>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from the cube and ETR, this is Breaking Analysis with Dave Vellante. >> Organizations have considerable room to improve their performance without making expensive changes to their talent, their structure, or their fundamental business model. You don't need a slew of consultants to tell you what to do. You already know. What you need is to immediately ratchet up expectations, energy, urgency, and intensity. You have to fight mediocrity every step of the way. Amp it up and the results will follow. This is the fundamental premise of a hard-hitting new book written by Frank Slootman, CEO of Snowflake, and published earlier this year. It's called "Amp It Up, Leading for Hypergrowth "by Raising Expectations, Increasing Urgency, "and Elevating Intensity." Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. At Snowflake Summit last month, I was asked to interview Frank on stage about his new book. I've read it several times. And if you haven't read it, you should. Even if you have read it, in this Breaking Analysis, we'll dig deeper into the book and share some clarifying insights and nuances directly from Slootman himself from my one-on-one conversation with him. My first question to Slootman was why do you write this book? Okay, it's kind of a common throwaway question. And how the heck did you find time to do it? It's fairly well-known that a few years ago, Slootman put up a post on LinkedIn with the title Amp It Up. It generated so much buzz and so many requests for Frank's time that he decided that the best way to efficiently scale and share his thoughts on how to create high-performing companies and organizations was to publish a book. Now, he wrote the book during the pandemic. And I joked that they must not have Netflix in Montana where he resides. In a pretty funny moment, he said that writing the book was easier than promoting it. Take a listen. >> Denise, our CMO, you know, she just made sure that this process wasn't going to. It was more work for me to promote this book with all these damn podcasts and other crap, than actually writing the book, you know. And after a while, I was like I'm not doing another podcast. >> Now, the book gives a lot of interesting background information on Slootman's career and what he learned at various companies that he led and participated in. Now, I'm not going to go into most of that today, which is why you should read the book yourself. But Slootman, he's become somewhat of a business hero to many people, myself included. Leaders like Frank, Scott McNealy, Jayshree Ullal, and my old boss, Pat McGovern at IDG, have inspired me over the years. And each has applied his or her own approach to building cultures and companies. Now, when Slootman first took over the reins at Snowflake, I published a Breaking Analysis talking about Snowflake and what we could expect from the company now that Slootman and CFO Mike Scarpelli were back together. In that post, buried toward the end, I referenced the playbook that Frank used at Data Domain and ServiceNow, two companies that I followed quite closely as an analyst, and how it would be applied at Snowflake, that playbook if you will. Frank reached out to me afterwards and said something to the effect of, "I don't use playbooks. "I am a situational leader. "Playbooks, you know, they work in football games. "But in the military, they teach you "situational leadership." Pretty interesting learning moment for me. So I asked Frank on the stage about this. Here's what he said. >> The older you get, the more experience that you have, the more you become a prisoner of your own background because you sort of think in terms of what you know as opposed to, you know, getting outside of what you know and trying to sort of look at things like a five-year-old that has never seen this before. And then how would you, you know, deal with it? And I really try to force myself into I've never seen this before and how do I think about it? Because at least they're very different, you know, interpretations. And be open-minded, just really avoid that rinse and repeat mentality. And you know, I've brought people in from who have worked with me before. Some of them come with me from company to company. And they were falling prey to, you know, rinse and repeat. I would just literally go like that's not what we want. >> So think about that for a moment. I mean, imagine coming in to lead a new company and forcing yourself and your people to forget what they know that works and has worked in the past, put that aside and assess the current situation with an open mind, essentially start over. Now, that doesn't mean you don't apply what has worked in the past. Slootman talked to me about bringing back Scarpelli and the synergistic relationship that they have and how they build cultures and the no BS and hard truth mentality they bring to companies. But he bristles when people ask him, "What type of CEO are you?" He says, "Do we have to put a label on it? "It really depends on the situation." Now, one of the other really hard-hitting parts of the book was the way Frank deals with who to keep and who to let go. He uses the Volkswagen tagline of drivers wanted. He says in his book, in companies there are passengers and there are drivers, and we want drivers. He said, "You have to figure out really quickly "who the drivers are and basically throw the wrong people "off the bus, keep the right people, bring in new people "that fit the culture and put them "in the right seats on the bus." Now, these are not easy decisions to make. But as it pertains to getting rid of people, I'm reminded of the movie "Moneyball." Art Howe, the manager of the Oakland As, he refused to play Scott Hatteberg at first base. So the GM, Billy Bean played by Brad Pitt says to Peter Brand who was played by Jonah Hill, "You have to fire Carlos Pena." Don't learn how to fire people. Billy Bean says, "Just keep it quick. "Tell him he's been traded and that's it." So I asked Frank, "Okay, I get it. "Like the movie, when you have the wrong person "on the bus, you just have to make the decision, "be straightforward, and do it." But I asked him, "What if you're on the fence? "What if you're not completely sure if this person "is a driver or a passenger, if he or she "should be on the bus or not on the bus? "How do you handle that?" Listen to what he said. >> I have a very simple way to break ties. And when there's doubt, there's no doubt, okay? >> When there's doubt, there's no doubt. Slootman's philosophy is you have to be emphatic and have high conviction. You know, back to the baseball analogy, if you're thinking about taking the pitcher out of the game, take 'em out. Confrontation is the single hardest thing in business according to Slootman but you have to be intellectually honest and do what's best for the organization, period. Okay, so wow, that may sound harsh but that's how Slootman approaches it, very Belichickian if you will. But how can you amp it up on a daily basis? What's the approach that Slootman takes? We got into this conversation with a discussion about MBOs, management by objective. Slootman in his book says he's killed MBOs at every company he's led. And I asked him to explain why. His rationale was that individual MBOs invariably end up in a discussion about relief of the MBO if the person is not hitting his or her targets. And that detracts from the organizational alignment. He said at Snowflake everyone gets paid the same way, from the execs on down. It's a key way he creates focus and energy in an organization, by creating alignment, urgency, and putting more resources into the most important things. This is especially hard, Slootman says, as the organization gets bigger. But if you do approach it this way, everything gets easier. The cadence changes, the tempo accelerates, and it works. Now, and to emphasize that point, he said the following. Play the clip. >> Every meeting that you have, every email, every encounter in the hallway, whatever it is, is an opportunity to amp things up. That's why I use that title. But do you take that opportunity? >> And according to Slootman, if you don't take that opportunity, if you're not in the moment, amping it up, then you're thinking about your golf game or the tennis match that's going on this weekend or being out on your boat. And to the point, this approach is not for everyone. You're either built for it or you're not. But if you can bring people into the organization that can handle this type of dynamic, it creates energy. It becomes fun. Everything moves faster. The conversations are exciting. They're inspiring. And it becomes addictive. Now let's talk about priorities. I said to Frank that for me anyway, his book was an uncomfortable read. And he was somewhat surprised by that. "Really," he said. I said, "Yeah. "I mean, it was an easy read but uncomfortable "because over my career, I've managed thousands of people, "not tens of thousands but thousands, "enough to have to take this stuff very seriously." And I found myself throughout the book, oh, you know, on the one hand saying to myself, "Oh, I got that right, good job, Dave." And then other times, I was thinking to myself, "Oh wow, I probably need to rethink that. "I need to amp it up on that front." And the point is to Frank's leadership philosophy, there's no one correct way to approach all situations. You have to figure it out for yourself. But the one thing in the book that I found the hardest was Slootman challenged the reader. If you had to drop everything and focus on one thing, just one thing, for the rest of the year, what would that one thing be? Think about that for a moment. Were you able to come up with that one thing? What would happen to all the other things on your priority list? Are they all necessary? If so, how would you delegate those? Do you have someone in your organization who can take those off your plate? What would happen if you only focused on that one thing? These are hard questions. But Slootman really forces you to think about them and do that mental exercise. Look at Frank's body language in this screenshot. Imagine going into a management meeting with Frank and being prepared to share all the things you're working on that you're so proud of and all the priorities you have for the coming year. Listen to Frank in this clip and tell me it doesn't really make you think. >> I've been in, you know, on other boards and stuff. And I got a PowerPoint back from the CEO and there's like 15 things. They're our priorities for the year. I'm like you got 15, you got none, right? It's like you just can't decide, you know, what's important. So I'll tell you everything because I just can't figure out. And the thing is it's very hard to just say one thing. But it's really the mental exercise that matters. >> Going through that mental exercise is really important according to Slootman. Let's have a conversation about what really matters at this point in time. Why does it need to happen? And does it take priority over other things? Slootman says you have to pull apart the hairball and drive extraordinary clarity. You could be wrong, he says. And he admits he's been wrong on many things before. He, like everyone, is fearful of being wrong. But if you don't have the conversation according to Slootman, you're already defeated. And one of the most important things Slootman emphasizes in the book is execution. He said that's one of the reasons he wrote "Amp It Up." In our discussion, he referenced Pat Gelsinger, his former boss, who bought Data Domain when he was working for Joe Tucci at EMC. Listen to Frank describe the interaction with Gelsinger. >> Well, one of my prior bosses, you know, Pat Gelsinger, when they acquired Data Domain through EMC, Pat was CEO of Intel. And he quoted Andy Grove as saying, 'cause he was Intel for a long time when he was younger man. And he said no strategy is better than its execution, which if I find one of the most brilliant things. >> Now, before you go changing your strategy, says Slootman, you have to eliminate execution as a potential point of failure. All too often, he says, Silicon Valley wants to change strategy without really understanding whether the execution is right. All too often companies don't consider that maybe the product isn't that great. They will frequently, for example, make a change to sales leadership without questioning whether or not there's a product fit. According to Slootman, you have to drive hardcore intellectual honesty. And as uncomfortable as that may be, it's incredibly important and powerful. Okay, one of the other contrarian points in the book was whether or not to have a customer success department. Slootman says this became really fashionable in Silicon Valley with the SaaS craze. Everyone was following and pattern matching the lead of salesforce.com. He says he's eliminated the customer service department at every company he's led which had a customer success department. Listen to Frank Slootman in his own words talk about the customer success department. >> I view the whole company as a customer success function. Okay, I'm customer success, you know. I said it in my presentation yesterday. We're a customer-first organization. I don't need a department. >> Now, he went on to say that sales owns the commercial relationship with the customer. Engineering owns the technical relationship. And oh, by the way, he always puts support inside of the engineering department because engineering has to back up support. And rather than having a separate department for customer success, he focuses on making sure that the existing departments are functioning properly. Slootman also has always been big on net promoter score, NPS. And Snowflake's is very high at 72. And according to Slootman, it's not just the product. It's the people that drive that type of loyalty. Now, Slootman stresses amping up the big things and even the little things too. He told a story about someone who came into his office to ask his opinion about a tee shirt. And he turned it around on her and said, "Well, what do you think?" And she said, "Well, it's okay." So Frank made the point by flipping the situation. Why are you coming to me with something that's just okay? If we're going to do something, let's do it. Let's do it all out. Let's do it right and get excited about it, not just check the box and get something off your desk. Amp it up, all aspects of our business. Listen to Slootman talk about Steve Jobs and the relevance of demanding excellence and shunning mediocrity. >> He was incredibly intolerant of anything that he didn't think of as great. You know, he was immediately done with it and with the person. You know, I'm not that aggressive, you know, in that way. I'm a little bit nicer, you know, about it. But I still, you know, I don't want to give into expediency and mediocrity. I just don't, I'm just going to fight it, you know, every step of the way. >> Now, that story was about a little thing like some swag. But Slootman talked about some big things too. And one of the major ways Snowflake was making big, sweeping changes to amp up its business was reorganizing its go-to-market around industries like financial services, media, and healthcare. Here's some ETR data that shows Snowflake's net score or spending momentum for key industry segments over time. The red dotted line at 40% is an indicator of highly elevated spending momentum. And you can see for the key areas shown, Snowflake is well above that level. And we cut this data where responses were greater, the response numbers were greater than 15. So not huge ends but large enough to have meaning. Most were in the 20s. Now, it's relatively uncommon to see a company that's having the success of Snowflake make this kind of non-trivial change in the middle of steep S-curve growth. Why did they make this move? Well, I think it's because Snowflake realizes that its data cloud is going to increasingly have industry diversity and unique value by industry, that ecosystems and data marketplaces are forming around industries. So the more industry affinity Snowflake can create, the stronger its moat will be. It also aligns with how the largest and most prominent global system integrators, global SIs, go to market. This is important because as companies are transforming, they are radically changing their data architecture, how they think about data, how they approach data as a competitive advantage, and they're looking at data as specifically a monetization opportunity. So having industry expertise and knowledge and aligning with those customer objectives is going to serve Snowflake and its ecosystems well in my view. Slootman even said he joined the board of Instacart not because he needed another board seat but because he wanted to get out of his comfort zone and expose himself to other industries as a way to learn. So look, we're just barely scratching the surface of Slootman's book and I've pulled some highlights from our conversation. There's so much more that I can share just even from our conversation. And I will as the opportunity arises. But for now, I'll just give you the kind of bumper sticker of "Amp It Up." Raise your standards by taking every opportunity, every interaction, to increase your intensity. Get your people aligned and moving in the same direction. If it's the wrong direction, figure it out and course correct quickly. Prioritize and sharpen your focus on things that will really make a difference. If you do these things and increase the urgency in your organization, you'll naturally pick up the pace and accelerate your company. Do these things and you'll be able to transform, better identify adjacent opportunities and go attack them, and create a lasting and meaningful experience for your employees, customers, and partners. Okay, that's it for today. Thanks for watching. And thank you to Alex Myerson who's on production and he manages the podcast for Breaking Analysis. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters. And Rob Hove is our EIC over at Silicon Angle who does some wonderful and tremendous editing. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can email me at david.vellante@siliconangle.com or DM me @dvellante or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in enterprise tech. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well. And we'll see you next time on Breaking Analysis. (upbeat music)

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

>> From around the globe, it's theCUBE, with coverage of KubeCon and CloudNativeCon Europe 2021 Virtual. Brought to you by Red Hat, the Cloud Native Computing Foundation and ecosystem partners. >> Hello, welcome back to theCUBE's coverage of KubeCon 2021 CloudNativeCon Europe Virtual, I'm John Furrier your host, preview with Brian Gracely from Red Hat Senior Director Product Strategy Cloud Business Unit Brian Gracely great to see you. Former CUBE host CUBE alumni, big time strategist at Red Hat, great to see you, always great. And also the founder of Cloudcast which is an amazing podcast on cloud, part of the cloud (indistinct), great to see you Brian. Hope's all well. >> Great to see you too, you know for years, theCUBE was always sort of the ESPN of tech, I feel like, you know ESPN has become nothing but highlights. This is where all the good conversation is. It's theCUBE has become sort of the the clubhouse of tech, if you will. I know that's that's an area you're focused on, so yeah I'm excited to be back on and good to talk to you. >> It's funny you know, with all the events going away loved going out extracting the signal from the noise, you know, game day kind of vibe. CUBE Virtual has really expanded, so it's been so much more fun because we can get more people easy to dial in. So we're going to keep that feature post COVID. You're going to hear more about theCUBE Virtual hybrid events are going to be a big part of it, which is great because as you know and we've talked about communities and ecosystems are huge advantage right now it's been a big part of the Red Hat story. Now part of IBM bringing that mojo to the table the role of ecosystems with hybrid cloud is so critical. Can you share your thoughts on this? Because I know you study it, you have podcasts you've had one for many years, you understand that democratization and this new direct to audience kind of concept. Share your thoughts on this new ecosystem. >> Yeah, I think so, you know, we're sort of putting this in the context of what we all sort of familiarly call KubeCon but you know, if we think about it, it started as KubeCon it was sort of about this one technology but it's always been CloudNativeCon and we've sort of downplayed the cloud native part of it. But even if we think about it now, you know Kubernetes to a certain extent has kind of, you know there's this feeling around the community that, that piece of the puzzle is kind of boring. You know, it's 21 releases in, and there's lots of different offerings that you can get access to. There's still, you know, a lot of innovation but the rest of the ecosystem has just exploded. So it's, you know, there are ecosystem partners and companies that are working on edge and miniaturization. You know, we're seeing things like Kubernetes now getting into outer space and it's in the space station. We're seeing, you know, Linux get on Mars. But we're also seeing, you know, stuff on the other side of the spectrum. We're sort of seeing, you know awesome people doing database work and streaming and AI and ML on top of Kubernetes. So, you know, the ecosystem is doing what you'd expect it to do once one part of it gets stable. The innovation sort of builds on top of it. And, you know, even though we're virtual, we're still seeing just tons and tons of contributions, different companies different people stepping up and leading. So it's been really cool to watch the last few years. >> Yes, interesting point about the CloudNativeCon. That's an interesting insight, and I totally agree with you. And I think it's worth double clicking on. Let me just ask you, because when you look at like, say Kubernetes, okay, it's enabled a lot. Okay, it's been called the dial tone of Cloud native. I think Pat Gelsinger of VMware used that term. We call it the kind of the interoperability layer it enables more large scale deployments. So you're seeing a lot more Kubernetes enablement on clusters. Which is causing more hybrid cloud which means more Cloud native. So it actually is creating a network effect in and of itself with more Cloud native components and it's changing the development cycle. So the question I want to ask you is one how does a customer deal with that? Because people are saying, I like hybrid. I agree, Multicloud is coming around the corner. And of course, Multicloud is just a subsystem of resource underneath hybrid. How do I connect it all? Now I have multiple vendors, I have multiple clusters. I'm cross-cloud, I'm connecting multiple clouds multiple services, Kubernetes clusters, some get stood up some gets to down, it's very dynamic. >> Yeah, it's very dynamic. It's actually, you know, just coincidentally, you know, our lead architect, a guy named Clayton Coleman, who was one of the Kubernetes founders, is going to give a talk on sort of Kubernetes is this hybrid control plane. So we're already starting to see the tentacles come out of it. So you know how we do cross cloud networking how we do cross cloud provisioning of services. So like, how do I go discover what's in other clouds? You know and I think like you said, it took people a few years to figure out, like how do I use this new thing, this Kubernetes thing. How do I harness it. And, but the demand has since become "I have to do multi-cloud." And that means, you know, hey our company acquires companies, so you know, we don't necessarily know where that next company we acquire is going to run. Are they going to run on AWS? Are they going to, you know, run on Azure I've got to be able to run in multiple places. You know, we're seeing banking industries say, "hey, look cloud's now a viable target for you to put your applications, but you have to treat multiple clouds as if they're your backup domains." And so we're, you know, we're seeing both, you know the way business operates whether it's acquisitions or new things driving it. We're seeing regulations driving hybrid and multi-cloud and, even you know, even if the stalwart were to you know, set for a long time, well the world's only going to be public cloud and sort of you know, legacy data centers even those folks are now coming around to "I've got to bring hybrid to, to these places." So it's been more than just technology. It's been, you know, industries pushing it regulations pushing it, a lot of stuff. So, but like I said, we're going to be talking about kind of our future, our vision on that, our future on that. And, you know Red Hat everything we end up doing is a community activity. So we expect a lot of people will get on board with it >> You know, for all the old timers out there they can relate to this. But I remember in the 80's the OSI Open Systems Interconnect, and I was chatting with Paul Cormier about this because we were kind of grew up through that generation. That disrupted network protocols that were proprietary and that opened the door for massive, massive growth massive innovation around just getting that interoperability with TCP/IP, and then everything else happened. So Kubernetes does that, that's a phenomenal impact. So Cloud native to me is at that stage where it's totally next-gen and it's happening really fast. And a lot of people getting caught off guard, Brian. So you know, I got to to ask you as a product strategist, what's your, how would you give them the navigation of where that North star is? If I'm a customer, okay, I got to figure out where I got to navigate now. I know it's super volatile, changing super fast. What's your advice? >> I think it's a couple of pieces, you know we're seeing more and more that, you know, the technology decisions don't get driven out of sort of central IT as much anymore right? We sort of talk all the time that every business opportunity, every business project has a technology component to it. And I think what we're seeing is the companies that tend to be successful with it have built up the muscle, built up the skill set to say, okay, when this line of business says, I need to do something new and innovative I've got the capabilities to sort of stand behind that. They're not out trying to learn it new they're not chasing it. So that's a big piece of it, is letting the business drive your technology decisions as opposed to what happened for a long time which was we built out technology, we hope they would come. You know, the other piece of it is I think because we're seeing so much push from different directions. So we're seeing, you know people put technology out at the edge. We're able to do some, you know unique scalable things, you know in the cloud and so forth That, you know more and more companies are having to say, "hey, look, I'm not, I'm not in the pharmaceutical business. I'm not in the automotive business, I'm in software." And so, you know the companies that realize that faster, and then, you know once they sort of come to those realizations they realize, that's my new normal, those are the ones that are investing in software skills. And they're not afraid to say, look, you know even if my existing staff is, you know, 30 years of sort of history, I'm not afraid to bring in some folks that that'll break a few eggs and, you know, and use them as a lighthouse within their organization to retrain and sort of reset, you know, what's possible. So it's the business doesn't move. That's the the thing that drives all of them. And it's, if you embrace it, we see a lot of success. It's the ones that, that push back on it really hard. And, you know the market tends to sort of push back on them as well. >> Well we're previewing KubeCon CloudNativeCon. We'll amplify that it's CloudNativeCon as well. You guys bought StackRox, okay, so interesting company, not an open source company they have soon to be, I'm assuring, but Advanced Cluster Security, ACS, as it's known it's really been a key part of Red Hat. Can you give us the strategy behind that deal? What does that product, how does it fit in that's a lot of people are really talking about this acquisition. >> Yeah so here's the way we looked at it, is we've learned a couple of things over the last say five years that we've been really head down in Kubernetes, right? One is, we've always embedded a lot of security capabilities in the platform. So OpenShift being our core Kubernetes platform. And then what's happened over time is customers have said to us, "that's great, you've made the platform very secure" but the reality is, you know, our software supply chain. So the way that we build applications that, you know we need to secure that better. We need to deal with these more dynamic environments. And then once the applications are deployed they interact with various types of networks. I need to better secure those environments too. So we realized that we needed to expand our functionality beyond the core platform of OpenShift. And then the second thing that we've learned over the last number of years is to be successful in this space, it's really hard to take technology that wasn't designed for containers, or it wasn't designed for Kubernetes and kind of retrofit it back into that. And so when we were looking at potential acquisition targets, we really narrowed down to companies whose fundamental technologies were you know, Kubernetes-centric, you know having had to modify something to get to Kubernetes, and StackRox was really the leader in that space. They really, you know have been the leader in enterprise Kubernetes security. And the great thing about them was, you know not only did they have this Kubernetes expertise but on top of that, probably half of their customers were already OpenShift customers. And about 3/4 of their customers were using you know, native Kubernetes services and other clouds. So, you know, when we went and talked to them and said, "Hey we believe in Kubernetes, we believe in multi-cloud. We believe in open source," they said, "yeah, those are all the foundational things for us." And to your point about it, you know, maybe not being an open source company, they actually had a number of sort of ancillary projects that were open source. So they weren't unfamiliar to it. And then now that the acquisition's closed, we will do what we do with every piece of Red Hat technology. We'll make sure that within a reasonable period of time that it's made open source. And so you know, it's good for the community. It allows them to keep focusing on their innovation. >> Yeah you've got to get that code out there cool. Brian, I'm hearing about Platform Plus what is that about? Take us through that. >> Yeah, so you know, one of the things that our customers, you know, have come to us over time is it's you know, it's like, I've been saying kind of throughout this discussion, right? Kubernetes is foundational, but it's become pretty stable. The things that people are solving for now are like, you highlighted lots and lots of clusters, they're all over the place. That was something that our advanced cluster management capabilities were able to solve for people. Once you start getting into lots of places you've got to be able to secure things everywhere you go. And so OpenShift for us really allows us to bundle together, you know, sort of the complete set of the portfolio. So the platform, security management, and it also gives us the foundational pieces or it allows our customers to buy the foundational pieces that are going to help them do multi and hybrid cloud. And, you know, when we bundle that we can save them probably 25% in terms of sort of product acquisition. And then obviously the integration work we do you know, saves a ton on the operational side. So it's a new way for us to, to not only bundle the platform and the technologies but it gets customers in a mindset that says, "hey we've moved past sort of single environments to hybrid and multi-cloud environments. >> Awesome, well thanks for the update on that, appreciate it. One of the things going into KubeCon, and that we're watching closely is this Cloud native developer action. Certainly end users want to get that in a separate section with you but the end user contribution, which is like exploding. But on the developer side there's a real trend towards adding stronger consistency programmability support for more use cases okay. Where it's becoming more of a data platform as a requirement. >> Brian: Right. >> So how, so that's a trend so I'm kind of thinking, there's no disagreement on that. >> Brian: No, absolutely. >> What does that mean? Like I'm a customer, that sounds good. How do I make that happen? 