost significantly without sacrificing reliability. >> So can your solution also help to optimize the application in the long run? Because, yes, of course-- >> Yep. >> The lowering fluid as you know optimize the deployment. >> Yeah. >> But actually the long-term is optimizing the application. >> Yes. >> Which is the real problem. >> Yep. >> So, we're fine with the former of what you just said, but we exist to do the latter. And so, we're squarely and completely focused at the application layer. As long as you can track or understand the metrics you care about for your application, we can optimize against it. We love that we don't know your application, we don't know what the SLA and SLO requirements are for your app, you do, and so, in our world it's about empowering the developer into the process, not automating them out of it and I think sometimes AI and machine learning sort of gets a bad rap from that standpoint. And so, at this point the company's been around since 2016, kind of from the very early days of Kubernetes, we've always been, squarely focused on Kubernetes, using our core machine learning engine to optimize metrics at the application layer that people care about and need to go after. And the truth of the matter is today and over time, setting a cluster up on Kubernetes has largely been solved. And yet the promise of Kubernetes around portability and flexibility, downstream when you operationalize, the complexity smacks you in the face and that's where StormForge comes in. And so we're a vertical, kind of vertically oriented solution, that's absolutely focused on solving that problem. >> Well, I don't want to play, actually. I want to play the devils advocate here and-- >> You wouldn't be a good analyst if you didn't. >> So the problem is when you talk with clients, users, there are many of them still working with Java, something that is really tough. I mean, all of us loved Java. >> Yeah, absolutely. >> Maybe 20 years ago. Yeah, but not anymore, but still they have developers, they have porting applications, microservices. Yes, but not very optimized, et cetera, cetera, et cetera. So it's becoming tough. So how you can interact with this kind of old hybrid or anyway, not well engineered applications. >> Yeah. >> We do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage and we, like Matt was saying, we can use any metric that you care about and we can work with any configuration for that application. So perfect example is Java, you have to worry about your heap size, your garbage collection tuning and one of the things that really struck me very early on about the StormForge product is because it is true machine learning. You remove the human bias from that. So like a lot of what I did in the past, especially around SRE and performance tuning, we were only as good as our humans were because of what they knew. And so, we kind of got stuck in these paths of making the same configuration adjustments, making the same changes to the application, hoping for different results. But then when you apply machine learning capability to that the machine will recommend things you never would've dreamed of. And you get amazing results out of that. >> So both me and Enrico have been doing this for a long time. Like, I have battled to my last breath the argument when it's a bare metal or a VM, look, I cannot give you any more memory. >> Yeah. >> And the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith you're cheap, my developer resources are expensive, buy bigger box. >> Yeah. >> Yap. >> Buying a bigger box in the cloud to your point is no longer a option because it's just expensive. >> Yeah. >> Talk to me about the carrot or the stick as developers are realizing that they have to be more responsible. Where's the culture change coming from? Is it the shift in responsibility? >> I think the center of the bullseye for us is within those sets of decisions, not in a static way, but in an ongoing way, especially as the development of applications becomes more and more rapid and the management of them. Our charge and our belief wholeheartedly is that you shouldn't have to choose. You should not have to choose between costs or performance. You should not have to choose where your applications live, in a public private or hybrid cloud environment. And so, we want to empower people to be able to sit in the middle of all of that chaos and for those trade offs and those difficult interactions to no longer be a thing. We're at a place now where we've done hundreds of deployments and never once have we met a developer who said, "I'm really excited to get out of bed and come to work every day and manually tune my application." One side, secondly, we've never met, a manager or someone with budget that said, please don't increase the value of my investment that I've made to lift and shift us over to the cloud or to Kubernetes or some combination of both. And so what we're seeing is the converging of these groups, their happy place is the lack of needing to be able to make those trade offs, and that's been exciting for us. >> So, I'm listening and looks like that your solution is right in the middle in application performance, management, observability. >> Yeah. >> And, monitoring. >> Yeah. >> So it's a little bit of all of this. >> Yeah, so we want to be, the intel inside of all of that, we often get lumped into one of those categories, it used to be APM a lot, we sometimes get, are you observability or and we're really not any of those things, in and of themselves, but we instead we've invested in deep integrations and partnerships with a lot of that tooling 'cause in a lot of ways, the tool chain is hardening in a cloud native and in Kubernetes world. And so, integrating in intelligently, staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for our users who