>>Hello and run. Welcome back to the Cube's live coverage here. Day three of Cube's coverage, two sets, wall to wall coverage. Third set upstairs in the Executive Briefing Center. I'm John Furry, host of the Cube with Dave Alon. Two other hosts here. Lot of action. Dave. The cheer here is the CEO of MongoDB, exclusive post on Silicon Angle for your prior to the event. Thanks for doing that. Great to see >>You. Likewise. Nice to see you >>Coming on. See you David. So it's great to catch up. Prior to the event for that exclusive story on ecosystem, your perspective that resonated with a lot of the people. The traffic on that post and comments have been off the charts. I think we're seeing a ecosystem kind of surge and not change over, but like a an and ISV and new platform. So I really appreciate your perspective as a platform ISV for aws. What's it like? What's this event like? What's your learnings? What's your takeaway from your customers here this year? What's the most important story going on? >>First of all, I think being here is important for us because we have so many customers and partners here. In fact, if you look at the customers that Amazon themselves announced about two thirds of those customers or MongoDB customers. So we have a huge overlap in customers here. So just connecting with customers and partners has been important. Obviously a lot of them are thinking about their plans going to next year. So we're kind of meeting with them to think about what their priorities are and how we can help. And also we're sharing a little bit of our product roadmap in terms of where we're going and helping them think through like how they can best use Mongadi B as they think about their data strategy, you know, going to next year. So it's been a very productive end. We have a lot of people here, a lot of sales people, a lot of product people, and there's tons of customers here. So we can get a lot accomplished in a few days. >>Dave and I always talk on the cube. Well, Dave always goes to the TAM expansion question. Expanding your total stressful market, the market is changing and you guys have a great position growing positioned. How do you look at the total addressable market for Mongo changing? Where's the growth gonna come from? How do you see your role in the market and how does that impact your current business model? >>Yeah, our whole goal is to really enable developers to think about Mongo, to be first when they're building modern applications. So what we've done is first built a fir, a first class transactional platform and now we've kind expanding the platform to do things like search and analytics, right? And so we are really offering a broad set of capabilities. Now our primary focus is the developer and helping developers build these amazing applications and giving them tools to really do so in a very quick way. So if you think about customers like Intuit, customers like Canva, customers like, you know, Verizon, at and t, you know, who are just using us to really transform their business. It's either to build new applications quickly to do things at a certain level of performance of scale they've never done before. And so really enabling them to do so much more in building these next generation applications that they can build anywhere else. >>So I was listening to McDermott, bill McDermott this morning. Yeah. And you listen to Bill, you just wanna buy from the guy, right? He's amazing. But he was basically saying, look, companies like he was talking about ServiceNow that could help organizations digitally transform, et cetera, but make money or save money or in a good position. And I said, right, Mongo's definitely one of those companies. What are those conversations like here? I know you've been meeting with customers, it's a different environment right now. There's a lot of uncertainty. I, I was talking to one of your customers said, yeah, I'm up for renewal. I love Mongo. I'm gonna see if they can stage my payments a little bit. You know, things like that. Are those conversations? Yeah, you know, similar to what >>You having, we clearly customers are getting a little bit more prudent, but we haven't seen any kind of like slow down terms of deal cycles or, or elongated sales cycles. I mean, obviously different customers in different sectors are going through different issues. What we are seeing customers think about is like how can I, you know, either drive more efficiency in my business like and big part of that is modernization of my existing legacy tech stack. How can maybe consolidate to a fewer set of vendors? I think they like our broad platform story. You know, rather than using three or four different databases, they can use MongoDB to do everything. So that that resonates with customers and the fact that they can move fast, right? Developer productivity is a proxy for innovation. And so being able to move fast to either seize new opportunities or respond to new threats is really, you know, top of mind for still C level executive. >>So can your software, you're right, consolidation is the number one way in which people are save money. Can your software be deflationary? I mean, I mean that in a good way. So >>I was just meeting with a customer who was thinking about Mongo for their transactional platform, elastic for the search platform and like a graph database for a special use case. And, and we said you can do all that on MongoDB. And he is like, oh my goodness, I can consolidate everything. Have one elegant developer interface. I can keep all the data in one place. I can easily access that data. And that makes so much more sense than having to basically use a bunch of peace parts. And so that's, that's what we're seeing more and more interest from customers about. >>So one of the things I want to get your reaction to is, I was saying on the cube, now you can disagree with me if you want, but at, in the cloud native world at Cuban and Kubernetes was going through its hype cycle. The conversation went to it's getting boring. And that's good cause they want it to be boring. They don't want people to talk about the run time. They want it to be working. Working is boring. That's invisible. It's good, it's sticky, it's done. As you guys have such a great sticky business model, you got a great install base. Mongo works, people are happy, they like the product. So it's kind of working, I won't wanna say boring cuz that's, it's irrelevant. What's the exciting things that Mongo's bringing on top of the existing base of product that is gonna really get your clients and prospects enthused about the innovation from Mongo? What's what cuz it's, it's almost like electricity in a way. You guys are very utility in, in the way you do, but it's growing. But is there an exciting element coming that you see that they should pay attention to? What's, what's your >>Vision that, right, so if you look back over the last 10, 15 years, there's been big two big platform shifts, mobile and cloud. I think the next big platform shift is from what I call dumb apps to smart apps. So building more intelligence into applications. And what that means is automating human decision making and embedding that into applications. So we believe that to be a fundamentally a developer problem to solve, yes, you need data scientist to build the machine learning algorithms to train the models. Yeah. But ultimately you can't really deploy, deployed at scale unless you give developers the tools to build those smart applications that what we focused on. And a big part of that is what we call application driven analytics where people or can, can embed that intelligence into applications so that they can instead rather having humans involved, they can make decisions faster, drive to businesses more quickly, you know, shorten it's short and time to market, et cetera. >>And so your strategy to implement those smart apps is to keep targeting the developer Yes. And build on that >>Base. Correct. Exactly. So we wanna essentially democratize the ability for any customer to use our tools to build a smart applications where they don't have the resources of a Google or you know, a large tech company. And that's essentially resonating with our customer base. >>We, we were talking about this earlier after Swami's keynote, is most companies struggle to put data at the core of their business. And I don't mean centralizing it all in a single place as data's everywhere, but, but really organizing their company and democratizing data so people can make data decisions. So I think what you're saying, essentially Atlas is the platform that you're gonna inject intelligence into and allow developers to then build applications that are, you know, intelligent, smart with ai, machine intelligence, et cetera. And that's how the ones that don't have the resources of a Google or an Amazon become correct the, that kind of AI company if >>You, and that's, that's the whole purpose of a developer data platform is to enable them to have the tools, you know, to have very sophisticated analytics, to have the ability to do very sophisticated indexes, optimized for analytics, the ability to use data lakes for very efficient storage and retrieval of data to leverage, you know, edge devices to be able to capture and synchronize data. These are all critical elements to build these next generation applications. And you have to do that, but you don't want to stitch together a thousand primitives. You want to have a platform to do that. And that's where we really focus. >>You know, Dave, Dave and I, three, two days, Dave and I, Dave Ante and I have been talking a lot about developer productivity. And one observation that's now validated is that developers are setting the pace for innovation. Correct? And if you look at the how they, the language that they speak, it's not the same language as security departments, right? They speak almost like different languages, developer and security, and then you got data language. But the developers are making choices of self-service. They can accelerate, they're driving the behavior behavior into the organizations. And this is one of the things I wrote about on Friday last week was the organizational changes are changing cuz the developers set the pace. You can't force tooling down their throat. They're gonna go with what's easy, what's workable. If you believe that to be true, then all the security's gonna be in the developer pipeline. All the innovations we've driven off that high velocity developer site, we're seeing success of security being embedded there with the developers. What are you gonna bring up to that developer layer that's going to help with security, help with maybe even new things, >>Right? So, you know, it's, it's almost a cliche to say now software is in the world, right? Because every company's value props is driven by, it's either enabled to find or created through software. What that really means is that developers are eating all the work, right? And you're seeing, you saw in DevOps, right? Where developers basically enro encroach into the ops world and made infrastructure a programmable interface. You see developers, to your point, encroaching in security, embedding more and more security features into their applications. We believe the same thing's gonna happen with data scientists and business analysts where developers are gonna embed that functionality that was done by different domains in the Alex world and embed that capability into apps themselves. So these applications are just naturally smarter. So you don't need someone to look at a dashboard and say, aha, there's some insight here now I need to go make a decision. The application will do that for you and actually make that decision for you so you can move that much more quickly to run your business either more efficiently or to drive more, you know, revenue. >>Well the interesting thing about your business is cuz you know, you got a lot of transactional activity going on and the data, the way I would say what you just described is the data stack and the application stacks are coming together, right? And you're in a really good position, I think to really affect that. You think about we've, we've operationalized so many systems, we really haven't operationalized our data systems. And, and particularly as you guys get more into analytics, it becomes an interesting, you know, roadmap for Mongo and your customers. How do you see that? >>Yeah, so I wanna be clear, we're not trying to be a data warehouse, I get it. We're not trying to be like, you know, go compete. In fact, we have nice partnership with data bricks and so forth. What we are really trying to do is enable developers to instrument and build these applications that embed analytics. Like a good analogy I'd use is like Google Maps. You think about how sophisticated Google Maps has, and I use that because everyone has used Google Maps. Yeah. Like in the old, I was old enough to print out the directions, map quest exactly, put it on my lap and drive and look down. Now have this device that tells me, you know, if there's a traffic, if there's an accident, if there's something you know, going will reroute me automatically. And what that app is doing is embedding real time data into, into its decision making and making the decision for you so that you don't have to think about which road to take. Right? You, you're gonna see that happen across almost every application over the next X number of years where these applications are gonna become so much smarter and make these decisions for you. So you can just move so much more quickly. >>Yeah. Talk about the company, what status of the company, your growth plans. Obviously you're seeing a lot of news and Salesforce co CEO just resigned, layoffs at cnn, layoffs at DoorDash. You know, tech unfortunately is not impacted, thank God. I'm not that too bad. Certainly in cloud's not impacted it is impacting some of the buying behavior. We talked about that. What's going on with the company head count? What's your goals? How's the team doing? What are your priorities? >>Right? So we we're going after a big, big opportunity. You know, we recognize, obviously the market's a little choppy right now, but our long term, we're very bullish on the opportunity. We believe that we can be the modern developer data platform to build these next generation applications in terms of costs. We're obviously being a little bit more judicious about where we're investing, but we see big, big opportunities for us. And so our overall cost base will grow next year. But obviously we also recognize that there's ways to drive more efficiency. We're at a scale now. We're a 1.2 billion business. We're gonna announce our Q3 results next week. So we'll talk a little bit more about, you know, what we're seeing in the business next week. But we, we think we're a business that's growing fast. You know, we grew, you know, over 50, 50% and so, so we're pretty fast growing business. Yeah. You see? >>Yeah, Tuesday, December 6th you guys announce Exactly. Course is a big, we always watch and love it. So, so what I'm hearing is you're not, you're not stepping on the brakes, you're still accelerating growth, but not at all costs. >>Correct. The term we're using is profitable growth. We wanna, you know, you know, drive the business in a way that we think continues to seize the opportunity. But we also, we always exercise discipline. You know, I, I'm old enough where I had to deal with 2000 and 2008, so, you know, seen the movie before, I'm not 28 and have not seen these markets. And so obviously some are, you know, emerging leaders have not seen these kinds of markets before. So we're kind of helping them think about how to continue to be disciplined. And >>I like that reference to two thousand.com bubble and the financial crisis of 2008. I mentioned this to you when we chat, I'd love to get your thoughts. Now looking back for reinvent, Amazon wasn't a force in, in 2008. They weren't really that big debt yet. Know impact agility, wasn't it? They didn't hit that, they didn't hit that cruising altitude of the value pro cloud agility, time of value moving fast. Now they are. So this is the first time that they're a part of the economic equation. You're on, you're on in the middle of it with Amazon. They could be a catalyst to recover faster if plan properly. What's your CEO take on just that general and other CEOs might be watching and saying, Hey, you know, if I play this right, I could leverage the cloud. You know, Adams is leading into the cloud during a recession. Okay, I get that. But specifically there might be a tactic. What's your view on >>That? I mean, what, what we're seeing the, the hyperscalers do is really continue to kind of compete at the raw infrastructure level on storage, on compute, on network performance, on security to provide the, the kind of the building blocks for companies like Monga Beach really build on. So we're leveraging that price performance curve that they're pushing. You know, they obviously talk about Graviton three, they're talking about their training model chip sets and their inference model chip sets and their security chip sets. Which is great for us because we can leverage those capabilities to build upon that. And I think, you know, if you had asked me, you know, in 2008, would we be talking about chip sets in 2022? I'd probably say, oh, we're way beyond that. But what it really speaks to is those things are still so profoundly important. And I think that's where you can see Amazon and Google and Microsoft compete to provide the best underlying infrastructure where companies like mongadi we can build upon and we can help customers leverage that to really build the next generation. >>I'm not saying it's 2008 all over again, but we have data from 2008 that was the first major tailwind for the cloud. Yeah. When the CFO said we're going from CapEx to opex. So we saw that. Now it's a lot different now it's a lot more mature >>I think. I think there's a fine tuning trend going on where people are right sizing, fine tuning, whatever you wanna call it. But a craft is coming. A trade craft of cloud management, cloud optimization, managing the cost structures, tuning, it's a crafting, it's more of a craft. It's kind of seems like we're >>In that era, I call it cost optimization, that people are looking to say like, I know I'm gonna invest but I wanna be rational and more thoughtful about where I invest and why and with whom I invest with. Versus just like, you know, just, you know, everyone getting a 30% increase in their opex budgets every year. I don't think that's gonna happen. And so, and that's where we feel like it's gonna be an opportunity for us. We've kind of hit scap velocity. We've got the developer mind share. We have 37,000 customers of all shapes and sizes across the world. And that customer crown's only growing. So we feel like we're a place where people are gonna say, I wanna standardize among the >>Db. Yeah. And so let's get a great quote in his keynote, he said, if you wanna save money, the place to do it is in the cloud. >>You tighten the belt, which belt you tightening? The marketplace belt, the wire belt. We had a whole session on that. Tighten your belt thing. David Chair, CEO of a billion dollar company, MongoDB, continue to grow and grow and continue to innovate. Thanks for coming on the cube and thanks for participating in our stories. >>Thanks for having me. Great to >>Be here. Thank. Okay, I, Dave ante live on the show floor. We'll be right back with our final interview of the day after this short break, day three coming to close. Stay with us. We'll be right back.