'Cause that's the critical discussion right now in the DevOps, DevSecOps day, two operations. What you want to call it. This is the number one concern for developers and that solution architect, consistency, programmability more use cases with data as a platform. >> Yeah, I think, you know the way I kind of frame this up was you know, for any for any organization, the last thing you want to to do is sort of keep investing in lots of platforms, right? So platforms are great on their surface but once you're having to manage five and six and, you know 10 or however many you're managing, the economies of scale go away. And so what's been really interesting to watch with Kubernetes is, you know when we first got started everything was Cloud native application but that really was sort of, you know shorthand for stateless applications. We quickly saw a move to, you know, people that said, "Hey I can modernize something, you know, a Stateful application and we add that into Kubernetes, right? The community added the ability to do Stateful applications and that got people a certain amount of the way. And they sort of started saying, okay maybe Kubernetes can help me peel off some things of an existing platform. So I can peel off, you know Java workloads or I can peel off, what's been this explosion is the data community, if you will. So, you know, the TensorFlows the PItorches, you know, the Apache community with things like Couchbase and Kafka, TensorFlow, all these things that, you know maybe in the past didn't necessarily, had their own sort of underlying system are now defaulting to Kubernetes. And what we see because of that is, you know people now can say, okay, these data workloads these AI and ML workloads are so important to my business, right? Like I can directly point to cost savings. I can point to, you know, driving innovation and because Kubernetes is now their default sort of way of running, you know we're seeing just sort of what used to be, you know small islands of clusters become these enormous footprints whether they're in the cloud or in their data center. And that's almost become, you know, the most prevalent most widely used use case. And again, it makes total sense. It's exactly the trends that we've seen in our industry, even before Kubernetes. And now people are saying, okay, I can consolidate a lot of stuff on Kubernetes. I can get away from all those silos. So, you know, that's been a huge thing over the last probably year plus. And the cool thing is we've also seen, you know the hardware vendors. So whether it's Intel or Nvidia, especially around GPUs, really getting on board and trying to make that simpler. So it's not just the software ecosystem. It's also the hardware ecosystem, really getting on board. >> Awesome, Brian let me get your thoughts on the cloud versus the power dynamics between the cloud players and the open source software vendors. So what's the Red Hat relationship with the cloud players with the hybrid architecture, 'cause you want to set up the modern day developer environment, we get that right. And it's hybrid, what's the relationship with the cloud players? >> You know, I think so we we've always had two philosophies that haven't really changed. One is, we believe in open source and open licensing. So you haven't seen us look at the cloud as, a competitive threat, right? We didn't want to make our business, and the way we compete in business, you know change our philosophy in software. So we've always sort of maintained open licenses permissive licenses, but the second piece is you know, we've looked at the cloud providers as very much partners. And mostly because our customers look at them as partners. So, you know, if Delta Airlines or Deutsche Bank or somebody says, "hey that cloud provider is going to be our partner and we want you to be part of that journey, we need to be partners with that cloud as well." And you've seen that sort of manifest itself in terms of, you know, we haven't gone and set up new SaaS offerings that are Red Hat offerings. We've actually taken a different approach than a lot of the open source companies. And we've said we're going to embed our capabilities, especially, you know OpenShift into AWS, into Azure into IBM cloud working with Google cloud. So we'd look at them very much as a partner. I think it aligns to how Red Hat's done things in the past. And you know, we think, you know even though it maybe easy to sort of see a way of monetizing things you know, changing licensing, we've always found that, you've got to allow the ecosystem to compete. You've got to allow customers to go where they want to go. And we try and be there in the most consumable way possible. So that's worked out really well for us. >> So I got to bring up the end user participation component. That's a big theme here at KubeCon going into it and around the event is, and we've seen this trend happen. I mean, Envoy, Lyft the laying examples are out there. But they're more end-use enterprises coming in. So the enterprise class I call classic enterprise end user participation is at an all time high in opensource. You guys have the biggest portfolio of enterprises in the business. What's the trend that you're seeing because it used to be limited to the hyperscalers the Lyfts and the Facebooks and the big guys. Now you have, you know enterprises coming in the business model is working, can you just share your thoughts on CloudNativeCons participation for end users? >> Yeah, I think we're definitely seeing a blurring of lines between what used to be the Silicon Valley companies were the ones that would create innovation. So like you mentioned Lyft, or, you know LinkedIn doing Kafka or Twitter doing you know, whatever. But as we've seen more and more especially enterprises look at themselves as software companies right. So, you know if you talk about, you know, Ford or Volkswagen they think of themselves as a software company, almost more than they think about themselves as a car company, right. They're a sort of mobile transportation company you know, something like that. And so they look at themselves as I've got to I've got to have software as an expertise. I've got to compete for the best talent, no matter where that talent is, right? So it doesn't have to be in Detroit or in Germany or wherever I can go get that anywhere. And I think what they really, they look for us to do is you know, they've got great technology chops but they don't always understand kind of the the nuances and the dynamics of open-source right. They're used to having their own proprietary internal stuff. And so a lot of times they'll come to us, not you know, "Hey how do we work with the project?" But you know like here's new technology. But they'll come to us and they'll say "how do we be good, good stewards in this community? How do we make sure that we can set up our own internal open source office and have that group, work with communities?" And so the dynamics have really changed. I think a lot of them have, you know they've looked at Silicon Valley for years and now they're modeling it, but it's, you know, for us it's great because now we're talking the same language, you know we're able to share sort of experiences we're able to share best practices. So it is really, really interesting in terms of, you know, how far that whole sort of software is eating the world thing is materialized in sort of every industry. >> Yeah and it's the workloads of expanding Cloud native everywhere edge is blowing up big time. Brian, final question for you before we break. >> You bet. >> Thanks for coming on and always great to chat with you. It's always riffing and getting the data out too. What's your expectation for KubeCon CloudNativeCon this year? What are you expecting to see? What highlights do you expect will come out of CloudNativeCon KubeCon this year? >> Yeah, I think, you know like I said, I think it's going to be much more on the Cloud native side, you know we're seeing a ton of new communities come out. I think that's going to be the big headline is the number of new communities that are, you know have sort of built up a following. So whether it's Crossplane or whether it's, you know get-ops or whether it's, you know expanding around the work that's going on in operators we're going to see a whole bunch of projects around, you know, developer sort of frameworks and developer experience and so forth. So I think the big thing we're going to see is sort of this next stage of, you know a thousand flowers are blooming and we're going to see probably a half dozen or so new communities come out of this one really strong and you know the trends around those are going to accelerate. So I think that'll probably be the biggest takeaway. And then I think just the fact that the community is going to come out stronger after the pandemic than maybe it did before, because we're learning you know, new ways to work remotely, and that, that brings in a ton of new companies and contributors. So I think those two big things will be the headlines. And, you know, the state of the community is strong as they, as they like to say >> Yeah, love the ecosystem, I think the values are going to be network effect, ecosystems, integration standards evolving very quickly out in the open. Great to see Brian Gracely Senior Director Product Strategy at Red Hat for the cloud business unit, also podcasts are over a million episode downloads for the cloud cast podcast, thecloudcast.net. What's it Brian, what's the stats now. >> Yeah, I think we've, we've done over 500 shows. We're you know, about a million and a half listeners a year. So it's, you know again, it's great to have community followings and, you know, and meet people from around the world. So, you know, so many of these things intersect it's a real pleasure to work with everybody >> You're going to create a culture, well done. We're all been there, done that great job. >> Thank you >> Check out the cloud cast, of course, Red Hat's got the great OpenShift mojo going on into KubeCon. Brian, thanks for coming on. >> Thanks John. >> Okay so CUBE coverage of KubeCon, CloudNativeCon Europe 2021 Virtual, I'm John Furrier with theCUBE virtual. Thanks for watching. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Global Partner Network. Hello, everyone. This is Dave Balanta. And welcome to the cubes Virtual coverage of AWS reinvent 2020 with a special focus on the A p N partner experience. I'm excited to have two great guests on the program. Fernando Castillo is the head s a p on AWS Partner Network and s A P Alliance and AWS and Stephen Jones is the general manager s a p E c to enterprise that aws Gentlemen, welcome to the Cube. Great to see you. >>I'm here. >>So guys ASAP on AWS. It's a core workload for customers. I call it the poster child for mission Critical workloads and applications. Now a lot has happened since we last talked to you guys. So So tell us it. Maybe start with Stephen. What's going on with Sapna Ws? Give us the update. >>I appreciate the question Day. Look, a lot of customers continue to migrate. These mission critical workloads State of us on a good example is the U. S. Navy right? Who moved their entire recipe landscape European workload AWS. This is a very large system of support. Over 72,000 users across 66 different navy commands. They estimate that 70 billion worth of parts and goods actually transact through the system every year. Just just massive. Right? And this this type of adoptions continued to accelerate a very rapid clip. And today, over 5000 customers now are running SFP workloads. I need to be us on there really trusting us, uh, to to manage and run these workloads. And another interesting stat here is that more than half of these customers are actually running asap, Hana, which is a safe He's flagship in memory database. >>Right, Fernando, can you add to that? >>Sure. So definitely about, you know, the customs are also SCP themselves continue to lose a dollar less to run their own offerings. Right? So think about conquer SCP platform. SCP analytics were when new offers like Hannah Cloud. In addition to that, we continue to see the P and L despondent network to grow at an accelerated pace. Today we have over 60 SNP company partners all over the world helping SFP customers s O that customers are my green. There s appeal asking CW's. They only look for reduced costs, improved performance but also toe again access to new capabilities. So innovate around their core business systems and transform their businesses. >>So for now, I wonder if I could stay with you for a minute. I mean, the numbers that Steve was putting out there, it's just massive scale. So you obviously have a lot of data. So I'm wondering when you talk to these customers, Are you discerning any common patterns that are emerging? What are some of the things that you're hearing or seeing when you analyze the data? >>Sure. So just to give a couple example right. Our biggest customers are doing complete ASAP. Transformations on Toe s four Hana. Their chance they're going to these new S a p r p code nine All customers have immediate needs, and they're taking their existing assets to AWS, so looking to reduce costs and improve performance, but also to sell them apart for innovation. This innovation is something that operation or something that they can wait. They need it right now. It's they This time to innovate is now right on some of these customers saying that while s and P has nice apart. So that is a multi year process on most organizations and have a look from waiting for this just before they start innovating. So instead of that, they focus on bringing what they have on start innovating right away on Steve has some great stories around here, so maybe Steve can share with that. Goes with that? >>Yeah, that'd be great, Steve. >>Yeah. Look, I think a good example here on and Fernando touched it, touched on it. Well, right. So customers coming from all kind of different places in their journey aws as it relates to this this critical workload and some are looking to really reap the benefits of the investments they made over the last couple decades sometimes. And Vista is a really good example Here, um there a subsidiary of Cook Industries, they migrated and moved their existing S a P r P solution called E c C. To AWS. They estimate that this migration alone from an infrastructure cost savings perspective, has netted them about two million per year. Additionally, you know, they started to bring some of the other issues they were trying to solve from a business perspective, together now that they were on the on the on the business on the AWS platform. And one thing that recognizes they had different data silos, that they had been operating in an on premises world. Right? So massive factories solution and bringing all of that data together on a single platform on AWS and enriching that with the SCP data has allowed them to actually improve their forecasting supply chain processes across multiple data sources and the estimate that that is saving them additional millions per year. So again, customers are not necessarily waiting to innovate. Um, but actually moving forward now. >>All right, so I gotta ask, you don't hate me for asking this question, but but everybody talks about how great they are. Supporting s a P is It's one of the top, of course, because s a p, you know, huge player in the in the application space. So I want you guys to address how aws specifically compares Thio some of your competitors that are, you know, the hyper scaler specifically as it relates to supporting S a P workloads. What's the rial differential value that you guys bring? Maybe Steve, you could start >>Sure, you're probably getting to know us a little bit. Way don't focus a lot on competition, Aziz mentioned week We continue to see customers adopt AWS for S a p a really rapid clip. And that alone actually brings a lot of feedback back into how we consider our own service offerings as it relates to this particular workload on that, that's it. That's important signal right for what we're building. But customers do tell us the security performance availability matters, especially for this workload, which, you know, to be honest, is the backbone of many, many organizations. Right? And we understand why. And there was a study that was done recently about a. D. C. Where they found that even a single hour of unplanned downtime as a released this particular workload could cost millions. And so it's it's super important. And if you look at, um, you know, publicly available data from an average perspective, um, it has considerably less downtime than the other hyper scale is out there way. Take the performance and availability of oh, our entire global footprint and in this workload in particular, super important. >>Well, you know, that's a great point, Steve. I mean, if you got critical mission critical applications like ASAP supporting the business, that's driving revenue. It's driving productivity. The higher the value of the application, the greater the impact when it's down, I wonder, Fernando, you know, Steve said, You guys don't focus on the competition. Well, is an analyst. You know, I always focused on the competition, So I wonder if you're gonna add anything to that. >>Sure. So again, as you can imagine, multiple analyst called Space right. And, uh, everybody shares information. And analysts have agreed that Italy's clean infrastructure services, including the three quite a for CP across the globe. So we feel very humble and honor about this recognition on this encourages to continue to improve ourselves to give you a couple examples for a 10 year in a row. Italy's US evaluated as a leader in the century Gardner Magic Quadrant, right for cloud infrastructure from services. And, as you know, the measure to access right they measure very execute on complete, insufficient were the highest, both of them. Another third party, just not keep with one is icy, right? You know, technology research dreamers, you already you might know advice for famous Well, the reason they publisher s a p on infrastructure service provider lands reports long name which, basically, the analyzers providers were best suited to host s a. P s four hana workloads on more broadly s a p Hannah landscapes, you know, very large scape ASAP 100 landscapes. So they recognize it, at least for the third year in a row. And conservative right, the best class enterprise. Great infrastructure towards security performances, Steve mentioned, but also making the panic community secure. Differentiation. Andi, they posted. They mentioned it all us as a little position in quadrant for the U. S. U K France, Germany, the Nordics in Brazil. So again, really honor and humble on discontinued in court just to continue to improve. >>You know, Steve, I just wrote a piece on Cloud 2030 trying to project what the next 10 years is gonna look like in one of the I listed a lot of things, but one of the things I talked about was some of the technical factors like alternative processors, specialized networks, and you guys have have have really, always done a good job of sort of looking at purpose built, you know, stuff that that can run workloads faster. How relevant is that in the the S A P community? >>Oh, that's a great question, David. It's It's absolutely relevant. You take a look at what? What we've done over the years with nitro and how we've actually brought the ability for customers to run on environmental infrastructure but still have that integrated, uh, native cloud experience. Uh, that is absolutely applicable to Unless if you workload and we're actually able toe with that technology, bring the capability to customers to run thes mission critical workloads on instances with up to 24 terabytes of brand, albeit bare metal, but fully integrated into the AWS network fabric, >>right? I mean, a lot of people, you know, need that bare metal raw performance on, and that makes sense that you've been, you know, prioritize such an important class of workload. I'm not surprised that that I mean, the numbers that you threw out a pretty impressive eso. It's clear you're leading the charge here. Maybe you could share a little glimpse of what's coming in the future. Show us a little leg, Steve. >>Yeah, well, look, uh, we know that infrastructure is super important. Thio. Our customers and in particular the customers are running these mission critical workloads. But there's a lot of heavy lifting, uh, that that we also want to simplify. And so you've seen some indications of what we've done here over the years, uh, ice G that Fernando mentioned actually called out. AWS is differentiating here, right? So for for many years, we've actually been leading in releasing tools for customers to actually orchestrate and automate the deployment of these types of worthless so ASAP in particular. I mean, if you think about it a customer who is coming to a to a hyper scale platforms like AWS and having to learn what that means, Plus understand all the best practices from S, A, P and AWS to make that thing really shine from a performance and availability perspective, that's a heavy asked. Right? So we put a lot of work from a tooling perspective into into automating this and making this super simple not just for customers, but also partners. >>Anything you wanna chime in on that particular the partner side, Fernando. >>Sure. So this is super important for public community, right? As you can imagine, the tooling that we're bringing together toe. The market is helping the Spanish to move quicker, right? So they don't have to reinvent. They will all the time. They will just take this and move and take it and move forward. Give an example. One of our parents in New York, three hosts. Thanks for lunch. We start with Steve just reference right. They want to create work clothes in an automated way. Speeding up the delivery time. 75% corporation is every environments. So it just imagine the the impact of these eso a thing here that is important is our goal is to help customers and partners move quicker, removing any undifferentiated heavy lifting, right, Andi, that's kind of the mantra of this group. >>You know, when you think about what Doug Young was saying is in the keynote, um, the importance of partners and I've been on this kick about we've moved in this industry from products to platforms, and the next 10 years is gonna be about leveraging ecosystems. The power of many versus the resource is of a few or even one is large is a W s so so partners air critical on I wonder if you could talk toe the role that that the network partners air playing in affecting S a p customer outcomes and strategies. Maybe Steve, you could take that first. >>Yeah, but look, we recognize that the migration on the management of these systems it's complex, right? And for years, we've invested in a global community of partners many partners who have been fundamental to s a p customer success over over a couple decades, Right? And so, um, that there are some nuances that that need to be realized when it comes to running ASAP on on a hyper scale platforms like AWS. And so we put a lot of work into making sure these partners are equipped to ensure customers have have a really good experience. And I mean, in a recent conversation I had with a CEO of a large, uh, CPG company, he told me he reflected that the partners really are the glue. That kind of brings it all together for them. And, uh, you know, just to share something with you today, our partners, our partner community network for S. If he is actually helping over 90% of net new customers who are coming toe migrate as if you were close to AWS, so they're just absolutely critical. >>So, Fernando, there's the m word, the migration, you know, it's you don't want to unless you have to, but people have to move to the cloud. So So what can you add to this conversation? >>Sure, they So again, just to echo what Steve mentioned, right? Uh, migration. Super important. We have ah group of partners that are right now specializing in migration projects. And they have built migration factories. You may have seen some of them. They have been doing press releases through the whole year saying that they're part of these and their special cells they're bringing to the helping customers adopt AWS. So they go through the next, you know, very detailed process. We call them map for ASAP partners. So they have these incremental value on top of being SCP competent funds, which I referred earlier on. This group has, as mentioned, you know, show additional capability to safeguard these migrations on. Of course, we appreciate and respect and we have put investment programs for them to help them support their own customers right in those in these migrations. But because the SNP ecosystem on it. But it's not about only migrations, right? One important topic that we need technologies as you as Steve mentioned, we have these great set of partner of customers have trusted us or 5000 through a year on these, uh, these customers asking for innovation right there, asking us how come the ecosystem help us innovate faster? So these partners are using a dollars a plan off innovation, creating new solutions that are relevant for SCP. So basically helping customers modernize their business processes so you can take an example like Accenture Data Accelerator writers taking SCP information and data legs Really harm is the power of data there or the Lloyd you know, kinetic finances helping, you know, deploy Central finance, which is a key component of SCP, or customer like partners like syntax that has created our industrial i o. T. Offering that connects with the SNP core. So more and more you will see thes ecosystem partners innovating on AWS to support SNP customers. >>You know, I think that's such an important point because for for decades have been around for a while. It's the migrations air like this. Oftentimes there's forced March because maybe a vendor is not going to support it anymore. Or you're just trying to, you know, squeeze Mawr costs out of the lemon. What you guys are talking about is leveraging an ecosystem for innovation and again that ties into the themes that we're talking about about Cloud 2030 in the next decade of innovation. Let's close, guys. What can customers ASAP customers AWS customers expect from reinvent this year? Um, you know, maybe more broadly, what can they expect from A W S in the coming 12 months? Maybe, Steve, you could give us a sense, and then Fernando could bring us home. >>You bet. Look, um, this year we've really tried to focus on customer stories, right? So we've we've optimized. There's a number of sessions here agreement this year. We want customers and partners to learn from other from other customer experiences, so customers will be able to listen to Bristol Myers Squibb talk about their performance, their their experiences, Alando Newmont's and Volkswagen. And I'll be talking about kind of different places where they are on this, this journey to cloud and this innovation life cycle, right, because it really is about choice and what's right for their business. So we're pretty excited about that. >>Yeah. Nice mix of representative Industries there. I Fernando bring us home, please. >>Sure. So, again, we think about 21 in the future. Rest assured, we'll continue to invest heavily to make sure it values remains the platform innovation. Right on choice for recipe customers where a customer wants to move their existing investments on continue to add value. So what they have already done for years or goto export transformation. We're here to support their choice. Right? And we're committed to that as part of our customers Asian culture. So we're super excited about the future. And we're thankful for you to spend time with us today. >>Great, guys, Look, these are the most demanding workloads we're seeing that that rapid movement to the cloud is just gonna accelerate over the coming years. Thanks so much for coming on The Cube. Really appreciate it. >>Our pleasure. Thank >>you. All >>right. Thank you for watching everyone keep it right there from or great content. You're watching the cube aws reinvent 2020

>> Narrator: From around the globe, it's theCUBE with digital coverage of AnsibleFest 2020, brought to you by Red Hat. >> The ascendancy of massive clouds underscored the limits of human labor. People, they simply don't scale at the pace of today's technology. And this trend created an automation mandate for IT which has been further accentuated by the pandemic. The world is witnessing the build-out of a massively distributed system that comprises on-prem apps, public clouds and edge computing. The challenge we face is how to go from managing things you can see and touch to cost effectively managing, securing and scaling these vast systems. It requires an automation first mindset. Hello, everyone. This is Dave Vellante and welcome back to AnsibleFest 2020. We have a great panel to wrap up this show. With me are our three excellent guests and CUBE alums. Ashesh Badani is the Senior Vice President of Cloud Platforms at Red Hat. Ashesh, good to see you again. Thanks for coming on. >> Yeah, likewise. Thanks for having me on again, Dave. >> Stefanie Chiras is Vice President and General Manager of the RHEL Business Unit and my sports buddy. Stefanie, glad to see you back in the New England area. I knew you'd be back. >> Yeah, good to see you, Dave. Thanks for having us today. >> You're very welcome. And then finally, Joe Fitzgerald, longtime CUBE alum, Vice President and General Manager of the Management Business Unit at Red Hat. Joe, good to see you. >> Hey, Dave, good to be here with you. >> Ashesh, I'm going to start with you. Lay out the big picture for us. So how do you see this evolution to what we sometimes talk about as hybrid cloud, but really truly a hybrid cloud environment across these three platforms that I just talked about? >> Yeah, let me start off by echoing something that most of your viewers have probably heard in the past. There's always this notion about developers, developers, developers. And you know, that still holds true. We aren't going away from that anymore. Developers are the new kingmakers. But increasingly, as the scope and complexity of applications and services that are deployed in this heterogeneous environment increases, it's more and more about automation, automation, automation. In the times we live in today, even, you know, before dealing with the crises that, you know, we have, just the sheer magnitude of requirements that are being placed on enterprises and expectations from customers require us to be more and more focused on automating tasks which humans just can't keep up with. So you know, as we look forward, this conversation here today, you know, what Ansible's doing, you know, is squarely aimed at dealing with this complexity that we all face. >> So Stefanie, I wonder if you could talk about what it's going to take to implement what I call this true hybrid cloud, this connection and management of this environment. RHEL is obviously a key piece of that. That's going to be your business unit, but take us through your thoughts there. >> Yeah, so I'm kind of building on what Ashesh said. When we look at this hybrid cloud world, right, which now hybrid is much more than it was considered five years ago. It used to be hybrid was on-prem versus off-prem. Now, hybrid translates to many layers in the stack. It can be VMs hybrid with containers. It can be on-prem with off-prem and clearly with edge involved, as well. Whenever you start to require the ability to bridge across these, that's where we focus on having a platform that allows you to access sort of all of those and be able to deploy your applications in a simple way. When I look at what customers require, it's all about speed of deploying applications, right, build, deploy and run your applications. It's about stability, which is clearly where we're focused on RHEL being able to provide that stability across multiple types of hybrid deployment models. And third is all about scale. It is absolutely all about scale and that's across multiple ranges in hybrid, be it on-prem, off-prem, edge and that's where all of this automation comes in, so to me, it's really about where do you make those strategic decisions that allow you to choose, right, for the flexibility that you need and still be able to deploy applications with speed, have that stability, resiliency, and be able to scale. >> So Joe, let's talk about your swim lane and it's weird to even use that term, right? 