have already invested likely, in a APM or observability. >> So to go a little bit deeper. What does it mean integration? I mean, do you provide data to this, other applications in the environment or are they supporting you in the work that you do. >> Yeah, we're a data consumer for the most part. In fact, one of our big taglines is take your observability and turn it into action ability, right? Like how do you take that, it's one thing to collect all of the data, but then how do you know what to do with it, right? So to Matt's point, we integrate with folks like Datadog, we integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. >> But also we want Datadog customers, for example, we have a very close partnership with Datadog so that in your existing Datadog dashboard, now you have-- >> Yeah. >> The StormForge capability showing up in the same location. >> Yep. >> And so you don't have to switch out. >> So I was just going to ask, is it a push pull? What is the developer experience when you say you provide developer this resolve ML learnings about performance, how do they receive it? Like, what's the developer experience. >> They can receive it, for a while we were CLI only, like any good developer tool. >> Right. >> And, we have our own UI. And so it is a push in a lot of cases where I can come to one spot, I've got my applications and every time I'm going to release or plan for a release or I have released and I want to pull in observability data from a production standpoint, I can visualize all of that within the StormForge UI and platform, make decisions, we allow you to set your, kind of comfort level of automation that you're okay with. You can be completely set and forget or you can be somewhere along that spectrum and you can say, as long as it's within, these thresholds, go ahead and release the application or go ahead and apply the configuration. But we also allow you to experience the same, a lot of the same functionality right now, in Grafana, in Datadog and a bunch of others that are coming. >> So I've talked to Tim Crawford who talks to a lot of CIOs and he's saying one of the biggest challenges or if not, one of the biggest challenges CIOs are facing are resource constraints. >> Yeah. >> They cannot find the developers to begin with to get this feedback. How are you hoping to address this biggest pain point for CIOs-- >> Yeah.6 >> And developers? >> You should take that one. >> Yeah, absolutely. So like my background, like I said at UnitedHealth Group, right. It's not always just about cost savings. In fact, the way that I look about at some of these tech challenges, especially when we talk about scalability there's kind of three pillars that I consider, right? There's the tech scalability, how am I solving those challenges? There's the financial piece 'cause you can only throw money at a problem for so long and it's the same thing with the human piece. I can only find so many bodies and right now that pool is very small, and so, we are absolutely squarely in that footprint of we enable your team to focus on the things that they matter, not manual tuning like Matt said. And then there are other resource constraints that I think that a lot of folks don't talk about too. Like, you were talking about private cloud for instance and so having a physical data center, I've worked with physical data centers that companies I've worked for have owned where it is literally full, wall to wall. You can't rack any more servers in it, and so their biggest option is, well, I could spend $1.2 billion to build a new one if I wanted to, or if you had a capability to truly optimize your compute to what you needed and free up 30% of your capacity of that data center. So you can deploy additional name spaces into your cluster, like that's a huge opportunity. >> So I have another question. I mean, maybe it doesn't sound very intelligent at this point, but, so is it an ongoing process or is it something that you do at the very beginning, I mean you start deploying this. >> Yeah. >> And maybe as a service. >> Yep. >> Once in a year I say, okay, let's do it again and see if something change it. >> Sure. >> So one spot, one single.. >> Yeah, would you recommend somebody performance test just once a year? Like, so that's my thing is, at previous roles, my role was to do performance test every single release, and that was at a minimum once a week and if your thing did not get faster, you had to have an executive exception to get it into production and that's the space that we want to live in as well as part of your CICD process, like this should be continuous verification, every time you deploy, we want to make sure that we're recommending the perfect configuration for your application in the name space that you're deploying into. >> And I would be as bold as to say that we believe that we can be a part of adding, actually adding a step in the CICD process that's connected to optimization and that no application should be released, monitored, and sort of analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, but for cost and performance. >> Almost a couple of hundred vendors on this floor. You mentioned some of the big ones Datadog, et cetera, but what happens when one of the up and comings out of nowhere, completely new data structure, some imaginative way to click to telemetry data. >> Yeah. >> How do, how do you react to that? >> Yeah, to us it's zeros and ones. >> Yeah. >> And, we really are data agnostic from the standpoint of, we're fortunate enough from the design of our algorithm standpoint, it doesn't get caught up on data structure issues, as long as you can capture it and make it available through one of a series of inputs, one would be load or performance tests, could be telemetry, could be observability, if we have access to it. Honestly, the messier the