>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to you by Dell Technologies. Hello, everyone. And welcome back to the cubes wall to wall coverage of Dell Technologies World, The digital experience 2020. The Virtual Cube is coming at you. I'm Dave Volonte. And with me or two Great guest, my colleague and longtime business friend Howard Elias. He's the chief customer officer and president of services and digital Adele. And also joining me is Sri Raj, aka Sri Can't him Nene, who is the managing director of digital insights at Cargo, which is one of the world's largest privately held companies in the top maker and distributor of agricultural products and the things that we eat every day. Gentlemen, thanks so much for your time and coming on the Cube. Great to see you. >>Great to see you, Dave. And three. Great to see you again as well. >>Good to be with you both. So >>I wanna Howard, I wanna talk about start by talking about digital transformation. I'm gonna make it laugh. So I was talking to a customer every day or the other day, and we all talk about, you know, digital transformation. And I said, What's digital transformation to you? He said, Dave, my S a P system was 15 years old and I have to upgrade. It was like, Okay, there's eso There's a spectrum, as you know, but what do you seeing as digital transformation? What does that mean to your customers? >>Well, what we're seeing is a glimpse of the future. And first of all, Dave, Great to be with you again, uh, free and all of you out there hope everybody's safe. And, well, thanks for joining us, Adele Technologies World today. But digital transformation from our customers perspectives the technology enablement of experiences with customers, partners and employees, a swells automating processes to deliver value to the all key stakeholders. And we've just seen a glimpse of the future. Customers are accelerating their adoption of technology. We see this through necessity, right when everybody had to pivot from or toe work from home, especially those professional workers and for the most part, whether companies plan forward or not, we all embraced and learned new ways of being productive remotely, and that was all enabled by technology. But we've seen it in every walk of life. It's really an acceleration of trends that were already underway, whether it was the remote experience for professional employees, whether it's e commerce experience, whether it's telemedicine, distance learning. All of these things have been available for a while, but we've seen them be embraced and accelerated tremendously due to what we've seen over the last six months in all industries. And free will talk about what's happening specifically in the agricultural industry, and what we've seen is customers that have made investments over the years have been ableto move even faster in their specific industries. We've just on a survey of about 4600 customers around the world, and 80% have accelerated their investments in digital technology to improve the experience of their employees of their customers and of their partners. >>Yes, so So thank you for that, Howard. Three. I mean, a lot of people might think of cargo. There's physical business, but it's anything but. I mean, you've got such a huge data component to your business, but I wonder what you would add. I mean, we're maybe talk a little bit. I mean, it's such amazingly, you know, rich and deep company. But maybe talk about your digital transformation journey and at least in your sphere of the world where you're at. >>Yeah, thanks, David. You know, Howard's absolutely right. What? What Cove it has done is just accelerated the need for technology on farm and with our customers. And and certainly in the last few months, we've seen that accelerate tremendously, right? A t end of the day. Agriculture has been a technology first, um, industry for for hundreds of years, and and so we're seeing that take fold in the form of digital adoption, the use of analytics, the use of really unique sensor technologies like cameras and computer vision. Um, sound I liken it to the senses that we all have every day that we used to make decisions. Well, we're now seeing that adopted with our with our customers. And so it's a really interesting time, and I think an opportunity for for the industry to really move forward. >>I mean, in terms of the three in terms of the pandemic, you know, we we talked to a lot of customers. Howard just mentioned a survey. You certainly saw the pivot in tow work from home you know, increase in laptop momentum. And in Dell's business, we saw that you're seeing identity access, management, cloud security and point security. Even even VD I These were big tail winds early on. What did the pandemic due to your business and just in terms of your your priorities did you have to obviously shift to those things to support work from home? What happened to your digital transformation was was anything put on hold and is restarting. Can you just Yeah, I don't know what you could tell us about that, but anything you could describe and add some color to that narrative would be really helpful to our audience. >>Certainly. Yeah. You know, I think overnight we had, ah, workforce that went from being in the office toe working from home and and that just accelerated the need for for collaboration tools. Things like like teams and and Skype and Zoom have just taken off right? But also technologies that allow for virtual engagement, like white boarding and brainstorming sessions that we used to do in the office with customers and suppliers. We're now having to do in a virtual setting. So so that has just transformed how we do business on the customer. And, you know, technologies like computer vision and and sound really transform the need to to leverage labor differently. Right? One of the biggest challenges that the cove it has has placed is how labor interacts with animals and and with food production. And we've just seen a significant adoption of technology to help alleviate some of those stresses. >>Now you guys probably have seen the tongue in cheek cartoons, the covert wrecking ball, you know, the guys in the audience or the building saying digital transformation. Not on my watch in the cove, it comes in. I've often joked, uh, I guess we have to have a sense of humor in these times, but But if it ain't broke, don't fix it. We'll cove. It kind of broke everything. And Howard, when you think about digital transformation, yes, was going on before co vid. But But there are a lot of industries that hadn't been disrupted. I think about health care. I think about financial services. I think about defense. I mean, the list goes on unlike publishing, for instance, which got totally disrupted by the Internet. But now it seems like If you're not a digital business, you're out of business. Eso Are you seeing like virtually every industry adopting digital? Or are you seeing any trends that are different by industry? What are you seeing out there >>were absolutely seeing every company in every industry adopted in their own way, thinking through their business models. I mean, even think about what's happened in your local town. How technology is able enabled restaurants to dio, you know, uh, take out and delivery through digital tools, your local dry cleaner, your your local butcher and your baker. I mean, everybody's having toe be creative and reinvent. It's not just the, you know, large professional industrial financial services companies who are also reinventing. But I go back to what I said before what we're seeing. These trends were already underway. They've just been put into hyperspeed what folks were thinking about doing in two or three years we're doing into two or three months. The pivot toe work from home worldwide happened in two or three weeks, and it's not the crisis we planned for, but we're always preparing for the future. The groundwork was laid, and now it's just been accelerated. We're seeing it everywhere, including inside Adele. You know, I think about all the processes and the way we serve our employees, our customers and partners we've accelerated were adopting the product model within our own Del digital organization, for example, that's been accelerated. The move to multi cloud on having a cloud operating model no matter where the infrastructure has been accelerated. And you know, everything we've talked about on the client experience. Security models, networking model software, defined models, every every industry, every company has had to embrace this >>so sorry. I mean, I'm fascinated by your business. I mean again, I think a lot of people think of it as a real physical business. But there's so much data. You're the head of digital insights, which is You've got data running through your your entire operations. There's other things. There's there's double take words I see in your your background like aqua culture. So So how are you re imagining the future of your industry? >>That's Ah, that's a fascinating question, Dave. You know, think, Imagine this. You could listen to a shrimp eat and then turn that into unique insights about the feeding patterns on behaviors of shrimp, right? Who would have imagined 10 years ago that we would have technology that enabled us to do things like that? Right? And so, from aquaculture thio the dairy industry to, you know, grain origination. We're leveraging digital and data to really help our customers and producers make better, more informed decisions where in in the past it was really experience that allowed them toe be good farmers and and good stewards of our planet. Now we're using technology, so it's really an opportunity toe harness, the power of digital for our industry. >>Well, you know, and it's critical because we have people to feed and actually it's working. I mean, the yields that air coming out of the industry or are amazing. I know there's a lot of discussion now, but hey, you know, we're actually getting a lot of food to people. And now there's a discussion around nutrition that's that's front and center, and I presume technology and data fit in there as well. Three. I wonder if you could comment. >>Yeah, you know, by 2050 day there will be nearly 10 billion people on this planet. And to feed that growing population, we're gonna need 70% more protein on DSO. As you think about the impacts that that that growing population has on the planet. There's also, you know, nutrition. But think about sustainability. How do we how do we grow this food and get it from the place that it's produced to the place where it's consumed in a way that's a resource efficient and effective? So there's nutrition in just the middle class in Asia, you know, having a higher propensity to spend and dealing with that challenge on one end of the spectrum and then on the other end of the spectrum, being ableto really deal with with sustainability. >>I would have watched your career over the decades, and you've had so many roles, and I always used to joke with you. They give you the hardest problems if you want. If you want to get stuff done, you give it to the busiest guy. It was always Howard, you know, help us with with our own transformations. Help us do the integrations, whether it was m and a or the course, the largest in just >>industry I love a good challenge is you know, >>I do know and so I want to get. Get the update on Dell's own transformation. I've been talking to a number of your executives this week, and it looks like you know, you guys air, drinking your own champagne, dog food and whatever you wanna call it. But but bring us up to date on what you guys are doing internally. >>We are, and we're no different than any of our customers. And having Thio focus on our digital transformation agenda, I mentioned earlier the adoption of our product model, you know, moving from a project based Dell Digital and I T Organization to one that's a product model. So these are balanced teams with a product manager, a designer and developers working closely with the business and the function in an agile manner and the C I. C. D pipeline manner. And all of this again has been accelerated. We have our own del digital cloud, which is our hybrid cloud that we leverage internally. We're software defining everything, and it's really paying dividends because what we've seen literally in the last 6 to 8 months is higher levels of security, higher levels of availability, higher levels of resiliency. We've been able to handle all of the increased transactions on our e commerce engines, all at higher quality and lower costs. Now we the groundwork for this with Jen Felch in the team over the last couple of years, but again, by necessity, had to accelerate. And we've done that. And we're even moving faster now on data pipelines and really understanding all of our key processes and understanding the work flows and the data flows, working with machine learning and artificial intelligence again, exactly the way Cargill and other of our customers are doing in their businesses. I know you're talking or have talked to Doug Schmidt. You know, we've digitized and automated thousands of processes and our services organization Theobald bility on a remote basis