'Cause as Stefanie just said, we're kind of breaking down all these silos that we talk in terms of platform, but how do you see this evolving, and specifically, what's the contribution from a management perspective? >> Right, so Stefanie and Ashesh talked about sort of speed, scale and complexity. Right, people are trying to deploy things faster or larger scale, and oh, by the way, keep everything highly available and secure. That's a challenge, right? And so, you know, interestingly enough, Red Hat, about five years ago, we recognized that automation was going to be a problem as people were moving into open hybrid clouds, which we've been working with our customers for years on. And so we acquired this small company called Ansible, which had some really early emerging technology, all open source, right, to do automation. And what we've done over the past five years is we've really amplified that automation and amplified the innovation in that community to be able to provide automation across a wide array of domains that you need to automate, right, and to be able to plug that in to all the different processes that people need in order to be able to go faster, but to track, manage, secure and govern these kind of environments. So we made this bet years ago and it's paying off for Red Hat in very big ways. >> I mean, no doubt about it. I mean, when you guys bought Ansible, so it wasn't clear that it was going to be the clear leader. It is now. I mean, it's pulled ahead of Chef, Puppet. You saw, you know, VMware bought Salt, but I mean, Ansible very clearly has, based on our surveys, the greatest market momentum. We're going to talk about that. I know some of the other analysts have chimed in on this, but let me come back to this notion of on-prem and cloud and edge and this is complicated. I mean, the edge, it's kind of its own island, isn't it? I mean, you got the IT and the OT schism, so maybe you could talk a little bit about how you see those worlds coming together, the cloud, the on-prem, the edge. Maybe Stefanie, you can start. >> Yeah, I think the magic, Dave, is going to happen when it's not its own island, right, as we start to see this world driven by data cause the spread of a data center to be really dis-aggregated and allow that compute to move out closer to the data, the magic happens when it doesn't feel like an island, right, that's the beauty and the promise of hybrid. So when you start to look at what can you provide that is consistent that serves as a single language that you can talk to from on-prem, off-prem and edge, you know, it all comes down to, for us, having a platform that you can build once and deploy across all of those, but the real delicacy with edge is there are some different deployment models. I think that comes into deployment space and we're clearly getting feedback from customers. We're working on some capabilities where edge requires some different deployment models in the ways you update, et cetera, and thanks to all of you out there who are working with us upstream in order to deliver that. And I think the second place where it's unique is in this ability to manage and automate out at the edge, but our goal is certainly at our platform levels, whether it be on RHEL, whether it be on OpenShift to provide that consistent platform that allows you that ease of deployment, then you got to manage and automate it and that's where the whole Ansible and the ecosystem really plays in. You need that ecosystem and that's always what I love about AnsibleFest is this community comes together and it's a vibrant community, for sure. >> Well, I mean, Ashesh, you guys are betting big on this and I often think of the cloud is just this one big cloud. You got the on-prem cloud, you got the public clouds. Edge becomes just an extension of that cloud. Is that how you think about it and what is it actually going to take to make that edge not an island? >> Yeah, great point, Dave, and that's exactly how we think about it. We've always thought about our vision of the cloud as being a platform and abstraction that spans all the underlying infrastructure that the user can take advantage of, so if it happens to reside in a data center, some in a private cloud running off a data center, more increasingly in the public cloud setting, and as Stefanie called out, we're also starting to see edge deployments come in. We're seeing, you know, big build-outs in the work we're doing with telecom providers from a 5G perspective that's helping drive that. We're seeing, if you will, IOT-like opportunities with, let's say, the automotive sector or some in the retail sector, as well. And so this fabric, if you will, needs to span this entire set of deployment that a customer will take advantage of. And Joe started touching on this a little bit, right, with this notion of the speed, scale and complexity, so we see this platform needing to expand to all these footprints that customers are using. At the same time, the requirements that they have, even when they're going out the edge, is the same with regard to what they see in the data center and the public cloud, so putting all that together really is our sweet spot. That's our focus. And to the point you're making, Dave, that's where we're making a huge bet across all of Red Hat. >> So I mentioned, you know, some of our research and I do these breaking analysis segments every week and recently I was digging into cloud and specifically was interested in hybrid and multi. And you know, hybrid been I think pretty well understood for awhile. Multi I think was a lot of, you know, a lot of talk, but it's becoming real and the data really shows that. It shows OpenShift and Ansible have momentum. I mentioned that before. Yeah, you know, obviously VMware is there, but clearly Red Hat is well positioned specifically in multicloud and hybrid. And I know some of the other analyst firms have picked up on this. What are you guys seeing in the market? Maybe Joe, you can chime in and Ashesh, you can maybe add some color. >> Yeah, so you know, there's a lot of fashion, right, around hybrid and multicloud today, so every vendor is jumping on with multicloud storing. And you know, a lot of the vendors' strategies are, pick my solution and vertically use my stuff in the public cloud on-premise, maybe even at the edge, right, and you'll be fine. And you know, obviously customers don't like lock-in. They like to be able to take advantage of the best services, availability, security, different things that are available in each of these different clouds, right? So there is a strong preference for hybrid and multicloud. Red Hat is sort of the Switzerland of hybrid and multicloud because we enable you to run your workloads across all these different substrates, whether it's in public clouds, multiple, right, into the data center and physical, virtual, bare metal, out to the edge and edge is not a single homogeneous, you know, set of hardware or even implementation. It varies a lot by vertical, so you have a lot of diversity, right? And so Red Hat is really good at helping provide the platforms like OpenShift and RHEL that are going to provide that consistency across those different environments or also in the case of Ansible to provide automation that's going to match the physics of management and automation that are required across each of those different environments. Trust me, managing or automating something at the edge and with very small footprint of some device across the constraint network is very, very different than managing things in a public cloud or in a data center and that's where I think Red Hat is really focused and that's our sweet spot, helping people manage those environments. >> And Ashesh, you guys have obviously put a lot of effort there. If you could maybe comment. >> Yeah, I was just going to say, Dave, I'll add just really quickly to what Joe said. He said it well. But the thing I will add is the way for us to succeed here is to follow the user, follow the customer. Right, instead of us just coming out with regard to what we believe the path to be, you know, we're really kind of working closely with the actual customers that we have. So for example, recently been working with a large water utility in Italy, but they're thinking about, you know, the world that they live in and how can they go off and, you know, have kiosks that are spread throughout Italy, able to provide reports with regard to the quality of the water that's available, as well as other services to all their citizens. But it's really interesting use case for us to go off and pursue because in some sense, you can ask yourself, well, is that public cloud? Are they going to take advantage of those services? Is that, you know, private cloud? Is that data center, is that IOT, is that edge? At a certain point in time, what you've got to think about is, well, we've got to provide integrated end-to-end solution that spans all of these different worlds, and so as long as I think we keep that focus, as long as we make sure our North Star is really what the user's trying to do, what problem they're trying to solve, I think we'll come out just fine on the other side of this. >> So I'd love to get all your thoughts, all three of you, on just what's going on in containers, generally, Kubernetes, specifically. I mean, everybody knows it's a hot space and the data shows that it is maturing, but it's amazing to me how much momentum it still has. I mean, it's like the new shiny toy, but it's everywhere and so it's able to sort of maintain that velocity and it's really becoming the go-to cloud native development platform, so the question is how is Red Hat, you know, helping your customers connect OpenShift to the rest of their IT infrastructure, platforms, their processes, the tools. I mean, who wants to start? I'd love to hear from all three of you. Ashesh, why don't you kick it off and then we'll just go left to right. >> So Dave, we've spoken to you and to folks the CUBE, as well, other for many years on this. We've made a huge investment in the Kubernetes market and been one of the earliest to do that and we continue to believe in the promise that it delivers to users, this notion of being able to have an environment that customers can use regardless of the underlying choices that they make. Here's an extremely powerful one, it's truly an open source, right? This is key to, you know, what we do. Increasingly, what we're working on is to ensure that one, if you make a commitment to Kubernetes and increasingly we see lots of customers around the world doing that, that we ensure that we're working closely, that our entire portfolio helps support that. So if you're going to make a choice with regard to Kubernetes base deployment, we help support you running it yourself wherever it is that you choose to run it, we help support you whether you choose to have us manage on your behalf and then also make sure we're providing an entire portfolio of services, both within Red Hat as well as from third parties so that you have the most productive, integrated experience possible. >> Okay, and Stefanie, loved your point of view on this, and Joe, I'd love to understand how you're bridging kind of the Ansible and Kubernetes communities, but Stefanie, why don't you chime in first? >> Yeah, I'll quickly add to what Ashesh said and talked about well on really the promise and the value of containers, but particularly from a RHEL perspective, we have taken all our capabilities and knowledge in the Linux space and we have taken that to apply it to OpenShift, right, because Kubernetes and containers is just another way to deploy Linux, so making sure that that underpinning is stable, secure and resilient and tied to an ecosystem, right? An ecosystem of various architectures, an ecosystem of ISVs and tooling, right? We've pulled that together and everything we've done in Linux for, you know, over decades now at Red Hat and we've put that into that customer experience around OpenShift to deploy containers, so we've really built, it has been a portfolio-wide effort, as Ashesh alluded to, and of course, it passes over to Ansible as well with Joe's portfolio. >> Yeah, we talked about this upfront, Joe. The communities are so crucial, so how are you bridging those Ansible and Kubernetes communities? What's your thought on that? >> Well, a quick note about those communities. So you know, OpenShift is built on Kubernetes and a number of other projects. Kubernetes is number seven in the top 10 open source projects based on the number of contributors. Turns out Ansible is number nine, right? So if you think about it, these are two incredibly robust communities, right? On the one hand, building the container platform in Kubernetes and in the other around Ansible and automation. It turns out that as the need for this digital acceleration and building these container-based applications comes along, there's a lot of other things that have to be done when you deploy container-based applications, whether it's infrastructure automation, right, to expand and manage and automate the infrastructure that you're running your container-based applications on, creating more clusters, you know, configuring storage, network, you know, counts, things like that, but also connecting to other systems in the environment that need to be integrated with around, you know, ITSM or systems of record, change management, inventory, cost, things like that, so what we've done is we've integrated Ansible, right, in a very powerful way with OpenShift through our advanced cluster management capability, which allows us to provide an easy way to instrument Ansible during critical points, whether it's you're deploying new clusters out there or you're deploying a new version of an application or a new application for the first time, whether you're checking policy, right, to ensure that, you know, the thing is secure and that, you know, you can govern these environments, right, that you're relying on. So we've really now tied together two sort of de facto standards, OpenShift built on Kubernetes and a number of other projects and then Ansible, or Red Hat, has taken this innovation in the community and created these certified content collections, platforms and capabilities that people can actually build and rely on and know that it's going to work. >> Ashesh, I mean, Red Hat has earned the right, really, to play in both the cloud native world and of course the traditional infrastructure world, but I'm interested in what you're seeing there, how you're bringing those two worlds together. Are they still, you know, largely separate? Are you seeing traditional IT? I mean, you're certainly seeing them lean in to more and more cloud native, but what are you guys doing specifically to kind of bring those worlds together? >> Yeah, increasingly it's really hard to be able to separate out those worlds, right? So in the past, we used to call it shadow IT. There really is no shadow IT anymore, right? This is IT. So we've embraced that completely. You know, our take on that is to say there are certain applications that are going to be appropriate for being run in a data center a certain way. There are certain other workloads that'll find their way appropriate for the public cloud. We want to make sure we're meeting them across, but what we want to do is constantly introduce technologies to help support the choices customers make. What do I mean by that? Let me give a couple examples. One is, you know, we can say customers have VMs that are based out in specific environments and they can only run as VMs. That code can't be containerized for a variety of reasons, right? You know, hard to re-architect that, don't have the funds, you know, have certain security compliance reasons. Well, what if we could take those VMs and then have them be run in containers in a native fashion? Wouldn't that be extremely powerful value proposition to run containers and then VMs as containers sort of side by side with Kubernetes orchestrating them all. So that's a capability we call open source virtualization. We've introduced that and made that generally available within our platform. Another one, which I think Joe starting to touch on a little bit here, is both around this notion of Ansible, as well as advanced cluster management. And say, once technologies like Ansible are familiar to our customers, how about if we find ways to introduce things like the operator framework to help support people's use of Ansible and introduce technologies like advanced cluster management, which allows for us to say, well, regardless of where you run your clusters, whether you run your Kubernetes clusters on premise, you run them in the cloud, right, we can imagine a consistent fashion and manage, you know, health and policy and compliance of applications across that entire state. So David, question's extremely good one, right, but what we are trying to do is try to be able to say, you know, we are going to just span those two worlds and provide as many tools as possible to ensure that customers feel like, you know, the shift, if you will, or the move between traditional enterprise software application development and the more modern cloud native can be bridged as seamlessly as possible. >> Yeah, Joe, we heard a lot of this at AnsibleFest, so the ACM as a key component of your innovation, and frankly, your competitive posture. Anything you would add to what Ashesh just shared? >> Well, I think that one of the things that Red Hat is really good at is we take management and automation as sort of an intrinsic part of what needs to go on. It's not an afterthought. You just don't go build something, go, "Oh I need management," go out and, you know, go get something, right, so we've been working on, sort of automation and management for many, many years, right, so we build it in concert with these platforms, right, and we understand the physics of these different environments, so we're very focused on that from inception, as opposed to an afterthought when people sort of paint themselves into a corner or have management challenges they can't deal with. >> There's a lot of analogs in our business, isn't there? Management is a bolt-on and security is a bolt-on. It just doesn't work that well and certainly doesn't scale. Stefanie, I want to come back to you and I want to come back to the edge. We hear a lot of people talking about extending their deployments to the edge in the future. I mean, you look at what IBM's doing. They're essentially betting its business on RHEL and OpenShift and betting that its customers are going to do the same as well are you. Maybe talk about, you know, what you're doing to specifically extend RHEL to the edge. >> Yeah, Dave, so we've been looking at this space consistent with our strategy, as Ashesh talked about, right? Our goal is to make sure that it all looks and feels the same and provides one single Linux experience. We've been building on a number of those aspects for quite some time, things like being able to deal with heterogeneous architectures, as an example, being able to deal with, you know, having Arm components and x86 components and power components and being able to leverage all of that from multiple vendors and being able to deploy. Those are things we've been focused on for a long time and now when you move into the space of the edge, certainly we're seeing, you know, essentially data center level hardware move out to be dis-aggregated and dispersed as they move it closer to the data and where that's coming in and where the analysis needs to be done, but some of those foundational things that we've been working on for years starts to pay off because the edge tends to be more heterogeneous all the way from an architecture level to an application level, so now we're seeing some asks. We've been working upstream in order to pull in some features that drive capabilities around specifically updating, deploying those updates, doing rollbacks and things like that, so we're focused on that. But really, it's about pulling together the capabilities of having multiple architectures, dealing with heterogeneous infrastructure out there at the edge, being able to reliably deploy it even when, for example, we have customers who they deploy their hardware and they can't touch it for years. How do they make sure that that's out there in a stable environment that they can count on? And then, you know, adding in things like containerization. We talked about the magic of that, being able to deploy an application consistently and being able to deploy a single container out there to the edge. We're thinking about it all the way from the architecture up to how the application gets deployed and it's going to take the whole portfolio to do that as you need to manage it, as you need to deploy containers, so it's a focus across the company for how we deal with that. >> And as we were talking about before, you know, it takes a village. You know that bromide, but it does, requires an ecosystem of jobs. I mean, there's some real technical challenges in R&D that has to happen. I mean, you've got to be, you know, you're talking about cloud native in all three different clouds, and you know, and not just the big three, but other clouds and then bringing that to the edge, so there's some clear technical challenges, but there's also some business challenges out there. So you know, what are you seeing in that regard? You know, what are some of those things that you hope to solve by bridging that gap? >> Well, I think one of the things we're trying to do and I'm focused on the management and automation side is to provide a common set of management tooling of automation, right, and I think Ansible fits that quite well. So for the past five years since Ansible's been part of Red Hat, we've expanded from, you know, they started off initially doing configuration management, right? We've expanded to include, you know, network and storage and security, now edge. At AnsibleFest, we demonstrated things like serverless event-driven automation, right, building an OpenShift serverless in Knative. We're trying to expand the use cases for Ansible so that there's a simplicity, there's a tool reduction, right, across all these environments and you don't have to go deal with nine vendors, and you know, 17 different tools to try to manage each element here to be able to provide a common set. It reduces complexity, cost and allows skills to be able to be reused across these different areas. It's going to all be about digital acceleration, right, and reducing that complexity. And one last comment. One of the reasons we bought Ansible years ago is the architecture, it's agent-less. Many of our competitors that you hear, the first thing they want to do is go deploy an agent somewhere and that creates its own ongoing burden of, do I have the latest version of the agent? Is it secure? Does it fit on the device? As Stefanie mentioned, is there a version that fits on the architecture the device is running on? It starts getting really, really complicated. So Ansible is just simple, elegant, agent-less. We've expanded the domains we can automate with it and we've expanded sort of the modality. How can I call it? User, driven by an event, as part of some life cycle management, app deployment, Ansible plugs right in. >> Well, Joe, you can tell you're a management guy, right? Agents, another thing that has to be managed. You just laundry list of stuff. (laughs) I want to come back to this notion Joe just touched on, this digital transformation. They say, "If it ain't broke, don't fix it." Well, COVID broke everything. And I got to say, I mean, all the talk about digital transformation over the last, you know, several years, yes, it was certainly happening, but there was also a lot of lip service going on and now if you're not digital, you're out of business. And so, you know, given everything that we've seen in the last, you know, whatever, 150, 200 days or so, what's the impact that you're seeing on customers' digital transformation initiatives, and you know, what is Red Hat doing to respond? Maybe Ashesh, you could start and we can get feedback from the others. >> Yeah, David, it's an unfortunate thing to say, right, but there's that meme going around with regard to who's responsible for digital transformation and it's a little bit of I guess gallows humor to call it COVID, but we're increasingly seeing that customers and the journey that they're on is one that they haven't really gotten off, even with this, if you will, change of environment that's come about. So projects that we've seen in play, you know, are still underway. We've seen acceleration, actually, in some places with regard to making services more easily accessible. Anyone who's invested in hybrid cloud or public cloud is seeing huge value with regard to being able to consume services remotely, being able to do this on demand and that's a big part of the value proposition, you know, that comes forward. And increasingly what we're trying to do is try to say, how can we engage and assist you in these times, right? So our services team, for example, has transformed to be able to help customers remotely. Our support team has gone off and work more and more with customers. For a company like Red Hat, that hasn't been completely, if you will, difficult thing to do mostly because we've been so used to working in a distributed fashion, working remotely with our customers, so that's not a challenge in itself, but making sure customers understand that this is really a critical journey for them to go on and how we can kind of help them, you know, walk through that has been good and we're finding that that message really resonates. Right, so both Stefanie and Joe talked a little bit about, you know, how essentially our entire portfolio is now built around, you know, ensuring that if you'd like to consume on demand, we can help support you, if you'd like to consume in a traditional fashion, we can help you. That amount of flexibility that we provide to customers is really coming to bear at this point in time. >> So maybe we could wrap with, we haven't really dropped any customer names. Stefanie and Joe and Ashesh, I wonder if you have any stories you can share or, you know, customer examples that we could close on that are exciting to you this year. >> So I can start, if that's okay. >> Please. >> So an area that I find super interesting from a customer perspective that we're increasingly seeing more and more customers go down is sheer interest in, if you will, kind of diversity of use cases that we're seeing, right? So we see this, for example, in automotive, right? So whether it's a BMW or a Volkswagen, we see this now in health care with the ACA, in we'll say a little bit more traditional industries like energy with Exxon or Schlumberger around increasingly embrace of AIML, right? So artificial machine learning, if you will, advanced analytics being much more proactive with regard to how they can take data that's coming in, adjust it, be able to make sense of the patterns and then be able to, you know, have some action that has real business impact. So this whole trend towards, you know, AIML workloads that they can run is extremely powerful. We work very closely with Nvidia, as well, and we're seeing a lot of interest, for example, in being able to run a Kubernetes-based platform, support Nvidia GPUs for specific class workloads. There's a whole bunch of customers, people in financial services that, you know, this is a rich area of interest. You know, we've seen great use cases for example around grid with Deutsche Bank. And so, to me, I'm personally really excited to see kind of that embrace the PC from our customers regard to saying there's a whole lot of data that's out there. You know, how can we essentially use all of these tools that we have in place? You know, we talk about containers, microservices, DevOps, you know, all of this and then put it to bear to really put to work and get business value. >> Great, thank you for that, Ashesh. Stefanie, Joe, Stefanie, anything you want to add or final thoughts? >> Yeah, just one thing to add and I think Ashesh talked to a whole number across industry verticals and customers. But I think the one thing that I've seen through COVID is that if nothing else, it's taught us that change is the only constant and I think, you know, our whole vision of open hybrid cloud is how to enable customers to be flexible and do what they need to do when they need to do it, wherever they want to deploy, however they want to build. We provide them some consistency, right, across that as they make those changes and I think as I've worked with customers here through since the beginning of COVID, it's been amazing to me the diversity of how they've had to respond. Some have doubled down in the data center, some have doubled down on going public cloud and to me, this is the proof of the strategy that we're on, right, that open hybrid cloud is about delivering flexibility, and boy, nothing's taught us the need for flexibility like COVID has recently, so I think there's a lot more to do. I think pulling together the platforms and the automation is what is going to enable the ability to do that in a simple fashion. >> So Joe, you get the final word. I mean, AnsibleFest 2020, I mean, it's weird, right? But that's the way these events are, all virtual. Hopefully, next year we got a shot at being face to face, but bring us home, please. >> Yeah, I got to tell ya, having, you know, 20,000 or so of your closest friends get together to talk about automation for a couple of days is just amazing. That just shows you sort of the power of it. You know, we have a lot of customers this week at AnsibleFest telling you their story, you know, CarMax and ExxonMobil, you know, BlueCross BlueShield. I mean, there's a number across all different verticals, globally, Cepsa from Europe. I mean, just an incredibly, you know, diverse array of customers and use cases. I would encourage people to look at some of the customer presentations that were on at AnsibleFest, listen to the customer telling you what they're doing with Ansible, deploying their networks, deploying their apps, managing their infrastructure, container apps, traditional apps, connecting it, moving faster. They have amazing stories. I encourage people to go look. >> Well, guys, thanks so much for helping us wrap up AnsibleFest 2020. It was really a great discussion. You guys have always been awesome CUBE guests. Really appreciate the partnership and so thank you. >> Thanks a lot, Dave. Appreciate it. >> Yeah, thanks, Dave. >> Thanks for having us. >> All right, and thank you for watching, everybody. This is Dave Vellante for theCUBE and we'll see you next time. (calm music)

to have Rodger Goodell fly to a tech conference to sit with you and then bring his team talk about the deal. >> Well, ya know, we've been partners with the NFL for a while with the Next Gen Stats that they use on all their telecasts and one of the things I really like about Roger is that he's very curious and very interested in technology and the first couple times I spoke with him he asked me so many questions about ways the NFL might be able to use the Cloud and digital transformation to transform their various experiences and he's always said if you have a creative idea or something you think that could change the world for us, just call me he said or text me or email me and I'll call you back within 24 hours. And so, we've spent the better part of the last year talking about a lot of really interesting, strategic ways that they can evolve their experience both for fans, as well as their players and the Player Health and Safety Initiative, it's so important in sports and particularly important with the NFL given the nature of the sport and they've always had a focus on it, but what you can do with computer vision and machine learning algorithms and then building a digital athlete which is really like a digital twin of each athlete so you understand, what does it look like when they're healthy and compare that when it looks like they may not be healthy and be able to simulate all kinds of different combinations of player hits and angles and different plays so that you could try to predict injuries and predict the right equipment you need before there's a problem can be really transformational so we're super excited about it. >> Did you guys come up with the idea or was it a collaboration between them? >> It was really a collaboration. I mean they, look, they are very focused on players safety and health and it's a big deal for their- you know, they have two main constituents the players and fans and they care deeply about the players and it's a-it's a hard problem in a sport like Football, I mean, you watch it. >> Yeah, and I got to say it does point out the use cases of what you guys are promoting heavily at the show here of the SageMaker Studio, which was a big part of your Keynote, where they have all this data. >> Andy: Right. >> And they're data hoarders, they hoard data but the manual process of going through the data was a killer problem. This is consistent with a lot of the enterprises that are out there, they have more data than they even know. So this seems to be a big part of the strategy. How do you get the customers to actually wake up to the fact that they got all this data and how do you tie that together? >> I think in almost every company they know they have a lot of data. And there are always pockets of people who want to do something with it. But, when you're going to make these really big leaps forward; these transformations, the things like Volkswagen is doing where they're reinventing their factories and their manufacturing process or the NFL where they're going to radically transform how they do players uh, health and safety. It starts top down and if the senior leader isn't convicted about wanting to take that leap forward and trying something different and organizing the data differently and organizing the team differently and using machine learning and getting help from us and building algorithms and building some muscle inside the company it just doesn't happen because it's not in the normal machinery of what most companies do. And so it always, almost always, starts top down. Sometimes it can be the Commissioner or CEO sometimes it can be the CIO but it has to be senior level conviction or it doesn't get off the ground. >> And the business model impact has to be real. For NFL, they know concussions, hurting their youth pipe-lining, this is a huge issue for them. the low level building blocks and stitch them together creatively however they see fit to create whatever's in their-in their heads. And then we have the second segment of customers that say look, I'm willing to give up some of that flexibility in exchange for getting 80% of the way there much faster.