better from time to time from a machine learning standpoint, it's pretty powerful to see. We've never had a deployment where we saved less than 30%, while also improving performance by at least 10%. But the typical results for us are 40 to 60% savings and 30 to 40% improvement in performance. >> And what happens if the application is, I mean, yes Kubernetes is the best thing of the world but sometimes we have to, external data sources or, we have to connect with external services anyway. >> Yeah. >> So, can you provide an indication also on this particular application, like, where the problem could be? >> Yeah. >> Yeah, and that's absolutely one of the things that we look at too, 'cause it's, especially when you talk about resource consumption it's never a flat line, right? Like depending on your application, depending on the workloads that you're running it varies from sometimes minute to minute, day to day, or it could be week to week even. And so, especially with some of the products that we have coming out with what we want to do, integrating heavily with the HPA and being able to handle some of those bumps and not necessarily bumps, but bursts and being able to do it in a way that's intelligent so that we can make sure that, like I said, it's the perfect configuration for the application regardless of the time of day that you're operating in or what your traffic patterns look like, or, what your disc looks like, right. Like 'cause with our low environment testing, any metric you throw at us, we can optimize for. >> So Matt and Patrick, thank you for stopping by. >> Yeah. >> Yes. >> We can go all day because day two is I think the biggest challenge right now, not just in Kubernetes but application re-platforming and transformation, very, very difficult. Most CTOs and EASs that I talked to, this is the challenge space. From Valencia, Spain, I'm Keith Townsend, along with my host Enrico Signoretti and you're watching "theCube" the leader in high-tech coverage. (whimsical music)

>> Okay. We're now going to go into the customer panel, and we'd like to welcome Angelo Fausti, who's a software engineer at the Vera C. Rubin Observatory, and Caleb Maclachlan who's senior spacecraft operations software engineer at Loft Orbital. Guys, thanks for joining us. You don't want to miss folks this interview. Caleb, let's start with you. You work for an extremely cool company, you're launching satellites into space. Of course doing that is highly complex and not a cheap endeavor. Tell us about Loft Orbital and what you guys do to attack that problem. >> Yeah, absolutely. And thanks for having me here by the way. So Loft Orbital is a company that's a series B startup now, who, and our mission basically is to provide rapid access to space for all kinds of customers. Historically, if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, have a big software teams, and then eventually worry about, a bunch like, just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access, to a infrastructure problem. So that it's almost as simple as deploying a VM in AWS or GCP is getting your programs, your mission deployed on orbit with access to different sensors, cameras, radios, stuff like that. So, that's kind of our mission and just to give a really brief example of the kind of customer that we can serve. There's a really cool company called Totum Labs, who is working on building IoT cons, an IoT constellation for, internet of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor IoT which means you have this little modem inside a container that container that you track from anywhere in the world as it's going across the ocean. So, and it's really little, and they've been able to stay a small startup that's focused on their product, which is the, that super crazy, complicated, cool radio, while we handle the whole space segment for them, which just, you know, before Loft was really impossible. So that's our mission is providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving a huge variety of customers with all kinds of different missions, and obviously generating a ton of data in space that we've got to handle. >> Yeah. So amazing Caleb, what you guys do. Now, I know you were lured to the skies very early in your career, but how did you kind of land in this business? >> Yeah, so, I guess just a little bit about me. For some people, they don't necessarily know what they want to do like earlier in their life. For me I was five years old and I knew I want to be in the space industry. So, I started in the Air Force, but have stayed in the space industry my whole career and been a part of, this is the fifth space startup that I've been a part of actually. So, I've kind of started out in satellites, spent some time in working in the launch industry on rockets, then, now I'm here back in satellites and honestly, this is the most exciting of the different space startups that I've been a part of. >> Super interesting. Okay. Angelo, let's talk about the Rubin Observatory. Vera C. Rubin, famous woman scientist, galaxy guru. Now you guys, the Observatory, you're up way up high, you get a good look at the Southern sky. And I know COVID slowed you guys down a bit, but no doubt you continued to code away on the software. I know you're getting close, you got to be super excited, give us the update on the Observatory and your role. >> All right. So, yeah. Rubin is a state of the art observatory that is in construction on a remote mountain in Chile. And, with Rubin we'll conduct the large survey of space and time. We're going to observe the sky with eight meter optical telescope and take 1000 pictures every night with 2.2 Gigapixel camera. And we are going to do that for 10 years, which is the duration of the survey. >> Yeah, amazing project. Now, you earned a doctor of philosophy so you