to service our customers were we've invented new and innovative ways the service our customers remotely versus going on site, not just in break fix, deployment, remote change, management, manage services, consulting. It's just, you know, great to see all this wonderful innovation come together serving our customers. >>Thank you for that, Howard. And you, you said something that triggered me in a good way. Data pipelines. I use that term a lot. And three I wonder if you could talk about this because you're You guys have been around since the 18 hundreds, I think the largest privately held company in United States, I think, right, and probably close to one of the largest in the world. And so >>you >>got a lot of data and a lot of different places. So a huge challenge for you is okay. How do you manage those data pipelines? Those data, the data lifecycle, And I would think the company the size of cargo to the extent that you can reduce the end to end time it takes to go from raw data to insights E. That's gonna be telephone numbers for for your business and your bottom line that you can then reinvest and get back to customers, etcetera and be competitive. I wonder if you could talk about >>you >>know, that whole concept of the data pipeline And how are you using data and and some of the challenges of compressing that end to end cycle time and Leighton >>see, to >>get to insights >>that day. You know, Carlos, 155 year old company and and at our core were a supply chain company. Right? Um, you know, taking food from where it's produced, getting it through the manufacturing process, toe customers. And so at the end of the day, I I joked that not only are we have physical supply chain company, but we're also a data supply chain company. So the data value chain right is really about taking all the different inputs in data that we have in turning that into unique insights. And I don't think there's ah company on the planet in the food space that has the ability to connect those dots in the way that we dio. And so our ability to create unique, actionable insights for our customers is going to be really powerful, especially in the in the coming years. >>So talk about let's talk about Dell a little bit. I always ask, uh, technology leaders how your vendors doing for you? How did they help you through the pandemic? How would you grade del uh, in terms of its support through the pandemic? >>Dell has been absolutely fantastic, right? I mean, I think it is really need to have partners like Dell helping us achieve our mission for our customers. And I know they feel that way about us as their customers. So it's really wonderful. Toe have the type of collaboration and partnership that we do. >>Alright, Howard, Same question for you. How would you grade Del Onda? How you guys have done through the through the pandemic with regard to supporting your customers. I mean, you're you're never one toe overhype, uh, in my experience with you. But give us the your take. >>Why would grade del by what our customers say? And we do it both through direct conversations as well as the data and telemetry we get and the data and telemetry we have in terms of our NPS r R C sat scores or service level objectives that were delivering all have remained in profile. The team has really risen to the occasion. Been super creative, passionate, full of grit. We heard Alison and Angela talk about that the Dell Technologies world this morning, and our team is embodied that spirit and that great to be able to deliver. But in the conversations we're having with customers three and his peers, uh, you know, look, it's it's been a challenging time, but as you know, Dell has always focused on delivering value for the long term. We're not in it for the short term, and that has served us well. That philosophy Theobald active. We have with working with customers, eyes always about what's in the best interests of our customers in the long term. Because if we do that, it will ultimately be in the best interest of Dell. >>Well, it's It's been amazing to just watch. I mean, it's just ironic that we got hit with this at the beginning of this decade. It's gonna It's obviously gonna define. You know what we do going forward. I think we've all talked about it. It's funny. Everybody in our business and the technology business. We've become covert experts in some way, shape or form overnight. But we've talked a lot about the the things that we see as as permanent, and I think that >>you >>know you clearly the your two companies are examples of agility leaning into technology. And, as you said, Howard here for the long term, 155 years old, I think story said so well, here's to another 155 years. Gentlemen, thanks so much for coming to Cuba. Awesome guests. >>Thanks. Day. Appreciate it. >>Thank you for watching everybody. Our continuing coverage of Dell Technologies World 2020. You're watching the Cube?

>>Well, good day and welcome back to the cube as we continue our segment featuring AWS star showcase we're with now Mike Bilodeau, who's in corporate development and operations at Kong. Mike, uh, thank you for joining us here on the cube and particularly on the startup showcase. Nice to have you and pong represented here today. Thanks for having me, John. Great to be here. You bet. All right, first off, let's just tell us about pong a little bit and, and, uh, con cadet, which I know is your, your feature program, um, or, um, service. Oh, I love the name by the way. Um, but tell us a little bit about home and then what connect is all about to? Sure. So, uh, Kong as a company really came about in the past five years, our two co-founders came over from Italy in, uh, in the late, in the late aughts, early 20 teens and, uh, had a company called Mashape. >>And so what they were looking at and what they were betting on at that time was that API APIs, uh, were going to be the future of how software was built and how developers interacted with software. And so what came from that was a piece of, uh, they were running that shape as a marketplace at the time. So connecting developers sit in for an API so they can consume them and use them to build new software. And what they found was that actually the most valuable piece of technology that they created was the backbone for running that marketplace. And that backbone is what Kong is. And so they created it to be able to handle a massive amount of traffic, a massive amount of API APIs, all simultaneously. This is a problem that a lot of enterprises have, especially now that we've started to get some microservices, uh, started to, to have more distributed technologies. >>And so what Kong is really is it's a way to manage all of those different API APIs, all of the connections between different microservices, uh, through a single platform, which is called connect. And now that we've started to have Coobernetti's, uh, the, sort of the birth and the, the nascent space of service mesh con connect allows all of those connections to be managed and to be secured and made reliable, uh, through a single platform. So what's driving this right. I mean, um, you, you mentioned micro services, um, and Coobernetti's, and that environment, which is kind of facilitating, you know, this, uh, I guess transformation you might say. Um, but what's the big driver in your opinion, in terms of, of what's pushing this microservices phenomenon, if you will, or this revolution. Sure. And when I think it starts out at, at the simple active of technology acceleration in general, um, so when you look at just the, the real shifts that have come in enterprise, uh, especially looking, you know, start with that at the cloud, but you could even go back to VMware and virtualization is it's really about allowing people to build software more rapidly. >>Um, all of these different innovations that have happened, you know, with cloud, with virtualization now with containers, Kubernetes, microservices, they're really focused on making it, uh, so that developers can build software a lot more quickly, uh, develop the, the latest and greatest in a more rapid way. >>A huge driver out of this is just making it easier for developers, for organizations to bring new technologies to market. Uh, and we see that as a kind of a key driver in a lot of these decisions that are being made. I think another piece of it that's really coming about is looking at, uh, security, uh, as a really big component, you know, do you have a huge monolithic app? Uh, it can become very challenging to actually secure that if somebody gets into kind of that initial, uh, into the, the initial ops space, they're really past the point of no return and can get access to some things that you might not want them to similar for compliance and governance reasons that becomes challenging. So I think you're seeing this combination of where people are looking at breaking things into smaller pieces, even though it does come with its own challenges around security, um, that you need to manage, it's making it so that, uh, there's less ability to just get in and cause a lot of damage kind of all at once. Often Melissa malicious attackers. >>Yeah. You bring up security. And so, yeah, to me, it's almost, in some cases it's almost counterintuitive. I think about, I've got the, if I got this model, the gap and I've got a big parameter around it, right. And I know that I can confine this thing. I can contain this. This is good. Now microservices, now I got a lot of, it's almost like a lot of villages, right. They're all around. And, and uh, I don't have the castle anymore. I've got all these villages, so I have to build walls around all these villages. Right. But you're saying that there that's actually easier to do, or at least you're more capable of doing that now as opposed to living >>Three years ago. Well, you can almost think of it, uh, as if you have this little just right, and you might, um, if you have one castle and somebody gets inside, they're going to be able to find whatever treasury may have, you know, to extend the analogy here a bit, but now it's different, uh, 50 different villages that, you know, uh, an attacker needs to look in, it starts to become really time-consuming and really difficult. And now when you're looking at, especially this idea of kind of cybersecurity, um, the ability to secure a monolithic app is typically not all that different from what you can do with a microservice or with a once you get past that initial point, instead of thinking of it, you know, I have my one wall around everything, you know, think of it almost as a series of walls where it gets more and more difficult. Again, this all depends on, uh, that you're, you're managing that security well, which can get really time-consuming more than anything else and challenging from a pure management standpoint, but from an actual security posture, it is a way of where you can strengthen it, uh, because you're, you're creating more, um, more difficult ways of accessing information for attackers, as well as just more layers potentially of security. >>But what do you do to lift that burden then from, from the customer? Because like you said, that that that's a concern they really don't want to have. Right. They want, they want you to do that. They want somebody to do that for them. So what can, what do you do to alleviate those kinds of stress >>On their systems? Yeah, it's a great question. And this is really where the idea of API management and, um, in it's in its infancy came from, was thinking about, uh, how do we extract a way these different tasks that people don't really want to do when they're managing, uh, how API, how people can interact with their API APIs, whether that be a device or another human, um, and part of that is just taking away. So what we do and what API gate management tools have always done is abstract that into a, a new piece of software. So instead of having to kind of individually develop and write code for security, for logging, for, you know, routing logic, all these different pieces of how those different APIs will communicate with each other, we're putting that into a single piece of software and we're allowing that to be done in a really easy way. >>And so what we've done now with con connect and where we've extended that to you, is making it even easier to do that at a microservices level of scale. So if you're thinking about hundreds or thousands of different microservices that you understand and be able to manage, that's what we're really building to allow people to do. And so that comes with, you know, being able to, to make it extremely easy, uh, to, to actually add policies like authentication, you know, rate-limiting, whatever it may be, as well as giving people the choice to use what they want to use. Uh, we have great partners, you know, looking at the Datadog's, the Okta's of the world who provide a pretty, pretty incredible product. We don't necessarily want to reinvent the wheel on some of these things that are already out there, and that are widely loved and accepted by, uh, technology, practitioners and developers. We just want to make it really easy to actually use those, uh, those different technologies. And so that's, that's a