>> Hi, my name is Andy Clemenko. I'm a Senior Solutions Engineer at StackRox. Thanks for joining us today for my talk on labels, labels, labels. Obviously, you can reach me at all the socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, and it'll take you to my GitHub page where I've got all of this documentation, socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, (upbeat music) >> Hi, my name is Andy Clemenko. I'm a Senior Solutions Engineer at StackRox. Thanks for joining us today for my talk on labels, labels, labels. Obviously, you can reach me at all the socials. Before we get started, I like to point you to my GitHub repo, you can go to andyc.info/dc20, and it'll take you to my GitHub page where I've got all of this documentation, I've got the Keynote file there. YAMLs, I've got Dockerfiles, Compose files, all that good stuff. If you want to follow along, great, if not go back and review later, kind of fun. So let me tell you a little bit about myself. I am a former DOD contractor. This is my seventh DockerCon. I've spoken, I had the pleasure to speak at a few of them, one even in Europe. I was even a Docker employee for quite a number of years, providing solutions to the federal government and customers around containers and all things Docker. So I've been doing this a little while. One of the things that I always found interesting was the lack of understanding around labels. So why labels, right? Well, as a former DOD contractor, I had built out a large registry. And the question I constantly got was, where did this image come from? How did you get it? What's in it? Where did it come from? How did it get here? And one of the things we did to kind of alleviate some of those questions was we established a baseline set of labels. Labels really are designed to provide as much metadata around the image as possible. I ask everyone in attendance, when was the last time you pulled an image and had 100% confidence, you knew what was inside it, where it was built, how it was built, when it was built, you probably didn't, right? The last thing we obviously want is a container fire, like our image on the screen. And one kind of interesting way we can kind of prevent that is through the use of labels. We can use labels to address security, address some of the simplicity on how to run these images. So think of it, kind of like self documenting, Think of it also as an audit trail, image provenance, things like that. These are some interesting concepts that we can definitely mandate as we move forward. What is a label, right? Specifically what is the Schema? It's just a key-value. All right? It's any key and pretty much any value. What if we could dump in all kinds of information? What if we could encode things and store it in there? And I've got a fun little demo to show you about that. Let's start off with some of the simple keys, right? Author, date, description, version. Some of the basic information around the image. That would be pretty useful, right? What about specific labels for CI? What about a, where's the version control? Where's the source, right? Whether it's Git, whether it's GitLab, whether it's GitHub, whether it's Gitosis, right? Even SPN, who cares? Where are the source files that built, where's the Docker file that built this image? What's the commit number? That might be interesting in terms of tracking the resulting image to a person or to a commit, hopefully then to a person. How is it built? What if you wanted to play with it and do a git clone of the repo and then build the Docker file on your own? Having a label specifically dedicated on how to build this image might be interesting for development work. Where it was built, and obviously what build number, right? These kind of all, not only talk about continuous integration, CI but also start to talk about security. Specifically what server built it. The version control number, the version number, the commit number, again, how it was built. What's the specific build number? What was that job number in, say, Jenkins or GitLab? What if we could take it a step further? What if we could actually apply policy enforcement in the build pipeline, looking specifically for some of these specific labels? I've got a good example of, in my demo of a policy enforcement. So let's look at some sample labels. Now originally, this idea came out of label-schema.org. And then it was a modified to opencontainers, org.opencontainers.image. There is a link in my GitHub page that links to the full reference. But these are some of the labels that I like to use, just as kind of like a standardization. So obviously, Author's, an email address, so now the image is attributable to a person, that's always kind of good for security and reliability. Where's the source? Where's the version control that has the source, the Docker file and all the assets? How it was built, build number, build server the commit, we talked about, when it was created, a simple description. A fun one I like adding in is the healthZendpoint. Now obviously, the health check directive should be in the Docker file. But if you've got other systems that want to ping your applications, why not declare it and make it queryable? Image version, obviously, that's simple declarative And then a title. And then I've got the two fun ones. Remember, I talked about what if we could encode some fun things? Hypothetically, what if we could encode the Compose file of how to build the stack in the first image itself? And conversely the Kubernetes? Well, actually, you can and I have a demo to show you how to kind of take advantage of that. So how do we create labels? And really creating labels as a function of build time okay? You can't really add labels to an image after the fact. The way you do add labels is either through the Docker file, which I'm a big fan of, because it's declarative. It's in version control. It's kind of irrefutable, especially if you're tracking that commit number in a label. You can extend it from being a static kind of declaration to more a dynamic with build arguments. And I can show you, I'll show you in a little while how you can use a build argument at build time to pass in that variable. And then obviously, if you did it by hand, you could do a docker build--label key equals value. I'm not a big fan of the third one, I love the first one and obviously the second one. Being dynamic we can take advantage of some of the variables coming out of version control. Or I should say, some of the variables coming out of our CI system. And that way, it self documents effectively at build time, which is kind of cool. How do we view labels? Well, there's two major ways to view labels. The first one is obviously a docker pull and docker inspect. You can pull the image locally, you can inspect it, you can obviously, it's going to output as JSON. So you going to use something like JQ to crack it open and look at the individual labels. Another one which I found recently was Skopeo from Red Hat. This allows you to actually query the registry server. So you don't even have to pull the image initially. This can be really useful if you're on a really small development workstation, and you're trying to talk to a Kubernetes cluster and wanting to deploy apps kind of in a very simple manner. Okay? And this was that use case, right? Using Kubernetes, the Kubernetes demo. One of the interesting things about this is that you can base64 encode almost anything, push it in as text into a label and then base64 decode it, and then use it. So in this case, in my demo, I'll show you how we can actually use a kubectl apply piped from the base64 decode from the label itself from skopeo talking to the registry. And what's interesting about this kind of technique is you don't need to store Helm charts. You don't need to learn another language for your declarative automation, right? You don't need all this extra levels of abstraction inherently, if you use it as a label with a kubectl apply, It's just built in. It's kind of like the kiss approach to a certain extent. It does require some encoding when you actually build the image, but to me, it doesn't seem that hard. Okay, let's take a look at a demo. And what I'm going to do for my demo, before we actually get started is here's my repo. Here's a, let me actually go to the actual full repo. So here's the repo, right? And I've got my Jenkins pipeline 'cause I'm using Jenkins for this demo. And in my demo flask, I've got the Docker file. I've got my compose and my Kubernetes YAML. So let's take a look at the Docker file, right? So it's a simple Alpine image. The org statements are the build time arguments that are passed in. Label, so again, I'm using the org.opencontainers.image.blank, for most of them. There's a typo there. Let's see if you can find it, I'll show you it later. My source, build date, build number, commit. Build number and get commit are derived from the Jenkins itself, which is nice. I can just take advantage of existing URLs. I don't have to create anything crazy. And again, I've got my actual Docker build command. Now this is just a label on how to build it. And then here's my simple Python, APK upgrade, remove the package manager, kind of some security stuff, health check getting Python through, okay? Let's take a look at the Jenkins pipeline real quick. So here is my Jenkins pipeline and I have four major stages, four stages, I have built. And here in build, what I do is I actually do the Git clone. And then I do my docker build. From there, I actually tell the Jenkins StackRox plugin. So that's what I'm using for my security scanning. So go ahead and scan, basically, I'm staging it to scan the image. I'm pushing it to Hub, okay? Where I can see the, basically I'm pushing the image up to Hub so such that my StackRox security scanner can go ahead and scan the image. I'm kicking off the scan itself. And then if everything's successful, I'm pushing it to prod. Now what I'm doing is I'm just using the same image with two tags, pre-prod and prod. This is not exactly ideal, in your environment, you probably want to use separate registries and non-prod and a production registry, but for demonstration purposes, I think this is okay. So let's go over to my Jenkins and I've got a deliberate failure. And I'll show you why there's a reason for that. And let's go down. Let's look at my, so I have a StackRox report. Let's look at my report. And it says image required, required image label alert, right? Request that the maintainer, add the required label to the image, so we're missing a label, okay? One of the things we can do is let's flip over, and let's look at Skopeo. Right? I'm going to do this just the easy way. So instead of looking at org.zdocker, opencontainers.image.authors. Okay, see here it says build signature? That was the typo, we didn't actually pass in. So if we go back to our repo, we didn't pass in the the build time argument, we just passed in the word. So let's fix that real quick. That's the Docker file. Let's go ahead and put our dollar sign in their. First day with the fingers you going to love it. And let's go ahead and commit that. Okay? So now that that's committed, we can go back to Jenkins, and we can actually do another build. And there's number 12. And as you can see, I've been playing with this for a little bit today. And while that's running, come on, we can go ahead and look at the Console output. Okay, so there's our image. And again, look at all the build arguments that we're passing into the build statement. So we're passing in the date and the date gets derived on the command line. With the build arguments, there's the base64 encoded of the Compose file. Here's the base64 encoding of the Kubernetes YAML. We do the build. And then let's go down to the bottom layer exists and successful. So here's where we can see no system policy violations profound marking stack regimes security plugin, build step as successful, okay? So we're actually able to do policy enforcement that that image exists, that that label sorry, exists in the image. And again, we can look at the security report and there's no policy violations and no vulnerabilities. So that's pretty good for security, right? We can now enforce and mandate use of certain labels within our images. And let's flip back over to Skopeo, and let's go ahead and look at it. So we're looking at the prod version again. And there's it is in my email address. And that validated that that was valid for that policy. So that's kind of cool. Now, let's take it a step further. What if, let's go ahead and take a look at all of the image, all the labels for a second, let me remove the dash org, make it pretty. Okay? So we have all of our image labels. Again, author's build, commit number, look at the commit number. It was built today build number 12. We saw that right? Delete, build 12. So that's kind of cool dynamic labels. Name, healthz, right? But what we're looking for is we're going to look at the org.zdockerketers label. So let's go look at the label real quick. Okay, well that doesn't really help us because it's encoded but let's base64 dash D, let's decode it. And I need to put the dash r in there 'cause it doesn't like, there we go. So there's my Kubernetes YAML. So why can't we simply kubectl apply dash f? Let's just apply it from standard end. So now we've actually used that label. From the image that we've queried with skopeo, from a remote registry to deploy locally to our Kubernetes cluster. So let's go ahead and look everything's up and running, perfect. So what does that look like, right? So luckily, I'm using traefik for Ingress 'cause I love it. And I've got an object in my Kubernetes YAML called flask.doctor.life. That's my Ingress object for traefik. I can go to flask.docker.life. And I can hit refresh. Obviously, I'm not a very good web designer 'cause the background image in the text. We can go ahead and refresh it a couple times we've got Redis storing a hit counter. We can see that our server name is roundrobing. Okay? That's kind of cool. So let's kind of recap a little bit about my demo environment. So my demo environment, I'm using DigitalOcean, Ubuntu 19.10 Vms. I'm using K3s instead of full Kubernetes either full Rancher, full Open Shift or Docker Enterprise. I think K3s has some really interesting advantages on the development side and it's kind of intended for IoT but it works really well and it deploys super easy. I'm using traefik for Ingress. I love traefik. I may or may not be a traefik ambassador. I'm using Jenkins for CI. And I'm using StackRox for image scanning and policy enforcement. One of the things to think about though, especially in terms of labels is none of this demo stack is required. You can be in any cloud, you can be in CentOs, you can be in any Kubernetes. You can even be in swarm, if you wanted to, or Docker compose. Any Ingress, any CI system, Jenkins, circle, GitLab, it doesn't matter. And pretty much any scanning. One of the things that I think is kind of nice about at least StackRox is that we do a lot more than just image scanning, right? With the policy enforcement things like that. I guess that's kind of a shameless plug. But again, any of this stack is completely replaceable, with any comparative product in that category. So I'd like to, again, point you guys to the andyc.infodc20, that's take you right to the GitHub repo. You can reach out to me at any of the socials @clemenko or andy@stackrox.com. And thank you for attending. I hope you learned something fun about labels. And hopefully you guys can standardize labels in your organization and really kind of take your images and the image provenance to a new level. Thanks for watching. (upbeat music) >> Narrator: Live from Las Vegas It's theCUBE. Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with it's ecosystem partners. >> Okay, welcome back everyone theCUBE's live coverage of AWS re:Invent 2019. This is theCUBE's 7th year covering Amazon re:Invent. It's their 8th year of the conference. I want to just shout out to Intel for their sponsorship for these two amazing sets. Without their support we wouldn't be able to bring our mission of great content to you. I'm John Furrier. Stu Miniman. We're here with the chief of AWS, the chief executive officer Andy Jassy. Tech athlete in and of himself three hour Keynotes. Welcome to theCUBE again, great to see you. >> Great to be here, thanks for having me guys. >> Congratulations on a great show a lot of great buzz. >> Andy: Thank you. >> A lot of good stuff. Your Keynote was phenomenal. You get right into it, you giddy up right into it as you say, three hours, thirty announcements. You guys do a lot, but what I liked, the new addition, the last year and this year is the band; house band. They're pretty good. >> Andy: They're good right? >> They hit the queen notes, so that keeps it balanced. So we're going to work on getting a band for theCUBE. >> Awesome. >> So if I have to ask you, what's your walk up song, what would it be? >> There's so many choices, it depends on what kind of mood I'm in. But, uh, maybe Times Like These by the Foo Fighters. >> John: Alright. >> These are unusual times right now. >> Foo Fighters playing at the Amazon Intersect Show. >> Yes they are. >> Good plug Andy. >> Headlining. >> Very clever >> Always getting a good plug in there. >> My very favorite band. Well congratulations on the Intersect you got a lot going on. Intersect is a music festival, I'll get to that in a second But, I think the big news for me is two things, obviously we had a one-on-one exclusive interview and you laid out, essentially what looks like was going to be your Keynote, and it was. Transformation- >> Andy: Thank you for the practice. (Laughter) >> John: I'm glad to practice, use me anytime. >> Yeah. >> And I like to appreciate the comments on Jedi on the record, that was great. But I think the transformation story's a very real one, but the NFL news you guys just announced, to me, was so much fun and relevant. You had the Commissioner of NFL on stage with you talking about a strategic partnership. That is as top down, aggressive goal as you could get to have Rodger Goodell fly to a tech conference to sit with you and then bring his team talk about the deal. >> Well, ya know, we've been partners with the NFL for a while with the Next Gen Stats that they use on all their telecasts and one of the things I really like about Roger is that he's very curious and very interested in technology and the first couple times I spoke with him he asked me so many questions about ways the NFL might be able to use the Cloud and digital transformation to transform their various experiences and he's always said if you have a creative idea or something you think that could change the world for us, just call me he said or text me or email me and I'll call you back within 24 hours. And so, we've spent the better part of the last year talking about a lot of really interesting, strategic ways that they can evolve their experience both for fans, as well as their players and the Player Health and Safety Initiative, it's so important in sports and particularly important with the NFL given the nature of the sport and they've always had a focus on it, but what you can do with computer vision and machine learning algorithms and then building a digital athlete which is really like a digital twin of each athlete so you understand, what does it look like when they're healthy and compare that when it looks like they may not be healthy and be able to simulate all kinds of different combinations of player hits and angles and different plays so that you could try to predict injuries and predict the right equipment you need before there's a problem can be really transformational so we're super excited about it. >> Did you guys come up with the idea or was it a collaboration between them? >> It was really a collaboration. I mean they, look, they are very focused on players safety and health and it's a big deal for their- you know, they have two main constituents the players and fans and they care deeply about the players and it's a-it's a hard problem in a sport like Football, I mean, you watch it. >> Yeah, and I got to say it does point out the use cases of what you guys are promoting heavily at the show here of the SageMaker Studio, which was a big part of your Keynote, where they have all this data. >> Andy: Right. >> And they're data hoarders, they hoard data but the manual process of going through the data was a killer problem. This is consistent with a lot of the enterprises that are out there, they have more data than they even know. So this seems to be a big part of the strategy. How do you get the customers to actually wake up to the fact that they got all this data and how do you tie that together? >> I think in almost every company they know they have a lot of data. And there are always pockets of people who want to do something with it. But, when you're going to make these really big leaps forward; these transformations, the things like Volkswagen is doing where they're reinventing their factories and their manufacturing process or the NFL where they're going to radically transform how they do players uh, health and safety. It starts top down and if the senior leader isn't convicted about wanting to take that leap forward and trying something different and organizing the data differently and organizing the team differently and using machine learning and getting help from us and building algorithms and building some muscle inside the company it just doesn't happen because it's not in the normal machinery of what most companies do. And so it always, almost always, starts top down. Sometimes it can be the Commissioner or CEO sometimes it can be the CIO but it has to be senior level conviction or it doesn't get off the ground. >> And the business model impact has to be real. For NFL, they know concussions, hurting their youth pipe-lining, this is a huge issue for them. This is their business model. >> They lose even more players to lower extremity injuries. And so just the notion of trying to be able to predict injuries and, you know, the impact it can have on rules and the impact it can have on the equipment they use, it's a huge game changer when they look at the next 10 to 20 years. >> Alright, love geeking out on the NFL but Andy, you know- >> No more NFL talk? >> Off camera how about we talk? >> Nobody talks about the Giants being 2 and 10. >> Stu: We're both Patriots fans here. >> People bring up the undefeated season. >> So Andy- >> Everybody's a Patriot's fan now. (Laughter) >> It's fascinating to watch uh, you and your three hour uh, Keynote, uh Werner in his you know, architectural discussion, really showed how AWS is really extending its reach, you know, it's not just a place. For a few years people have been talking about you know, Cloud is an operational model its not a destination or a location but, I felt it really was laid out is you talked about Breadth and Depth and Werner really talked about you know, Architectural differentiation. People talk about Cloud, but there are very-there are a lot of differences between the vision for where things are going. Help us understand why, I mean, Amazon's vision is still a bit different from what other people talk about where this whole Cloud expansion, journey, put ever what tag or label you want on it but you know, the control plane and the technology that you're building and where you see that going. >> Well I think that, we've talked about this a couple times we have two macro types of customers. We have those that really want to get at the low level building blocks and stitch them together creatively however they see fit to create whatever's in their-in their heads. And then we have the second segment of customers that say look, I'm willing to give up some of that flexibility in exchange for getting 80% of the way there much faster. In an abstraction that's different from those low level building blocks. And both segments of builders we want to serve and serve well and so we've built very significant offerings in both areas. I think when you look at microservices um, you know, some of it has to do with the fact that we have this very strongly held belief born out of several years of Amazon where you know, the first 7 or 8 years of Amazon's consumer business we basically jumbled together all of the parts of our technology in moving really quickly and when we wanted to move quickly where you had to impact multiple internal development teams it was so long because it was this big ball, this big monolithic piece. And we got religion about that in trying to move faster in the consumer business and having to tease those pieces apart. And it really was a lot of impetus behind conceiving AWS where it was these low level, very flexible building blocks that6 don't try and make all the decisions for customers they get to make them themselves. And some of the microservices that you saw Werner talking about just, you know, for instance, what we-what we did with Nitro or even what we did with Firecracker those are very much about us relentlessly working to continue to uh, tease apart the different components. And even things that look like low level building blocks over time, you build more and more features and all of the sudden you realize they have a lot of things that are combined together that you wished weren't that slow you down and so, Nitro was a completely re imagining of our Hypervisor and Virtualization layer to allow us, both to let customers have better performance but also to let us move faster and have a better security story for our customers. >> I got to ask you the question around transformation because I think that all points, all the data points, you got all the references, Goldman Sachs on stage at the Keynote, Cerner, I mean healthcare just is an amazing example because I mean, that's demonstrating real value there there's no excuse. I talked to someone who wouldn't be named last night, in and around the area said, the CIA has a cost bar like this a cost-a budget like this but the demand for mission based apps is going up exponentially, so there's need for the Cloud. And so, you see more and more of that. What is your top down, aggressive goals to fill that solution base because you're also a very transformational thinker; what is your-what is your aggressive top down goals for your organization because you're serving a market with trillions of dollars of spend that's shifting, that's on the table. >> Yeah. >> A lot of competition now sees it too, they're going to go after it. But at the end of the day you have customers that have a demand for things, apps. >> Andy: Yeah. >> And not a lot of budget increase at the same time. This is a huge dynamic. >> Yeah. >> John: What's your goals? >> You know I think that at a high level our top down aggressive goals are that we want every single customer who uses our platform to have an outstanding customer experience. And we want that outstanding customer experience in part is that their operational performance and their security are outstanding, but also that it allows them to build, uh, build projects and initiatives that change their customer experience and allow them to be a sustainable successful business over a long period of time. And then, we also really want to be the technology infrastructure platform under all the applications that people build. And we're realistic, we know that you know, the market segments we address with infrastructure, software, hardware, and data center services globally are trillions of dollars in the long term and it won't only be us, but we have that goal of wanting to serve every application and that requires not just the security operational premise but also a lot of functionality and a lot of capability. We have by far the most amount of capability out there and yet I would tell you, we have 3 to 5 years of items on our roadmap that customers want us to add. And that's just what we know today. >> And Andy, underneath the covers you've been going through some transformation. When we talked a couple of years ago, about how serverless is impacting things I've heard that that's actually, in many ways, glue behind the two pizza teams to work between organizations. Talk about how the internal transformations are happening. How that impacts your discussions with customers that are going through that transformation. >> Well, I mean, there's a lot of- a lot of the technology we build comes from things that we're doing ourselves you know? And that we're learning ourselves. It's kind of how we started thinking about microservices, serverless too, we saw the need, you know, we would have we would build all these functions that when some kind of object came into an object store we would spin up, compute, all those tasks would take like, 3 or 4 hundred milliseconds then we'd spin it back down and yet, we'd have to keep a cluster up in multiple availability zones because we needed that fault tolerance and it was- we just said this is wasteful and, that's part of how we came up with Lambda and you know, when we were thinking about Lambda people understandably said, well if we build Lambda and we build this serverless adventure in computing a lot of people were keeping clusters of instances aren't going to use them anymore it's going to lead to less absolute revenue for us. But we, we have learned this lesson over the last 20 years at Amazon which is, if it's something that's good for customers you're much better off cannibalizing yourself and doing the right thing for customers and being part of shaping something. And I think if you look at the history of technology you always build things and people say well, that's going to cannibalize this and people are going to spend less money, what really ends up happening is they spend less money per unit of compute but it allows them to do so much more that they ultimately, long term, end up being more significant customers. >> I mean, you are like beating the drum all the time. Customers, what they say, we encompass the roadmap, I got that you guys have that playbook down, that's been really successful for you. >> Andy: Yeah. >> Two years ago you told me machine learning was really important to you because your customers told you. What's the next traunch of importance for customers? What's on top of mind now, as you, look at- >> Andy: Yeah. >> This re:Invent kind of coming to a close, Replay's tonight, you had conversations, you're a tech athlete, you're running around, doing speeches, talking to customers. What's that next hill from if it's machine learning today- >> There's so much I mean, (weird background noise) >> It's not a soup question (Laughter) And I think we're still in the very early days of machine learning it's not like most companies have mastered it yet even though they're using it much more then they did in the past. But, you know, I think machine learning for sure I think the Edge for sure, I think that um, we're optimistic about Quantum Computing even though I think it'll be a few years before it's really broadly useful. We're very um, enthusiastic about robotics. I think the amount of functions that are going to be done by these- >> Yeah. >> robotic applications are much more expansive than people realize. It doesn't mean humans won't have jobs, they're just going to work on things that are more value added. We're believers in augmented virtual reality, we're big believers in what's going to happen with Voice. And I'm also uh, I think sometimes people get bored you know, I think you're even bored with machine learning already >> Not yet. >> People get bored with the things you've heard about but, I think just what we've done with the Chips you know, in terms of giving people 40% better price performance in the latest generation of X86 processors. It's pretty unbelievable in the difference in what people are going to be able to do. Or just look at big data I mean, big data, we haven't gotten through big data where people have totally solved it. The amount of data that companies want to store, process, analyze, is exponentially larger than it was a few years ago and it will, I think, exponentially increase again in the next few years. You need different tools and services. >> Well I think we're not bored with machine learning we're excited to get started because we have all this data from the video and you guys got SageMaker. >> Andy: Yeah. >> We call it the stairway to machine learning heaven. >> Andy: Yeah. >> You start with the data, move up, knock- >> You guys are very sophisticated with what you do with technology and machine learning and there's so much I mean, we're just kind of, again, in such early innings. And I think that, it was so- before SageMaker, it was so hard for everyday developers and data scientists to build models but the combination of SageMaker and what's happened with thousands of companies standardizing on it the last two years, plus now SageMaker studio, giant leap forward. >> Well, we hope to use the data to transform our experience with our audience. And we're on Amazon Cloud so we really appreciate that. >> Andy: Yeah. >> And appreciate your support- >> Andy: Yeah, of course. >> John: With Amazon and get that machine learning going a little faster for us, that would be better. >> If you have requests I'm interested, yeah. >> So Andy, you talked about that you've got the customers that are builders and the customers that need simplification. Traditionally when you get into the, you know, the heart of the majority of adoption of something you really need to simplify that environment. But when I think about the successful enterprise of the future, they need to be builders. how'l I normally would've said enterprise want to pay for solutions because they don't have the skill set but, if they're going to succeed in this new economy they need to go through that transformation >> Andy: Yeah. >> That you talk to, so, I mean, are we in just a total new era when we look back will this be different than some of these previous waves? >> It's a really good question Stu, and I don't think there's a simple answer to it. I think that a lot of enterprises in some ways, I think wish that they could just skip the low level building blocks and only operate at that higher level abstraction. That's why people were so excited by things like, SageMaker, or CodeGuru, or Kendra, or Contact Lens, these are all services that allow them to just send us data and then run it on our models and get back the answers. But I think one of the big trends that we see with enterprises is that they are taking more and more of their development in house and they are wanting to operate more and more like startups. I think that they admire what companies like AirBnB and Pintrest and Slack and Robinhood and a whole bunch of those companies, Stripe, have done and so when, you know, I think you go through these phases and eras where there are waves of success at different companies and then others want to follow that success and replicate it. And so, we see more and more enterprises saying we need to take back a lot of that development in house. And as they do that, and as they add more developers those developers in most cases like to deal with the building blocks. And they have a lot of ideas on how they can creatively stich them together. >> Yeah, on that point, I want to just quickly ask you on Amazon versus other Clouds because you made a comment to me in our interview about how hard it is to provide a service to other people. And it's hard to have a service that you're using yourself and turn that around and the most quoted line of my story was, the compression algorithm- there's no compression algorithm for experience. Which to me, is the diseconomies of scale for taking shortcuts. >> Andy: Yeah. And so I think this is a really interesting point, just add some color commentary because I think this is a fundamental difference between AWS and others because you guys have a trajectory over the years of serving, at scale, customers wherever they are, whatever they want to do, now you got microservices. >> Yeah. >> John: It's even more complex. That's hard. >> Yeah. >> John: Talk about that. >> I think there are a few elements to that notion of there's no compression