>>Hi, I'm John Ferry with the Cube. We're here for a special exclusive conversation with David Geria, the CEO of Mongo MongoDB. Well established leading platform. It's been around for, I mean, decades. So continues to become the platform of choice for high performance data. This modern data stack that's emerging, a big part of the story here at a reinvent 2022 on top of an already performing a cloud with, you know, chips and silicon specialized instances, the world's gonna be getting faster, smaller, higher performance, lower cost specialized. Dave, thanks for taking the time with me today, >>John. It's great to be here. Thank you for having me. >>Do you see yourself as a ISV or you just go with that, because that's kind of a nomenclature >>When, when I think of the term isv, I think of the notion of someone building an end solution for customer to get something done. Or what we're building is essentially a developer data platform and we have thousands of ISVs who build software applications on our platform. So how could we be an isv? Because by definition I, you know, we enable people to do so many different things and you know, they can be the, you know, the largest companies of the world trying to transform their business or startups who are trying to disrupt either existing industries or create new ones. And so that's, and, and that's how our customers view MongoDB and, and the whole Atlas platform basically enables them to do some amazing things. The reason for that is, you know, you know, we believe that what we are enabling developers to do is be able to reduce the friction and the work required to build modern applications through the document model, which is really intuitive to the way developers think and code through the distributed nature of platforms. >>So, you know, things like charting no other company on the planet offers the capabilities we do to enable people to build the most highly performant and scalable applications. And also what we also do is enable people to, you know, run different types of workloads on our platform. So we have obviously transactional, we have search, we have time series, we enable people to do things like sophisticated device synchronization from Edge to the back end. We do graph, we do real time analytics. So being able to consolidate all that with developers on one elegant unified platform really makes, you know, it attractive for developers to build on long >>Db. You know, you guys are a feature partner of aws and I would speculate, I don't know if you can comment on this, but I would imagine that you probably produce a lot of revenue for Amazon because you really can't turn off EC two when you do a database work. So, you know, you kind of crank it all the time. You guys are a top partner. How long have you guys been a partner with aws? What's the relationship? >>The relationship's been strong, actually, Amazon spoke at one of our first user conferences in 2013. And since then we've been working together. We've been at reinvent since essentially 2015. And we've been a premier partner, an Emerald sponsor for the last Nu you know, I think four or five years. And so we're very committed to the relationship and I think there's some things that we have a lot, we have a lot of things in common. We care a lot about customers and for us, our customers, our developers, we care a lot about removing friction from their day to day work to move, be able to move fast and be able to, in order to seize new opportunities and respond to new threats. And so consequently, I think the partnership, obviously by nature of our, our common objectives has really come together. >>Talk about the journey of Mongo. I mean, you look back at the history, I, you go back the old lamp stack days, right? So you know, the day developer traction is just really kind of stuck at the none. I mean, it's, it's really well known. And I remember over the conversations, Dave Mongo doesn't scale. I mean, every year we heard something along those lines cuz it just kept scaling. I heard the same thing with AWS back in 2013 timeframe. You, oh, it's just, it's really not for a real prime time. It's, it's for hobbyists, not so much builders, maybe startup cloud, but that developer traction is translated. Can you take us through the journey of Mongo where it is now and, and kinda look back and, and, and take us through what's the state of the art now, >>Right? So just for those of you who, who, those, you know, those in your audience who don't know too much about Mon Be I'll just, you know, start with the background. The company was astounded by developers. It was basically the CTO and some key developers from Double Click who really saw the challenges and the limitations of the relational database architecture because they're trying to serve billions of ads per day and they constantly need to work on the constraints and relational database. And so they essentially decided, why don't we just build a database that we'd want to use? And that was a catalyst to starting MongoDB. The first thing they focused on was, rather than having a tabler data structure, they focused on a document data structure. Why documents? Because there's much more natural and intuitive to work with data and documents in terms of you can set parent child relationships and how you just think about the relationship with data is much more natural in a document than trying to connect data in a, you know, in hundreds of different tables. >>And so that enabled developers to just move so much faster. The second thing they focused on was building a truly distributed architecture, not kind of some adjunct, you know, you know, architecture that maybe made the existing architecture a little bit more scalable. They really took from the ground up a truly distributed architecture. So where you can do native replication, you can do charting and you can do it on a global basis. And so that was the, the other profound, you know, thing that they did. And then since then, what we've also done is, you know, the document model is truly a super set of other models. So we enabled other capabilities like search you can do joins, so you can do very transaction intensive use case among be where fully asset compliant. So you have the highest forms of data guarantees you can do very sophisticated things like time series, you can do device synchronization, you can do real time analytics because we can carve off read only nodes to be able to read and query data in real time rather than have to offload that data into a data warehouse. >>And so that enables developers to just build a wide variety of, of application longing to be, and they get one unified developer interface. It's highly elegant and seamless. And so essentially the cost and tax of matching multiple point tools goes away when, when I think of the term isv, I think of the notion of someone building an end solution for a customer to get something done. Or what we're building is essentially a developer data platform and we have thousands of ISVs who build software applications on our platform. So how could we be an isv? Because by definition I, you know, we enable people to do so many different things and you know, they can be the, you know, the largest companies in the world trying to transform their business or startups or trying to disrupt either existing industries or create new ones. And so that's, and and that's how our customers view MongoDB and, and the whole Atlas platform basically enables them to do some amazing things. >>Yeah, we're seeing a lot of activity on the Atlas. Do you see yourself as a ISV or you just go with that because that's kind of a nomenclature? >>No, we don't view ourselves as ISV at all. We view ourselves as a developer data platform. And the reason for that is, you know, you know, we believe that what we are enabling developers to do is be able to reduce the friction and the work required to build modern applications through the document model, which is really intuitive to the way developers think and code through the distributed nature of platforms. So, you know, things like sharding, no other company on the planet offers the capabilities we do to enable people to build the most highly performant and scalable applications. And also what we also do is enable people to, you know, run different types of workflows on our platform. So we have obviously transactional, we have search, we have time series, we enable people to do things like sophisticated device synchronization from Edge to the back end. We do graph, we do real time analytics. So being able to consolidate all that with developers on one elegant unified platform really makes, you know, it attractive for developers to build on long ndb. >>You know, the cloud adoption really is putting a lot of pressure on these systems and you're seeing companies in the ecosystem and AWS stepping up, you guys are doing great job, but we're seeing a lot more acceleration around it, on staying on premise for certain use cases. Yet you got the cloud as well growing for workloads and, and you get this hybrid steady state as an operational mode. I call that 10 of the classic cloud adoption track record. You guys are an example of multiple iterations in cloud. You're doing a lot more, we're starting to see this tipping point with others and customers coming kind of on that same pattern. Building platforms on top of aws on top of the primitives, more horsepower, higher level services, industry specific capabilities with data. I mean this is a new kind of cloud, kind of a next generation, you knows next gen you got the classic high performance infrastructure, it's getting better and better, but now you've got this new application platform, you know, reminds me of the old asp, you know, if you will. I mean, so are you seeing customers doing things differently? Can you share your, your reaction to this role of, you know, this new kind of SaaS platform that just isn't an application, it's, it's more, it's deeper than that. What's going on here? We call it super cloud, but >>Like what? Yeah, so essentially what what, you know, a lot of our customers doing, and by the way we have over 37,000 customers of all shapes and sizes from the largest companies in the world to cutting edge startups who are building applications among B, why do they choose MongoDB? Because essentially it's the, you know, the fastest way to innovate and the reason it's the fastest way to innovate is because they can work with data so much easier than working with data on other types of architecture. So the document model is profoundly a breakthrough way to work with data to make it very, very easy. So customers are essentially building these modern applications, you know, applications built on microservices, event driven architectures, you know, addressing sophisticated use cases like time series to, and then ultimately now they're getting into machine learning. We have a bunch of companies building machine learning applications on top of MongoDB. And the reason they're doing that is because one, they get the benefits of being able to, you know, build and work with, with data so much easier than any other platform. And it's highly scale and performant in a way that no other platform is. So literally they can run their, you know, workloads both locally and one, you know, autonomous zone or they can basically be or available zone or they could be basically, you know, anywhere in the world. And we also offer multicloud capabilities, which I can get into later. >>Let's talk about the performance side. I know I was speaking with some Amazon folks every year it's the same story. They're really working on the physics, they're getting the chips, they wanna squeeze as much energy out of that. I've never met a developer that said they wanna run their workload on a slower platform or slower hardware. We know said no developer, right? No one wants to do that. >>Correct. >>So you guys have a lot of experience tuning in with Graviton instances, we're seeing a lot more AWS EC two instances, we're seeing a lot more kind of integrated end to end stories. Data is now security, it's tied into data stacks or data modern kind of data hybrid stack. A lot going on around the hardware performance specialization, the role of data, kind of a modern data stack emerging. What, what's your thoughts on the that that Yeah, >>I, I think if you had asked me, you know, when the cloud started going vogue, like you know, the, you know, the, the later part of the last decade and told me, you know, sitting here 12, 15 years later, would you know, would we be talking about, you know, chip processing speeds? I'd probably thought, nah, we would've moved on by then. But what's really clear is that customers, to your point, customers care about performance, they care about price performance, right? So AWS's investments in Graviton, we have actually deployed a significant portion of our at fleet on Amazon now runs on Graviton. You know, they've built other chip sets like train and, and inferential for like, you know, training models and running inferences. They're doing things like Nitro. And so what that really speaks to is that the cloud providers are focusing on the price performance of their, as you call it, their primitives and their infrastructure and the infrastructure layer that are still very, very important. >>And, and you know, if you look at their revenue, about 60 to 70% of the revenue comes from that pure infrastructure. So to your point, they can't offer a second class solution and still win. So given that now they're seeing a lot of competition from Azure, Azure's building their own chip sets, Google's already obviously doing that and and building specialized chip sets for machine learning. You're seeing these cloud providers compete. So they have to really compete to make their platform the most performant, the most price competitive in the marketplace. Which gives us a great platform to build on to enable developers to build these incredibly highly performant applications that customers are now demand. >>I think that's a really great point. I mean, you know, it's so funny Dave, because you know, I remember those, we don't talk speeds and feeds anymore. We're not talking about boxes. I mean that's old kind of school thinking because it was a data center mentality, speeds and feeds and that was super important. But we're kind of coming back to that in the cloud now in distributed architecture, as you put your platforms out there for developers, you have to run fast. You gotta, you can't give the developer subpar or any kind of performance that's, they'll, they'll go somewhere else. I mean that's the reality of what developers, no one, again, no one says I wanna go on the slower platform unless it's some sort of policy based on price or some sort of thing. But, but for the most part it's gotta run fast. So you got the tail of two clouds going on here, you got Amazon classic ias, keep making it faster under the hood. >>And then you got the new abstraction layers of the higher level services. That's where you guys are bridging this new, new generational shift where it's like, hey, you know what? I can go, I can run a headless application, I can run a SAS app that's refactored with data. So you've seen a lot more innovation with developers, you know, running stuff in, in the C I C D pipeline that was once it, and you're seeing security and data operations kind of emerging as a structural change of how companies are, are are transforming on the business side. What's your reaction to that business transformation and the role of the developer? >>Right, so I mean I have to obviously give amazing kudos to the, you know, to AWS and the Amazon team for what they've built. Obviously they're the ones who kind of created the cloud industry and they continue to push the innovation in the space. I mean today they have over 300 services and you know, obviously, you know, no star today is building anything not on the cloud because they have so many building blocks to start with. But what we though have found from our talking to our customers is that in some ways there is still, you know, the onus is on the customer to figure out which building block to use to be able to stitch together the applications and solutions they wanna build. And what we have done is taken essentially an opinionated point of view and said we will enable you to do that. >>You know, using one data model. You know, Amazon today offers I think 17 or 18 different types of databases. We don't think like, you know, having a tool for every job makes sense because over time the tax and cost of learning, managing and supporting those different applications just don't make a lot of sense or just become cost prohibitive. And so we think offering one data model, one, you know, elegant user experience, you know, one way to address the broadest set of of use cases is that we think is a better way. But clearly customers have choice. They can use Amazon's primitives and those second layer services as you as you described, or they can use us. Unfortunately we've seen a lot of customers come to us with our approach and so does Amazon. And I have to give obviously again kudos and Amazon is very customer obsessed and so we have a great relationship with them, both technically in terms of the product integrations we do as well as working with 'em in the field, you know, on joint customer opportunities. >>Speaking of, while you mentioned that, I wanna just ask you, how is that marketplace relationship going with aws? Some of the partners are really seeing great economic and joint selling or them selling your, your stuff. So there's a real revenue pop there in that religion. Can you comment on that? >>So we had been working the partner in the marketplace for many years now, more from a field point of view where customers could leverage their existing commitments to AWS and leverage essentially, you know, using Atlas and applying in an atlas towards their commits. There was also some sales incentives for people in the field to basically work together so that, you know, everyone won should we collectively win a customer? What we recently announced is as pay as you Go initiative, where literally a customer on the Amazon marketplace can basically turn up, you know, an Alice instance with no commitment. So it's so easy. So we're just pushing the envelope to just reduce the friction for people to use Atlas on aws. And it's working really very well. The uptake has been been very strong and and we feel like we're just getting started because we're so excited about the results we're >>Seeing. You know, one of the things that's kind of not core in the keynote theme, but I think it's underlying message is clear in the industry, is the developer productivity. You said making things easy is a big deal, self-service, getting in and trying, these are what developer friendly tools are like and platform. So I have to ask you, cuz this comes up a lot in our kind of business conversation, is, is if you take digital transformation concept to its completion, assuming now you know, as a thought exercise, you completely transform a company with technology that's, that is the business transformation outcome. Take it to completion. What does that look like? I mean, if you go there you'd say, okay, the company is the app, the company is the data, it's not a department serving the business, it's the business. And so I think this is kind of what we're seeing as the next big mountain climb, which is companies that do transform there, they are technology companies, they're not a department like it. So I think a lot of companies are kind of saying, wait a minute, why would we have a department? It should be the company. What's your your your view on this because this >>Yeah, so I I've had the for good fortune of being able to talk to thousand customers all over the world. And you know, one thing John, they never tell me, they never tell me that they're innovating too quickly. In fact, they always tell me the reverse. They tell me all the obstacles and impediments they have to be able to be able to be able to move fast. So one of the reasons they gravitate to MongoDB is just the speed that they wish they can build applications to, to your point, developer productivity. And by definition, developer productivity is a proxy for innovation. The faster you can make your developers, you know, move, the faster they can push out code, the faster they can iterate and build new solutions or add more capabilities on the existing applications, the faster you can innovate either to, again, seize new opportunities or to respond to new threats in your business. >>And so that resonates with every C level executive. And to your point, the developers not some side hustle that they kind of think about once in a while. It's core to the business. So developers have amassed enormous amount of power and influence. You know, their, their, their engineering teams are front and center in terms of how they think about building capabilities and and building their business. And that's also obviously enabled, you know, to your point, every software company, every company's not becoming a software company because it all starts with softwares, software enables, defines or creates almost every company's value proposition. >>You know, it makes me smile because I love operating systems as one of my hobbies in college was, you know, systems programming and I remember those network kind of like the operating systems, the cloud. So, you know, everything's got specialized capabilities and that's a big theme here at Reinvent. If you look at the announcements Monday night with Peter DeSantis, you got, you got new instances, new chips. So this whole engine kind of specialized component is like an engine. You got a core and you got other subsystems. This is gonna be an integral part of how companies architect their platform or you know, Adam calls it the landing zone or whatever they wanna call it. But you gotta start seeing a new architectural thinking for companies. What's your, can you share your experience on how companies should look at this opportunity as a plethora of more goodness on the hardware? On hardware, but like chips and instances? Cause now you can mix and match. You've got, you've got, you got everything you need to kind of not roll your own but like really build foundational high performance capabilities. >>Yeah, so I I, so I think this is where I think Amazon is really enabling all companies, including, you know, companies like Mon db, you know, push the envelope and innovation. So for example, you know, the, the next big hurdle for us, I think we've seen two big platform shifts over the last 15 years of platform shifts, you know, to mobile and the platform shift to cloud. I believe the next big platform shift is going from dumb apps to smart apps, which you're building in, you know, machine learning and you know, AI and just very sophisticated automation. And when you start automating human decision making, rather than, you know, looking at a dashboard and saying, okay, I see the data now, now I have to do this. You can automate that into your applications and make your applications leveraging real time data become that much more smart. And that ultimately then becomes a developer challenge. And so we feel really good about our position in taking advantage of those next big trends and software leveraging the price performance curves that, you know, Amazon continues to push in terms of their hardware performance, networking performance, you know, you know, price, performance and storage to build those next generation of modern applications. >>Okay, so let me get this straight. You have next generation intelligent smart apps and you have AI generative solutions coming out around the corner. This is like pretty good position for Mongo to be in with data. I mean, this is what you do, you're in that exactly of the action. What's it like? I mean, you must be like trying to shake the world and wake up. The world's starting to wake up now through this. So what's, what's it like? >>Well, I mean we're really excited and bullish about the future. We think that we're well positioned because we know as to your point, you know, we have amassed amazing amount of developer mindshare. We are the most popular modern data platform out there in the world. There's developers in almost every corner of the planet using us to do something. And to your point, leveraging data and these advances in machine learning ai. And we think the more AI becomes democratized, not, you know, done by a bunch of data scientists sitting in some corner office, but essentially enabling developers to have the tools to build these very, very sophisticated, smart applications will, you know, will position as well. So that's, you know, obviously gonna be a focus for us over the, frankly, I think this is gonna be like a 10 year, 10 15 year run and we're just getting started in this whole >>Area. I think you guys are really well positioned. I think that's a great point. And Adam mentioned to me and, and Mike interviewed, he said on stage talk about it, the role of a data analyst kind of goes away. Everyone's a data analyst, right? You'll still see specialization on, on core data engineering, which is kind of like an SRE role for data. So data ops and data as code is a big deal making data applications. So again, exciting times and you guys are well positioned. If you had to bumper sticker the event this week here at Reinvent, what would you, how would you categorize this this point in time? I mean, Adam's great leader, he is gonna help educate customers how to use technology to, for business advantage and transformation. You know, Andy did a great job making technology great and innovative and setting the table, Adam's gotta bring it to the enterprises and businesses. So it's gonna be an interesting point in time we're in now. What, how would you categorize this year's reinvent, >>Right? I think the, the, the tech world is pivoting towards what I'd call rationalization or cost optimization. I think people obviously in, you know, the last 10 years have, you know, it's all about speed, speed, speed. And I think people still value speed, but they wanna do it at some sort of predictable cost model. And I think you're gonna see a lot more focus around cost and cost optimization. That's where we think having one platform is by definition of vendor consolidation way for people to cut costs so that they can basically, you know, still move fast but don't have to incur the tax of using a whole bunch of different point tools. And so we think we're well positioned. So the bumper sticker I think about is essentially, you know, do more for less with MongoDB. >>Yeah. And the developers on the front lines. Great stuff. You guys are great partner, a top partner at AWS and great reflection on, on where you guys been, but really where you are now and great opportunity. David Didier, thank you so much for spending the time and it's been great following Mongo and the continued rise of, of developers of the on the front lines really driving the business and that, and they are, I know, driving the business, so, and I think they're gonna continue Smart apps, intelligent apps, ai, generative apps are coming. I mean this is real. >>Thanks John. It's great speaking with >>You. Yeah, thanks. Thanks so much. Okay.