probably spent some time thinking about what's out there, and then you went out to earn a PhD in astronomy and astrophysics. So, this is something that you've been working on for the better part of your career, isn't it? >> Yeah, that's right, about 15 years. I studied physics in college. Then I got a PhD in astronomy. And, I worked for about five years in another project, the Dark Energy Survey before joining Rubin in 2015. >> Yeah, impressive. So it seems like both your organizations are looking at space from two different angles. One thing you guys both have in common of course is software, and you both use InfluxDB as part of your data infrastructure. How did you discover InfluxDB, get into it? How do you use the platform? Maybe Caleb you could start. >> Yeah, absolutely. So, the first company that I extensively used InfluxDB in, was a launch startup called Astra. And we were in the process of designing our first generation rocket there, and testing the engines, pumps, everything that goes into a rocket. And, when I joined the company our data story was not very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. And at first, there, you know, that's the way that a lot of engineers and scientists are used to working. And at first that was, like people weren't entirely sure that that was, that needed to change. But, it's, something, the nice thing about InfluxDB is that, it's so easy to deploy. So as, our software engineering team was able to get it deployed and, up and running very quickly and then quickly also backport all of the data that we collected this far into Influx. And, what was amazing to see and is kind of the super cool moment with Influx is, when we hooked that up to Grafana, Grafana as the visualization platform we used with Influx, 'cause it works really well with it. There was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data, where they could just almost instantly easily discover data that they hadn't been able to see before, and take the manual processes that they would run after a test and just throw those all in Influx and have live data as tests were coming, and, I saw them implementing like crazy rocket equation type stuff in Influx, and it just was totally game changing for how we tested. >> So Angelo, I was explaining in my open, that you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about in the example that Caleb just gave, you have to have a purpose built time series database. Where did you first learn about InfluxDB? >> Yeah, correct. So, I work with the data management team, and my first project was the record metrics that measured the performance of our software, the software that we used to process the data. So I started implementing that in our relational database. But then I realized that in fact I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found InfluxDB, and that was back in 2018. The, another use for InfluxDB that I'm also interested is the visits database. If you think about the observations, we are moving the telescope all the time and pointing to specific directions in the sky and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, we call a visit. So we want to record the metadata about those visits in InfluxDB. That time series is going to be 10 years long, with about 1000 points every night. It's actually not too much data compared to other problems. It's really just a different time scale. >> The telescope at the Rubin Observatory is like, pun intended, I guess the star of the show. And I believe I read that it's going to be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hubble's widest camera view, which is amazing. Like, that's like 40 moons in an image, amazingly fast as well. What else can you tell us about the telescope? >> This telescope it has to move really fast. And, it also has to carry the primary mirror which is an eight meter piece of glass. It's very heavy. And it has to carry a camera which has about the size of a small car. And this whole structure weighs about 300 tons. For that to work, the telescope needs to be very compact and stiff. And one thing that's amazing about it's design is that, the telescope, this 300 tons structure, it sits on a tiny film of oil, which has the diameter of human hair. And that makes an, almost zero friction interface. In fact, a few people can move this enormous structure with only their hands. As you said, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So, each image has, in diameter the size of about seven full moons. And, with that, we can map the entire sky in only three days. And of course, during operations everything's controlled by software and it is automatic. There's a very complex piece of software called the Scheduler, which is responsible for moving the telescope, and the camera, which is recording 15 terabytes of data every night. >> And Angelo, all this data lands in InfluxDB, correct? And what are you doing with all that data? >> Yeah, actually not. So we use InfluxDB to record engineering data and metadata about the observations. Like telemetry, events, and commands from the telescope. That's a much smaller data set compared to the images. But it is still challenging because you have some high frequency data that the system needs to keep up, and, we need to store this data and have it around for the lifetime of the project. >> Got it. Thank you. Okay, Caleb, let's bring you back in. Tell us more about the, you got these dishwasher size satellites, kind of using a multi-tenant model, I think it's genius. But tell us about the satellites themselves. >> Yeah, absolutely. So, we have in space some satellites already that as you said, are like dishwasher, mini fridge kind of size. And we're working on a bunch more that are a variety of sizes from shoebox to, I guess, a few times larger than what we have today. And it