algorithm for experience and I think the first thing to know about AWS which is different is, we just come from a different heritage and a different background. We ran a business for a long time that was our sole business that was a consumer retail business that was very low margin. And so, we had to operate at very large scale given how many people were using us but also, we had to run infrastructure services deep in the stack, compute storage and database, and reliable scalable data centers at very low cost and margins. And so, when you look at our business it actually, today, I mean its, its a higher margin business in our retail business, its a lower margin business in software companies but at real scale, it's a high volume, relatively low margin business. And the way that you have to operate to be successful with those businesses and the things you have to think about and that DNA come from the type of operators we have to be in our consumer retail business. And there's nobody else in our space that does that. So, you know, the way that we think about costs, the way we think about innovation in the data center, um, and I also think the way that we operate services and how long we've been operating services as a company its a very different mindset than operating package software. Then you look at when uh, you think about some of the uh, issues in very large scale Cloud, you can't learn some of those lessons until you get to different elbows of the curve and scale. And so what I was telling you is, its really different to run your own platform for your own users where you get to tell them exactly how its going to be done. But that's not the way the real world works. I mean, we have millions of external customers who use us from every imaginable country and location whenever they want, without any warning, for lots of different use cases, and they have lots of design patterns and we don't get to tell them what to do. And so operating a Cloud like that, at a scale that's several times larger than the next few providers combined is a very different endeavor and a very different operating rigor. >> Well you got to keep raising the bar you guys do a great job, really impressed again. Another tsunami of announcements. In fact, you had to spill the beans earlier with Quantum the day before the event. Tight schedule. I got to ask you about the musical festival because, I think this is a very cool innovation. It's the inaugural Intersect conference. >> Yes. >> John: Which is not part of Replay, >> Yes. >> John: Which is the concert tonight. Its a whole new thing, big music act, you're a big music buff, your daughter's an artist. Why did you do this? What's the purpose? What's your goal? >> Yeah, it's an experiment. I think that what's happened is that re:Invent has gotten so big, we have 65 thousand people here, that to do the party, which we do every year, its like a 35-40 thousand person concert now. Which means you have to have a location that has multiple stages and, you know, we thought about it last year and when we were watching it and we said, we're kind of throwing, like, a 4 hour music festival right now. There's multiple stages, and its quite expensive to set up that set for a party and we said well, maybe we don't have to spend all that money for 4 hours and then rip it apart because actually the rent to keep those locations for another two days is much smaller than the cost of actually building multiple stages and so we thought we would try it this year. We're very passionate about music as a business and I think we-I think our customers feel like we've thrown a pretty good music party the last few years and we thought we would try it at a larger scale as an experiment. And if you look at the economics- >> At the headliners real quick. >> The Foo Fighters are headlining on Saturday night, Anderson Paak and the Free Nationals, Brandi Carlile, Shawn Mullins, um, Willy Porter, its a good set. Friday night its Beck and Kacey Musgraves so it's a really great set of um, about thirty artists and we're hopeful that if we can build a great experience that people will want to attend that we can do it at scale and it might be something that both pays for itself and maybe, helps pay for re:Invent too overtime and you know, I think that we're also thinking about it as not just a music concert and festival the reason we named it Intersect is that we want an intersection of music genres and people and ethnicities and age groups and art and technology all there together and this will be the first year we try it, its an experiment and we're really excited about it. >> Well I'm gone, congratulations on all your success and I want to thank you we've been 7 years here at re:Invent we've been documenting the history. You got two sets now, one set upstairs. So appreciate you. >> theCUBE is part of re:Invent, you know, you guys really are apart of the event and we really appreciate your coming here and I know people appreciate the content you create as well. >> And we just launched CUBE365 on Amazon Marketplace built on AWS so thanks for letting us- >> Very cool >> John: Build on the platform. appreciate it. >> Thanks for having me guys, I appreciate it. >> Andy Jassy the CEO of AWS here inside theCUBE, it's our 7th year covering and documenting the thunderous innovation that Amazon's doing they're really doing amazing work building out the new technologies here in the Cloud computing world. I'm John Furrier, Stu Miniman, be right back with more after this short break. (Outro music)

la from Las Vegas it's the cube covering AWS reinvent 2019 brought to you by Amazon Web Services and in care along with its ecosystem partners hey welcome back everyone cubes live coverage of eight of us reinvent 2019 this is the cube seventh year covering Amazon reinvent it's their eighth year of the conference and want to just shout out to Intel for their sponsorship for these two amazing sets without their support we would be able to bring our mission of great content to you I'm John Force to many men we're here with the chief of AWS the chief executive officer Andy chassis tech athlete and himself three our keynotes welcome to the cube again great to see you great to be here thanks for having me guys congratulations on a great show a lot of great buzz thank you a lot of good stuff your keynote was phenomenal you get right into you giddy up right into as you say three hours 30 announcements you guys do a lot but what I liked the new addition in the last year and this year is the band house man yeah they're pretty good they hit the Queen note so that keeps it balanced so we're going to work on getting a band for the cube awesome so if I have to ask you what's your walk-up song what would it be there's so many choices depends what kind of mood I'm in but maybe times like these by the Foo Fighters these are unusual times right now Foo Fighters playing at the Amazon intersect show they are Gandy well congratulations on the intersect you got a lot going on intersect is the music festival I'll get that in a second but I think the big news for me is two things obviously we had a one-on-one exclusive interview and you laid out essentially what looks like was gonna be your keynote it was transformation key for the practice I'm glad to practice use me anytime yeah and I like to appreciate the comments on Jedi on the record that was great but I think the transformation story is a very real one but the NFL news you guys just announced to me was so much fun and relevant you had the Commissioner of NFL on stage with you talking about a strategic partnership that is as top-down aggressive goals you could get yeah I have Roger Goodell fly to a tech conference to sit with you and then bring his team talk about the deal well you know we've been partners with the NFL for a while with the next-gen stats are they using all their telecasts and one of the things I really like about Roger is that he's very curious and very interested in technology in the first couple times I spoke with him he asked me so many questions about ways the NFL might be able to use the cloud and digital transformation to transform their various experiences and he's always said if you have a creative idea or something you think that could change the world for us just call me is it or text me or email me and I'll call you back within 24 hours and so we've spent the better part of the last year talking about a lot of really interesting strategic ways that they can evolve their experience both for fans as well as their players and the player health and safe safety initiative it's so important in sports and particularly important with the NFL given the nature of the sport and they've always had a focus on it but what you can do with computer vision and machine learning algorithms and then building a digital athlete which is really like a digital twin of each athlete so you understand what does it look like when they're healthy what and compare that when it looks like they may not be healthy and be able to simulate all kinds of different combinations of player hits and angles and different plays so that you can try to predict injuries and predict the right equipment you need before there's a problem can be really transformational so it was super excited about it did you guys come up with the idea it was the collaboration between there's really a collaboration I mean they look they are very focused on player's safety and health and it's it's a big deal for their you know they have two main constituents that the players and fans and they care deeply about the players and it's a it's a hard problem in a sport like football but you watch it yeah I gotta say it does point out the use cases of what you guys are promoting heavily at the show here of the stage maker studio which is a big part of your keynote where they have all this data right and they're dated hoarders they've the hoard data but they're the manual process of going through the data it was a killer problem this is consistent with a lot of the enterprises that are out there they have more data than they even know so this seems to be a big part of the strategy how do you get the customers to actually a wake up to the fact that they got data and how do you tie that together I think in almost every company they know they have a lot of data and there are always pockets of people who want to do something with it but when you're gonna make these really big leaps forward these transformations so things like Volkswagen is doing with they're reinventing their factories in their manufacturing process or the NFL where they're gonna radically transform how they do players health and safety it starts top-down and if they if the senior leader isn't convicted about wanting to take that leap forward and trying something different and organizing the data differently and organizing the team differently and using machine learning and getting help from us and building algorithms and building some muscle inside the company it just doesn't happen because it's not in the normal machinery of what most companies do and so it all wait almost always starts top-down sometimes it can be the commissioner or the CEO sometimes it can be the CIO but it has to be senior level conviction or it does get off the ground and the business model impact has to be real for NFL they know concussions hurting their youth pipelining this is a huge issue for them is their business model they they lose even more players to lower extremity injuries and so just the notion of trying to be able to predict injuries and you know the impact it can have on rules the impact it can have on the equipment they use it's a huge game changer when they look at the next 10 to 20 years all right love geeking out on the NFL but no more do you know off camera a 10 man is here defeated season so everybody's a Patriots fan now it's fascinating to watch you and your three-hour keynote Vernor in his you know architectural discussion really showed how AWS is really extending its reach you know it's not just a place for a few years people have been talking about you know cloud as an operation operational model it's not a destination or a location but I felt that really was laid out is you talked about breadth and depth and Verna really talked about you know architectural differentiation people talk about cloud but there are very there are a lot of differences between the vision for where things are going help us understand and why I mean Amazon's vision is still a bit different from what other people talk about where this whole cloud expansion journey but put over what tagger label you want on it but you know the control plane and the technology that you're building and where you see that going well I think that we've talked about this a couple times we we have two macro types of customers we have those that really want to get at the load level building blocks and stitch them together creatively and however they see fit to create whatever is in there in their heads and then we have this second segment of customers who say look I'm willing to give up some of that flexibility in exchange for getting 80% of the way they're much faster in an abstraction that's different from those low level building blocks in both segments of builders we want to serve and serve well and so we built very significant offerings in both areas I think when you look at micro services you know some of it has to do with the fact that we have this very strongly held belief born out of several years at Amazon where you know the first seven or eight years of Amazon's consumer business we basically jumbled together all of the parts of our technology and moving really quickly and when we wanted to move quickly where you had to impact multiple internal development teams it was so long because it was this big ball this big monolithic piece and we got religion about that and trying to move faster in the consumer business and having to tease those pieces apart and it really was a lot of the impetus behind conceiving AWS where it was these low-level very flexible building blocks that don't try and make all the decisions for customers they get to make them themselves and some of the micro services that you saw Verner talking about just you know for instance what we what we did with nitro or even what we do with firecracker those are very much about us relentlessly working to continue to to tease apart the different components and even things that look like low-level building blocks over time you build more and more features and all of a sudden you realize they have a lot of things that are they were combined together that you wished weren't that slowed you down and so nitro was a completely reimagining of our hypervisor and virtualization layer to allow us both to let customers have better performance but also to let us move faster and have a better security story for our customers I got to ask you the question around transformation because I think it all points to that all the data points you got all the references goldman-sachs on stage at the keynote Cerner and the healthcare just an amazing example because I mean this demonstrating real value there there's no excuse I talked to someone who wouldn't be named last night and then around the area said the CIA has a cost bar like this cost up on a budget like this but the demand for mission based apps is going up exponentially so there's need for the cloud and so seeing more and more of that what is your top-down aggressive goals to fill that solution base because you're also very transformational thinker what is your what is your aggressive top-down goals for your organization because you're serving a market with trillions of dollars of span that's shifting that's on the table a lot of competition now sees it too they're gonna go after it but at the end of the day you have customers that have that demand for things apps yeah and not a lot of budget increase at the same time this is a huge dynamic what's your goals you know I think that at a high level are top-down aggressive goals so that we want every single customer who uses our platform to have an outstanding customer experience and we want that outstanding customer experience in part is that their operational performance and their security are outstanding but also that it allows them to build and it build projects and initiatives that change their customer experience and allow them to be a sustainable successful business over a long period of time and then we also really want to be the technology infrastructure platform under all the applications that people build and they were realistic we know that that you know the market segments we address with infrastructure software hardware and data center services globally are trillions of dollars in the long term it won't only be us but we have that goal of wanting to serve every application and that requires not just the security operational performance but also a lot of functionality a lot of capability we have by far the most amount of capability out there and yet I would tell you we have three to five years of items on our roadmap that customers want us to add and that's just what we know today well and any underneath the covers you've been going through some transformation when we talked a couple years ago about how serverless is impacting things I've heard that that's actually in many ways glue behind the two pizza teams to work between organizations talk about how the internal transformations are happening how that impacts your discussions with customers that are going through that transformation well I mean there's a lot of a lot of the technology we build comes from things that we're doing ourselves you know and that we're learning ourselves it's kind of how we started thinking about microservices serverless - we saw the need we know we would have we would build all these functions that when some kind of object came into an object store we would spin up compute all those tasks would take like three or four hundred milliseconds then we spin it back down and yet we'd have to keep a cluster up in multiple availability zones because we needed that fault tolerance and it was we just said this is wasteful and that's part of how we came up with lambda and that you know when we were thinking about lambda people understandably said well if we build lambda and we build the serverless event-driven computing a lot of people who are keeping clusters of instances aren't going to use them anymore it's going to lead to less absolute revenue for us but we we have learned this lesson over the last 20 years at Amazon which is if it's something it's good for customers you're much better off cannibalizing yourself and doing the right thing for customers and being part of shaping something and I think if you look at the history of Technology you always build things and people say well that's gonna cannibalize this and people are gonna spend less money what really ends up happening is they spend spend less money per unit of compute but it allows them to do so much more that the ultimately long-term end up being you know more significant customers I mean you are like beating the drum all the time customers what they say we implement the roadmap I got that you guys have that playbook down that's been really successful for you yeah two years ago you told me machine learning was really important to you because your customers told what's the next tranche of importance for customers what's on top of mine now as you look at this reinvent kind of coming to a close replays tonight you had conversations your your tech a fleet you're running around doing speeches talking to customers what's that next hill from from my fist machine learning today there's so much I mean that's not it's not a soup question you know I think we're still in this in the very early days of machine learning it's not like most companies have mastered yet even though they're using it much more than they did in the past but you know I think machine learning for sure I think the edge for sure I think that we're optimistic about quantum computing even though I think it'll be a few years before it's really broadly useful we're very enthusiastic about robotics I think the amount of functions are going to be done by these robotic applications are much more expansive than people realize it doesn't mean humans won't have jobs they're just going to work on things that are more value-added I thought we're believers in augmented and virtual reality we're big believers and what's going to happen with voice and I'm also I think sometimes people get bored you know I think you're even bored with machine learning maybe already but yet people get bored with the things you've heard about but I think just what we've done with the chips you know in terms of giving people 40% better price performance in the latest generation of x86 processors it's pretty unbelievable and the difference in what people are going to be able to do or just look at big data I mean big date we haven't gotten through big data where people have totally solved it the amount of data that companies want to store process and analyze is exponentially larger than it was a few years ago and it will I think exponentially increase again in the next few years you need different tools the service I think we're not we're not for with machine learning we're excited to get started because we have all this data from the video and you guys got sage maker yeah we call it a stairway to machine learning heaven we start with the data move up what now guys are very sophisticated with what you do with technology and machine learning and there's so much I mean we're just kind of again in this early innings and I think that it was soaked before sage maker was so hard for everyday developers and data scientists to build models but the combination of sage maker and what's happened with thousands of companies standardizing on it the last two years Plus now sage maker studio giant leap forward we hope to use the data to transform our experience with our audience and we're on Amazon Cloud I really appreciate that and appreciate your support if we're with Amazon and Instant get that machine learning going a little faster for us a big that'll be better if you have requests so any I'm you talked about that you've got the customers that are builders and the customers that need simplification traditionally when you get into the you know the heart of the majority of adoption of something you really need to simplify that environment but when I think about the successful enterprise of the future they need to be builders yeah so has the model flipped if you know I normally would said enterprise want to pay for solutions because they don't have the skill set but if they're gonna succeed in this new economy they need to go through that transformation that yeah so I mean are we in just a total new era when we look back will this be different than some of these previous waves it's a it's a really good question Stu and I I don't think there's a simple answer to it I think that a lot of enterprises in some ways I think wish that they could just skip the low level building blocks and and only operate at that higher level abstraction it's why people were so excited by things like sage maker or code guru or Kendra or contact lens these are all services that allow them to just send us data and then run it on our models and get back the answers but I think one of the big trends that we see with enterprises is that they are taking more and more of their development in-house and they are wanting to operate more and more like startups I think that they admire what companies like Airbnb and Pinterest and slack and and you know Robin Hood and a whole bunch of those companies stripe have done and so when you know I think you go through these phases and errors where there are waves of success at different companies and then others want to follow that success and and replicate and so we see more and more enterprises saying we need to take back a lot of that development in-house and as they do that and as they add more developers those developers in most cases like to deal with the building blocks and they have a lot of ideas on how they can create us to creatively stitch them together on that point I want to just quickly ask you on Amazon versus other clouds because you made a comment to me in our interview about how hard it is to provide a service that to other people and it's hard to have a service that you're using yourself and turn that around and the most quoted line in my story was the compression algorithm there's no compression outliving for experience which to me is the diseconomies of scale for taking shortcuts yeah and so I think this is a really interesting point just add some color comments or I think this is a fundamental difference between AWS and others because you guys have a trajectory over the years of serving at scale customers wherever they are whatever they want to do now you got micro services it's even more complex that's hard yeah how about that I think there are a few elements to that notion of there's no compression algorithm I think the first thing to know about AWS which is different is we just come from a different heritage in a different background we sweep ran a business for a long time that was our sole business that was a consumer retail business that was very low margin and so we had to operate a very large scale given how many people were using us but also we had to run infrastructure services deep in the stack compute storage and database in reliable scalable data centers at very low costs and margins and so when you look at our our business it actually today I mean it's it's a higher margin business in our retail business the lower margin business and software companies but at real scale it's a it's a high-volume relatively low margin business and the way that you have to operate to be successful with those businesses and the things you have to think about and that DNA come from the type of operators that we have to be in our consumer retail business and there's nobody else in our space that does that you know the way that we think about cost the way we think about innovation and the data center and and I also think the way that we operate services and how long we've been operating services of the company it's a very different mindset than operating package software then you look at when you think about some of the issues and very large scale cloud you can't learn some of those lessons until you get two different elbows of the curve and scale and so what I was telling you is it's really different to run your own platform for your own users where you get to tell them exactly how it's going to be done but that's nothing really the way the real world works I mean we have millions of external customers who use us from every imaginable country and location whenever they want without any warning for lots of different use cases and they have lots of design patterns and we don't get to tell them what to do and so operating a cloud like that at a scale that's several times larger the next few providers combined is a very different endeavor and a very different operating rigor well you got to keep raising the bar you guys do a great job really impress again another tsunami of announcements in fact you had to spill the beans early with quantum the day before the event tight schedule I gotta ask you about the music festival because I think there's a really cool innovation it's the inaugural intersex conference yeah it's not part of replay which is the concert tonight right it's a whole new thing big music act you're a big music buff your daughter's an artist why did you do this what's the purpose what's your goal yeah it's an experiment I think that what's happened is that reinvent has gotten so big with 65,000 people here that to do the party which we do every year it's like a thirty five forty thousand person concert now which means you have to have a location that has multiple stages and you know we thought about it last year when we were watching it and we said we're kind of throwing like a four hour music festival right now there's multiple stages and it's quite expensive to set up that set for our partying we said well maybe we don't have to spend all that money for four hours in the rip it apart because actually the rent to keep those locations for another two days is much smaller than the cost of actually building multiple stages and so we we would try it this year we're very passionate about music as a business and I think we are I think our customers feel like we throw in a pretty good music party the last few years and we thought we were trying at a larger scale as an experiment and if you look at the economics the headliners real quick the Foo Fighters are headlining on Saturday night Anderson Park and the free Nashville free Nationals Brandi Carlile Shawn Mullins Willie Porter it's a good set Friday night it's back in Kacey Musgraves so it's it's a really great set of about 30 artists and we're hopeful that if we can build a great experience that people want to attend that we can do it it's scale and it might be something that you know both pays for itself and maybe helps pay for reinvent to overtime and you know I think that we're also thinking about it as not just a music concert and festival the reason we named it intersect is that we want an intersection of music genres and people and ethnicities and age groups and art and Technology all there together and this will be the first year we try it it's an experiment and we're really excited about I'm gone congratulations all your success and I want to thank you we've been seven years here at reinvent we've been documenting the history two sets now once-dead upstairs so appreciate a cube is part of reinvent you know you guys really are a part of the event and we really appreciate your coming here and I know people appreciate the content you create as well and we just launched cube 365 on Amazon Marketplace built on AWS so thanks for letting us cool build on the platform appreciate it thanks for having me guys Jesse the CEO of AWS here inside the cube it's our seventh year covering and documenting they're just the thunderous innovation that Amazon is doing they're really doing amazing work building out the new technologies here in the cloud computing world I'm John Force too many men be right back with more after this short break [Music]

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Buress every enterprise has to concern themselves with how they're going to go about ensuring the appropriate access to those crucial applications that run the business this is especially a key question in domains where the applications our seminal feature of the operations how can we set up IT so users see what they should see can access what they can access and that we have control over all about how these systems work and have that conversation we're here with Tony Ferguson an IT infrastructure architect at man energy solutions Tony welcome to the cube yeah thank you so Tony before we get into this crucial question about the appropriate level of visibility and the need for security between people users and applications tell us a little bit about man energy solutions yeah so we're a german-based company I'm working out of Copenhagen but we're part of the Volkswagen Group we have 16 thousand users globally across a hundred locations our company we we make large diesel entrants you also make smaller versions in our own factory and yeah in our company we have a course a lot of my irt on the actual engine and of course we have corporate IT and my job is to secure all of this infrastructure so specifically some of these big diesel engines as I understanding are being placed in locations and use cases that have an absolute requirements for security for example driving a ship is a major feature of the way that your engines are being used within the world so if I got that right yeah yeah that's correct and yeah and then the scale of this you know the number of engines and the number of vessels we need to access and the data we collect it is critical infrastructure we also have power plants so it's really important that we secure this infrastructure so it's a it's a it's a very it's an infrastructure that has very interesting physical characteristics but also has very interesting security characteristics as you went into thinking about how you're going to improve the applicability of the overall infrastructure that you use to drive your business use cases what were some of the issues that you find yourself struggling with yes so yeah a lot of issues actually one of the first things is that we wanted to authenticate the actual engineer and we wanted to make sure that the right people got to the right assets and we wanted to make sure that a thing dication was strong so like the two-factor multi-factor authentication and we wanted to show that the all the data between their engineer and the vessel was encrypted and another big problem for us is scale we need to scale the solution and one of the one of the things as these get brought for us is namespace routing we had the ability to really scale the system without using IP addresses were actually networking so this solved really a lot of problems for us and trying to get those engineers to all of the assets and the IOT on the engine now one of the things that you noted in your as you move forward was this notion of a black cloud where you could formalize the clock the types of relationships you wanted between your engineer users and other users and the Eric the applications you were running on a global scale basis to actually ensure the reliability of the product you had out in the field tell us a little bit about this notion of black cloud yeah so it ties it into a little bit around zero trust but how I see black cloud and how I would describe it is you know everything is dark right so if there's an attacker and he scans port scans of my infrastructure he won't see anything so so basically we would use their tech surface that means that there's no answer back and by doing this we we remove all these vulnerabilities all these zero-day vulnerabilities were remove this and in the same time we stall out that engineer to commit to their assets now how does that work in an environment that is as physically constrained as you know integrating or networking internet working with seagoing vessels yeah so of course a lot of this connectivity is over satellite and of course it's across the internet so it's important that we encrypt into end and it's important that we allow the right engineers to the right customers and we're able to access all these resources and to do Federation and make sure there's strong authentication for our customers we can we really tell them that this all the similar structure is completely secured dark and it's extremely difficult to to come into this black cloud so you've got a challenge the challenge that we've set up here is that you've got a use case that is constrained by the characteristics of the physical infrastructure where the security needs are absolutely paramount and still has to scale and very importantly be evolvable to allow you to be able to provide future classes of services that will further differentiate and improve your business that suggests that these decisions you had to make about the characteristics of the solution was gonna have an enormous impact ultimately on what you could achieve tell us a little bit about the thought process as you went through as you chose a set of sub technology suppliers to help you build out this black cloud and this application set yeah so we looked at a lot of different solutions but a lot of these solutions were based around the old knit work style right around VPNs around having files and around having ACLs and a lot of this is really network centric and what we were looking for is something that was more application centric something that moved up the stack and started to look at policy around what the user would want access to so putting those users and applications together and create meaningful policy based on the DNS rather than on the IP layer and this was really important for us to be able to scale and really make meaningful policy so in many respects it allowed you to not to necessarily de-emphasize but refocus your network design engineering and management efforts from device level assets and perimeter level assets to some of the assets that are really driving new classes of value the applications the users and the data that these engines are streaming and the models that you're using to assure optimal performance of them have I got that right yeah that's exactly right it's extremely important that that we don't have electrical movement you know we look today there's all sorts of were mobile malware attacks ransomware and you know you can imagine if something got into into this cloud that you wouldn't want to let remove so it's not just about the products but it's also about making sure that all these assets are designed from the ground up that that dark as well all right that even on the interns that they can't speak to each other all these very limited connectivity there Tony this has been a fascinating conversation about how you've taken this notion of a black cloud and applied it to a really crucial business case within man energy but I got to believe that this sets you up for a range of other use cases that the investments you've made here are gonna offer new classes of payback in a lot of different use cases how are you going to roll this black cloud concept using Z scalar out to the rest of the organization and the rest of the work that's being performed yeah it's a good question um so when we first looked at this technology we thought it was perfect for consultants because we could have very specific access policies and just allow them to the SS we will be required but then we also saw that there were so many other user cases here for example we are moving our applications from our data center to AWS and to Azura and as we move those applications the users need to connect to this so where would you have this black cloud and have the connectivity to it but we're not opening this to the Internet so you know as far as you're concerned I don't even have any resources or a service in AWS because it's black it's dark so there's a huge amount of security that we can add to this and then there's also a lot of other user cases like company mergers we had to buy a company so we could use this technology to to move to another company together because you don't need to worry about the network anymore you just worried about getting applications to users so I there's a number of great applications for this technology and I really see that this technology will really grow and I'm really excited about it so moving away from a physical orientation of the network to a more logical application and user oriented services or any care orientated a vision of the network has opened up a lot of strategic possibilities what's been the cost impact yes so it what's quite interesting we when you move to the cloud and move to a company like Z scalar is there a software company so forget about all the hardware you can imagine we have a hundred locations globally so we don't have to install all the hardware we don't have to have VPN concentrators we just have to have some software on the client some software the connectors in the cloud and then Z scalar do the magic so for the business they really love this technology because it is very simple it's sitting in the background they don't have to log on to the VPN all the time so it's very seamless for the user and for us we save a lot of money on buying hardware and appliances excellent Tony Ferguson I want to thank you very much for being on the cube Tony Tony Ferguson's the IT infrastructure architect at man energy solutions I'm Peter Burris once again until we have another cube conversation you [Music]