>> Welcome back to New York City everybody. This is The Cube's coverage of MongoDB World 2022, Dev Ittycheria, here is the president and CEO of MongoDB. Thanks for spending some time with us. >> It's Great to be here Dave, thanks for having me. >> You're very welcome. So your keynotes this morning, I was hearkening back to Steve Ballmer, running around the stage screaming, developers, developers, developers. You weren't jumping around like a madman, but the message was the same. And you've not deviated from that message. I remember when it was 10th Gen, so you've been consistent. >> Yes. >> Why is Mongo DB so alluring to developers? >> Yeah, because I would say the reason we're so popular Dave is that our whole business was founded on the ethos, so making developers incredibly productive. Just getting the infrastructure out of the way so that the developers is really focused on what's important and that's building great applications that transform their business. And the way you do that is you look at where they spend most of the time. and they spend most of the time working with data. How do you present data, the right data, the right time, at the right place, and the right way. And when you remove the friction of working with data, you unleash so much more productivity, which people just say, oh my goodness, I can move so much faster. Product leaders can get products out the door faster than the competitors. Senior level executives can seize new opportunities or respond to new threats. And that was so profound during COVID when everyone had to think about pivoting their business. >> When you came to MongoDB, why did you choose this company? What was it that excited you about it? >> I get that question a lot. I would say conventional wisdom would suggest that MongoDB was not a great choice. There weren't that many companies who were very successful in open source, Red Hat was the only one. No one had really built a deep technology company in New York city. They say, you got to do it in the valley. And database companies need a lot of capital. Now turns out that raising capital of this past decade was a lot easier, but it still takes a lot of time, and a lot of capitals, you have to have a lot of patience. When I did my diligence, I was actually a VC before I joined MongoDB. The whole next generation database segment was really taking off. And actually I looked at some competing investments to MongoDB, and when I did my diligence, it was clear even then. And this is circa 2012, that MongoDB is way ahead in terms of customer attraction, commercials, and even kind of developer mind share. And so I ended up passing those investments. and then lo and behold, I got a call from a very senior executive recruiter who said, Dev, you got to take a meeting with MongoDB, there's something really interesting going on. And they had raised a lot of capital and they had just not been able to kind of really execute in terms of the opportunity. And they realized they needed to make a change. And so one thing led to another. One of the things that really actually convinced me, is when I did my diligence, I realized the customers they had loved MongoDB. They just really weren't executing on all cylinders. And I always believe you never bet against a company whose customers love the product. And said, that's something here. The second thing I would say is open source. Yes, is true that open source was not very successful, but that was open source 1.0. Open source 2.0, the technology is much better than the commercial options. And so that convinced me. And then New York, I lived in New York a big part of my life. I think New York's a fabulous place to build a business. There's so much talent, your customers are right... You walk out the door, there's customers all over the place. And getting to Europe is very easy, Almost like flying to the west coast. So it's a very central place to build a business. >> And it's easier to fix execution, wouldn't you say? And maybe even go to market than it is to fix a product that customers really don't love. >> Correct, it's much easier to fix leadership issues, culture issues, execution issues. Nailing product market fit is very, very hard. And there were signs, there's still some issues, there's still some rough spots, but there a lot of signs that this company was very, very close, and that's why I took the bet. >> And this is before there was that huge influx of capital into the separating compute from storage and the whole cloud thing, which is interesting. Because you take a company like Cloudera, they got caught up in that and got kind of washed over. And I guess you could argue Hortonworks did too, and they could have dead ended both. And then that just didn't work. But it's interesting to see Mongo, the market kind of came to you. And that really does speak to the product. It wasn't a barrier for you. You guys have obviously a lot of work to get into the cloud with Atlas, but it seemed like a natural fit with the product. It wasn't like a complete fork. >> Well, I think the challenge that we had was we had a lot of adoption, but we had tough time commercializing the business. And at some point I had to tell the all employees, it's great that we have all these people who are using MongoDB, but if you don't start generating revenue, our investors are going to get tired of subsidizing this company. So I had to try and change the culture. And as you imagine, the engineers didn't really like the salespeople, the salespeople thought the engineers didn't really want to make any money. And what I said, like, let's all galvanize around customers and let's make them really excited and try and create a lot of value. And so we just put a lot more discipline in terms of how we prosecuted deals. We put a lot more discipline in terms of what are the problems we're trying to solve. And one thing led to another, we started building the business brick by brick. And one of the things that became clear for me was that the old open source model of trying to find that happy medium between what you give away and what you charge for, is always a tough game. Like because finding that where the paywall is, if you give away too much new features, you don't make any money. If you don't give away enough, you don't have any adoption. So you're caught in this catch-22. The best way to monetize open source, is open source as a service. And we saw Amazon do that frankly. We learned a lot from how Amazon did that. And one of the advantages that MongoDB had that I didn't fully appreciate when I joined the company, but I was very grateful. It is that they had a much more restrictive license. Which we ended up actually changing and made it even more restrictive, which allowed us to perfect ourselves from being cannibalized by the cloud providers, so that we could build our own business using our own IP that we had invested in and create a cloud service. >> That was a huge milestone. And of course you have great relationships with all the cloud providers, but it got contentious there for a while, but, you give the cloud providers an inch, they're going to take a mile. That's just the way, they're aggressive like that. But thank you for going through the history with me a little bit, because when you go back to the IPO, IPO was 2017, right? >> Correct. >> I always tell young investors, my kids especially, don't buy a stock at IPO, you're going to have a better chance, but the window from Mongo was very narrow. So, you didn't really get a much better chance a little bit. And then it's been a rocket ship since then. Sure, there's been some volatility, but you look at some of the big IPOs, like Facebook, or Snap, or even Snowflake, there was better opportunities. But you guys have executed really, really well. That's part of your ethos in your management team. And it came across on the earnings call recently. >> Yep. >> It was very optimistic, yet at the same time you set cautious tones and you got, I think high marks. >> Yes. >> For some of that caution but that execution. So talk about where you feel the business is today given the economic uncertainty? >> Well, what I'd say is we feel really good about the long term. We feel like the secular trends are really in our favor. Software's fundamentally transforming every industry. And people want to use modern software to either automate inefficient processes, enable new capabilities, drive better customer experiences. And the level of performance and scale you need for today's modern applications is profoundly different than applications yesterday. So we think we're well positioned for that. What we said on the earnings call was that we started seeing a moderation of growth, slight moderation of growth in our low end of the business in Europe. It was in our self-serve business and in the SMB space for the NQ1, towards the end of Q1. And we saw a little bit of that show up in the self-serve business in may in Q2. And that's why while we raised guidance, we basically quantified the impact, which is roughly about 30 to 35 million for the year, based on what we saw. And in that assumption, we assumed like... We just can't assume it's going to only be at the low in the market, probably some effect at the enterprise market. Maybe not as much, but there'll be some effect. So we need to factor that in. And we wanted to help kind of investors have some sort of framework to think about what the impact is. We don't want to be one of those companies that said absolutely nothing. And we don't want to be one of those companies that just waves the hand, but then it wasn't really that useful for investors. >> Yeah, I thought it was substantive. You talked about those market trends, you cited three things. The developers recognize that there are limits to legacy RDBMS. You talked about the, what I call point solutions creep. And then the document model is the best for developers. >> Great. >> And when the conversation turned to consumption, everybody's concerned about consumption obviously. You said... My take, somewhat insulated from that because you're running mission critical apps. It's not discretionary. My question to you is, should we rethink the definition of mission-critical? You think of Oracle mission critical running a bank. Mission -critical today in this digital world seems to be different, is that fair? >> Gosh, when's the last time you ever saw a website down? Like if you're running like any kind of digital channel, or engaging with the customers, or your partners, or your suppliers, you need to be up all the time. And so you need a very resilient, highly available data platform. It needs to be highly performance as you add more users, you need to be scale. And we saw a lot of that when COVID hit. Like companies had to completely repovit. And we talked about some examples where like a health and beauty retailer who was all kind of basically retail, had to suddenly pivot to e-commerce strategy. We've had streaming and gaming companies suddenly saw this massive influx of data that they scaled their operations very, very quickly. So I would say anytime you're engaging with customers, customers they're so used to the kind of the consumer facing applications. I almost joke like slow is the old down. If you're not performant, it doesn't matter. They're going to abandon you and go somewhere else. So if you're an e-commerce site and you're not performing well and not serving up the right skews, depending on what they're looking for, they're going to go somewhere else. >> So it's a click away. You talk about a hundred billion TAM, maybe that's even undercounted as you start to bring new capabilities in there. But there's no lack of market for you. >> Correct. >> How do you think about the market opportunity? >> Well, we believe... Again, software is transforming so many industries. IDC says that 715 million applications will be built over the next two to three years by 2025. To put that number of perspective, that's more apps that will be built the next three to four years than were built in the last 40. The rate and pace of innovation is as exploding. And people are building custom applications. Yes, Workday, Salesforce, other companies, commercial companies are great companies, but my competitors can use Workday or Salesforce, some of those commercial companies. That doesn't gimme a competitive advantage, what gives me a competitive advantage is building custom software that better engage my customers, that transforms my business in adding new capabilities or drives more efficiency. And the applications are only getting smarter. And so you're seeing that innovation explode and that plays to our strength. People need platforms like MongoDB to build the next generation of applications. >> So Atlas is now roughly 60% of your business, think is growing at 85%. So it's at least the midterm future. But my question to you is, is it the future? 'Cause when we start to think about the edge, it's not necessarily the cloud. You're not going to be able to go that round trip and the latency. And we had Verizon on earlier, talking about what they're doing with 5G, and the Mobile Edge. Is Mongo positioning for that edge? And is our definition of cloud changing? Where it's not just OnPrem and across clouds, but it's also out to the edge, this continuous experience. >> So I'll make two points. One, definitely we believe the applications of the future will be mobile first or purely mobile. Because one with the advent of 5G, the distinction between mobile and web is going to blur, with a hundred times faster networking speeds. But the second point I make is that how that shows up on our revenue on our income table will look like Atlas. Because we don't charge nothing for the end point, it's basically driving consumption of the back end. And so we've introduced a bunch of very, very sophisticated capabilities to synchronized data from the edge to the backend and vice versa with things like flexible sync. So we see so many customers now using that capability, whether you're field service technicians, whether you're a mobile first company, et cetera. So that will drive Atlas revenue. So on an income statement, it'll look like Atlas, but we're obviously addressing those broader set of mobile needs. >> You talk a lot about product market fit former VC, of course, Mark Andreen says, product market fit you kind of know when you see it, your hair's on fire, you can't buy a service. How do you know when you have product market fit? >> Well, one, we have the luxury of lots of customers. So they tell us pretty clearly when they're happy, and we can see that by usage behavior. Now the other benefit of a cloud service, is we can see the level of activity. We can see the level of engagement. We can see how much data they're consuming. We can see all the actions they're taking. So you get the fidelity of feedback you get from Atlas versus someone doing something behind their own firewall. And you kind of call 'em and check in on them is very, very different. So that level of insight gives us visibility in terms of what products and features have been used, gives us a sense how things going well, or is there something awry. Maybe they have misconfigured something or they don't know how to use some capabilities. So the level of engagement that we can have with a customer using a service is so much different. And so we've really invested in our customer success organization. So the byproduct of that is that our retention rates are also very, very strong. Because you have such better information about what's happening in terms of your customers. >> See retention in real time. You've been somewhat... Is just so hard to say this 'cause you're growing at 50% a year. But you're somewhat conservative about the pace of hiring for go to market. And I'm curious as to how you think about scaling, especially when you introduce new products. Atlas is several years ago. But as you extend your capabilities and add new products, how do you decide when to scale? >> So it's a constant process. We've been quite aggressive in scaling organization for a couple reasons. One, we have very low market share, so the market's vastly under penetrated. We still don't have reps in every NFL sitting in the United States, which just kind of crazy. There's other parts of the world that we are just still vastly under penetrated in. But we also look at how those organizations are doing. So if we see a team really killing it, we're going to deploy more resources. Because one, it tells us there's more opportunity there, and there's a strong team there. If we see a team that maybe is struggling a little bit, we'll try and uncover. Rather than just applying more resources in, we'll try and uncover what are the issues and make sure we stabilize the organization and then devote resources. It's all in the measure of like being very disciplined about where we deploy our resources, to get those kind of returns. And on the product side, we obviously go through a very iterative process and kind of do rank order all the projects and what we think the expected returns are. Obviously, we look at the customer feedback, we look at what our strategic priorities are. And that informs what projects we fund and what projects kind of are below the line. And we do that over and over again every quarter. So every quarter we revisit the business, we have a very QBR centric culture. So we're constantly checking in and seeing how the business is operating. And then we make those investment decisions. In general, we've been investing very aggressively in terms of expanding our reach around the world. >> It seems like, well, with Mongo, your product portfolios... From an outside observer standpoint, it seems like you've always had pretty good product market fit. But I was curious, in your VC days, would you ever encourage companies to scale go to market prior to having confidence in product market fit? Or did you always see those as sequential activities? >> Well, I think the challenge is this part it's analysis part is judgment. So you don't necessarily have to have perfect product market fit to start investing. But you also don't want to plow a bunch of resources and realize the product doesn't work and then how you're burning through a lot of cash. So there's a little bit of art to the process. When I joined MongoDB, I could tell that we had a strong engineering team. They knew how to build high quality products, but we just struggled with commercialization. The culture wasn't great across the company. And we had some leadership challenges. So that's when I joined, I kind of focused on those things and tried to bring the organization together. And slowly we started chipping away and making people feel like they were winners. And once you start winning, that becomes contagious. And then the nice thing is when you start winning, you get a lot more customer feedback. That feedback helps you refine your products even more, which then adds... It's like the flywheel effect that starts taking off. >> So it seems the culture's working now. Do you have a favorite product from the announcements today? >> Well, I really like our foray to analytics. And essentially what we're seeing is really two big trends. One you're seeing applications get smarter. What applications are doing is really automating a lot of processes and rather than someone having to press a button. Based on analytics, you can automate a lot of decision making. So that's one theme that we're seeing as applications get smarter. The second theme is that people want more and more insight in terms of what's happening. And the source of that is insights is your operational database. Because that's where you're having transactions, that's where you know what products are selling, that's where you know what customers are buying. So people want more and more real time data versus waiting to take that data, put it somewhere else and then run reports and then get some update at the end of the night or maybe at the week. So that's driving a lot of really interesting use cases. And especially when you marry in things like time series use cases where you're collecting a lot of data people want to see trend analysis what's happening. Which I think it's a very exciting area. We introduced a very cool feature called Queryable Encryption, which basically... The problem with encrypting data, is you can't really query it because my definition's encrypted. >> Yeah, you're right. >> But obviously data security is very important. What we announced, is we're using very sophisticated cryptography. People can query the data, but they don't have really access to the data. So it really protects you from like data breaches or malicious users accessing your data, but you still can kind of make that data usable. So that was a very interesting announcer that we made today. >> Sounds like magic without the performance hit. >> Yes. >> You can do that. Dev, thanks so much for coming in The Cube. Congratulations on all activity, bumper sticker on day one. >> Oh, it's super exciting. The energy was palpable, 3,300 people in the room, lots of customers, lots of users. We had lots of investors here as well for our investor day, have a dinner tonight with a bunch of senior execs, so it's been a busy day. >> Future is bright for MongoBD. Dev, thanks for so much for coming on The Cube. And thanks for watching, this is Dave Vellante and we'll see you next time. (upbeat music)

(upbeat music) >> The next normal is upon us. And the way we run corporate communications, brand accelerators and events has changed inextricably from 12 months ago. Will this last? Welcome to theCUBE. My name is Samme Allen. It's great to have you with us. Joining me today to discuss what looks like success for us all in terms of communications and events, we have long time industry analyst, TV host, entrepreneur and of course, many other accolades, please welcome co-founder and CEO of theCUBE, Dave Vellante. Dave, welcome to theCUBE. >> Hey Samme, thank you very much. I've been in theCUBE a lot, but really not often in this format, so thanks for having me. >> It is a pleasure to be interviewing you today. How does it feel being in the hot seat about to be grilled about the future of events? >> A little weird, little uncomfortable. But bring it on. >> So we talk about this next normal. Some people called it the new normal. We're coming out of the world of pandemic. Thank God. We are seeing returning to live events. We are seeing returning to travel. But what do you think this looks like for the big brands in terms of how they start building out their communications strategy, including events for, say, the next 12 months, the immediate strategy for the future? >> Well, that's a great question. And it's interesting when you look back in the last 12, 13, 14 months, and you compare, let's say, last April to this April in terms of the quality of the events that not only the production value, but also the content and the formats and the intensive attempt to engage with people, you're seeing people, big organizations especially, really raised the bar quite dramatically. And now just as they've sort of become comfortable with virtual events, they're trying to figure out, okay, what's next? So we've seen with theCUBE, we're getting demand now for hybrid events. We're going to be at Mobile World Congress. We're seeing other events that people are asking us to attend. We've got some events in the fall. Smatterings, you know. It's not huge. But when you talk to people, pretty much everybody now is planning on some type of physical activity in 2021. So there's huge pent-up demand. We would expect, Samme, to have these, let's call'em VIP events, where you might have an audience of, local audience, maybe it's 20, maybe it's 25 people, selected audience of CEOs or CTOs or business executives, and then broadcast that to a much wider audience. I personally think this notion of virtual events, which nobody really wanted, you know, a couple of years ago, everybody wanted belly-to-belly, I think it's here to stay, because the long tail of consumption post-event is actually paying dividens, even though it's taking much, much longer to see those results. >> And we're seeing here in the UK. As you know, I'm based in our London studio. We are, you know, we're hearing from Sir David Attenborough who pretty much everyone around the globe knows as the global voice of sustainability saying that actually what we do in the next 5 to 10 years could potentially have a much bigger impact on the world than Corona virus has done so far. Do you think brands are taking this seriously in terms of the evolution of how they communicate, how they attend events, where things like theCUBE will be placed in the future? Are you seeing that from your clients, Dave? >> You know, that's a really tough question. Because on the one hand, and I often joke that, you know, it used to be the case that, you know, the only goal of a public company was to make profit. And now, you're seeing companies from IBM and Cisco and Salesforce, name a company, a large company, they're standing up and saying ESG, diversity, inclusion, these are not only the right thing to do, but they're good business. And so tie that into your question, which is, you know, can we affect the environment, for example, maybe by, you know, being more productive with travel? And the reason I think it's such a tough question is because I think the sales people who are under such pressure to perform, and the companies are under pressure to perform, clearly can be more productive face-to-face, and they can accelerate time to close, for example. At the same time, nobody's really excited to get back on a plane on a Sunday night every week and fly back on a Friday and see their family, maybe, you know, for a day or two. So I think we've got to figure that out. And I think to answer your question specifically, I think there's no question