lot of what we're doing is providing a, a way to make it easy to add this, you know, these policies and this logic into each one of these different services. >>So w if you're providing these kinds of services, right. And, and, and, and they're, they're, they're new, right. Um, and you're merging them sometimes with kind of legacy, uh, components, um, that transition or that interaction I would assume, could be a little complex. And, and you've, you've got your work cut out for you in some regards to kind of retrofit in some respects to make this seamless, to make this smooth. So maybe shine a little light on that process in terms of not throwing all the, you know, the bath out, you know, with, with the baby, all the water here, but just making sure it all works right. And that it makes it simple and, and, um, takes away that kind of complexity that people might be facing. >>Yeah, that's really the name of the game. Uh, we, we do not believe that there is a one size fits all approach in general, to how people should build software. Uh, there are going to need instances aware of building a monolithic app. It makes the most sense. There are going to be instances where building on Kubernetes makes the most sense. Um, the key thing that we want to solve is making sure that it works and that you're able to, to make the best technical decision for your products and for your organization. And so in looking at, uh, sort of how we help to solve that problem, I think the first is that we have first class support for everything. So we support, you know, everything down to, to kind of the oldest bare metal servers to NAMS, to containers across the board. Uh, and, and we had that mindset with every product that we brought to market. >>So thinking about our service mesh offering, for instance, um, Kula is the open source project that under tens now are even, but looking at Kumo, one of the first things that we did when we brought it out, because we saw this gap in space was to make sure that that adds first-class support for and chance at the time that wasn't something that was commonly done at all. Uh, now, you know, there there's more people are moving in that direction because they do see it as a need, which is great for the space. Um, but that's something where we, we understand that the important thing is making sure your point, you said it kind of the exact way that we like to, which is it needs to be reliable. It needs work. So I have a huge estate of, you know, older applications, older, uh, you know, potentially environments, even. I might have data centers that might've cloud being, trying to do everything all at once. Isn't really a pragmatic approach. Always. It needs to be able to support the journey as you move to, to a more modern way of building. So in terms of going from on-premise to the cloud, running in a hybrid approach, whatever it may be, all of those things shouldn't be an all or nothing proposition. It should be a phase approach and moving to, to really where it makes sense for your business and for the specific problem >>Talking about cloud deployments, obviously AWS comes into play there in a major way for you guys. Um, tell me a little bit about that, about how you're leveraging that relationship and how you're partnering with them, and then bringing the, the value then to your customer base and kind of how long that's been going on and the kinds of work that you guys are doing together, uh, ultimately to provide this kind of, uh, exemplary product or at least options to your customers. >>Yeah, of course. I think the way that we're doing it first and foremost is that, um, we, we know exactly who AWS is and the space and, and, you know, a great number of our customers are running on AWS. So again, I think that first class support in general for AWS environments services, uh, both from the container service, their, their Kubernetes services, everything that they can have and that they offer to their customers, we want to be able to support, uh, one of the first areas of really that comes to mind in terms of first-class integration and support is thinking about Lambda and serverless. Um, so at the time when we first came out, was that, again, it was early for us, uh, or early in our journey as product and as company, uh, but really early for the space. And so how we were able to support that and how we were able to see, uh, that it could support our vision and, and what we wanted to bring as a value proposition to the market has been, you know, really powerful. So I think in looking at, you know, how we work with AWS, certainly on a partnership level of where we share a lot of the same customers, we share a very similar ethos and wanting to help people do things in the most cost-effective rapid manner possible, and to build the best software. Uh, and, you know, I mean, for us, we have a little bit of a backstory with AWS because Jeffrey's us was a, an early investor in, in common. >>Yeah, exactly. I mean, the, the whole memo that he wrote about, uh, you know, build an API or you're fired was, was certainly an inspiration to, to us and it catalyzed, uh, so much change in, in the technology landscape in general, about how everyone viewed API APIs about building a software that could be reused and, and was composable. And so that's something that, you know, we, we look at, uh, kind of carrying forward and we've been building on that momentum ever since. So, >>Well, I mean, it's just kind of take a, again, a high level, look at this in terms of microservices. And now that it's changing in terms of cloud connectivity. Thank you. Actually, I have a graphic to that. Maybe we can pull up and take a look at this and let's talk about this evolution. You know, what's occurring here a little bit, and, and as we take a look at this, um, tell us what you think those, these impacts are at the end of the day for your customers and how they're better able to provide their services and satisfy their customer needs. >>Absolutely. So this is really the heart of the connect platform and of our vision in general. Um, we'd spoken just a minute ago about thinking how we can support the entire journey or, uh, the, the enterprise reality that is managing a, a relatively complex environment of modelists different services, microservices, you know, circle assumptions, whatever it may be, uh, as well as lots of different deployment methods and underlying tech platforms. You know, if you have, uh, virtual machines and Kubernetes, whatever, again, whatever it may be. But what we look at is just the different sort of, uh, design patterns that can occur in thinking about a monolithic application. And, um, okay. Mainly that's an edge concern of thinking about how you're going to handle connectivity coming in from the edge and looking at a Kubernetes environment of where you're going to have, you know, many Kubernetes clusters that need to be able to communicate with each other. >>That's where we start to think about, uh, our ingress products and Kubernetes ingress that allows for that cross applic, uh, across application communication. And then within the application itself, and looking at service mesh, which we talked a little bit about of just how do I make sure that I can instrument and secure every transaction that's happening in a, a truly microservices, uh, deployment within Kubernetes or outside of it? How do I make sure that that's reliable and secure? And so what we look at is this is just a, uh, part of it is evolution. And part of it is going to be figuring out what works best when it, um, certainly if you're, if you're building something from scratch, it doesn't always make sense to build it, your MDP, as, you know, microservices running on Kubernetes. It probably makes sense to go with the shortest path, uh, at the same time, if you're trying to run it at massive scale and big applications and make sure they're as reliable as possible, it very well does make sense to spend the time and the effort to, to make humanize work well for you. >>And I think that's, that's the, the beauty of, of how the space is shifting is that, uh, it's, it's going towards a way of the most practical solution to get towards business value, to, to move software quicker, to give customers the value that they want to delight them to use. Amazon's, uh, you know, phrase ology, if that's, uh, if that's a word, uh, it's, it's something of where, you know, that is becoming more and more standard practice versus just trying to make sure that you're doing the, the latest and greatest for the sake of, of, uh, of doing it. >>So we've been talking about customers in, in rather generic terms in terms of what you're providing them. We talked about new surfaces that are certainly, uh, providing added value and providing them solutions to their problems. Can you give us maybe just a couple of examples of some real life success stories, where, where you've had some success in terms of, of providing services that, um, I assume, um, people needed, or at least maybe they didn't know they needed until, uh, you, you provided that kind of development that, but give us an idea of maybe just, uh, shine, a little light on some success that you've had so that people at home watching this can perhaps relate to that experience and maybe give them a reason to think a little more about calm. >>Yeah, absolutely. Uh, there, there's a number that come to mind, but certainly one of the customers that I spent a lot of time with, uh, you know, become almost friends would be with, uh, with the different, with a couple of the practitioners who work there is company called Cargill. Uh, it's a shared one with us and AWS, you know, it's one we've written about in the past, but this is one of the largest companies in the world. Um, and, uh, the, the way that they describe it is, is that if you've ever eaten a Vic muffin or eaten from McDonald's and had breakfast there, you you've used a Cargill service because they provide so much of the, the food supply chain business and the logistics for it. They had a, uh, it's a, it's an old, you know, it's a century and a half old company. >>It has a really story kind of legacy, and it's grown to be an extremely large company that's so private. Uh, but you know, they have some of the most unique challenges. I think that I've, I've seen in the space in terms of needing to be able to ensure, uh, that they're able to, to kind of move quickly and build a lot of new services and software that touch so many different spaces. So they were, uh, the challenge that was put in front of them was looking at really modernizing, you know, again, a century and a half old company modernizing their entire tech stack. And, you know, we're certainly not all of that in any way, shape or form, but we are something that can help that process quite a bit. And so, as they were migrating to AWS, as they were looking at, you know, creating a CICB process for, for really being able to ship and deploy new software as quickly as possible as they were looking at how they could distribute the, the new API APIs and services that they were building, we were helping them with every piece of that journey, um, by being able to, to make sure that the services that they deployed, uh, performed in the way that they expected them to, we're able to give them a lot of competence and being able to move, uh, more rapidly and move a lot of software over from these tried and true, uh, you know, older or more legacy of doing things to a much more cloud native built as they were looking at using Kubernetes in AWS and, and being able to support that handle scale. >>Again, we are something that was able to, to kind of bridge that gap and make sure that there weren't going to be disruptions. So there, there are a lot of kind of great reasons of why they're their numbers really speak for themselves in terms of how, uh, how much velocity they were able to get. You know, they saying them saying them out loud on the sense fake in some cases, um, because they were able to, you know, I think like something, something around the order of 20 X, the amount of new API APIs and services that they were building over a six month period, really kind of crazy crazy numbers. Um, but it is something where, you know, the, for us, we, we got a lot out of them because they were open source users. So calling is first and foremost, an open source company. >>And so they were helping us before they even became paying customers, uh, just by testing the software and providing feedback, really putting it through its paces and using it at a scale that's really hard to replicate, you know, the scale of a, uh, a couple of hundred thousand person company, right? Yeah. Talking about a win-win yeah. That worked out well. It's certainly the proof is in the pudding and I'm sure that's just one of many examples of success that you've had. Uh, we appreciate the time here and certainly the insights and wish you well on down the road. Thanks for joining us, Mike. Thanks, Sean. Thanks for having me. I've been speaking with Mike Villa from Kong. He is in corporate development and operations there on John Walls, and you're watching on the cube, the AWS startup showcase.