(upbeat music) >> Announcer: From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Hi and welcome to the CUBE studios for another CUBE conversation, where we go in-depth with thought leaders driving innovation across the tech industry, I'm your host, Peter Burris. Every enterprise has to concern themselves with how they're going to go about insuring the appropriate access to those crucial applications that run the business, this is especially a key question in domains where the applications are a seminal feature of the operations. How can we set up IT so users see what they should see, can access what they can access, and that we have control overall about how these systems work. No to have that conversation, we're here with Tony Ferguson, an IT infrastructure architect at MAN Energy solutions, Tony, welcome to theCUBE. >> Yeah, thank you. >> So, Tony, before we get into this crucial question about the appropriate level of visibility and the need for security between people, users, and applications, tell us a little bit about MAN Energy Solutions. >> Yeah, so we're a German-based company. I'm working out of Copenhagen, but we're a part of the Volkswagen group, we have 16,000 users globally across 100 locations. Our company, we make large diesel engines, we also make smaller versions in our German factory. In our company we have of course a lot of IoT on the actual engine, and of course we have corporate IT. My job is to secure all of this infrastructure. >> So, specifically, some of these big diesel engines as I understand it, are being placed in locations and use cases that have an absolute requirement for security. For example, driving a ship is a major feature of the way that your engines are being used within the world, have I got that right? >> Yeah, that's correct, and the scale of this, the number of engines and the number of vessels we need to access and the data we collect. It is critical infrastructure, we also have power plants, so it's really important that we secure this infrastructure. >> So it's an infrastructure that has very interesting physical characteristics but also has very interesting security characteristics. As you went into thinking about how you're going to improve the applicability of the overall infrastructure that you use to drive your business use cases, what were some of the issues that you find yourself struggling with? >> Yeah, a lot of issues actually, one of the first things is that we wanted to authenticate the actual engineer, and we wanted to make sure that right people got to the right assets, and we wanted to make sure that authentication was strong, so like the two-factor, multi-factor authentication. And we wanted to ensure that all the data between the engineer and the vessel was encrypted. And another big problem for us is scale, we need to scale the solution, and one of things that Zscaler brought for us is name-space routing, we had the ability to really scale this system without using IP addresses, or actually networking. So this solved really, a lot of problems for us in trying to get those engineers to all of the assets and IoT on the engine. >> Now one of the things that you noted as you moved forward, was this notion of a black cloud >> Yeah. >> Where you could formalize the types of relationships you wanted between your engineer users and other users, and the applications you were running on a global scalable basis to actually ensure the reliability of the product you had out in the field. Tell us a little bit about this notion of black cloud. >> Yeah, so it ties in to a little bit around zero crust, but how I see black cloud and how I sort of describe it is, everything is dark, right, so if there's an attacker and he scans, bulk scans my infrastructure he won't see anything, so basically we reduce the tech surface. That means that there's no answer back and by doing this, we remove all these vulnerabilities, all these zero day vulnerabilities, we remove this and in the same time we still allow that engineer to connect to the assets. >> Now, how does that work in an environment that is as physically constrained as integrating or inter-networking with sea-going vessels? >> Yeah, so of course a lot of this connectivity is over satellite, and of course it's across the internet, so it is important that we encrypt end to end. And it's important that we allow the right engineers to the right customers and we're able to access all these resources and to do federation and make sure there's strong authentication for our customers. We can really tell them that this, all this infrastructure is completely secured, dark, and it's extremely difficult to come into this black cloud. >> So you've got a challenge, the challenge that we've set up here is that you've got a use case that is constrained by the characteristics of the physical infrastructure, where the security needs are absolutely paramount and still has to scale, and very importantly be evolvable to allow you to be able to provide future classes of services that will further differentiate and improve your business. That suggests that these decisions you had to make about the characteristics of the solution was going to have an enormous impact ultimately on what you could achieve. Tell us little bit about the thought process you went through as you chose a set of technology suppliers to help you build out this black cloud and this application set. >> Yeah, so we looked at a lot of different solutions but a lot of these solutions were based around the old network style, around VPNs, around having firewalls, and around having ACLs. And a lot of this is really network-centric and what we were looking for is something that was more applications centric, something that moved up the stack and started to look at policy around what the user would want access to. So putting those users and applications together and creating meaningful policy based on the DNS, rather than on the IP layer, and this was really important for us, to be able to scale and really make meaningful policy. >> So in many respects, it allowed you to, not to necessarily de-emphasize, but refocus your network design, engineering, and management efforts from device-level assets and pre-liminal level assets-- >> Yes. >> To some of the assets that are really driving new classes of value, the applications of users and the data that these engines are streaming and the models that you're using to assure optimal performance of them, have I got that right? >> Yeah, that's exactly right. It's extremely important that that we don't have lateral movement, we look today, there's all sorts of wormable malware attacks, ransomware, and you can imagine if something got into this cloud that you wouldn't want it to laterally move. So it's not just about the products but it's also about making sure that all these assets are designed from the ground up, that they're dark as well, right. That even on the chance, that they can't speak to each other or there's very limited connectivity there. >> Tony this has been a fascinating conversation about how you've taken this notion of a black cloud and applied it to a really crucial business case within MAN energy, but I got to believe that this sets you up for a range of other use cases, the investments you've made here are going to offer new classes of payback in a lot of different use cases. How are you going to roll this black cloud concept using Zscaler, out to the rest of the organization and the rest of the work that's being performed? >> It's a good question, so when we first looked at this technology, we thought it was perfect for consultants because we could have very specific access policies and just allow them to the assets where we required. But then we also saw that there was so many other user cases here, for example, we are moving our applications from our data center to AWS and to Azure, and as we move those applications the users need to connect to this. So we're able to have this black cloud and have the connectivity to it, but we're not opening this to the internet. So as far as you're concerned, I don't even have any resources or servers in AWS because it's black, it's dark. So there's a huge amount of security that we can add to this, and then there's also a lot of other user cases, like company mergers. We had to buy companies so we could use this technology to merge another company together. Because you don't need to worry about the network anymore, you're just worried about getting applications to users. So I think there's a number of great applications for this technology, and I really see that this technology will really grow and I'm really excited about it. >> So moving away from a physical-orientation of the network to a more logical, application and user oriented, services orientated version of the network has opened up a lot of strategic possibilities. What's been the cost impact? >> Yeah so what's quite interesting, when you move to the cloud and move to a company like Zscaler, they're a software company, so forget about all the hardware. You can imagine we have a hundred locations globally, so we don't have to install all the hardware. We don't have to have VPN concentrators, we just have to have some software on the client, some software connectors in the cloud, then Zscaler do the magic. So for the business, they really love this technology because it is very simple, it's sitting in the background, they don't have to log on to the VPN all the time. So it's very seamless for the user, and for us, we save a lot of money on buying hardware and appliances. >> Excellent, Tony Ferguson, I want to thank you very much for being on theCUBE >> Thank you. >> Tony Ferguson's an IT infrastructure architect at MAN Energy Solutions, I'm Peter Burris, once again, until we have another Cube Conversation. (upbeat music)

>> Announcer: Live, from Boston, Massachusets, it's theCUBE covering Red Hat Summit, 2019. Brought to you by Red Hat. >> Well, welcome back here in Boston. We're at the BCEC as we are starting to wrap up our coverage here of day two of the Red Hat Summit, 2019. Along with Stu Miniman, I'm John Walls, and we're now joined by Ashesh Badani, who is the senior vice president of Cloud Platforms at Red Hat. Been a big day for you, hasn't it Mr. Badani? >> It sure has, thanks for having me back on! >> You bet! All right, so OpenShift 4, we saw the unveiling, your baby gets introduced to the world. What's the reaction been between this morning and this afternoon in terms of people, what they're asking you about, what they're most curious about, and maybe what their best reaction is. >> Yeah, so it's not necessarily a surprise for the folks who have been following OpenShift closely, we put the beta out for a little while, so that's the good news, but let me roll back just a little. >> John: Sure >> I think another part of the news that was really important for us is our announcement of a milestone that we crossed, which is a thousand customers, right? And it was at this very summit and theCUBE definitely knows this well, right, because they've been talking for a while. At this very Summit in 2015, four years ago, that we launched OpenShift Version 3. Right and so, you know you fast forward four years, right, and now the diversity of cases that we see, you know, spanning, established apps, cloud native apps, we heard Exxon talking about AIML data signs that they're putting on the platform, in a variety of different industries, is amazing. And I think the way OpenShift 4 has come along for us, is us having the opportunity to learn what have all these customers been doing well, and what else do we need to do on the platform to make that experience a better one. How do we reimagine enterprise kubernetes, to take it to the next level. And I think that's what we're introducing to the industry. >> Ashesh I think back four years ago, kubernetes was not something that was on the tip of the tongues of most people here. Congratulations on 1,000. >> Thank you. >> I hear what, 100, 150, new customers every quarter is the current rate there, but what I've really enjoyed, talked to a CIO and they're like okay, we're talking about digital transformation, we're talking about how we're modernizing all of our environments, and OpenShift is the platform that we do it. So, talk a little bit, from a customer's standpoint, the speeds, the feeds, the technical pieces, but that outcome, what is it an enabler of for your customers? >> Yeah, so excellent points Stu, we've seen whole sale complete digital transformations underway with our customers. So whether it's Deutsche Bank, who came and talked about running thousands of containers now, moving a whole bunch of workload onto the platform, which is incredible to see. Whether it's a customer like Volkswagen, who talking yesterday, if you caught that, about building an autonomous, self-driving, sets of technologies on the platform. What we're seeing is not just what we thought we would only see in the beginning which is one built, cloud native apps, and digital apps, and so on. Or, more nice existing apps, and bring them on the platform. But also, technologies that are making a fundamental difference, and I'll call one out. So I'm a judge for The Innovation Awards, we do this every year, I have been for many years, I love it, it's one of my favorite parts of the show. This year, we had one entry, which is one of the winners, which is HCA, which is a healthcare provider, talking about how they've been using the OpenShift platform as a means to make a fundamental difference in patients' lives. And when I say fundamental difference, actually saving lives. And you'll hear more about their story, but what they've done, is be able to say, look how can we detect early warning signals, faster than we have been, take some AI technology, and correlate against that, and see how we can reduce sepsis within patients. It's a very personal story for me, my mother died of sepsis. And the fact that they've been able to do this, and I think they're reporting they've already saved dozens of lives based on this. That's when you know, the things that you're doing are making a real difference, making a real transformation, not just in an actual customers' lives, but in users and people around the world. >> You were saying earlier too, Ashesh, about looking at what customers are doing and then trying to improve upon that experience, and give them a more effective experience, whatever the right adjective might be, in terms of what you're doing with 4. If you had to look at it, and say okay, these are the two or three pillars of this where I think we've made the biggest improvement or the biggest change, what would those be? >> Yes, so, one is to look at the world as it is in some sense, which is what a customer's doing. Customers weren't deployed to hybrid cloud, right? They want choice, they want independence with regard to which environments are rented on, whether it's physical, virtual, private, or any public cloud. Customers want one platform, to say I want to run these next generation, cloud native, market service based applications, along with my established stateful applications. Customers want a platform for innovation, right? So for example, we have customers that say, look, I really need a modern platform because I want to recruit the next generation of developers from colleges, if I don't give them the ability to play with Go, or Python, or new databases, they're gonna go to some Silicon Valley company, and I'm going to deplete my pool of talent that I need to compete, right? 'Cause digital transformation is about taking existing companies, and making them digitally enabled. Going forward, what we're also seeing is the ability for us to say well maybe the experience we've given existing customers can be improved. How do we for example, give them a platform, that's more autonomous in nature, more self-driving in nature, that can heal itself, based on for example, there's a critical update that's required that we can send over the air to them. How can we bring greater automation into the platform? It's all of those ideas that we've got based on how customers are using it today, is what we're bringing to bear, going forward. >> Ashesh, one of the errors we have trying to help customers parse through the language is, everybody's talking about platforms, if you look at the public clouds, everybody's all in on kubernetes, a few weeks ago, we were at the Google Cloud event, talked to Red Hat there, there's Anthos, there's OpenShift, look at Azure, we Satya Nadella up on stage, and you're like, okay they've got their own kubernetes platform, but I've got OpenShift fully integrated there. >> Ashesh: Yeah. >> Can you help is kinda understand how those fit together because it's an interesting and changing dynamic. >> Well it's a very Silicon Valley buzzword, right? Everyone wants a platform, everyone wants to build a platform, Facebook's a platform, Uber's a platform, Airbnb is, everything's seeming a platform, right? What I really want to focus on more is in regard to, we want to be able to give folks literally an abstraction level, an ability for companies to say I want to embrace digital transformation. Before we get there, someone's like what's digital transformation, I don't even understand what that means anymore. My simple definition is basically flipping the table. Typically companies spend 80% on maintenance, 20% innovation, how do we flip that? So they're spending 80% innovation, 20% maintenance. So if we're still thinking in those terms, let me give you a way to develop those applications, spend more time and energy on innovation, and then allow for you to take advantage of what I'll call a pool of resources. Compute, network, and storage. Across the environment that you have in place. Some of which you might own, some of which some third parties might provide for you, and some of which you get from public cloud. And take advantage of innovation that's being done outside. Innovative services that come from either public cloud providers, or ISPs, or separate providers, and then be able to do that innovated rapid fashion, you know, develop, deploy, iterate quickly. So to me that is really fundamentally what we're trying to provide customers, and it takes different forms, internal packaging. >> Maybe you can explain to me, the Azure OpenStack seems different than some of the other partnerships. Two years ago, when we were sitting in this building, we talked to you about AWS with OpenShift in that partnership, so what's differentiated and special about the Azure OpenStack integration. >> Yeah, so the Azure partnership, it's a good question because we've now taken our partnering with the public cloud providers to the next level, if you will. With Azure there's a few things in play, first it's a jointly offered managed service from Red Hat and Microsoft, where we're both supporting it together. So in the case of OpenShift and AWS, that's you know OpenShift directly to the ring of service, in this case, it's right out of Microsoft, working close together to make that happen. It's a native service to Azure, so if you saw in the keynote, you could use a command line to call OpenShift directly integrate into the Azure command line. It's available within the interface of Microsoft-Azure. So it feels like a native service, you can take advantages of other Azure services, and bring those to bear, so obviously increases developer experience from that perspective. We also inherit all the compliances, certifications, that Microsoft-Azure has, as well, for that service, as well as all the availability requirements that they put out there, so it's much more closely integrated together, much better developer experience, native to Azure, and then the ability for the Microsoft sales team to go out and sell it to their customers in conjunction. >> You talk a lot about different partnerships, and bringing this collaborative, open-mindset to each and every relationship, how hard is that to do? Because you have your of way of doing things and it's worked very well, and yet, you go out and you have these new partnerships or extensions of partnerships, and not everybody with whom you work does things the same way, and so, everybody's gotta be malleable to a certain extent, but just in terms of being that flexible all the time, what does that do for you? >> So, we take that for granted sometimes, the way we work. And I don't mean to say that to be boastful, or arrogant, in any fashion. I had an interview earlier today, and the reporter said why don't you put on your page, that you're 100% open source? And I said we never put that on our page because that's just how we work, we assume that, we assume everyone knows that about us, and we're going forward. And he says, well, I don't know, perhaps there's others that don't know. And he's right. The world's changing, we're expanding our opportunities in front of folks. In the same way we've only and always known, we used to collaborate with others in the community, before we fully embraced OpenStack, there were certain projects that Red Hat was investing in that were Red Hat driven, and we say maybe there wasn't as much community around it, we're gonna go down and embrace and fully parse an OpenStack community. Same's the case, for example, in kubernetes too. It's not necessarily a project that we created on our own, in conjunction with Google, and many others in the community. And so that's something that's part of our DNA, I'm not sure we're doing anything different, in engaging with communities, just how we work. >> So, Ashesh, I know your team's busy doing a lot of things. We've been hearing about what sessions are overflowing, down in the expo floor, so why don't you give us some visibility. But there was one specific one I wondered if you could start with. >> Ashesh: Sure. >> So down on the expo floor, it's a containerized environment and it has something to do with puppies, and therefor how does that connect with OpenShift 4 if we can start there. >> That's a tough one, you're gonna have to go and ask the puppies how to make a difference in the world. (laughing) >> John: So we go from kubernetes to canines, (laughing) that's what we're doing here. >> I do believe they're comfort dogs, but there was coding and some of the other stuff, so give us a little bit of the walk around, the expo flow, the breakouts and the like, in some of the hot areas, that your team's working on. >> Fair enough, fair enough. Maybe not puppies, but maybe we're trying to herd cats, close enough, right? >> John: Safer terrain. >> The amount of interest, the number of sessions, with OpenShift, or container based technologies, cloud based technologies, it's tremendous to see that. So regardless if whether you see the breakouts that are in place, the customer sessions, I think we've got over 100 customers, I think. Who are presenting on all aspects of their journey. So to me, that's remarkable. Lots of interest in our road map going forward, which is great to see, standing room only for OpenShift 4 and where we're taking that. Other technology that's interesting, the work, for example, we're doing in serverless. We announced an OpenSource collaboration with Mircrosoft, something called KEDA, the Kubernetes eventually. Our scaling project, so interesting how customers can kind of engage around that as well. And then the partner ecosystem, you can walk around and see just a plethora of ISVs, we're all looking to build operators, or have operators and are certifying operators within our ecosystem. And then it's ways for us to expose that to our joint customers. >> We're gonna cut you loose, and let you go, the floor's gonna be open for a few minutes, those puppies are just down behind Stu, we'll let you go check that out. >> Alright, thanks, I hear you can adopt them if you want to, as well. >> Before we let you go see the comfort dogs, 1,000 customers, where do you see, when we come back a year from now, where you are, where you wanna see it go, show us a little bit looking forward. >> So there's been some news around Red Hat that has probably happened over the last few months, the people are hearing this, I look at that as a great opportunity for us to expand our reach into markets, both in terms of industries perhaps we haven't necessarily gone into, that other companies have been. Perhaps we say it's manufacturing, perhaps this is the opportunity for us to cross the chasm, have a lot more trained consultants who can help get more customers on the journey, so I fully expect our reach increasing over a period time. And then you'll see, if you will, iterations of OpenShift 4 and the progress we've made against that, and hopefully many more success stories on the stage. >> Alright, looking forward to catching up next year, if not sooner. >> Ashesh: Okay, excellent. >> John: And congratulations on today, and best of luck down the road. >> Thanks again for having me. >> And good to see you! >> Ashesh: Yeah, likewise! >> Back with more on theCube, you are watching our coverage live, here from Red Hat Summit, 2019, in Boston, Massachusetts. (upbeat techno music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's The Cube. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody, Jeff Frick here with the Cube. We're at Western Digital in Milpitas, California at the Auto Tech Council, Autonomous vehicle meetup, get-together, I'm exactly sure. There's 300 people, they get together every year around a lot of topics. Today is all about autonomous vehicles, and really, this whole ecosystem of startups and large companies trying to solve, as I was just corrected, not the thousands of problems but the millions and billions of problems that are going to have to be solved to really get autonomous vehicles to their ultimate destination, which is, what we're all hoping for, is just going to save a lot of lives, and that's really serious business. We're excited to have the guy that's kind of running the whole thing, Derek Curtain. He's the chairman of the Auto Tech Council. Derek, saw you last year, great to be back, thanks for having us. >> Well, thanks for having me back here to chat. >> So, what's really changed in the last year, kind of contextually, since we were here before? I think last year it was just about, like, mapping for autonomous vehicles. >> Yes. >> Which is an amazing little subset. >> There's been a tremendous amount of change in one year. One thing I can say right off the top that's critically important is, we've had fatalities. And that really shifts the conversation and refocuses everybody on the issue of safety. So, there's real vehicles out there driving real miles and we've had some problems crop up that the industry now has to re-double down in their efforts and really focus on stopping those, and reducing those. What's been really amazing about those fatalities is, everybody in the industry anticipated, 'oh' when somebody dies from these cars, there's going to be the governments, the people, there's going to be a backlash with pitchforks, and they'll throw the breaks on the whole effort. And so we're kind of hoping nobody goes out there and trips up to mess it up for the whole industry because we believe, as a whole, this'll actually bring safety to the market. But a few missteps can create a backlash. What's surprising is, we've had those fatalities, there's absolutely some issues revealed there that are critically important to address. But the backlash hasn't happened, so that's been a very interesting social aspect for the industry to try and digest and say, 'wow, we're pretty lucky.' and 'Why did that happen?' and 'Great!' to a certain extent. >> And, obviously, horrible for the poor people that passed away, but a little bit of a silver lining is that these are giant data collection machines. And so the ability to go back after the fact, to do a postmortem, you know, we've all seen the video of the poor gal going across the street in the dark and they got the data off the one, 101 87. So luckily, you know, we can learn from it, we can see what happened and try to move forward. >> Yeah, it is, obviously, a learning moment, which is absolutely not worth the price we pay. So, essentially, these learning moments have to happen without the human fatalities and the human cost. They have to happen in software and simulations in a variety of ways that don't put people in the public at risk. People outside the vehicle, who haven't even chosen to adopt those risks. So it's a terrible cost and one too high to pay. And that's the sad reality of the whole situation. On the other hand, if you want to say silver lining, well, there is no fatalities in a silver lining but the upside about a fatality in the self-driving world is that in the human world we're used to, when somebody crashes a car they learn a valuable lesson, and maybe the people around them learned a valuable lesson. 'I'm going to be more careful, I'm not going to have that drink.' When an autonomous car gets involved in any kind of an accident, a tremendous number of cars learn the lesson. So it's a fleet learning and that lesson is not just shared among one car, it might be all Teslas or all Ubers. But something this serious and this magnitude, those lessons are shared throughout the industry. And so this extremely terrible event is something that actually will drive an improvement in performance throughout the industry. >> That's a really good, that's a super good point. Because it is not a good thing. But again, it's nice that we can at least see the video, we could call kind of make our judgment, we could see what the real conditions were, and it was a tough situation. What's striking to me, and it came up in one of the other keynotes is, on one hand is this whole trust issue of autonomous vehicles and Uber's a great example. Would you trust an autonomous vehicle? Or will you trust some guy you don't know to drive your daughter to the prom? I mean, it's a really interesting question. But now we're seeing, at least in the Tesla cases that have been highlighted, people are all in. They got a 100% trust. >> A little too much trust. >> They think level five, we're not even close to level five and they're reading or, you know, doing all sorts of interesting things in the car rather than using it as a driver assist technology. >> What you see there is that there's a wide range of customers, a wide range users and some of them are cautious, some of them will avoid the technology completely and some of them will abuse it and be over confident in the technology. In the case of Tesla, they've been able to point out in almost every one of their accidents where their autopilot is involved, they've been able to go through the logs and they've been able to exonerate themselves and say, 'listen, this was customer misbehavior. Not our problem. This was customer misbehavior.' And I'm a big fan, so I go, 'great!' They're right. But the problem is after a certain point, it doesn't matter who's fault it is if your tool can be used in a bad way that causes fatalities to the person in the car and, once again, to people outside the car who are innocent bystanders in this, if your car is a tool in that, you have reconsider the design of that tool and you have to reconsider how you can make this idiot proof or fail safe. And whether you can exonerate yourself by saying, 'the driver was doing something bad, the pedestrian was doing something bad,' is largely irrelevant. People should be able to make mistakes and the systems need to correct those mistakes. >> But, not to make excuses, but it's just ridiculous that people think they're driving a level five car. It's like, oh my goodness! Really. >> Yeah when growing up there was that story or the joke of somebody that had cruise control in the R.V. so they went in the back to fry up some bacon. And it was a running joke when I was a kid but you see now that people with level two autonomous cars are kind of taking that joke a little too far and making it real and we're not ready for that. >> They're not ready. One thing that did strike that is here today that Patty talked about, Patty Rob from Intel, is just with the lane detection and the forward-looking, what's the technical term? >> There's forward-looking radar for braking. >> For braking, the forward-looking radar. And the crazy high positive impact on fatalities just those two technologies are having today. >> Yeah and you see the Insurance Institute for Highway Safety and the entire insurance industry, is willing to lower your rates if you have some of these technologies built into your car because these forward-looking radars and lidars that are able to apply brakes in emergency situations, not only can they completely avoid an accident and save the insurer a lot of money and the driver's life and limb, but even if they don't prevent the accident, if they apply a brake where a human driver might not have or they put the break on one second before you, it could have a tremendous affect on the velocity of the impact and since the energy that's imparted in a collision is a function of the square of the velocity, if you have a small reduction of velocity, you could have a measurable impact on the energy that's delivered in that collision. And so just making it a little slower can really deliver a lot of safety improvements. >> Right, so want to give you a chance to give a little plug in terms of, kind of, what the Auto Tech Council does. 