that we can do much more virtually. And I think we will, over the next 10 years, learn how to do that in a much more productive way. >> You hit quite a true point from the brands that we've been speaking with in terms of the desire to see people, to hug people, to be in a room. I think the one thing we hear all the time is that you can't network. Well, we know you can network, because we have algorithms, we have AI and big data. But actually, that socialization. Do you think once we've all got to that first conference and then actually, we have maybe, exactly as you said, that fatigue of not being with our families when the world has changed so much, so after this initial rush, do you think that then that blend of the world of hybrid will remain stable? >> Another really tough question. I think, you know, having, for myself, I'm not fully baked. I've had my second vaccine. And so when I see people, I'm really confident. I'm kind of a, you know, chest pumper, a handshaker, a hugger, whatever. So I'm much more comfortable doing that. But we don't know what we don't know. You know, do we need a booster shot in six months? You know, what is the data telling us? The science, I mean. Everybody says follow the science. But the Alzheimer, the science doesn't know what's happening. I would say this. I think unquestionably, from a business standpoint, that this notion of being able to expose your brand to many, many more, a much, much larger audience, is going to continue. That has legs. And I think people are very comfortable that, if you do that, you're not going to limit the number of people who actually, you know, show up live. It's like when TED decided to actually broadcast, the brand went through the roof. I think the same thing will happen here that you're going to see a slow return of the face-to-face. And I think the virtual will stay. And I think they'll be related, but different teams. I mean, we've talked about this, you and I. There's different skillsets for virtual. So I can see organizations, at least I think smart ones, will invest in both. And I think we're going to see a new era of events that are going to combine virtual and physical. >> Talking about theCUBE, you know. We talked about theCUBE being, you know, they're front and center at an event to offer those expert insights. Can you see in that, well, give us your crystal ball, where's theCUBE going to be in five years time? Do you hope? And do you, where do you think it's going to be strategically wise? >> You know, the awesome thing for theCUBE is that we started in virtual events and hybrid events back in 2015. And so, but it was interesting is we sort of try to push that on our clients, and nobody wanted it. It's like I was saying before, everybody wanted physical. So when COVID hit, we were in a really good position to extend our portfolio into virtual. And that's exactly what we did with our two studios and our software stack. What was a little tricky for us was we had to retrain people. And it was like training by fire. So that took some time. And so you start to see, okay, who's, who really enjoys the virtual, who enjoys the physical. So where I see theCUBE in five years time is that hybrid combination. Very clearly, people want theCUBE at their events, because it's light. It's lights, camera, action. You know, the sports-center-like vibe with the live production, you know. But at the same time, we've got this great capability and team that can reach a much, much wider audience. And then what we've learned, the big learning or one of the big learnings from COVID in virtual was the post-event consumption, that long tail is actually quite amazing, especially if you keep nurturing it. And by the way, a lot of our clients still miss this, a lot of brands move on to the next one, move on to the next one, whereas you can see the consumption continuing. And so I think people are going to continue to fine tune that and really take advantage. So I see theCUBE in both places. And it's just, we're really excited, because it's just a great expansion of our business. >> And I think that strategy, as you said, that, you know, building out a 365 strategy when it comes down to communications and bringing people on a journey with you, which is what you're doing at theCUBE, I think that's the future. And it's an exciting future. My last question for you. You've been locked down like we all have here in the UK, You're in the US. What are you most looking forward to now you've had your second shot, the world is opening up? What's the first thing that you're going to be doing sort of post-lockdown? >> You know, I'll say this. I, again, I don't miss flying every week and dragging my big, heavy backpack through airports. What I have missed is that interaction post-event. So theCUBE is intense. You go to an event. You're doing 10 to 12 interviews a day. Sometimes three or four days. You're exhausted at the end of the day. But then you get to sit back. And that's when you go to the evening events. And you see people, for instance, that were on theCUBE. And people were pointing to you, "hey, you're theCUBE guys." And you build a really intimate relationship with them that is long lasting. And I really do miss that. We, John Furrier, my business partner and co-CEO, we've made some great business friendships that will last a lifetime. And you only form those with these face-to-face interactions. You just, as you know, Samme, you can't do it. You can't get that level of intimacy in a video call. You just can't. So I'm really looking forward to that. And maybe a little better life balance. That's what I'm most looking forward to. >> I think that's a wonderful way to close this out. So I'm looking forward to also seeing you in person, raising that glass, building those relationships. Thank you, Dave, so much for being with us today. Thank you all for watching. Stay tuned to theCUBE for breaking insights, expert insights front and center when you need them. Keep safe. And see you next time. (upbeat 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)

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>> In this session, we will be reviewing the power and benefits of implementing a secure software supply chain and how we can gain a cloud like experience with the flexibility, speed and security of modern software delivering. Hi, I'm Matt Bentley and I run our technical pre-sales team here at Mirantis. I spent the last six years working with customers on their containerization journey. One thing almost every one of my customers has focused on is how they can leverage the speed and agility benefits of containerizing their applications while continuing to apply the same security controls. One of the most important things to remember is that we are all doing this for one reason and that is for our applications. So now let's take a look at how we can provide flexibility to all layers of the stack from the infrastructure on up to the application layer. When building a secure supply chain for container focused platforms, I generally see two different mindsets in terms of where their responsibilities lie between the developers of the applications and the operations teams who run the middleware platforms. Most organizations are looking to build a secure, yet robust service that fits their organization's goals around how modern applications are built and delivered. First, let's take a look at the developer or application team approach. This approach falls more of the DevOps philosophy, where a developer and application teams are the owners of their applications from the development through their life cycle, all the way to production. I would refer to this more of a self service model of application delivery and promotion when deployed to a container platform. This is fairly common, organizations where full stack responsibilities have been delegated to the application teams. Even in organizations where full stack ownership doesn't exist, I see the self service application deployment model work very well in lab development or non production environments. This allows teams to experiment with newer technologies, which is one of the most effective benefits of utilizing containers. In other organizations, there is a strong separation between responsibilities for developers and IT operations. This is often due to the complex nature of controlled processes related to the compliance and regulatory needs. Developers are responsible for their application development. This can either include dock at the development layer or be more traditional, throw it over the wall approach to application development. There's also quite a common experience around building a center of excellence with this approach where we can take container platforms and be delivered as a service to other consumers inside of the IT organization. This is fairly prescriptive in the manner of which application teams would consume it. Yeah when examining the two approaches, there are pros and cons to each. Process, controls and compliance are often seen as inhibitors to speed. Self-service creation, starting with the infrastructure layer, leads to inconsistency, security and control concerns, which leads to compliance issues. While self-service is great, without visibility into the utilization and optimization of those environments, it continues the cycles of inefficient resource utilization. And a true infrastructure as a code experience, requires DevOps, related coding skills that teams often have in pockets, but maybe aren't ingrained in the company culture. Luckily for us, there is a middle ground for all of this. Docker Enterprise Container Cloud provide the foundation for the cloud like experience on any infrastructure without all of the out of the box security and controls that our professional services team and your operations teams spend their time designing and implementing. This removes much of the additional work and worry around ensuring that your clusters and experiences are consistent, while maintaining the ideal self service model. No matter if it is a full stack ownership or easing the needs of IT operations. We're also bringing the most natural Kubernetes experience today with Lens to allow for multi-cluster visibility that is both developer and operator friendly. Lens provide immediate feedback for the health of your applications, observability for your clusters, fast context switching between environments and allowing you to choose the best in tool for the task at hand, whether it is the graphic user interface or command line interface driven. Combining the cloud like experience with the efficiencies of a secure supply chain that meet your needs brings you the best of both worlds. You get DevOps speed with all the security and controls to meet the regulations your business lives by. We're talking about more frequent deployments, faster time to recover from application issues and better code quality. As you can see from our clusters we have worked with, we're able to tie these processes back to real cost savings, real efficiency and faster adoption. This all adds up to delivering business value to end users in the overall perceived value. Now let's look and see how we're able to actually build a secure supply chain to help deliver these sorts of initiatives. In our example secure supply chain, where utilizing Docker desktop to help with consistency of developer experience, GitHub for our source control, Jenkins for our CACD tooling, the Docker trusted registry for our secure container registry and the Universal Control Plane to provide us with our secure container runtime with Kubernetes and Swarm, providing a consistent experience, no matter where our clusters are deployed. You work with our teams of developers and operators to design a system that provides a fast, consistent and secure experience. For my developers, that works for any application, Brownfield or Greenfield, Monolith or Microservice. Onboarding teams can be simplified with integrations into enterprise authentication services, calls to GitHub repositories, Jenkins access and jobs, Universal Control Plan and Docker trusted registry teams and organizations, Kubernetes namespace with access control, creating Docker trusted registry namespaces with access control, image scanning and promotion policies. So, now let's take a look and see what it looks like from the CICD process, including Jenkins. So let's start with Docker desktop. From the Docker desktop standpoint, we'll actually be utilizing visual studio code and Docker desktop to provide a consistent developer experience. So no matter if we have one developer or a hundred, we're going to be able to walk through a consistent process through Docker container utilization at the development layer. Once we've made our changes to our code, we'll be able to check those into our source code repository. In this case, we'll be using GitHub. Then when Jenkins picks up, it will check out that code from our source code repository, build our Docker containers, test the application that will build the image, and then it will take the image and push it to our Docker trusted registry. From there, we can scan the image and then make sure it doesn't have any vulnerabilities. Then we can sign them. So once we've signed our images, we've deployed our application to dev, we can actually test our application deployed in our real environment. Jenkins will then test the deployed application. And if all tests show that as good, we'll promote our Docker image to production. So now, let's look at the process, beginning from the developer interaction. First of all, let's take a look at our application as it's deployed today. Here, we can see that we have a change that we want to make on our application. So our marketing team says we need to change containerize NGINX to something more Mirantis branded. So let's take a look at visual studio code, which we'll be using for our ID to change our application. So here's our application. We have our code loaded and we're going to be able to use Docker desktop on our local environment with our Docker desktop plugin for visual studio code, to be able to build our application inside of Docker, without needing to run any command line specific tools. Here with our code, we'll be able to interact with Docker maker changes, see it live and be able to quickly see if our changes actually made the impact that we're expecting our application. So let's find our updated tiles for application and let's go ahead and change that to our Mirantis sized NGINX instead of containerized NGINX. So we'll change it in a title and on the front page of the application. So now that we've saved that changed to our application, we can actually take a look at our code here in VS code. And as simple as this, we can right click on the Docker file and build our application. We give it a name for our Docker image and VS code will take care of the automatic building of our application. So now we have a Docker image that has everything we need in our application inside of that image. So, here we can actually just right click on that image tag that we just created and do run. This will interactively run the container for us. And then once our containers running, we can just right click and open it up in a browser. So here we can see the change to our application as it exists live. So, once we can actually verify that our applications working as expected, we can stop our container. And then from here, we can actually make that change live by pushing it to our source code repository. So here, we're going to go ahead and make a commit message to say that we updated to our Mirantis branding. We will commit that change and then we'll push it to our source code repository. Again, in this case, we're using GitHub to be able to use as our source code repository. So here in VS code, we'll have that pushed here to our source code repository. And then, we'll move on to our next environment, which is Jenkins. Jenkins is going to be picking up those changes for our application and it checked it out from our source code repository. So GitHub notifies Jenkins that there's a change. Checks out the code, builds our Docker image using the Docker file. So we're getting a consistent experience between the local development environment on our desktop and then in Jenkins where we're actually building our application, doing our tests, pushing it into our Docker trusted registry, scanning it and signing our image in our Docker trusted registry and then deploying to our development environment. So let's actually take a look at that development environment as it's been deployed. So, here we can see that our title has been updated on our application, so we can verify that it looks good in development. If we jump back here to Jenkins, we'll see that Jenkins go ahead and runs our integration tests for our development environment. Everything worked as expected, so it promoted that image for our production repository in our Docker trusted registry. We're then, we're going to also sign that image. So we're assigning that yes, we've signed off that has made it through our integration tests and it's deployed to production. So here in Jenkins, we can take a look at our deployed production environment where our application is live in production. We've made a change, automated and very secure manner. So now, let's take a look at our Docker trusted registry, where we can see our name space for our application and our simple NGINX repository. From here, we'll be able to see information about our application image that we've pushed into the registry, such as the image signature, when it was pushed by who and then, we'll also be able to see the results of our image. In this case, we can actually see that there are vulnerabilities for our image and we'll actually take a look at that. Docker trusted registry does binary level scanning. So we get detailed information about our individual image layers. From here, these image layers give us details about where the vulnerabilities were located and what those vulnerabilities actually are. So if we click on the vulnerability, we can see specific information about that vulnerability to give us details around the severity and more information about what exactly is vulnerable inside of our container. One of the challenges that you often face around vulnerabilities is how exactly we would remediate that in a secure supply chain. So let's take a look at that. In the example that we were looking at, the vulnerability is actually in the base layer of our image. In order to pull in a new base layer for our image, we need to actually find the source of that and update it. One of the ways that we can help secure that as a part of the supply chain is to actually take a look at where we get our base layers of our images. Docker hub really provides a great source of content to start from, but opening up Docker hub within your organization, opens up all sorts of security concerns around the origins of that content. Not all images are made equal when it comes to the security of those images. The official images from Docker hub are curated by Docker, open source projects and other vendors. One of the most important use cases is around how you get base images into your environment. It is much easier to consume the base operating system layer images than building your own and also trying to maintain them. Instead of just blindly trusting the content from Docker hub, we can take a set of content that we find useful such as those base image layers or content from vendors and pull that into our own Docker trusted registry, using our mirroring feature. Once the images have been mirrored into a staging area of our Docker trusted registry, we can then scan them to ensure that the images meet our security requirements. And then based off of the scan result, promote the image to a public repository where you can actually sign the images and make them available to our internal consumers to meet their needs. This allows us to provide a set of curated content that we know is secure and controlled within our environment. So from here, we can find our updated Docker image in our Docker trusted registry, where we can see that the vulnerabilities have been resolved. From a developer's point of view, that's about as smooth as the process gets. Now, let's take a look at how we can provide that secure content for our developers in our own Docker trusted registry. So in this case, we're taking a look at our Alpine image that we've mirrored into our Docker trusted registry. Here, we're looking at the staging area where the images get temporarily pulled because we have to pull them in order to actually be able to scan them. So here we set up mirroring and we can quickly turn it on by making it active. And then we can see that our image mirroring, we'll pull our content from Docker hub and then make it available in our Docker trusted registry in an automatic fashion. So from here, we can actually take a look at the promotions to be able to see how exactly we promote our images. In this case, we created a promotion policy within Docker trusted registry that makes it so that content gets promoted to a public repository for internal users to consume based off of the vulnerabilities that are found or not found inside of the Docker image. So our actual users, how they would consume this content is by taking a look at the public to them, official images that we've made available. Here again, looking at our Alpine image, we can take a look at the tags that exist and we can see that we have our content that has been made available. So we've pulled in all sorts of content from Docker hub. In this case, we've even pulled in the multi architecture images, which we can scan due to the binary level nature of our scanning solution. Now let's take a look at Lens. Lens provides capabilities to be able to give developers a quick opinionated view that focuses around how they would want to view, manage and inspect applications deployed to a Kubernetes cluster. Lens integrates natively out of the box with Universal Control Plane clam bundles. So you're automatically generated TLS certificates from UCP, just work. Inside our organization, we want to give our developers the ability to see their applications in a very easy to view manner. So in this case, let's actually filter down to the application that we just employed to our development environment. Here, we can see the pod for application. And when we click on that, we get instant detailed feedback about the components and information that this pod is utilizing. We can also see here in Lens that it gives us the ability to quickly switch contexts between different clusters that we have access to. With that, we also have capabilities to be able to quickly deploy other types of components. One of those is helm charts. Helm charts are a great way to package up applications, especially those that may be more complex to make it much simpler to be able to consume and inversion our applications. In this case, let's take a look at the application that we just built and deployed. In this case, our simple NGINX application has been bundled up as a helm chart and is made available through Lens. Here, we can just click on that description of our application to be able to see more information about the helm chart. So we can publish whatever information may be relevant about our application. And through one click, we can install our helm chart. Here, it will show us the actual details of the helm charts. So before we install it, we can actually look at those individual components. So in this case, we can see this created an ingress rule. And then this will tell Kubernetes how did it create this specific components of our application. We'd just have to pick a namespace to deploy it to and in this case, we're actually going to do a quick test here because in this case, we're trying to deploy the application from Docker hub. In our Universal Control Plane, we've turned on Docker content trust policy enforcement. So this is actually going to fail to deploy. Because we're trying to employ our application from Docker hub, the image hasn't been properly signed in our environment. So the Docker content trust policy enforcement prevents us from deploying our Docker image from Docker hub. In this case, we have to go through our approved process through our secure supply chain to be able to ensure that we know where our image came from and that meets our quality standards. So if we comment out the Docker hub repository and comment in our Docker trusted registry repository and click install, it will then install the helm chart with our Docker image being pulled from our DTR, which then it has a proper signature. We can see that our application has been successfully deployed through our home chart releases view. From here, we can see that simple NGINX application and in this case, we'll get details around the actual deployed helm chart. The nice thing is, is that Lens provides us this capability here with helm to be able to see all of the components that make up our application. From this view, it's giving us that single pane of glass into that specific application, so that we know all of the components that is created inside of Kubernetes. There are specific details that can help us access the applications such as that ingress rule that we just talked about, gives us the details of that, but it also gives us the resources such as the service, the deployment and ingress that has been created within Kubernetes to be able to actually have the application exist. So to recap, we've covered how we can offer all the benefits of a cloud like experience and offer flexibility around DevOps and operations control processes through the use of a secure supply chain, allowing our developers to spend more time developing and our operators, more time designing systems that meet our security and compliance concerns.