(upbeat music) >> Well, good day and welcome back to the Cube as we continue our segment, featuring AWS Startup Showcase and we're with now Mike Bilodeau who's in corporate development and operations at Kong. Mike, thank you for joining us here on the Cube and particularly on the Startup Showcase. Nice to have you and Kong represented here today. >> Thanks for having me, John. Great to be here. >> You better and first off let's just tell us about Kong a little bit and column cadet which I know is your feature program or service. I love the name by the way, but tell us a little bit about Kong and then what Kong is all about too? >> Sure, so Kong as a company really came about in the past five years. Our two co-founders came over from Italy in the late aughts early to 20 teens and had a company called Mashape. And so what they were looking at and what they were betting on at that time, was that APIs were going to be the future of how software was built and how developers interacted with software. And so what came from that was a piece of they were running Mashape as a marketplace at the time. So connecting developers to different APIs so they can consume them and use them to build new software. And what they found was that actually the most valuable piece of technology that they had created was the backbone for running that marketplace. And that backbone is what Kong is. And so they created it to be able to handle a massive amount of traffic, a massive amount of APIs, all simultaneously. This is a problem that a lot of enterprises have especially now that we've started to get some microservices, started to have more distributed technologies. And so what Kong is really is, it's a way to manage all of those different APIs. All of the connections between different microservices through a single platform which is Kong connect. And now that we've started to have Kubernetes the sort of the birth and the nascent space of service mesh. Kong connect allows all of those connections to be managed and to be secured and made reliable through a single platform. >> So what's driving this, right? I mean you mentioned microservices and Kubernetes and that environment which is kind of facilitating this, I guess transformation you might say. But what's the big driver in your opinion in terms of what's pushing this microservices phenomenon if you will or this revolution? >> Sure, and when I think it starts out at the simple active of technology acceleration in general. So when you look at just the real shifts that have come in enterprise to hack especially looking, you know start with that at the cloud but you could even go back to VMware and virtualization is it's really about allowing people to build software more rapidly. All of these different innovations that have happened with cloud, with virtualization, now with containers, Kubernetes, microservices they're really focused on making it so that developers can build software a lot more quickly. Develop the latest and greatest in a more rapid way. I think a huge driver out of this is just making it easier for developers, for organizations to bring new technologies to market. And we see that as a key driver in a lot of these decisions that are being made. I think another piece of it that's really coming about is looking at security as a really big component. You know we have a huge monolithic app. It can become very challenging to actually secure that. If somebody gets into the initial Ops space they're really past the point of no return and can get access to some things that you might not want them to. Similar for compliance and governance reasons, that becomes challenging. So I think you're seeing this combination of where people are looking at breaking things into smaller pieces, even though it does come with its own challenges around security that you need to manage. It's making it so that there's less ability to just get in and cause a lot of damage all at once from malicious attackers. >> Yeah, you bring up security and so, yeah to me it's almost in some cases it's almost counterintuitive. I think about if I got this model to gap and I've got a big parameter around it, right. And I know that I can confine this thing. I can contain this, this is is good. Now microservices, now got a lot of, it's almost like a lot of villages, right? They're all around. And I don't have the castle anymore. I've got all these villages. So I have to build walls around all these villages. But you're saying that that's actually easier to do or at least you're more capable of doing that now as opposed to maybe where we were two, three years ago. >> Well you can almost think of it as if you have those villages, right. And if you have one castle and somebody gets inside they're going to be able to find whatever treasure you may have you know, to extend the analogy here a bit. But now if you have 50 different villages that an attacker needs to look in it starts to become really time consuming and really difficult. And now when you're looking at, especially this idea of cybersecurity, the ability to secure a monolithic app is typically not all that different from what you can do with a microservice or once you get past that initial point. Instead of thinking of it as, you know I have my one wall around everything you now think of it almost as a series of walls where it gets more and more difficult. Again this all depends on that you're managing that security well which can get really time-consuming more than anything else and challenging from a pure management standpoint. But from an actual security posture it is a way of where you can strengthen it because you're you're creating more difficult ways of accessing information for attackers as well as just more layers potentially of security that they need to get them. >> But what do you do to lift that burden then from the customers because like you said that's a concern they really don't want to have. They want you to do that. They want somebody to do that before them. So what do you do to alleviate those kinds of stresses on their systems? >> Yeah, it's a great question. And this is really where the idea of API management in its infancy came from. Was thinking about, how do we abstract a way these different tasks that people don't really want to do when they're managing how people can interact with their APIs whether that be a device or another human? And part of that is just taking away. So what we do and what API game management tools have always done is abstract that into a new piece of software. So instead of having to kind of individually develop and write code for security, for logging, for routing logic, all these different pieces of how those different APIs will communicate with each other we're putting that into a single piece of software, And we're allowing that to be done in a really easy way. And so what we've done now with Kong connect and where we've extended that to is making it even easier to do that at a microservices level of scale. So if you're thinking about hundreds or thousands of different microservices that you need to understand and be able to manage that's what we're really building to allow people to do. And so that comes with being able to make it extremely easy to actually add policies like authentication, rate limiting whatever it may be as well as giving people the choice to use what they want to use. We have great partners looking at the Datadog's, the Okta's of the world who provide a pretty, pretty incredible product. We don't necessarily want to reinvent the wheel on some of these things that are already out there and that are widely loved and accepted by technology practitioners and developers. We just want to make it really easy to actually use those different technologies. And so that's a lot of what we're doing is providing a a way to make it easy to add these policies and this logic into each one of these different services. >> So what if you're providing these kinds of services and they're new and you're merging them sometimes with kind of legacy components? That transition or that interaction I would assume could be a little complex. And you've got your work cut out for you in some regards to kind of retrofit, right? In some respects to make this seamless, to make this smooth. So maybe you shine a little light on that process in terms of not throwing all the bath out with the baby or the water here, but just making sure it all works. And that it makes it simple and takes away that kind of complexity that people might be facing. >> Yeah, that's really the name of the game. We do not believe that there is a one size fits all approach in general to how people should build software. There are going to be instances of where building a monolithic app makes the most sense. There are going to be instances where building a Kubernetes makes the most sense. The key thing that we wonna solve is making sure that it works and that you're able to make the best technical decision for your products and for your organization. And so in looking at how we help to solve that problem, I think the first is that we have first-class support for everything. So we support everything down to kind of the oldest bare metal servers, to IBMs to containers across the board. And we've had that mindset with every product that we brought to market. So thinking about our service mesh for instance Kuma is the open-source project that all depends now on an enterprise one. But looking at Kuma, one of the first things that we did when we brought it out because we saw this gap in the space was to make sure that they have first-class support for virtual machines. At the time that wasn't something that was commonly done at all. Now more people are moving in that direction because they do see it as it need which is great for the space. But that's something where we understand that the important thing is making sure your point you said it kind of the exact way that we like to which is it needs to be reliable. It needs to work. So I have a huge estate of older applications, older potentially environments even I might have data centers, I might have cloud been trying to do everything all at once. Isn't really a pragmatic approach always. It needs to be able to support the journey as you move to a more modern way of building. So in terms of going from on-premise to the cloud, running in a hybrid approach, whatever it may be, all of those things shouldn't be an all-or-nothing proposition. It should be a phased approach and moving to really where it makes sense for your business and for the specific product. >> You've been talking about cloud deployments obviously. AWS comes into play there in a major way for you guys. Tell me a little bit about that. About how you're leveraging that relationship and how you're partnering with them and then bringing the value then to your customer base. And how long that's been going on and the kinds of work that you guys are doing together ultimately to provide this kind of exemplary product or at least options to your customers. >> Yeah, of course. I think the way that we're doing it first and foremost is that we know exactly who AWS is in the space. And great number of our customers are running on AWS. So again, I think that first-class support in general for AWS environments, services both from the container