'Cause I think what's great with the automotive industry right, is clearly, you know, is born in the U.S. and in Detroit and obviously Japan and Europe those are big automotive presences. But there's so much innovation here and we're seeing them all set up these kind of innovation centers here in the Bay area, where there's Volkswagen or Ford and the list goes on and on. How is the, kind of, your mission of bringing those two worlds together? Working, what are some of the big hurdles you still have to go over? Any surprises, either positive or negative as this race towards autonomous vehicles seems to be just rolling down the track? >> Yeah, I think, you know, Silicone Valley historically a source of great innovation for technologies. And what's happened is that the technologies that Silicone Valley is famous for inventing, cloud-based technology and network technology, processing, artificial intelligence, which is machine learning, this all Silicone Valley stuff. Not to say that it isn't done anywhere else in the world, but we're really strong in it. And, historically, those may not have been important to a car maker in Detroit. And say, 'well that's great, but we had to worry about our transmission, and make these ratios better. And it's a softer transmission shift is what we're working on right now.' Well that era is still with us but they've layered on this extremely important software-based and technology-based innovation that now is extremely important. The car makers are looking at self-driving technologies, you know, the evolution of aid as technologies as extremely disruptive to their world. They're going to need to adopt like other competitors will. It'll shift the way people buy cars, the number of cars they buy and the way those cars are used. So they don't want to be laggards. No car maker in the world wants to come late to that party. So they want to either be extremely fast followers or be the leaders in this space. So to that they feel like well, 'we need to get a shoulder to shoulder with a lot of these innovation companies. Some of them are pre-existing, so you mentioned Patti Smith from Intel. Okay we want to get side by side with Intel who's based here in Silicone Valley. The ones that are just startups, you know? Outside I see a car right now from a company called Iris, they make driver monitoring software that monitors the state of the driver. This stuff's pretty important if your car is trading off control between the automated system and the driver, you need to know what the driver's state is. So that's startup is here in Silicone Valley, they want to be side by side and interacting with startups like that all the time. So as a result, the car companies, as you said, set up here in Silicone Valley. And we've basically formed a club around them and said, 'listen, that's great! We're going to be a club where the innovators can come and show their stuff and the car makers can come and kind of shop those wares. >> It's such crazy times because the innovation is on so many axis for this thing. Somebody used in the keynote care, or Case. So they're connected, they're autonomous, so the operation of them is changing, the ownership now, they're all shared, that's all changing. And then the propulsion in the motors are all going to electric and hybrid, that's all changing. So all of those factors are kind of flipping at the same time. >> Yeah, we just had a panel today and the subject was the changes in supply chain that Case is essentially going to bring. We said autonomy but electrification is a big part of that as well. And we have these historic supply chains that have been very, you know, everyone's going as far GM now, so GM will have these premier suppliers that give them their parts. Brake stores, motors that drive up and down the windows and stuff, and engine parts and such. And they stick year after year with the same suppliers 'cause they have good relationships and reliability and they meet their standards, their factories are co-located in the right places. But because of this Case notion and these new kinds of cars, new range of suppliers are coming into play. So that's great, we have suppliers for our piston rods, for example. Hey, they built a factory outside Detroit and in Lancing real near where we are. But we don't want piston rods anymore we want electric motors. We need rare earth magnets to put in our electric motors and that's a whole new range of suppliers. That supply either motors or the rare earth magnets or different kind of, you know, a switch that can transmit right amperage from your battery to your motor. So new suppliers but one of the things that panel turned up that was really interesting is, specifically, was, it's not just suppliers in these kind of brick and mortar, or mechanical spaces that car makers usually had. It's increasing the partners and suppliers in the technology space. So cloud, we need a cloud vendor or we got to build the cloud data center ourselves. We need a processing partner to sell us powerful processors. We can't use these small dedicated chips anymore, we need to have a central computer. So you see companies like Invidia and Intel going, 'oh, that's an opportunity for us we're keen to provide.' >> Right, exciting times. It looks like you're in the right place at the right time. >> It is exciting. >> Alright Derek, we got to leave it there. Congratulations, again, on another event and inserting yourself in a very disruptive and opportunistic filled industry. >> Yup, thanks a lot. >> He's Derek, I'm Jeff, you're watching The Cube from Western Digital Auto Tech Council event in Milpitas, California. Thanks for watching and see you next time. (electronic music)

Live from Berlin Germany, it's the queue Covering NetApp insight 2017 brought to you by Neda Welcome back to the cubes live coverage of net app insight here in Berlin Germany I'm your host Rebecca Knight along with my co-host Peter Burris. We are joined by Mark Bregman. He is the CTO of net app Thanks so much for coming on the cube Thanks for taking the time so you have been recently looking into your crystal ball to predict the future and you have some some fun sometimes counterintuitive Predictions about what we're going to be seeing in the next Year and decade to come right so so your first pitch in you said data will become Self-aware right what do you mean by that? Well the title is kind of provocative really the idea is that? Data is going to carry with it much more of its metadata Metadata becomes almost more important than the data in many cases and we can anticipate Sort of architectures in which the data drives the processing whereas today? We always have data is sort of a pile of data over here And then we have a process that we execute against the data that's our been our tradition in the computing world for a long long time as data becomes more self-aware the data as it passes through Will determine what processes get executed on it? So let me give you a simple analogy from a different field from the past in The communications world we used to have circuit switched systems There was some central authority that understood the whole network If you and I wanted to communicate it would figure out the circuit set up the circuit And then we would communicate and that's sort of similar to traditional Processing of data the process knows everything it wants to do it knows where to find the data. It does that it puts It somewhere else But in the communications world we move to packets which data, so now the packet the data Carries with it the information about what should happen to it And I no longer have to know everything about the network nobody has to know everything about the network I pass it to the nearest neighbor who says well I don't know where it's ultimately going, but I know it's going generally in that direction and eventually it gets there now Why is that better? It's very robust it's much more scalable and Particularly in a world where the rules might be changing. I don't have to necessarily redo the program I can change the the markup if you will the tagging of the data You can think of different examples imagine the data That's sitting in a autonomous vehicle and there's an accident now There are many people who want access to that data the insurance company the authorities the manufacturer the data has contained within it the Knowledge of who can do what would that data? So I don't have to now have a separate program that can determine Can I use that data or not the data says sorry you're not allowed to see this. This is private data You can't see this part of it Maybe the identify our data for the obviously the insurance company needs to know who the car owner is But maybe they don't need to know something else like where I came from The authorities might need both well he came from a bar So you can imagine that as an example if you the implications, yes marker are important for example if I Wanted to develop an application. That would be enhanced by having access to data I had to do programming to get to that data because some other application control that data and that data was defined contextually by that application right and so everything was handled by the application by moving the metadata into the data now I can bring that data to my Application more easily less overhead and that's crucial because the value of data accretes It grows as you can combine it in new and interesting ways so by putting the metadata end of the data I can envision a world where it becomes much faster much more Fasil to combine data and new and Exactly it. Also is easier to move the Processing through the data to the data because the processing is no longer a monolithic program It's some large set of micro services and the data organizes which ones to execute So I think we'll see I mean this is not a near-term prediction This is not one for next year because it requires rethinking How we think about data and processing, but I think we'll see it with the emergence of micro services compositional programming Metadata together with the data will see more functional programs little programs well That's your quick rush before we go on to the next one. It's almost like in the early night or the late 1970s It was networks of devices ARPANET the became the Internet and then the web was networks of pages And then we moved into networks of application services Do you foresee a day where it's going to be literally networks of data? Yes, and in fact That's a great example because if you think about what happened in the evolution of the web through what we called web 2.0 That the pages were static data They came alive in the web 2.0, and there was a much less of a distinction between the data and the program In the web layer right so that's what we're saying we see that emerging even further Next prediction was about virtual machines becoming rideshare machines well this is somewhat complementary to the first one they all kind of fit together and Here the idea is you know if we go back in the earlier days of IT it wasn't that long ago that if you needed? Something you ordered the server, and you installed it you owned it and then we got to the model of the public cloud, which is like a rental and by the same analogy if in the past if I wanted a vehicle I had to buy it and Then the rental car agencies came up, and I said well, you know when I go to Berlin I'm not gonna buy a car for three days I'll rent a car, but I can choose which car I want do I want the BMW, or do I want you know of Volkswagen That's very similar to the way the cloud works today. I pick what instances I want and They they meet my needs And if I make the right choice great and by the way I pay for it while I have it not for the work It's getting done so if I forget to return that instance. I'm still getting charged But the rideshare is kind of like uber and we're starting to see that with things like serverless computing In the model that I say I want to get this work done The infrastructure decides what shows up in the same way that when I call uber I don't get to pick what car shows up they send me the one that's most convenient for them and me and I get charged for the work going from point A to point B. Not for the amount of time There's some differentiation if there is so cool Ah, they come to that and and so that's more like a rideshare But as you point out even in the rideshare world. I have some choices. I can't choose if I want a large SUV I might get a BMW SUV or I might get a Mercedes SUV I can't choose that I can't choose it the silver or black But I get a higher class and what we're seeing with the cloud Or these kind of instances virtual solutions is they are also becoming more specialized I might it might be that for a particular workload I want some instance that has have GPUs in them or some neural chip or something else In much the same way that The rental model would say go choose the exact one you want The rideshare model would say I need to get this work done and the infrastructure might decide this is best serviced by five instances with GPU or Because of availability and cost maybe it's 25 instances of standard processors because you don't care about how long it takes so It's this compromise and it's really very analogous to the rideshare model now coming back to the earlier discussion as The units of work gets smaller and smaller and smaller and become really micro services Now I can imagine the data driving that decision hailing the cab hailing the rideshare and driving What needs to be done? So that's why I see them in somewhat complementary and so what's the upshot though? For the employee and for the company I think there are two things one is you got to make the right decision? You know if I were to use uber to commute to Sunnyvale every day It'd break the bank, and it would be kind of stupid so for that particular task I own my vehicle But if I'm gonna go to Tahoe for the weekend, and I meet an SUV I'm not gonna buy one neither am I going to take an uber I'm in a rent one because that's the right vehicle on the other hand when I'm going from you know where I live to the marina within San Francisco, that's a 15 minute drive I On demand I take an uber and I don't really care now if I have 10 friends I might pick a big one or a small one But again that the distinction is there so I think for companies They need to understand the implications and a lot of times as with many people they make the wrong initial choice And then they have then they learn from it so You know there are people who take uber everywhere And I talked and I said I had a friend who was commuting to HP every day by uber from the city from San Francisco That just didn't make sense he kind of knew that but The next one is data will grow faster than the ability to transport it, but that's ok it doesn't sound ok it Doesn't sound ok and for a long time. We've worried about that. We've done compression, and we've done all kinds of things We've built bigger pipes And we've but we were fundamentally transporting data between data centers or more recently between the data center and the cloud big chunks of data What this really talks about is with the emergence of quality IOT in a broad sense? Telematics IOT digital health many different cases there's going to be more and more and more data both generated and ultimately stored at the edge and That will not be able to be shipped all of that will not be able to be shipped back to the core And it's okay not to do that because there's also Processing at the edge so in an autonomous vehicle where you may be generating 20 megabytes per hour or more You're not gonna ship that all back You're gonna store it you're gonna do some local processing you're gonna send the summary of it the appropriate summary back But you're also gonna keep it there for a while because maybe there's an accident and now I do need all that data I didn't ship it back from every vehicle But that one I care about and now I'm gonna bring it back or I'm gonna do some different processing than I originally Thought I would do so again the ability to Manage this is going to be important, but it's managed in a different way. It means we need to figure out ways to do overall Data lifecycle management all the way from the edge where historically that was a silo we didn't care about it Probably all the way through the archive or through the cloud where we're doing machine learning rules generation and so on but it also suggests that we're going to need to do a better job of Discriminating or demarcating different characteristic yen classes of data, and so that data at the edge Real-world data that has real-world implications right now is different from data that summarizes business events which is different from data that Summarized as things models that might be integrated something somewhere else And we have to do a better job of really understanding the relationships between data It's use its asset characteristics etcetera, would you agree with that absolutely and maybe you see the method in my madness now? Which is that data will have? Associated with it the metadata that describes that so that I don't misuse it you know think about The video data off of a vehicle I might want to have a sample of that every I don't know 30 seconds, but now if there's really a problem and it may be not an accident Maybe it's a performance problem. You skidded I'd like to go back and see why was there a Physical issue with the vehicle that I need to think about as an engineering problem was it Your driving ability was it a cat jumped in front of the car so But I need to be able to as you pointed out in a systematic way distinguish what data I'm looking at and where it belongs and where it came from The final prediction it concerns the evolution from Big Data to huge data so that is Really driven by the Increasing need we have to do machine learning AI Very large amounts of data being analyzed in near real time to meet new needs for business And there's again a little like many of these things There's a little bit of a feedback loop so that drives us to new architectures for example being able to do in memory analytics But in-memory analytics with all that important data. I want to have persistence technologies are coming along like Storage class memories that are allowing us to build persistent storage persistent memory We'll have to re our Kotak the applications, but at the same time that persistent memory data I don't want to lose it so it has to be thought of also as a part of the storage system Historically we've had systems the compute system, and there's a pipe and there's a storage system And they're separate they're kind of coming together, and so you're seeing the storage Impinge on the system the compute system our announcement of Plexus store acquisition is how we're getting there But at the same time you see what might have been thought of is the memory of the computer System really be an extended part of the storage system with all the things related to copy management backup and and And so on so that's really what that's talking about and you know it's being driven by another factor I think which is a higher level factor. We started in the first 50 years of the IT industry was all about automating processes That ran the business they didn't change the business. They made it more efficient accounting systems etc since probably 2000 there's been a little bit of a shift Because of the web and mobile to say oh I can use this to change the relationship with my customer Customer in density I can use mobile and and I can change the banking business Maybe you don't ever come to the bank for cash anymore even to an ATM because they've changed that The wave that's starting now which is driving This is the realization in many organizations, and I truly believe eventually in all organizations that They can have new data-driven businesses That are transforming their fundamental view of their business so an example I would use is imagine a shoe maker a shoe manufacturer well for 50 years. They made better shoes They had better distribution, and they could do better inventory management and get better cost and all of that with IT in the last Seven or ten years, they've started to be able to build a relationship with their client. Maybe they put some Sensors in the shoe, and they're doing you know Fitbit like stuff mostly for them That was about a better client relationship, so they could sell better shoes cuz I wrench eiated now The next step is what happens if they wake up and say wait a minute We could take all this data and sell it to the insurance companies or healthcare companies or the city planners Because we now know where everyone's walking all the time That's a completely different business But that requires new kind of lytx that we can't almost not imagine in the current storage model so it drives these new architectures And there is one more prediction, okay? Which is that and it comes back again? It kind of closed the whole cycle as we see these Intelligence coming to the data and new processing forms and so on we also need a way to change data management to give us really Understanding of data through its whole lifecycle one of the one example would be how can I ensure? That I understand the chain of custody of data the example of an automobile there's an accent well How do I know that data was an alter or? how can I know whose touch this data along the way because I might have an audit trail and So we see the emergence of a new Distributed and mutable management framework if when I say those two words together you probably think Blockchain which is the right thing to think but it's not the blockchain. We know today there may be something It's something like that But it will be a distributed and immutable ledger that will give us new ways to access and understand our data Once you open up the once you open up Trying to get the metaphor once you decide to put the metadata next to the data Then you're going to decide to put a lot more control information in that metadata Exactly, so this is just an extension said it kind of closes the loop exactly Mark well, thanks so much for coming on the show and for talking about the future with us It was really fun to have you on the show we should come back in a year and see if maybe you're right exactly exactly Thank you. I'm Rebecca night. We will have more from NetApp insight. Just after this

that are aimed at educating you, When they come to theCUBE, it's a more conversational interview. We have big technology executives, from Michael Dell, for example, who come to theCUBE, share different messages, and reach a much bigger global digital audience than they could in person. Whether you're watching from home, in Europe, in Asia, it doesn't matter where you are, you can access and watch live, and access on-demand content 24 by seven. We come to you wherever you are. We bring to you real, live conversations so you understand how you can make a difference >> Announcer: Live from Nice, France, at your company. >> Technology industry for over 12 years now, so I've had the opportunity of a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin, and I am a host of theCUBE. Being a host on theCUBE has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago, and got the courage up to say, "Hey, I'm really interested in this, "I love talking with customers, "give me a shot, "let me come into the studio and do an interview, "and see if we can work together." I think where I really impact theCUBE is being a female in technology. We interview a lot of females in tech. We do a lot of women in technology events, and one of the things I personally love is understanding what their career path was. They're very inspiring, and it's a great vehicle, theCUBE, to showcase and raise the profile of females in the technology and software space. The benefits for vendors to have theCUBE at events are manyfold. I think the first one is de-mystifying the messaging. You're going to hear great guys and gals as keynotes, prepared speeches. it's theCUBE. Covering .NEXT Conference 2017 Europe. Brought to you by Nutanix. >> Hi, I'm Stu Miniman, and we're here at the Nutanix European Conference .NEXT, and, being in Europe, one of the hot topics of conversation leading up to May, 2018, is, of course, GDPR. So happy to welcome to the program two guests that are here talking about this, Famke Krumbmuller and Nina Vassilief. Thank you so much for joining us today. So, Famke, we'll start with you. You're a partner with OpenCitiz. Tell us a little bit about your background and what your organization does. >> Sure. So OpenCitiz is a consultancy. We work together with companies and businesses helping them to understand the impact of policy and politics on their business, especially European politics and national politics of countries in Europe. We're based in Paris. I'm here to talk about GDPR. >> Alright. And Nina, you're a security and GDPR consultant. Tell us a little bit about your background, also. >> Well, I used to work with Volkswagen Group France as CTO and I'm a CISO, and then I was asked to work basically on GDPR and security as a consultant, because many companies want to be compliant by the due date of May 25th next year. And many companies are having a hard time, so I'm doing business analysis, audits, and creating roadmaps for GDPR. >> Yeah. That's great. Famke, there's been many times when policy and technology come together, but GDPR feels like this growing buzz that's been around the industry. Like, I've heard people, "It's the new Y2K," it's this impending thing, there's a lot of uncertainty, it seems, for something that has a big legal document on it. Give us where people are, how are they feeling, how are you helping people go with it when there is so much uncertainty there. >> First of all, it's very interesting to know that a lot of companies, even in Europe but also outside of Europe, who will be concerned by GDPR, not even aware of the fact that this regulation exists and that they are concerned and they need to comply. And then the other half, roughly, it's unclear whether they will be able to comply by the deadline in May of next year, because it's a huge burden on the majority of the companies. They really have to review all of their processes, maybe do something new, get outside expertise on how to do so, and if they don't, they actually face huge fines from the European Union, which is obviously a way to try and incentivize these companies to do all that hard work to be able to comply. >> Yeah Nina, as if IT organizations didn't already have enough challenges to work with, it's like security is kind of keeping people pretty busy these days. So where does GDPR fit into the discussion? How do they bring it up? Where in the organization does it usually bubble up? And in which teams need to address this? >> Well GDPR actually concerns everyone, so it really concerns the business, but IT has a big role to play in the sense that, for example, many companies don't know what applications they're running. I've seen three companies, and they might be running, let's say they say, "Okay, we're running on the cloud 40 applications," but when they start looking at it with shadow IT, they might be running the triple. So it's actually good, because it's forcing best practice, it's forcing inventories, audits, and it's cutting costs at the same time. >> Yeah, I moderated a customer panel towards the beginning of the week here, and there was one research organization, they're like, "Look, we've anonymized all of our data, "I think we're pretty good," and one that was like, "Well, I'm doing a lot of cloud stuff, you know, "Amazon will take care of this for me," something like this. But if you ask all of them, "Are you ready?" most of them kind of said, "Yeah, we think we're ready." What do you find, Nina? Are most companies really ready? >> I'd say most are not. I find that in the UK, they've understood most companies that they need to be thinking about the big companies. Some of the small companies are just waking up. In the US, they're really thinking about it too, how it's going to touch them, because all services and goods concerning European citizens are concerned. So companies are really, and executives, are waking up. And for France, for example, I'd say about a third of the companies realize it's really going to hit them, and there's many others that are not ready at all. >> Yeah, you mentioned, Famke, that half of all companies barely heard about this, and absolutely, most companies, they are global. Even if you're some local place, you have customers and everything, so what's the step beyond becoming aware? Where do people need to go? >> Right. I mean, there's several things they should be doing when they start to realize that they are actually concerned by this regulation. One of the most important thing is just to educate yourself about it. What do I need to do? Do relevant people within the company know that we need to respond to this, and are they aware that something needs to be done quickly? And then conduct internal information audit, like what data do we hold, why do we hold it, do we really need it? What are our processes? And then maybe appoint a data protection officer, somebody who is on the inside of the company and will have the legal responsibility to inform them about how to be able to comply with GDPR. Things like these, I think, are the most important things to do in the very beginning. >> Nina, I've heard the most from companies that handle data protection, you know, IBM, Veritas, Veeam, all ones that I've heard kind of a strong push from them. A company like Nutanix, do they fit into the picture? Is it something that they're just part of the landscape and they're trying to help and be good citizens to make sure their customers are aware? What from a technology standpoint? >> From my perspective, it concerns every technological company that's running a service concerning European citizens. So of course it concerns Nutanix. And yesterday we had a really great session for executives to explain, and quite a few of them were actually saying, "Hey, I didn't know GDPR really concerns me," and so it's good that Nutanix realized they also need to wake up. >> It does even concern companies who are not based in the EU, but simply by the fact of holding data that concerns EU residents, they need to comply, and that's something which is obviously extremely important, because it affects companies globally, even though they might think, "We're not based in the EU, "we don't have any headquarters in the EU, "we're totally not concerned," well, yes they are, as soon as they hold EU residents' data. >> Yeah, well, the clock's been ticking. It was only towards the beginning of this year that I first heard about GDPR, I've done a number of interviews and talked to many companies. Any chance it's going to get delayed? Or are the lawsuits just going to start as soon as we get to May? >> Well I guess the authorities who will be responsible for overseeing where their companies are compliant, if there is a data breech or something like this happens, I think they will really look at the processes that companies have put into place, and if there is a good amount of goodwill and work has been done, and it's a minor breech to a certain extend, they will probably be lenient in the very beginning, because they know what a burden it is on companies to comply. On the other hand, obviously they also need to set a precedent and show that they're actually serious about enforcing this regulation. So depending on what company they have, if the Amazons and the Facebooks don't comply, that will be a huge problem. If a small business doesn't comply, that's maybe a little bit different. >> Just following up on that, companies have so many different challenges they need to work through. Any, kind of, first steps that they need to make sure that they're doing to kind of meet with what is expected? >> Well I'd say just for them to be compliant to start with is to find out what they're really running on systems and on the cloud, applications to do inventories, to do business assessments to see what risk is involved around it, and I find that most companies that are starting to wake up, the first thing they do is they realize they don't know what they're running. So operations has a lot of work to do, and the security staff. >> Nina the other question I have is, we've spend the last few years, a lot of companies are getting excited about what they can do with information. Is this going to be now a headwind that's going to stop companies and say, "Wait, hold on, "maybe I shouldn't be holding onto everything," or is it just having the right governance in place to make sure I have protections for personal information as opposed to more anonymized information? >> I would think that it's the governance. It will make a big difference in many companies for the governance in IT. It might change the roles of CIOs and operations staff. I don't know, what do you think? >> I think companies will have to re-evaluate what kind of data do they hold and for what purpose, and ultimately, GDPR actually really introduces this principle of you need to have, first of all, consent to hold the data, and then second it needs to be data that you really need for your operations and to deliver the service, or whatever you're delivering. So if there's no good reason for you to hold certain data, then you're actually, strictly speaking, not even allowed to do so. That should probably change a little bit, how companies view the type of data that they hold and for how long. >> And I've even heard, we've talked about some of the global impact, because, even if this is the EU, if it affects people that work there, but other governments are looking and might copy that, if this becomes the template to go forward. >> Right, and we've seen already. I think South Africa, Singapore, have published papers that are relatively similar. And it does make sense. This regulation was negotiated for four years in the EU, so it took some time to agree. And if it's globally applicable, and if it's extremely strict and high standards, it makes sense that it's being copied. >> I want to give you both the final word as to takeaways for this topic. >> Well, I'd say that if anybody's thinking about GDPR, I know there are 99 articles, but in most companies, 30 to 40 really concern the company, not to be scared. You need to start from something, and just to see what really concerns you. >> And I think the most important thing to know is that there are many legal and IT experts out there who really know this topic, which is very technical, extremely well, so it's probably a good idea to get outside consultants look at your processes and how you should go about things. >> Nina and Famke, I think that's part of the reason why Nutanix brought you in as to socialize some of their customers, and even some of their executives with the expertise that you bring. So thank you so much for sharing with our audience. We'll be back with more coverage here, getting towards the end of two days of live coverage from Nutanix .NEXT Conference in Nice, France. I'm Stu Miniman, and you're watching theCUBE.