>> So, hi, my name is Daniel Terry, I work as Lead Designer at SEB. So, today we will go through why we are why we are Mirantis' customer, why we choose Docker Enterprise, and mainly what challenges we were facing before we chose to work with Docker, and where we are today, and our keys to success. >> Hi, my name is Johan, I'm a senior developer and a Tech Lead at SEB. I was in the beginning with Docker for like, four years ago. And as Daniel was saying here, we are going to present to you our journey with Docker and the answers. >> Yeah, who are we? We are SEB group. So we are a classic, financial large institutions. So, classic and traditional banking services. In Sweden, we are quite a big bank, one of the largest. And we are on a journey of transforming the bank so it has to be online 24-seven. People can do their banking business every day, whenever they want, nothing should stop them to be online. So this is putting a lot of pressure on us on infrastructure to be able to give them that service. (drum fill) >> So our timeline here. Is look, we started out with how to facilitate the container technology it has to be. 2016. And, in 2018, we had the first Docker running in SEB in a standalone mode. You need that. We didn't have any swarm, or given up this cluster since a while. For 2019, we have our first Docker-prise enterprise cluster at SEB. And today, 2020, we have the latest and greatest version of Docker installed. We are running around approximately two and a fifth at 450 specs. Around a thousand services and around 1500 containers. So, developer challenges. As for me as a developer, previous to Docker was really, really hard to get things in production. Times. It took big things and ordering services and infrastructures was a pain in the... yeah, you know what I mean? So for me, it was all about processes. We use natural processes and meaning that I wasn't able to, to see maintaining my system in production. I was handing that over to our operations teams and operation teams in that time, they didn't know how the application works. They didn't know how to troubleshoot it and see, well, what's going wrong. They were experts on the infrastructure and the platforms, but not on our applications. We were working in silos, meaning that I as a developer, only did developing things. The operations side did their things, and the security side did their things. But we didn't work as a team. I mean, today we have a completely different way of working. We will not see shapes. I mean, we have persons that were really good in maybe MQ technologies, or in some programming language and so on, but we didn't have the knowledge in the team techs to solve things, as we should have. Long lead times. I mean, everything we were trying to do had to follow the processes as we had. I mean that we should fill in some forms, send it away, hopefully someone was getting, getting back to us and saying, yeah guys, we can help you out with these services or this infrastructure, but it takes a really long time to do that. I mean, ordering infrastructure is when you're not an expert on that really hard to do. And often the orders we made or placed were wrong. When we have forms to fill in, it wasn't possible for us to do things automatically. Meaning that we didn't have the code, or the infrastructure as code. Meaning that if we didn't get the right persons into the meetings the first time, we didn't have the possibility to do it the right way, meaning that we had to redo and redo, and hopefully sometimes we got the right. We didn't have consistent set ups between the environments. When we order, as for example, a test environment, we could maybe order it with some minor resources, less CPU, so less memory, less disc or whatever. Or actually less performance on the hardware, but then we moved up to production. We realized that we have different hardware, different discs, different memories, and that could actually cause some serious problems in applications, access-wise. I mean, everyone likes to have exercise, especially if you are the maintainer of the system. That was really, really hard to get. I mean, every system has their own services, their own service, and therefore they need to apply for access to those other services. But today there's a complete difference since we only have one class to produce. Since we don't have infrastructure as a code back then, there were really lots of human errors. I mean, everyone was doing things manually. When you're coming from the Windows perspective, everything is a UI. You tend to prefer that way of working, meaning that if you used to click something in between the environments, the environments will not look the same. Life cycles. I mean, just imagine. When we have the server installed, it's like a pet. You have everything configured all from certificates to port openings, cartels, install patches, you name it. And then imagine that Windows are terminating a version and you need to reinstall that. Everything needs to be redone from the beginning. So there was a really long time taking to, to do the LCM activities, General lack of support of Microservice architecture was really also, a thing that are driving us forward with the containers technology, since we can't scale our applications in the same way as for containers. We, for example, couldn't have two applications or two processes using the same TCP port. For example, if you'd like to scale a web server, you can't do that on the same hardware. You need to have two different servers. And just imagine replacing all the excesses, replacing all the orders again for more hardware, and then manually a setting up there. The low balancer in front is a really huge task to do. And necessarily if you don't have the knowledge how the infrastructure is where you're working, then it's also really hard for you as a developer to do things right. Traditionalist. I mean, the services for us are like pets. They were really, really hard to set up. It'd take maybe a week or so. And if something was wrong with them, we will try to fix them as a pet. I mean, we couldn't just kill them and throw them away. It will actually destroy the application as this, our, like a unit box where all our things are installed. >> So, coming in from the infrastructure part of this, we've also seen challenges. For my team, we're coming from a Windows environment. So doing like a DevOps journey, which we want to do, makes it harder due to our nature in our environments. We are not used to, maybe use API, so we are not used to giving open APIs to our developers to do changes on the servers. Since we are a bank, we don't allow users to log into the servers, which means we have to do things for them all the time. This was very time consuming. And a lot of the challenges we actually still are seeing is the existing infrastructure. You can't just put that container platform on it, and thinking you're sold and everything. One of the biggest issues for us is, has been to getting servers. Windows servers usually takes like 15 minutes, Linux servers can takes up to two week in a bad day. So we really lack like, infrastructure as code. If we want a low balancer, that is also an order form. If we want the firewall opening, that's an order form. Hopefully they will not deny it. So it will go faster. So it's a lot of old processes that we need to go through. So what we wanted to do is that we want to move all of these things to the developers, so they can do it. They can own up their problems, but with our old infrastructure, that wasn't possible. We are a heavily ITIL-based organization, meaning that everything went from a cab. Still does in some way, we have one major service window every month where we take everything down. There is a lot of people involved in everything. So it's quite hard to know what will be done during the maintenance window. We lack supporting tools, or we lacked supporting tools, like log-in, good log-in tools. We have a bunch of CI/CD tools, but the maturity level of the infrastructure team wasn't that good. Again, order form and processes. If we want to, like, procure, do our procurement on a new like, storage system, or a backup system, we talk about here. So to do it is, for us, with containers, it would solve a lot of problems, because we cause we would then move the problems, not maybe move the problems to the developer, but we would make it able for them to own their own problems. So everything that we have talked about up till now boils down to business drivers. So the management's gave- gave us some policies to, or what they, how they want to change the company, so we can be this agile and fast moving bank. So one of the biggest drivers are cloud readiness, where Containers comes in perfectly. So we can build it on premises, and then we can move it to the cloud when we are ready but we can't, but we also need an exit strategy to move it back on premises if we need, due to hard regulations. Maybe you can throw it in the air. >> Absolutely. I definitely can. You're absolutely right. We need to develop things in a certain way. So we can move from infrastructure to infrastructure depending, or regardless of the vendor. Meaning that if we are able to run it on-prem, we should be able to run it in cloud or vice versa. We should also be able to move between clouds, and not be forced into one cloud provider. So that's really important for us at SEB. Short time to market is also a thing here. I mean, we are working with the huge customers. I can't name them, but they're really huge. And they need to have us being moving forward. I mean, able to really fast switch from one technology, maybe to another, we are here for them. And it's really important to us to be really fast for us to get new things out in production. All right, maybe. Nothing else? >> I don't, don't really. From the upside, we are in a huge staff DevOps transition. So, or a forced DevOps transition, which means we need to start looking at new infrastructure solutions, maybe deploy our infrastructure parts inside of containers to be able to use it the same way in the cloud. That's what we do prior, do here on premises, we have private clouds which are built on techno- technology, container technology today. So this fits quite good to have the Docker platform being one part of that one. >> Yeah. And this is solid, we are also working really, really actively on open source platforms and open source drivers. We can see that we have a huge amount of vendors in SEB, really huge ones, but we can also see that we can, facilitate open source platforms, and open source technology as well. So container technology will bring that for us. I mean, instead of having a SaaS platform and SaaS services, we can actually instantiate our own with containers and stuff. >> Also we are, since we are quite heavily regulated, the process of going through to you as like a SaaS service can take up to two years for us to go through, and then maybe the SaaS service, is it, is it what we want to use anymore? So, also we want to develop the things in our own premises and maybe, and scale it to the cloud if we need. And also we want to be an attractive employer, where maybe it's not that, the coolest thing for a young student to work in mainframe, we have a mainframe it's, it's not going anywhere, but it's hard to get people, and we want to be an attractive employer, and everyone is talking Kubernetics and containers or, and clouds. So we need to transition into those technologies. >> Yeah, we need to be open minded and necessarily facilitate the new technologies. So we can actually attract new employees. So it's really important to us to have an open mind. Our experience with Docker Containers. I mean, as I said before, scalability is a really important thing for us today. When we are using a more microservice architecture, we need to be able to Skype. We need to be scaling horizontally instead of vertically. So for that, containers are perfect storage. As we said before, we have a huge problem with environments being differently set up, since it was often manually done. Today, as we have a infrastructure as a code, it's really, really nice to have the same things exactly configured, the same in all environments. And we also have the same tooling, meaning that if I can run it on my machine, it's the same tooling I will be using to run it for test purposes or in production. That's a huge benefit for us as a developer. Time to market. I mean, today, we don't have to order service, we are using the service approach here. So we have a container cluster that are actually just sitting there waiting for our services to be hosted. So no more forms, no more calls, no more meetings before we can set up anything. We also own our problems. I mean, before, as I said, we have the processes, meaning that we ship our applications to any server. And then the operation sites take over. That's not the case now. We are actually using this as we should in DevOps. Meaning the other teams are actually responsible for all their errors as well. Even if it's on the infrastructure part, it's completely different if it's a platform's problem, because then it's the platform's team, and we can use different windows. We can try stuff out, we have an open mind. And that says that I can download and try any container image I would like on my developer machine. It's not maybe, okay to run it in production without having the security people look at it. But normally it's really, really much faster instead of waiting maybe six months, we can maybe wait one week or so. And of course less to none LCM activities. I mean, as I said before, it will take months, maybe, to do an LCM activity on multiple servers. Today, our LCM activities more or less are just switching to a new version of the image from Docker hub. That's all we have to do. So that's actually maintained during the processes we have in CI/CD pipelines. >> And the last one. So our keys to success: you should get demanded from the managers and management that everything should be a container. All the new development has to go through a container before you start ordering servers. Everything shall go through a CI/CD pipeline. We don't actually, here at SEB. Our developers build their own CI/CD pipeline. We just provide a platform for them to use it against, and the CI/CD to systems, but they build everything for themselves. Cause they know how their application works, how it should be deployed, with what tools. We just provide them with a tool set. Build a Cross Team. So you should incorporate all the processes that you need, but you should focus on the developer part, because you are building a platform for the developers, not for operations or security. >> And then maybe >> A lot of... >> you'll be able to take flight >> Yeah. Luck has nothing to do with it? Yes, it has. Of course, luck has something to do with it, even if you're really passionate, even if you're really good at some things. I mean, we got some really nice help from Dr. Inc. We were really... Came in with the technology in the right time for us to be, and we had really engaged people with these projects and that's a really luck for us to have. >> Yeah. And also we... I want to thank our colleagues, because we have another container team who started before us. And they have actually run into a lot of organizational problems, which they have sold, so we could piggyback on that, on those solutions. Also, start small and scale it. This is where Docker swarm comes, fits perfectly. So we have actually, we started with swarm. We are moving into Kubernetics in this platform. We will not force-move anything. The developers just should show us, what their- fits their needs. Thank you! >> Thank you very much.

>>this session will be reviewing the power benefits of implementing a secure software supply chain and how we can gain a cloud like experience with flexibility, speed and security off modern software delivery. Hi, I'm Matt Bentley, and I run our technical pre sales team here. Um Iran. Tous I spent the last six years working with customers on their container ization journey. One thing almost every one of my customers is focused on how they can leverage the speed and agility benefits of contain arising their applications while continuing to apply the same security controls. One of the most important things to remember is that we are all doing this for one reason, and that is for our applications. So now let's take a look at how we could provide flexibility all layers of the stack from the infrastructure on up to the application layer. When building a secure supply chain for container focus platforms, I generally see two different mindsets in terms of where the responsibilities lie between the developers of the applications and the operations teams who run the middleware platforms. Most organizations are looking to build a secure yet robust service that fits the organization's goals around how modern applications are built and delivered. Yeah. First, let's take a look at the developer or application team approach. This approach follows Mawr of the Dev ops philosophy, where a developer and application teams are the owners of their applications. From the development through their life cycle, all the way to production. I would refer this more of a self service model of application, delivery and promotion when deployed to a container platform. This is fairly common organizations where full stack responsibilities have been delegated to the application teams, even in organizations were full stack ownership doesn't exist. I see the self service application deployment model work very well in lab development or non production environments. This allows teams to experiment with newer technologies, which is one of the most effective benefits of utilizing containers and other organizations. There's a strong separation between responsibilities for developers and I T operations. This is often do the complex nature of controlled processes related to the compliance and regulatory needs. Developers are responsible for their application development. This can either include doctorate the development layer or b'more traditional throw it over the wall approach to application development. There's also quite a common experience around building a center of excellence with this approach, where we can take container platforms and be delivered as a service to other consumers inside of the I T organization. This is fairly prescriptive, in the manner of which application teams would consume it. When examining the two approaches, there are pros and cons to each process. Controls and appliance are often seen as inhibitors to speak. Self service creation, starting with the infrastructure layer, leads to inconsistency, security and control concerns, which leads to compliance issues. While self service is great without visibility into the utilization and optimization of those environments, it continues the cycles of inefficient resource utilization and the true infrastructure is a code. Experience requires Dev ops related coding skills that teams often have in pockets but maybe aren't ingrained in the company culture. Luckily for us, there is a middle ground for all of this Doc Enterprise Container Cloud provides the foundation for the cloud like experience on any infrastructure without all of the out of the box security and controls that are professional services Team and your operations team spend their time designing and implementing. This removes much of the additional work and worry Run, ensuring that your clusters and experiences are consistent while maintaining the ideal self service model, no matter if it is a full stack ownership or easing the needs of I T operations. We're also bringing the most natural kubernetes experience today with winds to allow for multi cluster visibility that is both developer and operator friendly. Let's provides immediate feedback for the health of your applications. Observe ability for your clusters. Fast context, switching between environments and allowing you to choose the best in tool for the task at hand. Whether is three graphical user interface or command line interface driven. Combining the cloud like experience with the efficiencies of a secure supply chain that meet your needs brings you the best of both worlds. You get Dave off speed with all the security controls to meet the regulations your business lives by. We're talking about more frequent deployments. Faster time to recover from application issues and better code quality, as you can see from our clusters we have worked with were able to tie these processes back to real cost savings, riel efficiency and faster adoption. This all adds up to delivering business value to end users in the overall perceived value. Now let's look at see how we're able to actually build a secure supply chain. Help deliver these sorts of initiatives in our example. Secure Supply chain. We're utilizing doctor desktop to help with consistency of developer experience. Get hub for our source Control Jenkins for a C A C D. Tooling the doctor trusted registry for our secure container registry in the universal control playing to provide us with our secure container run time with kubernetes and swarm. Providing a consistent experience no matter where are clusters are deployed. You work with our teams of developers and operators to design a system that provides a fast, consistent and secure experience for my developers that works for any application. Brownfield or Greenfield monolith or micro service on boarding teams could be simplified with integrations into enterprise authentication services. Calls to get help repositories. Jenkins Access and Jobs, Universal Control Plan and Dr Trusted registry teams and organizations. Cooper down his name space with access control, creating doctor trusted registry named spaces with access control, image scanning and promotion policies. So now let's take a look and see what it looks like from the C I c D process, including Jenkins. So let's start with Dr Desktop from the doctor desktop standpoint, what should be utilizing visual studio code and Dr Desktop to provide a consistent developer experience. So no matter if we have one developer or 100 we're gonna be able to walk through the consistent process through docker container utilization at the development layer. Once we've made our changes to our code will be able to check those into our source code repository in this case, abusing Get up. Then, when Jenkins picks up, it will check out that code from our source code repository, build our doctor containers, test the application that will build the image, and then it will take the image and push it toward doctor trusted registry. From there, we can scan the image and then make sure it doesn't have any vulnerabilities. Then we consign them. So once we signed our images, we've deployed our application to Dev. We can actually test their application deployed in our real environment. Jenkins will then test the deployed application, and if all tests show that is good, will promote the r R Dr and Mr Production. So now let's look at the process, beginning from the developer interaction. First of all, let's take a look at our application as is deployed today. Here, we can see that we have a change that we want to make on our application. So marketing Team says we need to change containerized injure next to something more Miranda's branded. So let's take a look at visual studio coat, which will be using for I D to change our application. So here's our application. We have our code loaded, and we're gonna be able to use Dr Desktop on our local environment with our doctor desktop plug in for visual studio code to be able to build our application inside of doctor without needing to run any command line. Specific tools here is our code will be able to interact with docker, make our changes, see it >>live and be able to quickly see if our changes actually made the impact that we're expecting our application. Let's find our updated tiles for application and let's go and change that to our Miranda sized into next. Instead of containerized in genetics, so will change in the title and on the front page of the application, so that we save. That changed our application. We can actually take a look at our code here in V s code. >>And as simple as this, we can right click on the docker file and build our application. We give it a name for our Docker image and V s code will take care of the automatic building of our application. So now we have a docker image that has everything we need in our application inside of that image. So here we can actually just right click on the image tag that we just created and do run this winter, actively run the container for us and then what's our containers running? We could just right click and open it up in a browser. So here we can see the change to our application as it exists live. So once we can actually verify that our applications working as expected, weaken, stop our container. And then from here, we can actually make that change live by pushing it to our source code repository. So here we're going to go ahead and make a commit message to say that we updated to our Mantis branding. We will commit that change and then we'll push it to our source code repository again. In this case we're using get Hub to be able to use our source code repository. So here in V s code will have that pushed here to our source code repository. And then we'll move on to our next environment, which is Jenkins. Jenkins is gonna be picking up those changes for our application, and it checked it out from our source code repository. So get Hub Notifies Jenkins. That there is a change checks out. The code builds our doctor image using the doctor file. So we're getting a consistent experience between the local development environment on our desktop and then and Jenkins or actually building our application, doing our tests, pushing in toward doctor trusted registry, scanning it and signing our image. And our doctor trusted registry, then 2.4 development environment. >>So let's actually take a look at that development environment as it's been deployed. So here we can see that our title has been updated on our application so we can verify that looks good and development. If we jump back here to Jenkins, will see that Jenkins go >>ahead and runs our integration tests for a development environment. Everything worked as expected, so it promoted that image for production repository and our doctor trusted registry. Where then we're going to also sign that image. So we're signing that. Yes, we have signed off that has made it through our integration tests, and it's deployed to production. So here in Jenkins, we could take a look at our deployed production environment where our application is live in production. We've made a change automated and very secure manner. >>So now let's take a look at our doctor trusted registry where we can see our game Space for application are simple in genetics repository. From here we will be able to see information about our application image that we've pushed into the registry, such as Thean Midge signature when it was pushed by who and then we'll also be able to see the scan results of our image. In this case, we can actually see that there are vulnerabilities for our image and we'll actually take a look at that. Dr Trusted registry does binary level scanning, so we get detailed information about our individual image layers. From here, these image layers give us details about where the vulnerabilities were located and what those vulnerabilities actually are. So if we click on the vulnerability, we can see specific information about that vulnerability to give us details around the severity and more information about what, exactly is vulnerable inside of our container. One of the challenges that you often face around vulnerabilities is how, exactly we would remediate that and secure supply chain. So let's take a look at that and the example that we were looking at the vulnerability is actually in the base layer of our image. In order to pull in a new base layer of our image, we need to actually find the source of that and updated. One of the ways that we can help secure that is a part of the supply chain is to actually take a look at where we get our base layers of our images. Dr. Help really >>provides a great source of content to start from, but opening up docker help within your organization opens up all sorts of security concerns around the origins of that content. Not all images are made equal when it comes to the security of those images. The official images from Docker, However, curated