service, their Kubernetes services, everything that they can have and that they offer to their customers we wonna be able to support. One of the first areas really that comes to mind in terms of first-class integration and support is thinking about Lambda and serverless. So at the time when we first came out with that, again it was early for us or early in our journey as product and as company, but really early for the space. And so how we were able to support that and how we were able to see that it could support our vision and what we wanted to bring as a value proposition to the market has been really powerful. So I think in looking at how we work with AWS certainly on a partnership level of where we share a lot of the same customers we share a very similar ethos and wanting to help people do things in the most cost-effective rapid manner possible and to build the best software. And I mean for us we have a little bit of a backstory with AWS 'cause Jeff Bezos was an early investor in Kong. >> That didn't hurt really. Yeah exactly, I mean the whole memo that he wrote about build an API or you're fired was certainly an inspiration to us. And just it catalyzed so much change in the technology landscape in general about how everyone viewed APIs about building a software that could be reused and and was composable. And so that's something that we look at and kind of carry it forward and we've been building on that momentum ever since. >> So I'm going to just kind of take, again a high level. Look at this in terms of microservices and how that's changing in terms of cloud connectivity. Think you actually have a graphic too that maybe we can pull up and take a look at this and let's talk about this evolution. What's occurring here a little bit and as we take a look at this tell us what you think these impacts are at the end of the day for your customers and how they're better able to provide their services and satisfy their customer needs. >> Absolutely, so this is really the heart of the connect platform and of our vision in general. We've spoken just a minute ago about thinking how we can support the entire journey or the enterprise reality that is managing a relatively complex environment of monoliths, different services, microservices, serverless functions, whatever it may be as well as lots of different deployment methods and underlying tech platforms. If you have virtual machines and Kubernetes whatever it may be. But what we look at is just the different design patterns that can occur in thinking about a monolithic application. Okay, mainly that's an edge concern of thinking about how you going to handle connectivity coming in from the edge in looking at a Kubernetes environment of where you going to have many Kubernetes clusters that need to be able to communicate with each other. That's where we start to think about our ingress products and Kubernetes ingress that allows for that cross application communication. And then within the application itself and looking at service mesh which we talked a little bit about of just how do I make sure that I can instrument and secure every transaction that's happening in a truly microservices deployment within Kubernetes or outside of it? How do I make sure that that's reliable and secure? And so what we look at is part of it is evolution. And part of it is going to be figuring out what works best when. Certainly if you're building something from scratch it doesn't always make sense to build it. Your MDP as microservices running on Kubernetes it probably makes sense to go with the shortest path. At the same time if you're trying to run it at massive scale and big applications and make sure they're as reliable as possible it very well does make sense to spend the time and the effort to make Kubernetes work well for you. And I think that's the beauty of how the space is shifting is that it's going towards a way of the most practical solution to get towards business value to move software quicker to give customers the value that they want to delight them to use Amazon's phraseology if that's a word. It's something that is becoming more and more standard practice versus just trying to make sure that you're doing the latest and greatest for the sake of doing it. >> So we've been talking about customers in rather generic terms in terms of what you're providing them. We've talked about new services that are certainly providing added value and providing them with solutions to their problems. Can you give us maybe just a couple of examples of some real life success stories where you've had some success in terms of providing services that I assume people needed or at least maybe they didn't know they needed until you provided that kind of development. But give us an idea, maybe just shine a little light on some success that you've had so that people at home and are watching this can perhaps relate to that experience and maybe give them a reason to think a little more about Kong and Kong connect. >> Yeah, absolutely, there's a number that come to mind but certainly one of the customers that I have spent a lot of time with, become almost friends with a couple of the practitioners who work there, is company called Cargill. It's a shared one with us and AWS. It's one we've written about in the past but this is one of the largest companies in the world. And the way that they describe it as is that if you've ever eaten a McMuffin or eaten from McDonald's and had breakfast there, you've used a Cargill service because they provide so much of the food supply chain business and the logistics for it. You know, it's a century and a half old company. It has a really story and a legacy and it's grown to be an extremely large company that's still private. But they have some of the most unique challenges, I think that I've seen in the space in terms of needing to be able to ensure that they're able to kind of move quickly and build a lot of new services and software that touch so many different spaces. So the challenge that was put in front of them was looking at really modernizing a century and a half old company. Modernizing their entire tech stack. And we're certainly not all of that in any way shape or form but we are something that can help that process quite a bit. And so as they were migrating to AWS as they were looking at creating a CICB process for really being able to shape and deploy new software as quickly as possible. As they were looking at how they could distribute the new APIs and services that they were building, we were helping them with every piece of that journey by being able to to make sure that the services that they deployed performed in the way that they expected them to. We're able to give them a lot of confidence in being able to move more rapidly and move a lot of software over from these tried and true older or more legacy ways of doing things to a much more cloud native build. As they were looking at using Kubernetes in AWS and being able to support that handle scale, again we're something that was able to kind of bridge that gap and make sure that there weren't going to be disruptions. So there are a lot of great reasons of why their numbers really speak for themselves in terms of how much velocity they were able to get. Saying them out loud will sound fake in some cases because they were able to, you know, I think like something around the order of 20 X the amount of new APIs and services that they were building over a six month period. Really kind of crazy, crazy numbers. But it is something where, for us we got a lot out of them because they were open-source users. So Kong is first and foremost an open-source company. And so they were helping us before they even became paying customers. Just by testing the software, providing feedback, really putting it through its paces and using it at a scale that's really hard to replicate. You know the scale of a couple of hundred thousand person company, yeah. >> Talk about a win-win. That worked out well. Certainly the proof is in the pudding and I'm sure that's just one of many examples of success that you've had. We appreciate the time here and certainly the insights and I wish you well on down the road. Thanks for joining us Mike. >> Thanks John, thanks for having me. >> Been peaking with Mike Bilodeau from Kong. He is in corporate development and operations there. I'm John Walls and you're watching "On the Cube" the AWS Startup Showcase. (soft music)

>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, >>Hi. We are the Cube live covering AWS reinvent 2020. I'm Lisa Martin, and I've got one of our cube alumni back with me. Mike Miller is here. General manager of A W s AI Devices at AWS. Mike, welcome back to the Cube. >>Hi, Lisa. Thank you so much for having me. It's really great to join you all again at this virtual reinvent. >>Yes, I think last year you were on set. We have always had to. That's at reinvent. And you you had the deep race, your car, and so we're obviously socially distance here. But talk to me about deepracer. What's going on? Some of the things that have gone on the last year that you're excited >>about. Yeah, I'd love to tell. Tell you a little bit about what's been happening. We've had a tremendous year. Obviously, Cove. It has restricted our ability to have our in person races. Eso we've really gone gone gangbusters with our virtual league. So we have monthly races for competitors that culminate in the championship. Um, at reinvent. So this year we've got over 100 competitors who have qualified and who are racing virtually with us this year at reinvent. They're participating in a series of knockout rounds that are being broadcast live on twitch over the next week. That will whittle the group down to AH Group of 32 which will have a Siris of single elimination brackets leading to eight finalists who will race Grand Prix style five laps, eight cars on the track at the same time and will crown the champion at the closing keynote on December 15th this year. >>Exciting? So you're bringing a reinforcement, learning together with with sports that so many of us have been missing during the pandemic. We talked to me a little bit about some of the things that air that you've improved with Deep Racer and some of the things that are coming next year. Yeah, >>absolutely so, First of all, Deep Racer not only has been interesting for individuals to participate in the league, but we continue to see great traction and adoption amongst big customers on dare, using Deep Racer for hands on learning for machine learning, and many of them are turning to Deep Racer to train their workforce in machine learning. So over 150 customers from the likes of Capital One Moody's, Accenture, DBS Bank, JPMorgan Chase, BMW and Toyota have held Deep Racer events for their workforces. And in fact, three of those customers Accenture, DBS Bank and J. P. Morgan Chase have each trained over 1000 employees in their organization because they're just super excited. And they find that deep racers away to drive that excitement and engagement across their customers. We even have Capital one expanded this to their families, so Capital One ran a deep raise. Their Kids Cup, a family friendly virtual competition this past year were over. 250 Children and 200 families got to get hands on with machine learning. >>So I envisioned some. You