>> Announcer: It's The Cube covering OpenStack Summit 2017 brought to you by the OpenStack Foundation, Red Hat, an additional ecosystem of support. >> Welcome back, I'm Stu Miniman joined by my cohost John Troyer. Happy to welcome back to the program the two Keynote emcees for the first two days, Jonathan Bryce who's the executive director and Mark Collier who's the COO of OpenStack Foundation. Both of you, thanks so much for joining us. >> Jonathan: Yeah, thanks for having us back. >> It's great to be on The Cube. >> Thank you for the foundation. Without your guys' support, we couldn't do this. It's our fifth year doing the show. I remember the first year, John Furrier went. They were like, "Hey, OpenStack has arrived. "The Cube's there!" And now, it's part of our regular rotation. I know our community loves it. Community, opensource, big part of the show. I wish we had two hours to tease out all the pieces, but Mark, I got to start with you. You just did a live Q&A with Edward Snowden. Somebody joked, they said the quality and the sound was too good. He was sitting in the backroom somewhere. Can you just tell us, how did this come about and how do you make that work? >> Yeah. I mean, pinch me. Is this real life? I keep asking myself 'cause it seems kind of surreal. Just briefly, I got a lot of people that ask, "How did we get connected with him?" It's kind of a funny story, but basically, several years ago when the whole story came out about somebody from the government, from NSA had leaked these documents, but nobody knew who it was. I was on vacation, it was in the summer. I forget what year, I was on vacation with family. We were in the lobby of this hotel where we were on vacation and I've been following the story with some interest. All of a sudden, I see on the TV screen in the lobby of the hotel, "Breaking news, we're about to reveal "the name of the leaker." I look up and I'm watching it and it says, "Here it is. It's Edward Snowden." The first thing I did is I pull up my phone. I immediately look and see if edwardsnowden.com was available, so I registered it thinking, "Well, this might come in handy." Some person just became the most famous person in the world, possibly. It was available, so I'm like, I'm furiously typing on my phone trying to register the domain. I register the domain edwardsnowden.com. No idea what I would actually do with it, just thinking, "If it's there, "this name is about to become really famous," so I registered it. Didn't do much with it, I just put some Twitter feeds on there, just thought, "We'll see what comes of this." A little while later as things developed, he ended up in Russia. I was contacted by some of his team that said, "We're putting together a legal defense fund. "It'd be great if we could host it at edwardsnowden.com. "Could we buy the domain from you?" I was like, "You can have it, I'll donate it. "I just grabbed it 'cause I figured "this might come in handy someday, "just was an impulse." They said, "Great, thank you. "Edward thanks you, we're going to really use "this domain for his legal defense fund webpage," and all that stuff. Overtime, I occasionally would ping them and say, "Look, the domain's free. You've got it. "I want you to have it, it's not my name. "I don't have any need, I don't have any right to this. "You guys use it, but it would be great "if he could come on the Summit thing that we do." This was three or four years ago. They were like, "Oh yeah, he would love to do it "to thank you for donating the domain," but each time we talked, it was always like, the schedule didn't lineup. I've been literally asking him for six or seven Summits. This was the first time the schedules lined up. I didn't tell anybody 'cause I thought, this is never going to happen, this is a pipe dream. I don't want to promise anything. It was only just a few weeks ago that we found out the schedule's lined up, it's on. Got connected from there. He's obviously an opensource-person, has a lot of passion behind that. We thought this is pretty interesting for our audience, so it worked out. >> All right, so Jonathan. Let's reset for a second here (Jonathan laughs) and step back. One of the things we'd love to see is the foundation is self-aware. There's always that balance when you get into, you don't want to read the press or things like that because they don't understand what we're doing or where we're going or things like that. In your opening Keynote and throughout the show, we called it, it's a little bit of a reset. If you think about where people thought OpenStack was and where it was going three years ago, it was like, the Amazon this or the cheaper VMware or how that is, where it is, where it's going, who's leading, who's involved, winning-and-losing type stuff, you guys did a good job of laying that out, so congrats on that. Take us in a little bit, and what message did you guys want to get out this week? >> Yeah, I think that you're right, we are very self-aware. I think that some of that comes from our role. At the foundation, we are not selling a product. We don't have anything to sell off the back of a truck, so to speak. What we actually really care about is moving the state of the community and the technology we produce forward. The thing that's great about that is we can look at the portfolio of technologies that we have. We can look at the things that are in the market and if we see a shift there, it's not like we have a $500 million dollar line of business that, "Uh-oh, we need to keep milking this cash cow "and turn a blind eye to these changes over here." I think over the last couple of years, I talked about a shift in what private clouds can do now and how they're built and operated. We seen that and we've sort of been teasing that out a little bit at previous Summits whether it's demos with Kubernetes or different integrations with Cloud Foundry and other things like that. What we decided this time is coming out of last year, there was a lot of news. What we saw really picking up is there would be these rumors or misperceptions that somebody would put out there, you know? Not based on fact, not based on reality. We were like, "You know what? "We can't just try to subtly hint at what's going on. "Let's just go out there and actually address "the state of things," and I think what you mentioned is actually what's at the root of a lot of these misconceptions as people look at opensource now. Because so much technology gets developed that way, they look at it and they expect it to be like the old world of IT where you need to have Microsoft versus Linux, and you need to have Oracle versus MySQL. Actually, what we see is just the cloud overall is growing so quickly. Public cloud, everybody believes that's growing. What we see is, private clouds are growing. We see that servers, there are more servers this year than there were last year. There are more virtual machines this year than there were last year. Far more containers this year than last year. All of these technologies are growing, so it's not a zero-sum game where in order for OpenStack to succeed, AWS has to lose. I think that we feel that way and we see that, but we realize that this is... We need to just go at it directly. >> Mark, I've heard good feedback from people when, you know, core, where it is, how it's matured. People like the component piece. They'd be able to take some digital pieces which, my understanding, they could do that before, it's becoming highlighted a bit. We talked about some of the opensource days and Cloud Foundry, Kubernetes. The piece where we've heard some people poking holes in is what big tent we discussed last year. Big tent, we poked a hole, is it dead? How do we reposition that? >> Yeah, that's a great question. I think first of all, one of the things that this just this strange stroke of luck that maybe turned out to be bad luck was, one of the few times when a handful of developers went off and organized something, gave it a random name and the name really stuck. It actually was almost too good of a name. You heard Big Tent and everyone's just rolling off the tongue all the time, "Big Tent, Big Tent, Big Tent," so everyone had to have an opinion about it and was like, "This is a huge change." It really wasn't meant to be a huge change. It wasn't even meant to be broadcast that widely to everyone who's just observing OpenStack. That's just kind of what happens, people talk about it. I do think that we are entering a point now when we're thinking about composable, open infrastructure, yes, you need to have different components. You need to be able to pick them, but we're also getting more serious about what things need to exist in OpenStack. I talked about that a little bit this morning. Not every single thing that we've launched needs to continue to be an OpenStack project. Whether you call it the Big Tent or not, or if you give it different names, the reality is we need to adopt and integrate technologies from other communities. Any opensource community out there is potentially developing something really powerful. >> Jonathan: Did you mention the FCB thing this morning in your, I can't remember if-- >> Yeah I mentioned it briefly. A perfect example of this is a lot of OpenStack services have said, "You know, we need to distributed lock management function "in order to evolve as a service. "Where should we go build it? "How're we going to write it?" Then, this culture of, "Well, hold on. "There are a lot of them out there, they're proven. "What about Etcd?" So the forum, which is the first time we've really had a dedicated space at the Summit for both developers and operators to be in the same room, not just next door to each other. They had a discussion yesterday on this and they said, "Yes, we're going to go forward with Etcd." That's an opensource project, very proven, it solves this particular function, it's not developed inside of OpenStack, but who cares? It's opensource. We can work, we can be friends with anybody who builds great opensource software. Let's not reinvent the wheels. I think that does represent a bit of a shift in the philosophy and culture at OpenStack of not trying to just build every single thing from scratch 'cause that's not the best thing for our users or the market. >> I think the ecosystem message and the landscape message came through really clearly. This is my first OpenStack Summit. I was very curious about what is the shape of OpenStack? Where does it fit in? Talking about the upper layers and Kubernetes and the app layers, and now talking about the overall landscape, right. Why rewrite that something like Etcd write. The whole ecosystem has grown up around OpenStack. During the 70's, the whole foundation has been working on it, all the members. One thing that impressed me, we are post-hype cycle. There are real customers here. There are people building their first clouds right now on OpenStack. Could you talk a little bit about just the community in general, the composition of it and the actual real use cases that we're seeing that happen. >> We had some new companies that spoke here for the first time, GE was one. The U.S. Army Cyber School is another one. We had some companies that came back as well. I think that you hit on a key point which is the maturity of the software. A company like GE, especially in their healthcare division, this is a highly regulated company. It's probably the most regulated company out there when you consider the things they do with aviation, nuclear power, healthcare, finance and all these things. They don't take those decisions lightly at all. I think that is an indicator of that maturity. What we see in the makeup of the community is a broader set of industries than ever before. We had strong representation among IT companies early on and continued with that, but now we have industrial companies. We have manufacturing companies like Volkswagen, BMW, you know, a number of car manufacturers, and defense companies. I think that kind of plays into that. I think the other thing that we've seen... When we talk about the OpenStack community and the platform overall, we think of it as an ecosystem that has three main parts. There's the users, which, that's why we exist. We create software for it to be used. There are the developers who are doing that, and then there's the ecosystem of companies who create commercial products and services. I think that's actually just as important. Right now, at the phase that we're at is how that is also reaching maturity. In the earlier days of OpenStack, I think that we had a lot of startups and we had a lot of activity, but the market didn't know how to consume it. It didn't understand what it was. I think that actually scared off some companies and it made a little bit of it more confusing, but as you get a few years into that, some of those companies succeed. Some of them don't succeed, but what you arrive at is a clear understanding of what the market wants, how the products should shape up. You get companies that stop trying to build it all themselves, kind of along with the not-invented-here, and they partner with people who know how to do opensource or they come up with new delivery models. I think that, actually, just as important is the maturing that we've seen in the commercial ecosystem because that leads to sustainable business models for these companies like Red Hat and Rackspace and others that then drive the development, but it also leads to clear adoption choices for users. >> One of the things that I think came out of last year at the Austin Summit was just where OpenStack fits in in this hybrid world. I think about GE, Rackspace, Red Hat, all of those companies clearly span both sides of it. Back to that winning-and-losing discussion we had at the beginning, it was always public cloud versus the private and the infrastructure piece. We know it's a multi-cloud, hybrid cloud world. How do you see that fitting in the conversations? The other piece on that, I see a large number, it was a 74% of deployment according to your latest survey, are not U.S. which is the inverse of we see such. North America's where we have a lot of public cloud adoption so does that fit in? What dynamics may be mixed up with you, Mark? >> A couple things, I would say that what we're finding is a few years ago, it was like, are we going to do cloud? Okay, now it's yes. Then it was, which app it's going to be? It's going to be as many as we can get. Then it was, are we going to do public or private? Well, we picked one. Now it's, okay, yes to everything. It's going to be cloud, we're going to put as many apps as we can. We're going to do public and private, so what happens next? Now, it's a question of where. Where do you place each workload? Some of them belong in the public cloud, some of them don't. Economics plays a big factor, performance, compliance, all the things that he said. The three C's, capabilities. I think that's the next discussion point that's happening inside of these boardrooms with CTOs and IT leaders at the major companies. How do we get a sophisticated strategy for where to place the workload? In terms of the geographic dynamic, I think one of the things Jonathan hit on yesterday is that it's just the nature of opensource that you never know where it's going to go. You just have no clue. Really, any new technology development, the market's going to go somewhere you could've never predicted like, the crystal ball is dead. It's really roadmaps or almost obsolete. It's like, you need to create a structure for how you respond and adopt to change 'cause you know it's coming. What's happened with OpenStack 'cause it's been used in all these new and different ways, and part of that's geographic. It's used to power cell phone networks in all these different countries. It's being used to fit within regulatory requirements in certain countries in data locality, both for performance and other reasons. I think that's why you see it, it's a big world out there. More than 74% of the world doesn't live in the United States, so I think we're closer to the real percentage out there. >> I want to jump in with one thing that you said that I might disagree with slightly. >> Mark: Okay, let's have a debate. On the right... >> Well, you said that these are the conversations that CTOs and CIOs are having is the strategy about how to do it. I think it's a conversation they should be having... >> Mark: Okay, fair point. >> But I think that what we see is, we see a lot of companies-- >> After they hear this, maybe they'll start talking about the right thing. >> I think that we see that, but we're kind of on the front edge of cloud adoption >> That's a good point. >> in the OpenStack community. >> Mark: I concede your point, sir. >> And I think that one of the issues that we see still is that people are thinking about it too simplistically, almost. As Larry Ellison famously said, "The IT industry is the most "fashion-driven industry out there." I think that right now, there are a lot of companies that they still think that there's some shiny object that's going to fix it all for them. Right now, it might be public cloud or containers. They've heard this word and they think that's... Never happened. Never happened in the history of IT ever before. There has never been at technology that came along and fixed the stuff before it. They all get edited. So, yes. We were talking with a CEO just this week, and it was real interesting to hear his perspective because he said that he actually thinks that the pendulum is going to shift back towards private cloud for people who run any significant amount of software. He goes, "I know that is not a popular viewpoint right now, "and if I said that to most other "technology C-level execs, "they would probably disagree with me and go, "No, cloud first, containers," but I think that just the fundamentals behind it, over the next few years, I don't know if it will shift all the way back. It may, who knows? But that's definitely something that I think is going to change from where the current fab might be. >> We'll have to have you back later to talk about how public is now moving to edge. Edge, of course, lives. >> Yeah. Oh, yes. >> Edge is the new data center, is what they have. I do have one final question before we let you go. That whole new shiny stuff? The last couple years, I'd been hearing, everybody's like, "Containers are going to subsume and take over. "DockerCon will be the new thing. "Oh wait, Kubernetes is just "going to dominate and take it over," and we have CubeCon and the CNCF. There's lots of Linux Foundation shows that do partnerships with what you do in Cloud Foundry Summit and on all these other pieces. What do you see as the future for the OpenStack Summit? Does it get pulled? This is being pulled into pieces, but for the show itself, for the foundation, and how it fits with that whole broad ecosystem of opensource. >> Well, the OpenStack Summit has always had some specific purposes. Again, this gets back to the fact that we are an opensource community and a foundation built to support that opensource community. The primary purposes of the OpenStack Summit are basically to strengthen those three pillars that I talked about earlier, especially on the software angle. Mark mentioned that this time around, we are doing what we call the forum. We used to have the Design Summit here, and we actually split that into two parts: one that's very technical and it's really gets down into implementation details. That's split out into a separate event. It happened in February, it's going to happen in September. What we did here is we set up time where developers and operators can get together and talk about strategic issues. Instead of talking about, "How do we fix this issue on line X of file Y?" they're talking about, "What should we use for distributed storage "and lock management? "Should we do Etcd? Should we do Zoo?" They're having more strategic conversation. That is a very critical piece for our community and for the people who run on it. We do a lot of education here. I think that what we've seen is that the OpenStack Summit is becoming more focused around users and the strategic needs of them as we build out the technology versus what it used to be. It originally started as a hacking event for 75 software developers. That's where I think it's going. Just to address the other point, all of the other opensource projects, a lot of them are here and we go to their events because, again, like we've been saying, it's not a zero-sum game. What we care about is that there are open alternatives and that they work well together. One of the things that I think we've seen and we've seen it proven over and over again with OpenStack is that getting communities together in person, those high-bandwidth interactions are actually really critical to getting work done and making things happen. I think they're all valuable and we're going to continue to participate in all of them. >> Yeah, well, Jonathan Bryce, Mark Collier. Really appreciate you joining us. I'm sure we'll see you at many of those other shows that The Cube will be covering throughout the years. Stay tuned with us, we've got lots more covered here at OpenStack Summit 2017 in Boston. Thanks for watching The Cube. (minimal electronic music)

>> Announcer: It's The Cube. Covering HPE Big Data Conference 2016. Now here are your hosts, Dave Vellante and Paul Gillin. >> Welcome back to Boston, everybody, this is The Cube, we're here live at HP's big data conference, hashtag seize the data. Steve Spear is here, he's an author, MIT professor, author of The High Velocity Edge, welcome to The Cube, thanks for coming on. >> Oh, thanks for having me. >> I got to tell you, following Phil Black, you were coming onstage, I have never heard you speak before, I said, "Oh, this poor guy," and you did awesome, you were great, you held the audience, so congratulations, you were very dynamic and he was unbelievable and you were fantastic, so. >> Today was second-worst speaking setup, one time I was on a panel where it was three admirals, a general, and then the other guy wearing a suit, I said, "Well at least another schmo in a suit," and his opening lines were, "You know, this reminds me, "when I was on the space shuttle and we were flying "to the Hubble," and I'm like, "A flipping astronaut, "I got to follow an astronaut?" So anyway, this was only a SEAL, there were a lot of them, there were far fewer astronauts, so that was easy. >> What I really liked about your talk is, first of all, you told the story of Toyota, which I didn't know, you may. >> No, my experience with Toyota was in the early '70s, I remember the Toyota sort of sweeping into the market but you talked about 20 years before it when they were first entering and how this really was a company that had a lot of quality problems and it was perceived as not being very competitive. >> Yeah, Toyota now people look at as almost, they just take for granted the quality, the productivity, they assume good labor relations and that kind of thing, it's non-unionized, not because the unions haven't tried to unionize, but the employees don't feel the need. And again, in the '50s, Toyota was absolutely an abysmal auto-maker, their product was terrible, their productivity was awful and they didn't have particularly good relations with the workforce either. I mean, it's a profound transformation. >> And you gave this test, in the 50s, I forget what it was, it was one-tenth the productivity of the sort of average automobile manufacturer and then they reached parity in '62, by '68 they were 2X, and by '73, they were off the charts. >> Right, right, right. >> Right, so amazing transformation and then you try to figure out how they did it and they couldn't answer, but they said, "We can show you," right? And that sort of led to your research and your book. >> Yeah, so the quick background is in some regards, this fellow Kenneth Bowen, who was my mentor and advisor when I was doing my doctorate, he could argue we were late to the game because people started recognizing Toyota as this paragon of virtue, high quality at low cost, and so that in the 1980s prompted this whole investigation and the term lean manufacturing came out of the realization that on any given day, Toyota and suppliers were making basically twice the product with half the effort and so you had this period of '85 to about '95 where there was this intense attempt to study Toyota, document Toyota, imitate Toyota, General Motors had a joint venture with Toyota, and then you have the mid-'90s and there's no second Toyota, despite all this investment, so we go to the Toyota guys and say, "Look, clearly if everyone is studying you, imitating you, "copying you, and they haven't replicated you, "they've missed something, so what is it?" And they say, "I'm sorry, but we can't tell you." And we said, "Well you got to be kidding, I mean, "you have a joint venture with your biggest competitor, "General Motors," and they said, "No, no, it's not that we wouldn't tell you, "we just actually don't know how to explain what we do "'cause most of us learn it in this very immersive setting, "but if you'd like to learn it, "you can learn it the way we do." I didn't realize at the time that it would be this Karate Kid wax-on, wax-off, paint-up, paint-down experience, which took years and years to learn and there are some funny anecdotes about it but even at the end, their inability to say what it is, so I went years trying to capture what they were doing and realizing I was wrong 'cause different things wouldn't work quite right, and I can tell you, I was on the Shinkansen with the guy who was my Toyota mentor and I finally said, "Mr. Oba, I think I finally "figured it out, it all boils down to these basic "approaches to seeing and solving problems." And he's looking over my cartoons and stuff and he says, "Well, I don't see anything wrong with this." (laughs) >> That was as good as it got. >> That was as good as it got, I was like, "Score, nothing wrong that he can see!" So anyway. >> But so if you talk about productivity, reliability, you made huge gains there, and the speed of product cycles, were the three knobs that Toyota was turning much more significantly than anybody else and then fuel efficiency came. >> Right, so if you start looking at Toyota and I think this is where people first got the attraction and then sort of the dismissive of, we don't make cars, so the initial hook was the affordable reliability, they could deliver a much higher-quality car, much more affordable based on their productivity. And so that's what triggered attention which then manifest itself as this lean manufacturing and its production control tools. What then sort of started to fall off people's radar is that Toyota not only stayed ahead on those dimensions but they added to the dimensionality of the game, so they started introducing new product faster than anybody else and then they introduced new brand more successfully so all the Japanese, Nissan, Honda, Toyota, all came out with a luxury version, but no one came out with Lexus other than Toyota. The Affinity and the Acura, I mean, it's nice cars, but it didn't become this dominant brand like the Lexus. And then in trying to hit the youth market, everyone tried to come up with, like Honda had the Element but nothing like the Scion, so then Toyota's, and that's much further upstream, a much more big an undertaking than just productivity in a factory. And then when it came time to this issue around fuel efficiency, that's a big technology play of trying to figure out how you get these hybridized technologies with a very very complex software engineering overlay to coordinate power flow in this thing and that, and everyone has their version of hybrid, but no one has it through six generations, 21 platforms, and millions of copies sold. So it didn't matter where you were, Toyota figured out how to compete on this value to market with speed and ease which no one else in their industry was replicating. >> You're talking about, this has nothing to do with operational efficiency, when you talk about the Scion for example, you're talking about tapping into a customer, into an emotional connection with your customer and being able to actually anticipate what they will want before they even know, how do you operationalize that? >> So I think, again, Toyota made such an impression on people with operational efficiency that a lot of their genius went unrecognized, so what I was trying to elaborate on this morning is that Toyota's operational efficiency is not the consequence of just more clever design of operations, like you have an algorithm which I lack and so you get to a better answer than I do, it was this very intense almost empathetic approach to improving existing operations, so you're working on something and it's difficult so we're perceptive of that difficulty and try to understand the source of that difficulty and resolve it, and just do that relentlessly about everything all the time, and it's that empathy to understand your difficulty which then becomes the trigger for making things better, so as far as the Scion comes in, what you see is the same notion of empathic design apply to the needs of the youth market. And the youth market unlike the folks who are, let's say at the time, middle-aged, was less about reliable affordability, but these were people who were coming of age during the Bannatyne era where, very fast mass customization or the iPod era, which was common Chassis but very fast, inexpensive personalization and the folks at Toyota said, "You know what, "the youth market, we don't really understand that, "we've been really successful for this older mid-market, "so let's try to understand the problems that the youth "are trying to solve with their acquisitions," and it turned out personalization. And so if you look at the Scion, it wasn't necessarily a technically or technologically sophisticated quote-unquote sexy product, what it did was it leant itself towards very diverse personalization, which was the problem that the youth market was trying to solve. And you actually see, if I can go on this notion of empathic design, so you see this with the Lexus, so I think the conventional wisdom about luxury cars was Uber technology and bling it, throw chrome and leather and wood and when Toyota tried that initially, they took what was I guess now the Avalon, full-sized car, and they blinged it up and it was contradictory 'cause if you're looking for a luxury car, you don't go to a Toyota dealer, and if you go to a Toyota dealer and you see something with chrome and leather and wood veneer, you're like, you have dissonance. So they tried to understand what luxury meant from the American consumer perspective and again, it wasn't, you always wish you'd get this job, but they sent an engineering team to live in Beverly Hills for some months. (laughs) It's like, ooh, twist my arm on that one, right? But what they found was that luxury wasn't just the physical product, it was the respectful service around it, like when you came back to your hotel room, you walked in, people remembered your name or remembered that, oh we noticed that you used a lot of bath towels so we made sure there were extra in your room, that sort of thing, and if you look at the Lexus, and people were dismissive of the Lexus, saying, "It looks like slightly fancier Toyota, "but what's the big deal, it's not a Beamer or Mercedes." But that wasn't the point, it was the experience you got when you went for sales and service, which was, you got treated so nice, and again, not like hoity toity but you got treated respectfully, so anyway, it all comes back to this empathic design around what problem is the customer or someone inside a plan trying to solve. >> So Toyota and Volkswagen trying to vie for top market share but Toyota, as you say, has got this brand and this empathy that Volkswagen doesn't. You must get a lot of questions about Tesla. Thoughts on Tesla. >> Yeah, cool product, cool technology and time will tell if they're actually solving a real problem. And I don't mean to be dismissive, it's just not an area where I've spent a lot of time. >> And we don't really know, I mean, it's amazing and a software-defined automobile and autonomous, very difficult to predict, we're very tight on time. >> All the cool people seem to drive them though. >> Yeah, that's true. Last question I have is, what the heck does this have to do with analytics at a conference like this? >> Right, so you start thinking about the Toyota model, really, it's not that you can sit down and design something right, it's that you design things which you know deep-rooted in your DNA is that what you've designed is wrong, and that in order to get it right and actually much righter than anything else in the marketplace, what you need to do is understand what's wrong about it and so the experience of the user will help inform what's wrong, the worker rounds they do, the inconveniences they experience, the coping, the compensation they do, and that you can not only use that to help inform what's wrong, but then help shape your understanding of how to get to right, and so where all this fits in is that when you start thinking about data, well first of all, these are gigantic systems, right, which it's probably well-informed to think in terms of these systems are being designed by flawed human beings so the systems themselves have flaws, so it's good to be attentive to the flaws that are designed in it so you can fix them and make them more usable by your intended clientele. But the other thing is that these systems can help you gain much greater precision, granularity, frequency of sampling and understanding of where things are misfiring sooner than later, smaller than larger, so you can adjust and adapt and be more agile in shaping the experience. >> Well Steve, great work, thanks very much for coming on The Cube and sharing and great to meet you. >> Yeah likewise, thanks for having me. >> You're welcome. Alright, keep it right there, everybody, Paul and I will be back with our next guest, we're live from Boston, this is The Cube, we'll be right back. (upbeat music)