is, we do shoot to have effectively something like a multi-tenant model where we will buy a bus off the shelf. The bus is what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power, it has the solar panels, it has some radios attached to it. It handles the attitude control, basically steers the spacecraft in orbit, and then we build also in-house, what we call our payload hub which is, has all, any customer payloads attached and our own kind of Edge processing sort of capabilities built into it. And, so we integrate that, we launch it, and those things because they're in lower Earth orbit, they're orbiting the earth every 90 minutes. That's, seven kilometers per second which is several times faster than a speeding bullet. So we have one of the unique challenges of operating spacecraft in lower Earth orbit is that generally you can't talk to them all the time. So, we're managing these things through very brief windows of time, where we get to talk to them through our ground sites, either in Antarctica or in the North pole region. >> Talk more about how you use InfluxDB to make sense of this data through all this tech that you're launching into space. >> We basically, previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was so slow and the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. So we migrated to InfluxDB to store our time series telemetry from the spacecraft. So, that's things like power level, voltage, currents, counts, whatever metadata we need to monitor about the spacecraft, we now store that in InfluxDB. And that has, now we can actually easily store the entire volume of data for the mission life so far without having to worry about the size bloating to an unmanageable amount, and we can also seamlessly query large chunks of data. Like if I need to see, you know, for example, as an operator, I might want to see how my battery state of charge is evolving over the course of the year, I can have, plot in an Influx that loads that in a fraction of a second for a year's worth of data because it does, intelligent, it can intelligently group the data by assigning time interval. So, it's been extremely powerful for us to access the data. And, as time has gone on, we've gradually migrated more and more of our operating data into Influx. >> Yeah. Let's talk a little bit about, we throw this term around a lot of, you know, data driven, a lot of companies say, "Oh yes, we're data driven." But you guys really are, I mean, you got data at the core. Caleb, what does that mean to you? >> Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astra where our engineer's feedback loop went from a lot of kind of slow researching, digging into the data to like an instant, instantaneous almost, seeing the data, making decisions based on it immediately rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. But to give another practical example, as I said, we have a huge amount of data that comes down every orbit and we need to be able to ingest all of that data almost instantaneously and provide it to the operator in near real time, about a second worth of latency is all that's acceptable for us to react to see what is coming down from the spacecraft. And building that pipeline is challenging from a software engineering standpoint. My primary language is Python which isn't necessarily that fast. So what we've done is started, and the goal of being data-driven is publish metrics on individual, how individual pieces of our data processing pipeline are performing into Influx as well. And we do that in production as well as in dev. So we have kind of a production monitoring flow. And what that has done is allow us to make intelligent decisions on our software development roadmap where it makes the most sense for us to focus our development efforts in terms of improving our software efficiency, just because we have that visibility into where the real problems are. And sometimes we've found ourselves before we started doing this, kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. But now that we're being a bit more data driven there, we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale from supporting a couple of satellites to supporting many, many satellites at once. >> Yeah, of course is how you reduced those dead ends. Maybe Angelo you could talk about what sort of data-driven means to you and your teams. >> I would say that, having real time visibility to the telemetry data and metrics is crucial for us. We need to make sure that the images that we collect with the telescope have good quality, and, that they are within the specifications to meet our science goals. And so if they are not, we want to know that as soon as possible and then start fixing problems. >> Caleb, what are your sort of event, you know, intervals like? >> So I would say that, as of today on the spacecraft, the event, the level of timing that we deal with probably tops out at about 20 Hertz, 20 measurements per second on things like our gyroscopes. But, the, I think the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications and I'll give an example from when I worked at, on the rockets at Astra. There, our baseline data rate that we would ingest data during a test is 500 Hertz. So 500 samples per second, and in some cases we would actually need to ingest much higher rate data, even up to like 1.5 kilohertz, so extremely, extremely high precision data there where timing really matters a lot. And, you know, I can, one of the really powerful things about Influx is the fact that it can handle this. That's one of the reasons we chose it, because, there's, times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job we often zoom out to look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second, and you need to see same thing as Angelo just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers, so