know, this being a big facilitator during the pandemic when there's been this massive shift to remote work has have you seen an uptick in it for companies that talking about training need to be ableto higher? Many, many more people remotely but also train them? Is deep Racer facilitator of that? Yeah, >>absolutely. Deep Racer has ah core component of the experience, which is all virtualized. So we have, ah, console and integration with other AWS services so that racers can participate using a three d racing simulator. They can actually see their car driving around a track in a three D world simulation. Um, we're also selling the physical devices. So you know, if participants want to get the one of those devices and translate what they've done in the virtual world to the real world, they can start doing that. And in fact, just this past year, we made our deep race or car available for purchase internationally through the Amazon Com website to help facilitate that. >>So how maney deep racers air out there? I'm just curious. >>Oh, thousands. Um, you know, And there what? What we've seen is some companies will purchase you, know them in bulk and use them for their internal leagues. Just like you know, JP Morgan Chase on DBS Bank. These folks have their own kind of tracks and racers that they'll use to facilitate both in person as well as the virtual racing. >>I'm curious with this shift to remote that we mentioned a minute ago. How are you seeing deepracer as a facilitator of engagement. You mentioned engagement. And that's one of the biggest challenges that so Maney teams develops. Processes have without being co located with each other deep Brister help with that. I mean, from an engagement perspective, I think >>so. What we've seen is that Deep Racer is just fun to get your hands on. And we really lower the learning curve for machine learning. And in particular, this branch called reinforcement Learning, which is where you train this agent through trial and error toe, learn how to do a new, complex task. Um, and what we've seen is that customers who have introduced Deep Racer, um, as an event for their employees have seen ah, very wide variety of employees. Skill sets, um, kind of get engaged. So you've got not just the hardcore deep data scientists or the M L engineers. You've got Web front end programmers. You even have some non technical folks who want to get their hands dirty. Onda learn about machine learning and Deep Racer really is a nice, gradual introduction to doing that. You can get engaged with it with very little kind of coding knowledge at all. >>So talk to me about some of the new services. And let's look at some specific use case customer use cases with each service. Yeah, >>absolutely. So just to set the context. You know, Amazon's got hundreds. A ws has hundreds of thousands of customers doing machine learning on AWS. No customers of all sizes are embedding machine learning into their no core business processes. And one of the things that we always do it Amazon is We're listening to customers. You know, 90 to 95% of our road maps are driven by customer feedback. And so, as we've been talking to these industrial manufacturing customers, they've been telling us, Hey, we've got data. We've got these processes that are happening in our industrial sites. Um, and we just need some help connecting the dots like, how do we really most effectively use machine learning to improve our processes in these industrial and manufacturing sites? And so we've come up with these five services. They're focused on industrial manufacturing customers, uh, two of the services air focused around, um, predictive maintenance and, uh, the other three services air focused on computer vision. Um, and so let's start with the predictive maintenance side. So we announced Amazon Monitor On and Amazon look out for equipment. So these services both enable predictive maintenance powered by machine learning in a way that doesn't require the customer to have any machine learning expertise. So Mono Tron is an end to end machine learning system with sensors, gateway and an ML service that can detect anomalies and predict when industrial equipment will require maintenance. I've actually got a couple examples here of the sensors in the gateway, so this is Amazon monitor on these little sensors. This little guy is a vibration and temperature sensor that's battery operated, and wireless connects to the gateway, which then transfers the data up to the M L Service in the cloud. And what happens is, um, the sensors can be connected to any rotating machinery like pump. Pour a fan or a compressor, and they will send data up to the machine learning cloud service, which will detect anomalies or sort of irregular kind of sensor readings and then alert via a mobile app. Just a tech or a maintenance technician at an industrial site to go have a look at their equipment and do some preventative maintenance. So um, it's super extreme line to end to end and easy for, you know, a company that has no machine learning expertise to take advantage of >>really helping them get on board quite quickly. Yeah, >>absolutely. It's simple tea set up. There's really very little configuration. It's just a matter of placing the sensors, pairing them up with the mobile app and you're off and running. >>Excellent. I like easy. So some of the other use cases? Yeah, absolutely. >>So So we've seen. So Amazon fulfillment centers actually have, um, enormous amounts of equipment you can imagine, you know, the size of an Amazon fulfillment center. 28 football fields, long miles of conveyor belts and Amazon fulfillment centers have started to use Amazon monitor on, uh, to monitor some of their conveyor belts. And we've got a filament center in Germany that has started using these 1000 sensors, and they've already been able to, you know, do predictive maintenance and prevent downtime, which is super costly, you know, for businesses, we've also got customers like Fender, you know, who makes guitars and amplifiers and musical equipment. Here in the US, they're adopting Amazon monitor on for their industrial machinery, um, to help prevent downtime, which again can cost them a great deal as they kind of hand manufacture these high end guitars. Then there's Amazon. Look out for equipment, which is one step further from Amazon monitor on Amazon. Look out for equipment. Um provides a way for customers to send their own sensor data to AWS in order to build and train a model that returns predictions for detecting abnormal equipment behavior. So here we have a customer, for example, like GP uh, E P s in South Korea, or I'm sorry, g S E P s in South Korea there in industrial conglomerate, and they've been collecting their own data. So they have their own sensors from industrial equipment for a decade. And they've been using just kind of rule basic rules based systems to try to gain insight into that data. Well, now they're using Amazon, look out for equipment to take all of their existing sensor data, have Amazon for equipment, automatically generate machine learning models on, then process the sensor data to know when they're abnormalities or when some predictive maintenance needs to occur. >>So you've got the capabilities of working with with customers and industry that that don't have any ML training to those that do have been using sensors. So really, everybody has an opportunity here to leverage this new Amazon technology, not only for predicted, but one of the things I'm hearing is contact list, being able to understand what's going on without having to have someone physically there unless there is an issue in contact. This is not one of the words of 2020 but I think it probably should be. >>Yeah, absolutely. And in fact, that that was some of the genesis of some of the next industrial services that we announced that are based on computer vision. What we saw on what we heard when talking to these customers is they have what we call human inspection processes or manual inspection processes that are required today for everything from, you know, monitoring you like workplace safety, too, you know, quality of goods coming off of a machinery line or monitoring their yard and sort of their, you know, truck entry and exit on their looking for computer vision toe automate a lot of these tasks. And so we just announced a couple new services that use computer vision to do that to automate these once previously manual inspection tasks. So let's start with a W A. W s Panorama uses computer vision toe improve those operations and workplace safety. AWS Panorama is, uh, comes in two flavors. There's an appliance, which is, ah, box like this. Um, it basically can go get installed on your network, and it will automatically discover and start processing the video feeds from existing cameras. So there's no additional capital expense to take a W s panorama and have it apply computer vision to the cameras that you've already got deployed, you know, So customers are are seeing that, um, you know, computer vision is valuable, but the reason they want to do this at the edge and put this computer vision on site is because sometimes they need to make very low Leighton see decisions where if you have, like a fast moving industrial process, you can use computer vision. But I don't really want to incur the cost of sending data to the cloud and back. I need to make a split second decision, so we need machine learning that happens on premise. Sometimes they don't want to stream high bandwidth video. Or they just don't have the bandwidth to get this video back to the cloud and sometimes their data governance or privacy restrictions that restrict the company's ability to send images or video from their site, um, off site to the cloud. And so this is why Panorama takes this machine learning and makes it happen right here on the edge for customers. So we've got customers like Cargill who uses or who is going to use Panorama to improve their yard management. They wanna use computer vision to detect the size of trucks that drive into their granaries and then automatically assign them to an appropriately sized loading dock. You've got a customer like Siemens Mobility who you know, works with municipalities on, you know, traffic on by other transport solutions. They're going to use AWS Panorama to take advantage of those existing kind of traffic cameras and build machine learning models that can, you know, improve congestion, allocate curbside space, optimize parking. We've also got retail customers. For instance, Parkland is a Canadian fuel station, um, and retailer, you know, like a little quick stop, and they want to use Panorama to do things like count the people coming in and out of their stores and do heat maps like, Where are people visiting my store so I can optimize retail promotions and product placement? >>That's fantastic. The number of use cases is just, I imagine if we had more time like you could keep going and going. But thank you so much for not only sharing what's going on with Deep Racer and the innovations, but also for show until even though we weren't in person at reinvent this year, Great to have you back on the Cube. Mike. We appreciate your time. Yeah, thanks, Lisa, for having me. I appreciate it for Mike Miller. I'm Lisa Martin. You're watching the cubes Live coverage of aws reinvent 2020.