by docker, open source projects and other vendors, one of the most important use cases is around how you get base images into your environment. It is much easier to consume the base operating system layer images than building your own and also trying to maintain them instead of just blindly trusting the content from doctor. How we could take a set >>of content that we find useful, such as those base image layers or content from vendors, and pull that into our own Dr trusted registry using our rearing feature. Once the images have been mirrored into a staging area of our DACA trusted registry, we can then scan them to ensure that the images meet our security requirements and then, based off the scan result, promote the image toe a public repository where we can actually sign the images and make them available to our internal consumers to meet their needs. This allows us to provide a set of curated content that we know a secure and controlled within our environment. So from here we confined our updated doctor image in our doctor trust registry, where we can see that the vulnerabilities have been resolved from a developers point of view, that's about a smooth process gets. Now let's take a look at how we could provide that secure content for developers and our own Dr Trusted registry. So in this case, we're taking a look at our Alpine image that we've mirrored into our doctor trusted registry. Here we're looking at the staging area where the images get temporarily pulled because we have to pull them in order to actually be able to scan them. So here we set up nearing and we can quickly turn it on by making active. Then we can see that our image mirroring will pull our content from Dr Hub and then make it available in our doctor trusted registry in an automatic fashion. So from here, we can actually take a look at the promotions to be able to see how exactly we promote our images. In this case, we created a promotion policy within docker trusted registry that makes it so. That content gets promoted to a public repository for internal users to consume based off of the vulnerabilities that are found or not found inside of the docker image. So are actually users. How they would consume this content is by taking a look at the public to them official images that we've made available here again, Looking at our Alpine image, we can take a look at the tags that exist. We could see that we have our content that has been made available, so we've pulled in all sorts of content from Dr Hub. In this case, we have even pulled in the multi architectural images, which we can scan due to the binary level nature of our scanning solution. Now let's take a look at Len's. Lens provides capabilities to be able to give developers a quick, opinionated view that focuses around how they would want to view, manage and inspect applications to point to a Cooper Days cluster. Lindsay integrates natively out of the box with universal control playing clam bundles so you're automatically generated. Tell certificates from UCP. Just work inside our organization. We want to give our developers the ability to see their applications and a very easy to view manner. So in this case, let's actually filter down to the application that we just deployed to our development environment. Here we can see the pot for application and we click on that. We get instant, detailed feedback about the components and information that this pot is utilizing. We can also see here in Linz that it gives us the ability to quickly switch context between different clusters that we have access to. With that, we also have capabilities to be able to quickly deploy other types of components. One of those is helm charts. Helm charts are a great way to package of applications, especially those that may be more complex to make it much simpler to be able to consume inversion our applications. In this case, let's take a look at the application that we just built and deployed. This case are simple in genetics. Application has been bundled up as a helm chart and has made available through lens here. We can just click on that description of our application to be able to see more information about the helm chart so we can publish whatever information may be relevant about our application, and through one click, we can install our helm chart here. It will show us the actual details of the home charts. So before we install it, we can actually look at those individual components. So in this case, we could see that's created ingress rule. And then it's well, tell kubernetes how to create the specific components of our application. We just have to pick a name space to to employ it, too. And in this case, we're actually going to do a quick test here because in this case, we're trying to deploy the application from Dr Hub in our universal Control plane. We've turned on Dr Content Trust Policy Enforcement. So this is actually gonna fail to deploy because we're trying to deploy application from Dr Hub. The image hasn't been properly signed in our environment. So the doctor can to trust policy enforcement prevents us from deploying our doctor image from Dr Hub. In this case, we have to go through our approved process through our secure supply chain to be able to ensure that we know our image came from, and that meets our quality standards. So if we comment out the doctor Hub repository and comment in our doctor trusted registry repository and click install, it will then install the helm chart with our doctor image being pulled from our GTR, which then has a proper signature, we can see that our application has been successfully deployed through our home chart releases view. From here, we can see that simple in genetics application, and in this case we'll get details around the actual deploy and help chart. The nice thing is that Linds provides us this capability here with home. To be able to see all the components that make up our application from this view is giving us that single pane of glass into that specific application so that we know all the components that is created inside of kubernetes. There are specific details that can help us access the applications, such as that ingress world that we just talked about gives us the details of that. But it also gives us the resource is such as the service, the deployment in ingress that has been created within kubernetes to be able to actually have the application exist. So to recap, we've covered how we can offer all the benefits of a cloud like experience and offer flexibility around dev ups and operations controlled processes through the use of a secure supply chain, allowing our developers to spend more time developing and our operators mawr time designing systems that meet our security and compliance concerns

[Music] [Music] [Applause] [Music] me [Music] [Applause] [Music] [Music] so [Music] [Music] [Applause] [Music] so [Applause] [Music] [Applause] [Music] [Music] me [Applause] [Music] [Music] [Music] [Music] [Applause] [Music] [Music] [Applause] so [Music] [Music] [Music] [Music] so [Applause] [Music] so [Applause] [Music] [Applause] [Music] [Music] um [Applause] [Music] [Music] [Music] [Music] [Applause] [Music] so so [Applause] so [Music] so welcome to cyber security insights we're excited to talk to you today about some of the key developments in the cyber security area let me start off by saying you know security's always been a board room topic boards care about it but right now it's actually getting even more important given what's happening covered 19 given the risk the world faces the fact that 70 percent of the workforce is now really working from home at vmware we have all of our employees working for we made that a mandate not just required but we're taking a cautious approach as to how they come back that's the reality of many of our customers but the bad guys are not staying still 148 increase in ransomware during this time they're just looking for every way to take advantage of innocent people working at home and then we've seen 52 percent increase of all attacks in the march time frame targeting the financial sector so it's very important that you we have a different approach to security because our belief is the security industry has been broken uh you'll see on this chart 5000 odd vendors 15 or 20 different categories and it's often i described like going to a doctor to stay healthy and she tells you you've got to take 5 000 tablets and you fall off your chest and that's just not possible you know so how do you prevent staying having 5000 tablets taking 5000 tablets to stay healthy you eat your vegetables your fruit your proteins drink your water you make it part of your hygiene and that's what needs to happen in security we've got to move away from this bolted on approach siloed approach where you've got you know various differences feels like even 5000 tablets 5000 security tools are all kind of like healthcare deem themselves very important and also from security that's just focused on threats and the new approach needs to be one that's more built-in intrinsically part of the platform like making a part of your diet more unified as opposed to just siloed across all of the key pillars of security and a lot more context-centric rather than just threat centric to do this we've been looking at kind of the value proposition of vmware we're you know about a 10.8 billion dollar company and have played across these three or four layers off being a digital foundation for the world any cloud any app any device with intrinsic security you've seen this from us several uh over the last several years what we've sought to do is layer into that diagram five or six important control points in security that we think are going to be super important to make security intrinsic let's start off on the bottom right corner of this with network security we think a new approach for network security means that if you look at data center networking or firewalls or load balancing or sd-wan what is a 30 billion dollar opportunity a new approach you know could be one way you could have in one platform all of those capabilities in something that's more software-defined that's what we've been doing uh in with nsx a platform some customers call us sort of the tesla of networking because we're taking a somewhat you know traditional hardware-defined approach to networking and building a more software-defined networking stack for security much the same way a tesla is building a software-defined car if you go to the left-hand side you see kind of the endpoints but it's two different forms of endpoint an endpoint that's on the client side near the device a laptop tablet a phone or a endpoint that's closer to the server a workload or a container and in both areas we believe we have an opposition proposition to really be the best uh security solution for endpoint and workload security identity we think there's a tremendous opportunity to be the best solution that not just some ourselves but also partners with the best of breed players for example um octa or azure active directory in cloud security we're going to do a lot ourselves for example cloud security posture management but we're also going to partner with the likes of well web gateways and and proxies like z scale or netscope and then analytics is the big kahuna because the more data that you have the more equipped you are to prevent breaches and what we believe here is this notion of what the analysts are now calling xdr collecting telemetry from all of these control points which we have exposure to network endpoint workload identity cloud and having one big data lake where you reason over this with a variety of behavioral and ai algorithms and then provide the best way by which you can protect customers from possible future security events this is something we well best because we actually collecting the most telemetry of anybody from disparate different sources and you're gonna only see this increase so vmware's proposition uh as you look at this we today have a billion dollar security business i know you're gonna listen to that and say wow where did that come from some customers call us one of the best kept uh security secrets in the industry uh a significant about that comes from network security a growing part of it now comes from endpoint security we think the opportunity is to take that billion dollar business it's about 20 000 odd customers and double or triple that by really focusing in these five or six control points you're going to see us build the best products in each of these categories but one that's intrinsic and also works between them in ways that are incredible let me give you a couple examples with carbon black we're going to make it agentless on the server side with vsphere nobody else can do that we're going to do that and you're going to see that very soon with carbon black we're going to make it unified with workspace 1 on the console so you have a unified approach there on both the console and the agent something that you also start seeing from us very soon these are things that nobody else in users can do network security you're going to see from one platform data center networking load balancing firewalls and sd-wan beautiful security-centric networking story so this is the approach for folks and now i think as we listen to several of the thought leaders and analysts you're going to hear them get into this story in more detail thank you very much let's continue in this show cyber security insights and now we'd like to explore the unified approach of security and i.t how do you unify them as a foundation for success our special guest today is chris sherman who's senior analyst at forrester and a pretty renowned security uh researcher and thought leader himself chris welcome to the show great to be here with you sanjay you know i'm sitting here in my living room in cleveland ohio as we uh ride down the curve right fighting off a cabin fever and staying healthy hope you're doing the same chris i'm doing well but listen i look at your beautiful looking um you know i can't confess that my background is my natural i've got a virtual background is that actually your living room or is that a virtual background it is this is my living room we built the house last year and it's also my little private iot lab because you know i'm a huge nerd and i love my devices we've been you know kind of a big fan of a lot of the forester research zero trust security you mentioned your research and iot uh i.t security and i'd like to explore this a little further with you chris i'm a big fan of your research read a lot of your stuff uh but let's kind of focus in you know clearly in this time having security strategy and i.t strategy be together in this current climate many organizations have had to pivot uh due to covert 19. you know one example is employees having to work at home which raises a whole host of cyber security issues and you know having reviewed the research results it makes them i think even more relevant the need for security and i.t to join forces i believe right now to defeating the cyber criminals during the pandemic um so that we don't have this risk and quite frankly you know we've been finding the risk is even higher because the bad guys aren't sleeping uh even if there's a crisis going on so maybe you can tell us a little bit more about this research and your findings absolutely yeah so you know i think the genesis of this research really started with a conversation i had with some of your team members back in november uh we talked about you know the high level of friction between these two teams right between i.t and security and frankly the lack of support that a lot of the existing tools in the market really have for you know integrating the two and when you look across the industry there really aren't a whole lot of resources for buyers or you know technology strategists that you know want to understand these dynamics and you know this is really what led to vmware commissioning forester to uh you know this past february to survey over 1400 security and it ops decision makers across the globe we really wanted to probe those dynamics right you know what's holding companies back from eliminating this friction right this really was actually the largest sample size of any commissioned study that i've been a part of here at forester and it really led to some excellent results and and data as you know from the uh published research i'm looking forward to to reading them and knowing more about it and you know i think if you think about the research and uh you know there's a shift in security driving alignment and collaboration security and it's you know kind of the top initiative we see in the next 12 months uh maybe even tell us about why the relationship between these security and id teams um you know are important whys have been strained across both you know all three of people process and technology yeah i mean so i team security really are two sides of the same coin right but unfortunately their teams have struggled to work well together for many years according to our survey date it's gotten to the point where 83 of both team staff report a negative relationship between the two it's very unfortunate but there are many reasons for this you know many reasons for this friction especially with the vp director and manager roles between the security and the ite teams you know at a high level most of this is driven by the fact that security and i.t have differing priorities right our data backs us up you know you have i.t on one side that's focused on technology efficiency and uptime and from our conversations with it staff it's clear you know they view security as philosophically opposite you know to this right often as roadblocks to accomplishing their goals and then on the other side security's top priority is as you'd expect responding to security events and incidents and preventing compromises and this difference in priorities is the source of a lot of friction also both security and i.t staff are really unhappy with the technology that the tools specifically that they're using or the security tools the c cios and csos you know that we talked to all had the same complaint they have too many disjointed tools in fact the average across our study was 27 security products on average in each organization and even the most established security solutions like take firewalls for example you know it caused some serious angst right we found that only 52 percent of respondents felt that their firewalls were satisfactory in terms of the performance and the security uh efficacy i think you know listen a couple of points i'll point point out from what you talked about that resonate deeply with us one is when you talked about uh i don't know it was 25 or 27 odd tools i'd be surprised the number of csos i talked to who say it's in the dozens one i think i always sort of keep a record for the number of tools i've heard one tell me it was like 100 different security tools i asked you know him was there a hundred different consoles so it's just the number of tools and consoles uh the other one that you resonated with me was even in one of the more mature areas like firewalls you would have thought oh people are really happy there we find the same level of dissatisfaction with people saying listen traditional hardware-based approaches appliance-based approaches lots of policy way way too complicated um now let's talk a little bit about staffing i think it's it's you know listen at the end of the day security is a team sport it does depend on products and processes and technology but there's also people and you know we security teams are understaffed they're increasingly dealing with a complex portfolio of these non-integrated products how uh is this impacting teams and what can companies you do as you advise them to reduce complexity from the plethora of different products that are often point products today well you're right right finding and training the right item security staff is really critical to the success of the respective teams unfortunately this continues to be a major pain point right across the whole industry in fact 64 of the security teams that we surveyed and 53 of the it teams reported they're understaffed but yeah i mean amid this global pandemic when most organizations are focused on surviving and you know maybe keeping the lights on or i guess in this case maybe the vpn's running right and getting by with limited resources and protecting an increasingly remote workforce it's much more difficult to collaborate and work together across teams but our data showed that one of the major results of this you know the formation of communication silos you know teams aren't communicating enough right they're they're communicating within their or organization designed for their particular use case right with very little integration and collaboration across those silos and you know this is where tools could help right most of the time though they the tools actually just reflect or amplify those silos by reinforcing the division right between the two teams ultimately organizations may be looking for technologies that can support the needs of both it and security right this will help alleviate any tension that might arise over things like competition over limited resources right ideally once the teams come together and agree on goals as well as objectives and and measures of success for that matter right they can address their technology stack inherent complexity wisely said listen the security attacks are becoming more sophisticated uh organizations are considering now i think the approach as you've described is a unified strategy to address these critical issues uh can you tell us more about how you've seen these unified approaches to security strategy being effective well so i mean it seems like we've been talking about unifying the tools and strategies by you know i.t ops and security for years right but it's only been recently that we've seen the two sides really demonstrate any appetite to actually do so unfortunately most of the tools again right on the market are focused on one or the other and integrations are only starting to really accelerate to the point where our true unified vision is even possible this not only aligns teams under common goals right having a common tool set but it also aligns workflows between those two teams and helps foster collaboration uh listen uh you mentioned a couple of these these examples are really good for people to kind of grop you know in this have you uh outside of these exams or any other sort of tangible results uh that you think companies can expect uh as they bring together their security and id strategies and make them more unified what are the results from your research you think customers can expect to gain yeah there are several other you know clear benefits right that we identified in this research right the benefits to unifying the tech stacks between it ops and security our research showed that companies with a unified strategy reported fewer security incidents fewer data breaches which makes sense right given how critical endpoint configuration and overall i.t hygiene is to the security posture of an organization also you know building security capabilities directly into the it infrastructure helps to motivate non-security staff to take some ownership right over basic security fundamentals and this all helps speed right this this increases the speed to you know both detect new threats and uh respond once they're you know identified you know time to containment right this was also validated by our survey data a common strategy really can empower both to you know mitigate risk ensure continuous compliance and improve you know their threat response uh workflows you know between the two teams really companies need to find tools that meet the needs of both teams and at the end of the day as you pointed out security is a team sport right we all benefit from working together to protect the business and its employees right from malicious actors especially in these difficult times that's great chris thank you for uh your research um um so i just encourage all of you are listening um if you want to um you know get chris's research um you know go to this url on the screen here and you'll be able to download it uh we're excited about it i mean listen you know personally when i watch it teams and security teams sometimes sort of spar each other um you know i i i think that increasingly whether the security team reports under the cio sometimes that's the case sometimes security teams report into the chief legal officer or they report maybe into the cfo wherever reporting structures are only you have to build a team sport because there's aspect of this that's policy aspects of this that are technology there are aspects of this that are people uh thank you for this research chris as always i'm a fan of uh the stuff as are all of we and what you're right so it's always good to be able to see more this is also much of the other extended uh forest to work like zero trust that have become kind of the things that i've seen now becoming more pervasive in the industry so thank you all for listening to this uh and we hope we'll continue to serve you in the course of this program cyber security insights with more insights like this it's my pleasure right now to also continue this uh cyber security insights series now with a wonderful interview um with the head of security and infrastructure at circle k suzanne hall um i've had a chance to briefly meet her prior to this and she's got an incredible vision of how infrastructure security comes together uh in the context of retail so i'm looking forward to the discussion suzanne thank you for joining us today thanks sanjay glad to be here great hey listen maybe i'll start with um you know circle okay some folks may know you in the locality in the areas where they shop or whatever have you but many folks around the country may not and we're assuming there'll be a very large audience watching this tell us a little bit about the company what you guys do uh what's your vision and how are you serving uh customers and consumers oh terrific oh well yeah so circle k uh many people do not realize it's actually a canadian-owned company we are a global uh convenience and fuel service organization uh with with offices all across north america uh large part of northern europe um and with franchises in a large part of asia as well we're the second largest convenience store company in the world and the 11th largest retailer we yeah we acquired circle k the brand um back in the early 2000's and uh our goals right now over the next five years are to try and double in size um which is a pretty aggressive goal goal considering uh our organization which really is taking a you know 60 billion dollar organization and trying to double that in the next five years so wish us luck let's focus now a little bit more on the infrastructure and security part of it um it's interesting that you own both as you think about those areas um you know how are they linked together and what have you been doing to tie uh infrastructure topics and security topics which are often you know you have a ciso and then a cto owns infrastructure in your case you own both and i think it's a classic way in which you know we're trying to kind of get traditional it teams the security work world to go you're living it then you're breathing and you're implementing your team uh how is it working out and how are you making it work yeah oh sorry it was actually a key part of me being attracted to the to this world i've been here about 18 months um i really feel for certain organizations culturally if you can make it work where security operations can function together um it really empowers your security team to move things quickly and it also gives me the opportunity to take ultimately super scarce resources from the security side and build uh more security acumen within my network teams and my hosting teams and my infra um so that i get actually really smart technologists that also get security collaborating with really great security folks that also get technology there's a lot of synergies that i that i get from that from combining these two organizations and where circle k was before i got here you know we we um did need to rapidly mature a lot of our security program um because it had just um grown uh i think the organization grew beyond the competencies of the security team before i got here and so by having both sides of that house i was really able to move things quickly um kind of i don't