that can be something like, "Hey, I opened this valve at exactly this time," and that goes, we want to have that at, micro, or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at this exact moment, was that before or after this valve opened? That kind of visibility is critical in these kind of scientific applications, and absolutely game changing to be able to see that in near real time, and with, a really easy way for engineers to be able to visualize this data themselves without having to wait for us software engineers to go build it for them. >> Can the scientists do self-serve or do you have to design and build all the analytics and queries for your scientists? >> Well, I think that's absolutely, from my perspective that's absolutely one of the best things about Influx and what I've seen be game changing is that, generally I'd say anyone can learn to use Influx. And honestly, most of our users might not even know they're using Influx, because, the interface that we expose to them is Grafana, which is a generic graphing, open source graphing library that is very similar to Influx zone Chronograf. >> Sure. >> And what it does is, it provides this almost, it's a very intuitive UI for building your queries. So, you choose a measurement and it shows a dropdown of available measurements. And then you choose the particular fields you want to look at, and again, that's a dropdown. So, it's really easy for our users to discover and there's kind of point and click options for doing math, aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality that Influx provides. >> Putting data in the hands of those who have the context, the domain experts is key. Angelo, is it the same situation for you, is it self-serve? >> Yeah, correct. As I mentioned before, we have the astronomers making their own dashboards because they know what exactly what they need to visualize. >> Yeah, I mean, it's all about using the right tool for the job. I think for us, when I joined the company we weren't using InfluxDB and we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations. >> Guys, this has been really formative, it's pretty exciting to see how the edge, is mountaintops, lower Earth orbits, I mean space is the ultimate edge, isn't it? I wonder if you could answer two questions to wrap here. You know, what comes next for you guys? And is there something that you're really excited about that you're working on? Caleb maybe you could go first and then Angelo you can bring us home. >> Basically what's next for Loft Orbital is more satellites, a greater push towards infrastructure, and really making, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, making that happen. It's extremely exciting, an extremely exciting time to be in this company and to be in this industry as a whole. Because there are so many interesting applications out there, so many cool ways of leveraging space that people are taking advantage of, and with companies like SpaceX and the, now rapidly lowering cost of launch it's just a really exciting place to be in. We're launching more satellites, we are scaling up for some constellations, and our ground system has to be improved to match. So, there's a lot of improvements that we're working on to really scale up our control software to be best in class and make it capable of handling such a large workload, so. >> Are you guys hiring? >> We are absolutely hiring, so I would, we have positions all over the company, so, we need software engineers, we need people who do more aerospace specific stuff. So absolutely, I'd encourage anyone to check out the Loft Orbital website, if this is at all interesting. >> All right, Angelo, bring us home. >> Yeah. So what's next for us is really getting this telescope working and collecting data. And when that's happened is going to be just a deluge of data coming out of this camera and handling all that data is going to be really challenging. Yeah, I want to be here for that, I'm looking forward. Like for next year we have like an important milestone, which is our commissioning camera, which is a simplified version of the full camera, it's going to be on sky, and so yeah, most of the system has to be working by then. >> Nice. All right guys, with that we're going to end it. Thank you so much, really fascinating, and thanks to InfluxDB for making this possible, really groundbreaking stuff, enabling value creation at the Edge, in the cloud, and of course, beyond at the space. So, really transformational work that you guys are doing, so congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave Vellante, and you're watching theCUBE, the leader in high tech enterprise coverage. >> Welcome. Telegraf is a popular open source data collection agent. Telegraf collects data from hundreds of systems like IoT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists, to large corporate teams. The Telegraf project has a very welcoming and active Open Source community. Learn how to get involved by visiting the Telegraf GitHub page. Whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraf. We'd love to hear what you're building. >> Thanks for watching Moving the World with InfluxDB, made possible by Influx Data. I hope you learned some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you want to scale cost effectively with the highest performance, and you're analyzing metrics and data over time, times series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link in the resources below. Remember, all these recordings are going to be available on demand of thecube.net and influxdata.com, so check those out. And poke around Influx Data. They are the folks behind InfluxDB, and one of the leaders in the space. We hope you enjoyed the program, this is Dave Vellante for theCUBE, we'll see you soon. (upbeat music)