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube and ETR, this is Breaking Analysis with Dave Vellante. >> Despite it's faster growth and infrastructure as a service, relative to AWS and Azure, Google Cloud platform remains a third wheel in the race for cloud dominance. Google begins its Cloud Next online event starting July fourteenth in a series of nine rolling sessions that go through early September. Ahead of that, we want to update you on our most current data on Google's cloud business. Hello everyone, this is Dave Vellante, and welcome to this week's Wikibon Cube insights, powered by ETR. In this session, we'll review the current state of cloud, and Google's position in the market. We'll drill into the ETR data and share fresh insights from our partner and the Cube community. So let's get right into it. You know, Google, if you think about it, was actually very early into the cloud game. Google's 2004 IPO was a milestone event for the tech industry, and in you know many ways, it really marked the end of the post-dotcom malaise. It signaled the beginning of a new era of innovation. During this time, Google was busy building out its massive, global cloud infrastructure, probably the largest in the world, with undersea cables, global data centers, and tools like the Google file system, and of course Bigtable. But it took many years for Google to pull its head out of its ad serving butt and realize the opportunity to sell its cloud services to global enterprises. Bigtable, Google's no-sequel database, for example, was released in 2005, but it wasn't until 2015 that Google made this service available to its customers. That was the same year Google brought in VMware founder, Diane Greene to begin its enterprise journey in earnest. Now Google, they have a dizzying array of services in compute, storage, database, networking, IT ops, dev tools, machine learning, AI, analytics, big data, security, on and on and on. Name a category and it's likely that Google has something in it as a cloud service. But Google, to this day, still hasn't figured out how to sell to the enterprise. It really struggles to find the right formula. So, as you know, Google brought in Thomas Kurian from Oracle, to figure this out. Of course Kurian is, he's going to go with Google's strengths like analytics and database, but it has to have differentiation, so it comes up with unique pricing models like sustained discounts, which automatically apply discount for heavy usage, as opposed to forcing users to buy reserved instances such as what AWS does. You know Google is more aggressive partnering around multi-cloud, for instance, with Anthos, and it's smartly open-sourced Kubernetes really to minimize the importance of, physically, where workloads run. The bottom-line, however, is that these moves are necessary for Google to compete because it lags behind the leaders. And it has a long way to go before it's going to be satisfied with its cloud business. Let's look at the IaaS market in context. Now, I don't want to say it's all gloom and doom for Google. Far from it. Earnings for Q2, they're going to start rolling out later this month, but this chart shows our latest estimates of IaaS and PaaS for the big three cloud players. Now, I got to caution you, as I did before, other than AWS, which reports very clean numbers each quarter on IaaS and PaaS, we have to estimate Azure and GCP revenue because they bundle in other things. I'll give an example. Google reports its overall cloud numbers which include G Suite. Microsoft reports a category they call intelligent cloud. Now that includes public, private clouds, hybrid, sequel server, Windows server, system center, GitHub, enterprise support and consulting services. And Azure, the IaaS and PaaS numbers are also in there too. So what we have to do is to squint through the earnings reports and the 10 Ks and try to get a clean IaaS and PaaS figure for these players, and that's what we show here. Now there's really two points that we want to stress with this data. First, on a trailing 12 month basis, the big three cloud players now account for nearly 60 billion dollars in IaaS and PaaS revenue. And this 60 billion dollars, on a weighted average basis, is growing in the mid 40% range. So well on its way to being a 100 billion dollar business. Just for these three firms. And as we've reported, that's eating directly into the on-premises infrastructure install base, which is a flat to declining market. And that trend is going to play out in a big way this decade. We've predicted that public cloud is going to out pace on-prem infrastructure by more that 1800 basis points over the next 10 years, from a spending standpoint. Now the second point that I want to make relates to Google IaaS and PaaS growth. We peg it at greater than 70%, based on public statements, reading the 10 Ks and ETR data, which we'll discuss in a moment. So, very healthy growth, but from a much smaller install base than, or base than AWS and Azure. But in our view it's not enough, because AWS and Azure are so large and strong still, growth wise, that we feel Google is going to remain a distant third, really indefinitely. Nonetheless, a lot of companies would be thrilled to have a four billion dollar cloud business and there's certainly good news in the data for Google. So let's look at some of that survey data. Now, as we've reported in the past, Google pushes G Suite very hard, as part of its cloud story, and it leads often times with G Suite in its messaging. You know, but to us that's never really been that compelling. So let me start with some anecdotal data from ETR. ETR runs a regular program, they call it VENN, and in the VENN they invite clients into a private session to listen to named CIOs talk about their experience with vendors and overall spending intentions. It's a facilitated session. And we've had ETR's Eric Bradley on as a guest who directs the VENN program, and does much of the facilitation, and here's a statement from a recent VENN session quoting a CIO at a midsize Telco, that I think sums it up nicely. He says Google's G Suite is fine and dandy, but I don't see that truly as an enterprise solution. And frankly, it's still not of the quality of an Office application, talking about Microsoft. All in all I really like the infrastructure-as-a-service and the platform-as-a-service components that GCP had. And I thought they were coming along very very well in that space. Now, the reason that I share this is because the IT buyers that we speak with, you know they're very serious about exploring Google. They want options other than Azure and AWS and they see Google as having great tech and as a viable alternative. So let's talk about GCP and the enterprise. We looking, when we look into the ETR data for the most recent survey, which ran in June and early July, GCP is showing strength in one really important bellwether category, the giant public and private companies. These are the largest firms in the ETR dataset and often point to secular trends. Now, before we get into that, let's look at the picture for GCP using ETR's net score up methodology. This is fundamental to the ETR approach, and remember, each quarter ETR goes out and asks its respondents, are you planning to spend more or less? In its July survey, ETR focuses on second half spending. The next chart captures results across Google's entire portfolio. So here's the breakdown for, for Google across all sectors. 14% of the respondents are adopting new, that's the lime green. 39% plan to increase spending in the second half versus the first half, that's the forest green. Then there's a big fat middle, that's flat, and you see that in the gray area. And the 7% are spending less, with 2% replacing, that's the pinkish and dark red, respectively. So, I would say this result is mixed, in my opinion. Yeah, it's not bad, don't get me wrong, and we've, we'll see once ETR comes out of its quite period, how this compares to Azure and AWR, so remember, I can only share limited data until ETR clients get the data and have time to act on it. But this calculates out to a net score of 44%, which is respectable, but frankly not overly inspiring. So let's look across the GCP portfolio using the ETR taxonomy and see what it looks like. This chart shows the net score comparisons across three different surveys, October 19, April 20, and July 20. So reading the bars left to right, you can see Google's strong suit really is machine learning and AI. Container platforms are also very strong, as are functions, or server-less, and databases, very solid, we'll talk more about that in a minute. You know, video conferencing was just added by ETR and sure it pops up with the work from home. Cloud is actually holding firm when compared to October of last year. But surprisingly, analytics is looking a bit softer. And ETR for the first time added G Suite with, it shows a 26% net score, first time out, which is pretty tepid. I mean not very impressive at all. But overall, the picture looks pretty good for Google. So let's dig further into the giant public and private sector, that bellwether I talked about. And let's peal the onion a bit and look closer at the results from the largest companies in the dataset. So this chart shows the giant public, plus private organizations. So it would include like monster public companies but also large companies like a Cargill or a Coke Industries, if in fact they responded in this survey. And you can see, in that all important sector, it's a story of a lot of green with hardly any red, so quite a positive sign for Google within those bellwethers. Here's what I think is happening here. Is these large, and often far flung organizations, have realized that they have multiple cloud vendors, and they're asking their senior IT leadership to bring some consistency and sanity to their cloud strategies. So they look at the big three and say, okay, what's the best strategic fit for each workload? So they might say for instance let's use AWS for core IaaS, let's use Azure for productivity workloads, and we'll sprinkle some Google in for machine learning and related projects. So we do see some real strength in some of the larger strongholds for Google, although interestingly ETR sort of tells me that there's softness in the midsize and smaller companies that have powered AWS for so many years. And of course this, with Google's base, but compare that to AWS and AWS is much stronger in those smaller companies, start-ups and the like, and of course COVID's the wild car in all this. You know, we have to take that into account, and we will with Sagar Kadakia, who's ETR's director of research in the coming weeks. But I want to look at Google in the all important database category. So before we wrap, let's look at database. You remember, Google's playing catch up in the cloud and its marketing takes a more open posture around partners and things like multi-cloud and you know you can contrast that with AWS for example, but look, make no mistake, Google wants you data in their cloud, and that's why database is so strategic and so important. Look, it's the mother of all lock specs. All you got to do is look at Oracle and their success. Now, as we've reported many times, there's a new workload emerging in the cloud around this idea of the modern data warehouse. I mean I don't even like that term anymore, data warehouse, because it sounds just so static. But anyway, any rate, I'm talking about workloads that bring database, machine learning, AI, data science, compute and storage along with visualization tools to deliver real-time insights and operational analytics. Database is at the heart of everything here. Win the database and everything else falls into place. Now, Google has six or seven database products and one of the most impressive, in my opinion, is BigQuery. I mean, for those who have followed me over the years you know I love the technology behind Google's banner, but BigQuery is where much of the action is around this new workload that I'm talking about. So, let's look at, deeper at Google's position in database. This chart shows one of my favorite views. On the Y axis is the net score, or spending momentum, and on the X axis is market share or pervasiveness in the ETR dataset. The chart plots various database companies and their position within the all important giant public plus private sector. So these are the companies in the ETR survey that are the largest, and oftentimes, again, are a bellwether. And you can see Microsoft and Oracle and AWS have very strong presence on the horizontal axis. Mongo, MongoDB looms large, MemSQL, they just raised 50 million dollars this past May, MariaDB just raised another 25 million this month. You can see Couchbase and Redis, they show up, and they're on my radar. I'm learning more about those companies. Folks, database is hot. VC's are pouring money in and it's something that's very important to the Cube community to look at. And of course you see Google in the chart, with a strong net score, you know, but not the type of market presence that you see from the other big cloud players. In fact, they've pulled back a little somewhat in this last ETR survey. So despite some bright spots in the enterprise in terms of spending momentum, just not quite enough presence yet. Oh, by the way, look who's right there with Google. I know I sound like a broken record, but Snowflake is everywhere. You'll find them in AWS, you'll find them in Azure and on GCP. Now remember, Snowflake is only about one tenth the size of Google's IaaS and PaaS business. But it has stronger spending momentum than all the big guys, and it continues to creep its way to the right in terms of market share or presence. You know, but Google has great database tech and BigQuery is at the heart of its strategy to support analytics at scale, and automate the data pipeline. BigQuery's very well designed, it started as a cloud native database, it's based on server-less, it's highly scalable, and it's very cost-effective. In fact, ESG, enterprise strategy group, wrote a report comparing the TCO of the cloud databases. Let me pull that up and show you. Now the report was commissioned by Google, so I got to caution you there. But it was very well done in my opinion by a guy named Aviv Kaufmann, and you can see here it compares BigQuery with the other cloud databases, and of course, you know, BigQuery wins, got the lowest TCO, but again I thought the report was really detailed and well researched. I have no doubt that Snowflake has an answer for the big brown bar, which is on-demand cloud cost. I think ESG was making certain assumptions, maybe worst case assumptions, about the need to over-provision resources for Snowflake, which I'm sure ESG can defend, but I'll bet dollars to donuts that Snowflake, you know, has an answer to that or a comeback. I'm going to ask them. But the point I want to make here is that BigQuery was designed from day one, again, as a cloud-native database. We've been talking about that a lot. It's very efficient and is going to be competitive. So you can see, there are some bright spots in the enterprise, for Google. Okay, let's wrap up. Now, having called out some of the positives, and there are many, Google is still not getting it done in the enterprise, in my opinion. I certainly would not say too little too late, but I would say they spotted the competition a huge lead, and the only reason is Google just didn't act on the opportunity staring them in the face, within the enterprise, fast enough, and they finally woke up. But enterprise sales are, they're really hard. Thomas Kurian, for all his experience, is coming from way, way behind with regard to the enterprise go to market, systems and processes, pricing, partnerships, special deals for the enterprise. Google's still learning how to sell the business outcomes and is relying far too much on its technology chops, which, while impressive, are not going to win the day without better enterprise sales, marketing, and ecosystem integration. Now I feel like for years, Google has said to the enterprise market, give me heat and I'll add the wood. Meaning we have the best tech, go ahead and use it. That strategy just doesn't work in the enterprise. Kurian knows it and I suspect that's why Google's showing some strength within these large, giant public and private companies. They're probably applying focused sales resources to nail customer success with some of its top accounts where they have a presence, and then once they nail that they'll broaden to the market. But they got to move fast. We'll learn more about Google's intentions and its progress over the next few, next few months as they try their online event experiment, and of course we'll be there providing our wall to wall coverage. Remember, these Breaking Analysis episodes, they're all available as podcasts. ETR is shortly exiting its quiet period, this week, and will be rolling out the data, so check out etr.plus. I publish weekly on wikibon.com and siloconeangle.com and as always please comment on my LinkedIn posts, I really appreciate the feedback. This is Dave Vellante for the Cube Insights, powered by ETR. Thanks for watching everyone. We'll see you next time.