have to i don't have to uh negotiate between the network team and the hosting team the security team because they all report up to me and i get i get to pick who wins all the time so it works really well i'd love to talk to you but just cover it it's on on everybody's mind it's changed transformed how we all work you and i are doing this interview work from home uh if we were doing it in different concerts i have to come to you or come to us we have done this in the studio together or in an event um and certainly it's you know kind of changing the ways in which we work and family life and so on and so forth but how is it changing your business how is it changing your i.t organization uh and how have you had to adapt to um you know this time that we're sheltering place work at home yeah well it's really it's changed everything for us as i'm sure for for most of your of your clients as well um you know obviously serp okay being convenience we are uh on the front lines we are open across the globe we may have some small stores that may get closed for periodic periods of time or maybe some shortened hours but we've got convenience workers and gas station workers working around the globe through coven so we've had to change how the stores look and feel um we've had to rapidly deploy things like curbside delivery to really adjust to uh customers um wants and expectations and then we've had to take the entire back office and put people working at home which was not our culture um before this all happened and we had to do that almost like in watching a wave go across the globe as it started uh offices started closing in northern europe first uh and then and then all the way through to ireland and then and then obviously the east coast and canada and all the way through to the west coast so um we actually had a very short period of time to create a remote working uh operation um luckily enough um we had some really talented folks we put a couple different solutions in place and uh within two weeks or so we were able to get everybody working remotely that could work remotely and then that really empowered us to support all those operations folks that needed to get things like plexiglass into the stores hand sanitizers into the stores masks uh um into the stores uh to serve our customers and to serve our staff i'd like to move on um then to the um the kind of the context of this infrastructure and i.t workers and security work i.t teams and security teams working better together one of the things we find often and we did some research with forester that where companies performed well and had great you know security prevention practices breaches places where i t and security work well together and traditionally often csos uh may be separate from the infrastructure team sometimes csos don't even report into ci support elsewhere and that can be uh not intensely so sometimes intentionally but often just a silo or a warring mentality you're good evidence now where you're bringing these together let's talk a little away from technology for a second and the people process collaboration how have you been able to bring these cultures together so that they work together for the common good of either cost saving protection whatever have you yeah you know um and so i've had the benefit of being a cso and a cio and a couple different organizations and also i was in i was in consulting for many years i worked for a big four uh from a letter of cyber practice with one of the big four firms and i'll tell you cyber programs uh move fast forward best when there's a couple of key elements in place and the first one is you have to have shared goals anytime that the cyber team is trying to implement something um in that the network team isn't on board with or the network team picked a tool they don't want to implement the tool that the cyber team is as um and has selected i mean that's that's always a recipe for failure so somehow you have to really work on aligned goals and i do that even though i own the infrastructure teams and the security teams um nobody's successful if we're not all successful together and really focusing on what does success look like for for each one of the each one of our areas and look sometimes you know we do have to take some uh educated risks in the environment you know for responding to things quickly but we also don't take we don't um let those risks sort of linger and and never get remediated right so we really work together to make sure that any new risks that we're taking on we have a focus on how we're going to mitigate that and we hold ourselves accountable and um and the network team is equally accountable for responding to security events as a security team is the key element i also say to my security teams is when you're working with production operations teams and and folks you've got to have skin in the game you've got to recognize that they're trying to keep systems up and running 24 7 you know for the operations of the organization right so we can take credit cards and cash in the stores and make the sales and deliver the goods and services when we need to if the security team isn't seen as fully on board with that mission and that um that responsibility then there's there's a non-equity sort of relationship going on between the two different teams so you really need to bring them all together and make sure that everybody um understands supports each other's wins and goals it's awesome that you've been a cio and a ciso and you've seen all of these in various different companies i'm sure maybe in smaller bigger wherever have you so you're able to really relate to that uh i find the csos i talk to uh most of my relationships in the years past have been with cfos and cios uh i set myself a personal goal this year as we started getting more into security as i've been shaping that strategy of the company to meet a thousand cesars i was 15 years ago at symantec and most of the csos i know are retired and moved on so uh it's a good new way of my understanding and i find as i talk to them so refreshing the ones who are strategic like yourself uh have had tremendous experience in id or are also owned them and are able to paint a vision that's very collaborative as to as opposed to ones who don't then are also able to strategically bring teams together so it's really good to to see that i'd like to kind of just work a little bit more into security because i mean your strategy plays into the reason we're quite carbon black um and you i have some obviously you know knowledge and investment vmware but i'm listening as i was listening to prior to getting on to this you know program together you're probably doing more with carbon black which is awesome i mean it'll probably strengthen our relationship with vmware too and of course but we can talk a little bit about that what's been your history carbon black why you picked them and where do you see that going on the endpoint security um and then i'll talk a little bit about how we're trying to try that into infrastructure too yeah so um so my relationship with carbon black goes back to uh almost right after i first arrived at circle k um obviously i know uh from having come from consulting a number of different uh tools and products out there um although carbon black always had a really good reputation and strength and um i went to carbon black pretty early on and said you know here's my here's my situation i've got a little bit of carbon black and a little bit of other things in different places i really want to standardize on a single tool i really want to get to a better visibility of my overall network and of my of my risks and ultimately i want to have a single pane of glass but um that you know i've got folks working from an eyes on 24 7. um you know carbon black hands a table really quickly and had a great vision uh for how they could get us uh standardized across some different versions that we had um and when i said okay i want to do this in six weeks or fewer um they didn't say we can't make that happen um i think a lot of people on my team wish that they'd said that we can't make that happen but um but now we were able to really rather quickly um deploy and and get up to speed across all of our stores across all of our networks all of our you know we're a very distributed organization i've got offices all across north america and europe um and uh and we were able to in six weeks get get standardized and get things up and running and i had gained great visibility uh in that and i'm a big believer when looking at all sorts of tools whether they're input tools or security tools that you know you can tell whether or not you've picked the right solution if it's fit for purpose relatively quickly if it feels like it's too hard to implement if it just feels like it's you're not getting the value out of out of something in a relatively quick period of time you really do need to look at whether or not the tool you're looking at is fit for purpose in your environment and i would say the carbon black team and the carbon black tool that made it really easy for us and um you know it's giving us great visibility we have been able to uh detect and respond to a number of different instances you know retail is a very uh high threat high target industry these days um so it's been it's been super helpful in us defending um circle k in our environment and with 130 000 employees i suspect your number of endpoints are in the tens of thousands on the client side and probably just as many in terms of server-side endpoints right so your your kind of surface area of potential endpoints is pretty large oh indeed and you know but you know you have over 15 000 stores every store has multiple point of sale systems and at multiple uh computers laptops tablets devices um and that's and that's even before i go out into the uh what we call the forecourt which is where the gas dispensers and pumps are so yeah it's very complex well listen we look forward to that journey together part of what she has talked about here is a key part to our vision uh folks listening to this is to basically bring together security to make it key parts of the infrastructure both in the endpoint the network and the cloud thank you for your partnership i look forward to getting to know you and your team better um thank you also for all you're doing to serve the community during these tough times especially those workers at circle key that are the front line in the stores we appreciate you tremendously and we look forward to continuing this dialogue thank you very much thank you thank you everybody for watching this cyber security insight segments titled security as a team sport we talked about the shift in security and how security is moving to a shared responsibility model in this team sport in this segment we also discussed the benefits of a consolidated security and an i.t strategy that allows for fewer breaches and a faster response to security incidents as key benefits that have implemented a common strategy for those who have done this i encourage all of you to watch this part two of cyber security insights the securities of dual mission and we will have two security leaders discussing how security helps not only protect but help drives the business forward thank you all for watching this segment [Music] you

I'm going alive I'm live right now let's send you this link and see if you can get on here so this is private see if I can break this out this is [Music] [Music] [Music] [Music] hello they're coming you live from Chuck alley studio here in Mountain View California and I'm on YouTube live I hope I'm not securing anything outta been out there for two minutes now let's be able to do a live private stream and be able to have that account that link to people - yeah okay yes you see me voice what's up what's up what's up so this is a private link I don't know if you can hear me that's a private link and if you give the link to whoever you want to see it oh you can't hear me hmm one two one two one two three four stop that

[Music] hi and welcome to this cube conversations in the cube in the cube Studios in Palo Alto California I'm your host Sonia - Gauri and today we're joined by Justin Warren the chief analyst and managing director for pivot 9 Justin welcome to the cube thanks for having me absolutely so tell us more about pivot 9 and more about your role yes so I found a pivot 9 back in 2011 and we help customers with their positioning in marketing and their messaging that's most of what we do these days we have a background in infrastructure enterprise consulting so we most of our clients tend to be focused on the enterprise and we also perform a bunch of analyst services basic research and understanding what the market is doing which helps us to to advise our clients on what makes a good position and message to take into the market that's great and you also founded this company so tell us about how you started this company and how you navigated funding well we're entirely so funded and have been profitable for for a while now it was kind of an accident in in the early days my background was in all traditional kind of consulting working with his clients on actually building infrastructure so I've done time in the trenches in in most of the different fields so I was once a DBA rapidly de-skilling and I got bored and decided that fairly company seemed like a good idea which was of course insane as anyone who is founded the company will gladly tell you but it has worked out okay for me in the end that's great and you're also you also do a couple other things you're a co-host on the cube or you're a host on the cube and you're also contributor of Forbes so tell us about how you got into hosting the cube and how that experience has been like for you host oh you can it was was kind of a happy accident I had known Stu for many years and an opportunity came up which I happened to be at a conference that he was he was at and said hey would you like to come on the cube and do a little bit of hosting and I will we said yes and have been doing a bit of it ever since every every now and again so yeah well it's when I happened to be at the same place and I do go to most of the major tech conferences it's it's always a pleasure to come on and guest host the Q but a little bit that's awesome and we love having you on the cube and you're also contributor on Forbes so tell us more about what articles you write what what topics in fields you mostly focus on yes oh uh mostly there I focus on enterprise and and cloud a little bit of networking and information security those are my interests and and it's my background so I know the enterprise technology field pretty well and now it's just interesting it gives me an opportunity to talk to a lot of different customers and find out or both customers and vendors and find out how they think about the market what what are they trying to build why are they trying to do that and whenever I'm talking to them I'm always trying to find a way that I can educate the audience about what what this means for them so it does dovetail nicely with the work we do through pivot nine but I just found it personally interesting and quite useful to be able to communicate what people are really doing and why it's why it's a good idea I think a lot of my readers value that that honesty and the insight that they get from that writing I certainly that's what they've told me so I like listening to customer feedback so if they tell me that I start to suck then I'll have to change what I do it but until when I'll keep doing it the way up and doing it that's awesome Justin thank you so much for being on the Kuban we really appreciate you have having you here no problem thank you so much absolutely thank you so much for watching the cube this has been a cube conversation at the cube studios and pellet [Music] you [Music]

[Music] hi and welcome to this cube conversation in the cube Studios in Palo Alto California I'm your host Sonia - Gauri and today we're joined by two guests Justin Warren who is the chief analyst and managing director of pivot 9 and John Troy the chief reckoner of tech reckoning John and Justin welcome to the cube Thanks thanks for having us great so Justin you're in Melbourne Australia John your local to California let's start with Justin Justin you work at pivot 9 tell us a little bit about your role and what you do so I'm the founder and chief analyst steered pivot know and so everything is my fault we we like to help customers with positioning and messaging that's what most of them come to us for so we we maintain a pretty good research focus on the market focus on enterprise infrastructure cloud and information security and our clients come to us for help with positioning into those markets that's awesome and John you're the chief reckoner at Tech reckoning so tell us more about tech reckoning and what you do sure in in a way my keep reckoner is just might know I guess I am also the bottle washer and analyst as well we work with companies that help them with their ecosystem of technologists we work community and influence and advocacy and Deverell is the term of art that people like right now but basically we work we help communities communicate with their their their the ecosystems of which that's great and you're both a host of the cube so let's go down the line John tell us how did you get into hosting the cube and how has that experience been like I was here at cube number one we we started to realize that video streaming was available in a reasonable way at events and I believe we worked we worked with John and Dave and some of the few boats who were Bill around now to bring them to VMworld over ten years ago I was also doing it home at myself with him disappear that we bought it electronic door I'm very quickly looking very welcome to have them take over a functionality for a lot of people and Justin how about you how's your experience been yeah it's been great it's a again happy accident as things started off I happen to nice to I've known him for a few years and they he was in need of submersed hosting spots at a conference that I I happen to be at anyway and I foolishly said yes and now I've done it more than once oh it's is it gets a lot easier after you've done it two or three time are there any tips and tricks you would give okay thank you so much for being on the cube and we will see you next time [Music] you [Music]

(electronic music) >> Voiceover: Live from San Francisco, it's theCUBE, covering DevNet Create 2017. Brought to you by Cisco. >> Welcome back everyone. We are here live in San Francisco for Cisco's inaugural event. First time they're having DevNet Create, an extension of their classic DevNet program. I guess not so classic, Peter, it's been only three years. I'm John Furrier with theCUBE, and here my co-host, Peter Burris, general manager of Wikibon.com. Our next guest is Jim Bugwadia who is the founder and CEO of Nirmata startup. Welcome to theCUBE. >> Thank you, John. >> So, thanks for coming on. First question before you get started, what do you guys do? Take a minute to talk about what your company does, and why are you here at Cisco DevNet Create. >> Right. Yes, so Nirmata is a SaaS for cloud application delivery and management. So what we do is, you can think of us as a logical layer above the big three cloud providers as well as private clouds, and we provide a common set of application services for developers who are looking at multi-cloud use cases, and even edge computing moving forward, to provide a common layer. >> I was just covering SAP Sapphire last week, again, on multi-cloud again coming out. Multi-cloud is the hottest trend right now in terms of what people are seeing. And that makes a lot of sense. No one cloud is going to win it all. There's never been a winner-take-all, Jerry Chen at Greylock said that many years ago. Turns out he's right. However, you got the big cloud guys lining up. The question is, multi-cloud, is it reality yet? Or it's just hybrid IT, hybrid cloud, just the stepping stone to potentially a multi-cloud world. Your thoughts. >> Yeah, good point, and hybrid is certainly the stepping stone but what we're seeing more and more is the application sort of being chosen to go on one cloud or another. So it's not at a point where we're seeing the same application span multiple clouds but based on the workload, based on the application type, enterprises deciding whether to put them on private cloud, public cloud or a choice of public clouds. >> So, define multi-cloud real quick. Take a minute. So let's get your definition of what is multi-cloud. >> Right, so to me it's a combination of being able to choose your infrastructure services primarily, and being able to have a portable set of application components and constructs which can span either these public or private cloud deployments. And today of course it's a lot of momentum towards public cloud but private cloud is also going to continue to grow and will continue to grow for various reasons. So having that choice of deployment is really what we're seeing as multi-cloud today. >> And of course put a plug in for Wikibon and Peter's research. They just put out on a new true private cloud report and they had it pegged at a market of what? 260 billion? >> For a true private cloud, yeah! >> For a true private cloud, yes. So you're right true is going to be big. >> And John, just another point. We are actually doing a multi-cloud crowd chat tomorrow at 9 a.m. Pacific. So, anybody that wants to participate in a crowd chat about multi-cloud, 9 a.m. Pacific tomorrow. >> Okay, good plug, check it out Crowdchat.net, check it, it's going to be right there on the front page. You should get on that Jim. But I want to ask you to go to the next level. Multi-cloud, let's peel the onion a little bit. Does that mean I can run workloads on any cloud, or do I put a workload on one cloud and then I put another workload on another cloud. Or, can this workload, if the capacity is bad, move over to another cloud. It just smells like a latency problem to me. It just seems like ungettable at this point. What's your definition, is that multi-cloud? What is multi-cloud? >> Yeah, so what is happening in the developer space of course with the big adoption of containers and the push towards containerizing applications, now we have that ability to rapidly spin up services as needed on different cloud platforms. And really, a cloud becomes a place where you can have a container host and an end-point for deployment. So you combine that with management services, application management services like Nirmata, and now you do have that choice of being able to set policies either based on demand and scale or usage, or based on recovery from faults in the infrastructure to span different clouds from the same workload. >> Okay, next question for you. Great to have you on, great subject matter expert there. Thanks for answering the questions. But this one is a little bit different. If I want to secure cloud, with say Amazon, put my stuff there. You've seen mostly Test/Dev, and the Oracle CEO talks about this all the time . It's pretty much all Test/Dev. Okay that ship has sailed. Pretty much no brainer. What percentage of the workloads now, or what workloads specifically are going beyond Test and Dev that you've seen that are going into production. Because now with hybrid, it opens up more range of apps beyond Test and Dev. So certainly Test/Dev is happening, we get that. Low hanging fruit. What's the next level? >> Yeah, so I think the one way to categorize it is systems of engagement and systems of record of course. So we're seeing anything public facing whether it's mobile, web-app properties, web applications, more and more micro-services style SOA applications. Those are the next wave that's going to cloud. Data residence tends to stay with private cloud for a longer term. But even that, over time we're seeing with VPC is, with the right security constructs, being a viable public cloud, being a viable option there. >> One of the top questions we have in our CrowdChat community, that comes up all the time around DevOps. So I'm going to get your thoughts on this. What advise would you give to operations practitioners who are afraid DevOps is going to automate away their jobs. >> Jim Bugwadia: Yeah (laughing) So, yeah, great great question, and that's very far away from the reality. What's happening with DevOps is now we're getting to a better definition of what Devs need to be concerned about and what Ops needs to be concerned about, right?. And again pointing to containers as one of the enablers, microservices as another. We're seeing where application developers want to operate their own applications. They want control of their destiny. But the furthest thing from their minds is to worry about IP addressing and security concerns and things like that. So there is, and it's interesting, because enterprise DevOps is very different than what you would find in a start-up or in a cloud or internet giant, right. And there is no mythical enterprise developer who can do all of this themselves. You need a Dev and you need an Ops. >> The mythical mammoth kind of goes out of the window. We had CMO, EVP earlier on. We had, it was Matt Howard, and he is an experienced guy. But he was saying, 100 developers have ten IT supports and one security person. He sees that completely flipping around. So if you take this whole notion of the jobs are going to go away. Which I think is BS. Certainly things have to be automated, machine learning is great for that. But you can see the shift happening. There're certainly more security guys. More operational IT guys not doing escalation, doing actual, real IT. So I think, there's going to be a shift of jobs. So you might be displaced functionally. You're a plumber, now you you're a machinist. I get that. Where are the hot jobs? If that's the case, if you believe, which I think you do. >> Right >> Where are they going to shift to, what does the job profile look like. >> Yes, much like we're seeing even in software development itself. The level of abstraction and the amount of knowledge that has to be absorbed, keeps increasing. So it's more similarly in operations what we're seeing, like you mentioned, rather than being something, doing something at a low level. Now its understanding what are the best policies for, let's take security as an example, in AWS, in Azure, in private cloud. How do you now make sure you have the right visibility and governance with things like containers, microservices, where the applications are so dynamic, it cross various environments. So it is a transformation in the type of role and skill-set, and I think it's for the better. Because now you really have time to step back and look at this holistically and contribute back to the business. >> Here's a philosophical question for you, and may be Peter you could weigh in too. What single misperception about DevOps would you like to see change out in there? As people try to grasp DevOps, we hear it's a movement, we hear it's a playbook, with this, it's an Agile Manifesto, grow organically, you know, Conway's Law, All kinds of stuff we've been talking about so bottom line, what is the most misunderstood or misperceived issue about DevOps >> Yeah >> That you would like to see changed. >> Yeah, so to us, the one issue that we always emphasize is there will be a Dev and there will be an Ops. And any product that tries to minimize one role or another is not a good fit for enterprises. So, what's needed is a transformation of that Ops role to the role, from just being the direct service provider, the hands-on ops person to more of a governance curation. In some ways an architect type of role, right? And that's what we're seeing, is that Ops role is not diminished. It's actually heightened and highlighted. >> John Furrier: Great point! >> We've already talked about it in 6many respects, the idea that we're going to go from application development to pushing a button and having the business suddenly run differently is just silly. At the end of the day-- >> You think people think that's what DevOps is? Just a magical, rub the bottom and the genie pops out. >> There is a lot of people that think that DevOps is a step on the path to no Ops. To having no people involved in operations at all. And that's just not going to happen. >> So you believe that Ops is still going to be relevant. >> I think Ops is always going to be relevant. I think that Dev is going to evolve to better understand, and have greater data and visibility on what's going on in Ops. And Ops going to have greater predictability in what's going to happen from a development standpoint. So I think we will see a combination of roles. We'll see the productivity of Ops continue to grow. But the idea that this is going to be, that there is magic in here, and Gandalf is going to wave his DevOps-- >> What would Trump say about DevOps? Oh we're great at it! I've done it 10 times! >> What would Trump say? Trump would say, I think Trump would say, "I've never been to Mexico." (laughing) >> I'm going to make it amazing. We'll build a wall of IT. (laughing) I needed to bring that in, sorry, laughing about Trump earlier with the whole thing going on. Okay. Good point. Some are saying in the community, not no Ops, but new Ops. It's a new kind of Ops. >> Yeah, the way we see it is that what we think of as DevOps is splitting more into functions like application operations, security operations, and infrastructure. So really all three need to be accommodated and they need to work together. And that's sort of how we have built up Nirmata as our private software. >> And there is ops for all three of them. In fact, the last conversation we had John was, and test you on this, is that, it is the inherent quality, or the inherent distributed quality of a lot of the new applications that we're building. Absolutely dictates that we start to parse Ops up differently. >> Jim Bugwadia: Right. >> That it's no longer running it on a single machine or on a single database with a network out in a client server domain. It is inherently distributed and therefore the tasks and the responsibilities and roles associated with the operations side of that are themselves going to be inherently distributed. Which requires new ways of thinking, new conventions, and new tools. >> Jim, I want to give you a final word. Give a plug about your company. Thanks for sharing your insight by the way. Appreciate you answering the questions. What do you guys do and what's up with the company? Talk about the status, the employees, how much funding you have, how much revenue you have, what's your goals. Go lay it all out. >> Yeah, so myself, my other co-founders, our background is enterprise software and we come from a network management background where we build centralized management systems for complex networks, distributed devices, etc. What we saw happening is with cloud applications are starting to mimic that complexity. And as applications move from back-office productivity functions to these hybrid distributed mission-critical, real-life functions that we use day-to-day, there is a need for this enterprise-grade management. So that's the type of centralized management we're delivering as a service to our customers. >> You have to become network of provides so you have to have app management. I mean that's pretty much what you're doing you're bringing network management paradigm to apps versus a monolithic app in some dashboard and now it's all over the place. Multiple form factors, access methods. It's a network in the app. >> It is. Yeah and today the customers are left to cobble together about 12 to 14 different tools correlate data across tools. And what we need to do is move beyond systems with just observe and report. To being able to observe, react and learn, and do things in real-time. >> John: Be actionable. >> Exactly! >> So you guys are simplifying that process. >> Jim Bugwadia: Absolutely. >> And is it a single pane of glass, is it a service, is it a software product? >> It's a cloud service. So you can think of us an overlay across any public or private cloud. And early on, we kind of decided, the best way to deliver infrastructure is as a service and we've learned that in real life. >> People who are doing that are winning. That's what Trump would say, winning. (laughing) He would say, I am going to the data lake swamp. >> Who knows what he'd say. (laughing) >> Of course I couldn't get that in there. Drain the swamp, he didn't get the data lake swamp. >> No I got it. >> Okay, go ahead. >> So we've built Nirmata completely as a cloud service because of that philosophy that we started with. And we want to give developers and DevOps teams the choice of any platform, right? And today it's all about cloud. The edge is also very real. We have industrial IoT customers who are looking at containers. >> Yes, your world is getting your TAM, your total customer market is getting bigger and bigger as every IoT device has data on it. Because data is an asset. It's part of the app. >> I want to bring that up. Just if we have just a second John. >> Yeah go ahead. >> I'm curious because on of the things that we believe is that increasingly the whole concept of digital business is how will data feature as an asset in your business? Especially if we're creating sustaining customers. Totally buy in to the idea of the external view versus the internal view. For customers versus for employees. That for customer side, the engagement side is really driving a lot of this. But at some point in time it makes me wonder if we're going to move from a DevOps orientation to a data ops orientation. Where at the end of the day, the physics of how things run is, where is the data, what saliency to get at it, how do you handle the state of it, etc. Do you foresee a... at least, or an extension of the DevOps concept so the data as an object is something that we act upon, and we understand what role it plays in this whole bringing together a lot of piece parts to create distributed digital systems. >> I think so. Starting point of that, that we're seeing is the split between data services and behavioral services. Look, any form of programming it's all about packaging behaviors and data, right? So whether it's in a programming language, and with object-oriented it was about putting things together in a object. Now with service oriented in microservices, it's the service bound rates. So having the data services and then having the behavioral services separated gives a lot of flexibility. And then being able to move the compute to the data versus the other way around that is also very interesting. So we're working with some partners where we're looking at cross cloud data. Can we, as even services in containers are spun up under one cloud. Can we clone an entire environment into another cloud. Can we migrate some of the data efficiently? Challenges like that. >> Well Jim, we're going to recruit you. I just made a note to ping you for tomorrow's CrowdChat. To see if you could make it or one of your co-founders. Love to get your input at the community as part of sharing insight into this really fast growing, changing world of management with all this complexity. I mean there are more tools out there than ever before. They are all different types, a lot of complexity. So we hope to bring you back in the studio, or have you come in via Skype, or CrowdChat. This is theCUBE's exclusive coverage. Cisco's inaugural event, DevNet Create. I'm John Furrier, Peter Burris. Stay with us for more coverage after this short break. (electronic music) Hi, I'm April Mitchell, and I'm the senior director of strategy and planning for--

(upbeat techno music) >> Okay, welcome back to SiliconANGLE's Cube special two-day coverage of Mobile World Congress 2017. The hashtag is #MWC17. My next guess is Emily Mui who is with SAP Cloud, formerly SAP HANA Cloud. Great to see you. Thanks for coming in. >> Good seeing you again, John. It's been a ... Over a year. >> Since Sapphire, since the big news of ... >> That's right. >> The cloud team kind of really showing its stuff. >> Yes. >> That was called the the HANA Cloud. >> Yes. >> Now it's called SAP Cloud. The name changed. Give us a little bit more deeper ... Meaning behind the name, why the name changed, 'cause, you know, everyone knows what HANA is. >> Yes. >> HANA's got a great brand name. >> Right. >> Why drop HANA? What's the deal? >> Well, very good question. I like to talk about ... I've been with this product for over two years now, and I've really seen the evolution of the product. We have so many more capabilities than we did about three years ago, and a lot of it is customer-driven and demand-driven and market-driven. So what we realized is that yes, we have a lot of customers that wanted to do real-time decision, but then we also had a lot of customers that wanted to talk about IOT, use IOT. They want to talk about machine learning, they want to talk about analytics, so it's not just about HANA. So the name change really helps reflect the product and the evolution of this platform as a service that is now known as SAP Cloud Platform. >> So mainly what I hear you saying is that it's gone broader than that. So it's not ... HANA was like a Ferrari, something really good and was great at what it did ... >> [Emily] Yes. >> And that's all great, but the Cloud is more, right? >> Exactly. >> [ John] And what specifically more would you mean? Non-HANA solutions, or ... Greenfield opportunities? >> We have so many customers that do different things, and they're the ones that are helping us understand what is needed to be in the product. So there are many, and what we've learned is that there's a lot of business value that they're seeing from it, and they're the ones telling us that they're trying to be more agile, they're trying to optimize their business processes, and what's interesting is they want to become digital, and I'm not talking about the Ubers of the world, or the Airbnb. I'm talking about those traditional brick-and-mortar companies, manufacturers that are trying to figure out, how do I stay competitive? How do I get one step ahead of the game, and how do I use technology to do that? >> One of the things I love about Mobile World Congress is that it's like CES but in a different way. CES is hardcore early adopters. Yeah, Mobile World Congress is a lot of people who love the device news, yeah, so-and-so's got a new phone, 5G's going to be amazing, it's going to power autonomous vehicles. So, there's some glam and sex appeal inside with some of the tech, but it's almost like a meat and potatoes kind of show in the sense that it's mostly, it's a very business deal-oriented show. A lot of telecos trying to figure out their future, a lot of enterprises trying to figure out how things like network function virtualization works with mobile apps, so you're seeing kind of what I call the early adopter market be more of a CES, and Mobile World Congress be more of a ... Okay, how do you make it real? So this seems to be the topic that we're seeing across the hundreds of events that we go to at theCUBE a year, which is you have the Ubers and Airbnbs, the pioneers, the Facebooks. Then you have the settlers who come in and say, okay, I get it now. I understand what digital transformation means. Now I want to operationalize it. And Amazon Web Services has been so much success with their cloud, in the enterprise, of all places, now. So that's a tell sign that ... Real businesses ... >> [Emily] Yes. >> Not the unicorns, want to use the technology. >> [Emily] Right. >> Do you see the same thing, and can you give some anecdotal or specific examples of how a normal business gets SASSified, and what path does it take? >> So, a really good point and really good question. So one of the customers that is actually going to be at Mobile World Congress is Mapal, and they are a mid-size German precision tool manufacturer. And you think, how are they going to use the cloud and cloud technology to help them improve their business, and it's quite interesting, because they're trying to become digital. They are, you know, and this is ... Their way of doing business is not different from how anyone else is doing. They're trying to connect their suppliers, their customers together, and then be able to track what's happening with the tools that they're manufacturing. The whole life cycle of that tool, from the minute they actually start manufacturing to the point of selling it. But they're using technology to do that, right? And so they're using that SAP Cloud platformm creating the application, and then being able to track what's happening and then providing visibility to their customers, to everyone on the plant floor, to their suppliers, so they're connecting everyone together. >> You know, Emily, I was just talking with Jeff Frick, who runs theCUBE. We had our Silicon Valley Friday show last week, and we were talking about some of the conversations that we hear in cloud from some of the normal businesses out there, and things like microservices ... It's a geeky term, but microservices, containers, a lot of application conversations happening, so you hear that, and also you hear about integration. So these are the two hottest areas that we see, because basically, the SAP has been in the process business. We value chains and manufacturing, customer support, and CRM, ZRP, all that good stuff that goes on, but now, those are being completely shattered and reconfigured with cloud. So integration is top of mind, whether it's an IOT, internet of things or a new application. How does this all get threaded together? Can you share some insight into the SAP Cloud strategy, and what things do you offer to those customers, because that seems to be the critical decision point for most CXOs on the cloud SASSification. >> That's another good point, because we see a lot of customers trying to connect. They're trying to figure out how to get to the cloud, and no one is immediately jumping to it, so they've got different applications that they're trying to build out, but in order to do that, they have to connect their backend, right? And not all of it is cloud application. Most of it is on-premises, and so you've got legacy systems, you've got some SAP applications, you've got some other ... I shouldn't mention venture applications, and then they're trying to figure out how do you extend and create new applications? So how do you bring it all together? So integration is one of the key services that we provide. APIs, integration ... We've also invested in microservices technology. SAP's heavily looking into that and seeing how we can help those companies out there who want to leverage that type of technology. How do they bring all that together? Build small applications, connect everything together, and then build out an application that will help support their business. New opportunities for their customers to make their customer experience better, for their employees, and trying to track talent. So there are a lot of different use cases where ... >> What are the top three use cases that you're seeing there right now from your customer base, as they look at the HANA Cloud ... Well, it's not HANA Cloud. The SAP Cloud. >> Yes. >> New name. When they look at it, what do they gravitate to? What does the ... I mean, it's not all the same, but I mean, some low-hanging fruit. >> Right. >> Most people say, oh, test/dev, but probably in SAP. What is that low-hanging fruit for you guys, and where do you see more of these ... >> Integration. I mean, a lot of times, they start with integration, because they need to bring that together, but integration's kind of a means to an end. So, an example I can think of is we have a customer named Owens-Illinois. They're a glass manufacturer, another real business, right? It doesn't always sound so sexy, but the reality ... >> They're billion ... These are billion-dollar businesses out there ... >> Yes, exactly. >> That aren't called Uber, and no one's ever heard of them, but they're businesses, doing their thing. >> Exactly. And they need to be able to integrate their backend. They had this one specific requirement where they had to quickly meet the requirements of the Peruvian government, because they needed to create e-invoicing, and if they weren't able to bring together their backend systems, build out this application to do e-invoicing, their plant in Peru was going to get shut down. So, really good example ... >> [John] A critical path item. >> Exactly. Integration, and then being able to extend that. So those are really key examples of what our customers are doing, and then of course innovation, just coming up with something completely brand new. You know, there's so many examples of of those types of ... >> You know, you mention some of these traditional businesses, whether they're a glass company or a tooling company or whatever. This is really highlighting the big trend, internet of things, or IOT. AI kind of gets bolted into that 'cause it's got machine learning and using data and things. Is the digitization of business ... It's not just like IT and getting your email and things of that nature. Seeing the industrial, analog side of the business being digitized, so, with sensors ... You can't look any further than some of the more obvious consumer examples, the Tesla car, self-driving cars, drones, all have data. And so that's kind of a mental model for most folks, but it could be plant and machinery, it could be airplanes, flown off data ... This is the industrialization of this new era. >> Right. >> [John] Of data. >> Yep. >> That's connected to the internet. Therefore, it is an internet-connected device that needs to be managed. So this is a new use case that points to some of these businesses that are now digitizing. Is that a big part of the new IOT service, and how do you guys talk to that market, because some of it's not an IT market, they're like a normal business market, that might have SAP accounting software, or manufacturing software... >> Well, I mean, I think, like most companies and most people out there, everyone's a consumer, right? We talk about companies, but within those companies, we're talking about employees, people, and everyone has a phone, a smartphone of some sort, if not an iPhone, an Android device. There's so much data that's being generated. I could give an example of my teenage ... Just turned teenage boy, and I don't want him to carry cash around. He wants to go to Starbucks, so I make sure that he has an account set up. So it's easy. All that ... Just think about the way he's transacting. He walks into Starbucks, and he can pay. I can see how much he's paying, what he's buying, right? So there's so much data, and businesses are transacting in such a way that they've never had to do before. >> [John] Do you track his location? >> That too. I know when he's going in the wrong direction. He's on the wrong bus, right? So, there's so much data, and businesses have to figure out what's the best way to monetize that, to create opportunities from it, right? And to provide that experience for their customers and then come up with new solutions and new products and new services. >> That's a great parent story. I feel the same. My wife and I have the surveillance tracker, and that's part and parcel to us paying for the phone, so. >> [Emily] Right. >> Quid pro quo. If they want to pay for their own phone, they can be anonymous. But that brings us back to the customer. I want to get back to the customer impact, because the challenges are also opportunities, so what are some of the key challenges that your top customers face in the cloud. Because I think right now, it's pretty obvious that Mobile World Congress is kind of proving it's no branch of the cloud. It's really the business model behind it. Okay, I need to have my business model align with the value preposition for what we sell to customers, and how do we execute that operationally? >> [Emily] Right. >> So, take us through how you guys help customers through those challenges and turn them into opportunities. >> Well, first, John, we listen to what those challenges are. We've heard it over and over again. How do I ... How does the company become agile? How can they stay competitive? And you're always trying to stay one step ahead of your competition, and how else do you do it? So agility is really important, and when we talk about agility, we're not just talking about being able to create an opportunity quickly. It's how can you become flexible? How can you integrate your backend quickly? How do you support your new business requirements? If you're IT, how do you support your business partner very quickly? So it's about agility, and we provide the software that will help them do that. The cloud platform allows them to quickly integrate and extend those applications, and then of course, optimizing business processes. Who doesn't want to be efficient? I don't know how many businesses out there who wants to do things this old-fashioned, slow way. They're always trying to do it better and quicker. >> They got to preserve the old, but kind of bring in the new at the same time, it's a ... >> Right. So how do we help them optimize that? So they're asking us that all the time, and we're SAP, right? Our bread and butter, ERP, CRM, applications. We know business processes, so we understand what it takes to help them optimize those business processes. >> I didn't get a chance to ask Dan Lahl, who I interviewed earlier, about ... Who's Vice President of Product Marketing at SAP Cloud, your colleague. I didn't get to ask him this question, but this is important. Customers want to know ... That their partner, in this case, SAP Cloud, has a healthy ecosystem around it. Why is an ecosystem important, a healthy ecosystem important for customers, and then what does SAP Cloud doing to foster more innovation and openness and relevance in that ecosystem? >> Another really good question, because SAP has a history of building out an ecosystem for partners, and with SAP Cloud platform, what's great about it is it's technology that our partners are, today, leveraging and creating applications. So for those integrators, systems integrators who work really closely with our customers or their customers, they understand their businesses. They're very intimate and close with them. So they're developing applications that will help support their needs, and there are actually a lot of these partners. We have over a thousand applications that have been built by partners today. We have 600 partners that are building applications with SAP Cloud platform, and that's quite remarkable, considering the product has been around ... for just three, four years. Four years. So, it's really good news. Our partners are really invested in this technology. >> Can you comment on some of the big news that's happening at Mobile World Congress, specifically around this concept of an integrated solution set? So we see 5G was a big announcement by Intel. You're seeing autonomous vehicles as a showcase. You saw them at CES by the way, too ... It was an auto show there, too, but it allows people to really get a sense that it's not a stovepipe or a silo anymore of software stack solutions in that, you know, you need some bandwidth, you need some glue software, you need some third-party solution. You need to have things componentized or Lego-blocked kind of designed in, so this is kind of this new fabric. Could be IOT from machine manufacturing equipment, to wearable computers, all kind of coming in. That's kind of the new solution set. What's the vision for you guys on that? >> You know, at Mobile World Congress, we actually have a couple really cool demos. I should probably say they're not just demos, but they're actually exhibits. We've got a connected vehicle. We talk about the connected stadium, and when we talk about the connected stadium, we're talking about the whole experience of someone coming to an event and then being able to use their iPhone or their Android device and be able to buy their food, be able to understand what's happening and know what, you know, be able to go to their seats, and things like that. Help them through the whole experience with a connected vehicle. Be able to rent a car, and then be able to create an expense report, all on their phone. All of that needs integration. >> [John] It's a mashup of all kinds of stuff. >> Exactly. >> An accounting system is now part of feature of a stadium. >> [Emily] Right. >> A cool sports venue. >> Think about all those business processes that have to be integrated, and not just on the IT side, but all those business processes. So, like you said. >> The speed is critical. You have to have low latency ... >> Yes. >> And great software to make that work. >> A repository, right? To be able to collect all that data, streaming data, bring all that together, and then be able to analyze and then make decisions and then trigger actions immediately, so. >> All right, so, let's go through some of the cool highlights real quick. I know we have limited time. I want to get to it. In terms of the demos, you mentioned the stadium thing. What else do you have? Explain some of the demos, and kind of give a little bit of a quick synopsis of each demo, and the coolness of it. >> Yeah, so, definitely, like I mentioned, the connected stadium's going to be a cool factor. The connected vehicle. We're going to have a car there, so that's going to be fun to watch, so, the fact that it's all connected. It's all IOT. It's through your phone. It's rental. >> [John] What's going to be in the car demo? >> Lots. (both laugh) Through the iPad, you can see certain things. I don't want to give it all away. >> So go to the demo. If you're in Barcelona, we're here in Palo Alto. >> [Emily] We'll have examples of what exactly the ... >> But what is in the car, because, if you think about it, obviously, over the years, I've seen tons of demos on stage, certainly at Sapphire and the big events. And there's a lot of real-time dashboarding stuff. Is that some of the ... The glam and flair going on at the demos? >> That's some aspect, yep. Yes. So, I can't give anything away yet. We want people to watch when we're there, but yeah. So there's going to be some cool demos there. And then we're actually going to be showcasing ... Intel, who's also a sponsor, for this particular show. This time around. Yeah, so we're going to be showing a prototype of a really simple IOT example, where we're going to connect it with Google Home and Amazon Echo, and we're able to control this little prototype building, send elevators up and down, all through bot technology. >> So SAP as a company's moving from a back office powering 80% of the world's businesses to a much more front-end, agile solution provider with technology ... >> [Emily] Exactly. >> Using the cloud and big data. >> And digital. >> [John] And digital. >> Yeah. And all of that is because our customers are demanding it. They see it, they know that ... They trust that we can help them along the way, on the backend as well as on the integration front, and help them become digital. >> But this is the transformation you guys have been at HANA. The system of record, that's the database and software. System of engagement, that's free-flowing data, and now you have AI ... >> [Emily] Yes. >> Kind of automating a lot of that real-world examples, so that seems to be the same. Nothing changes on the SAP vision on that front. >> No, it's an evolution. So I think all the technology components are in place. So AI, predictive, machine learning, that's been around forever. It seems like it's the holy grail for marketers, for people in risk management, you name it. Everyone wants to be able to use analytics. >> It's all integrated. >> Yeah, and now you've got the database, you've got the in-memory database, you've got the streaming capabilities, you've got ... There's so many different components that are now ready and in place to make it actually a reality. So it's exciting. >> Emily Mui with SAP Cloud Group. Final words, somewhere that you'd like folks to walk away with from a customer standpoint and impact here, Mobile World Congress this week. What's the big story from your perspective? >> Big story is that we've got a great cloud platform solution that people are just learning more about, and they should learn more about it, because we've got all the components, all the services available to help them become a much more agile business, help them optimize all the business processes they have in place today and the ones they're looking to create, and then of course becoming digital. It's become a benefit for them. It's an actual benefit to become digital. >> The IOT really highlights your value proposition as a company in general, and the cloud opportunity is just right ... Right lockstep with that. Congratulations. Thanks for coming out. >> Thank you. >> Emily Mui, here inside theCUBE in Palo Alto breaking down and talking about Mobile World Congress. Special two days of coverage here at Palo Alto. I'm John Furrier, thanks for watching. (upbeat techno music) (bright instrumental music)