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> As you may recall, coming into the second part of 2019 we reported, based on ETR Survey data, that there was a narrowing of spending on emerging tech and an unplugging of a lot of legacy systems. This was really because people were going from experimentation into operationalizing their digital initiatives. When COVID hit, conventional wisdom suggested that there would be a flight to safety. Now, interestingly, we reported with Eric Bradley, based on one of the Venns, that a lot of CIOs were still experimenting with emerging vendors. But this was very anecdotal. Today, we have more data, fresh data, from the ETR Emerging Technology Study on private companies, which really does suggest that there's a notable decline in experimentation, and that's affecting emerging technology vendors. Hi, everybody, this is Dave Vellante, and welcome to this week's Wikibon Cube Insights, powered by ETR. Once again, Sagar Kadakia is joining us. Sagar is the Director of Research at ETR. Sagar, good to see you. Thanks for coming on. >> Good to see you again. Thanks for having me, Dave. >> So, it's really important to point out, this Emerging Tech Study that you guys do, it's different from your quarterly Technology Spending Intention Survey. Take us through the methodology. Guys, maybe you could bring up the first chart. And, Sagar, walk us through how you guys approach this. >> No problem. So, a lot of the viewers are used to seeing a lot of the results from the Technology Spending Intention Survey, or the TSIS, as we call it. That study, as the title says, it really tracks spending intentions on more pervasive vendors, right, Microsoft, AWS, as an example. What we're going to look at today is our Emerging Technology Study, which we conduct biannually, in May and November. This study is a little bit different. We ask CIOs around evaluations, awareness, planned evaluations, so think of this as pre-spend, right. So that's a major differentiator from the TSIS. That, and this study, really focuses on private emerging providers. We're really only focused on those really emerging private companies, say, like your Series B to Series G or H, whatever it may be, so, two big differences within those studies. And then today what we're really going to look at is the results from the Emerging Technology Study. Just a couple of quick things here. We had 811 CIOs participate, which represents about 380 billion in annual IT spend, so the results from this study matter. We had almost 75 Fortune 100s take it. So, again, we're really measuring how private emerging providers are doing in the largest organizations. And so today we're going to be reviewing notable sectors, but largely this survey tracks roughly 356 private technologies and frameworks. >> All right, guys, bring up the pie chart, the next slide. Now, Sagar, this is sort of a snapshot here, and it basically says that 44% of CIOs agree that COVID has decreased the organization's evaluation and utilization of emerging tech, despite what I mentioned, Eric Bradley's Venn, which suggested one CIO in particular said, "Hey, I always pick somebody in the lower left "of the magic quadrant." But, again, this is a static view. I know we have some other data, but take us through this, and how this compares to other surveys that you've done. >> No problem. So let's start with the high level takeaways. And I'll actually kind of get into to the point that Eric was debating, 'cause that point is true. It's just really how you kind of slice and dice the data to get to that. So, what you're looking at here, and what the overall takeaway from the Emerging Technology Study was, is, you know, you are going to see notable declines in POCs, of proof-of-concepts, any valuations because of COVID-19. Even though we had been communicating for quite some time, you know, the last few months, that there's increasing pressure for companies to further digitize with COVID-19, there are IT budget constraints. There is a huge pivot in IT resources towards supporting remote employees, a decrease in risk tolerance, and so that's why what you're seeing here is a rather notable number of CIOs, 44%, that said that they are decreasing their organization's evaluation and utilization of private emerging providers. So that is notable. >> Now, as you pointed out, you guys run this survey a couple of times a year. So now let's look at the time series. Guys, if you bring up the next chart. We can see how the sentiment has changed since last year. And, of course, we're isolating here on some of larger companies. So, take us through what this data means. >> No problem. So, how do we quantify what we just saw in the prior slide? We saw 44% of CIOs indicating that they are going to be decreasing their evaluations. But what exactly does that mean? We can pretty much determine that by looking at a lot of the data that we captured through our Emerging Technology Study. There's a lot going on in this slide, but I'll walk you through it. What you're looking at here is Fortune 1000 organizations, so we've really isolated the data to those organizations that matter. So, let's start with the teal, kind of green line first, because I think it's a little bit easier to understand. What you're looking at, Fortune 1000 evaluations, both planned and current, okay? And you're looking at a time series, one year ago and six months ago. So, two of the answer options that we provide CIOs in this survey, right, think about the survey as a grid, where you have seven answer options going horizontally, and then 300-plus vendors and technologies going vertically. For any given vendor, they can essentially indicate one of these options, two of them being on currently evaluating them or I plan to evaluate them in six months. So what you're looking at here is effectively the aggregate number, or the average number of Fortune 1000 evaluations. So if you look into May 2019, all the way on the left of that chart, that 24% roughly means that a quarter of selections made by Fortune 1000 of the survey, they selected plan to evaluate or currently evaluating. If you fast-forward six months, to the middle of the chart, November '19, it's roughly the same, one in four technologies that are Fortune 1000 selected, they indicated that I plan or am currently evaluating them. But now look at that big drop off going into May 2020, the 17%, right? So now one out of every six technologies, or one out of every selections that they made was an evaluation. So a very notable drop. And then if you look at the blue line, this is another answer option that we provided CIOs: I'm aware of the technology but I have no plans to evaluate. So this answer option essentially tracks awareness levels. If you look at the last six months, look at that big uptick from 44% to over 50%, right? So now, essentially one out of every two technologies, or private technologies that a CIO is aware of, they have no plans to evaluate. So this is going to have an impact on the general landscape, when we think about those private emerging providers. But there is one caveat, and, Dave, this is what you mentioned earlier, this is what Eric was talking about. The providers that are doing well are the ones that are work-from-home aligned. And so, just like a few years ago, we were really analyzing results based on are you cloud-native or are you Cloud-aligned, because those technologies are going to do the best, what we're seeing in the emerging space is now the same thing. Those emerging providers that enable organizations to maintain productivity for their employees, essentially allowing their employees to work remotely, those emerging providers are still doing well. And that is probably the second biggest takeaway from this study. >> So now what we're seeing here is this flight to perceive safety, which, to your point, Sagar, doesn't necessarily mean good news for all enterprise tech vendors, but certainly for those that are positioned for the work-from-home pivot. So now let's take a look at a couple of sectors. We'll start with information security. We've reported for years about how the perimeter's been broken down, and that more spend was going to shift from inside the moat to a distributed network, and that's clearly what's happened as a result of COVID. Guys, if you bring up the next chart. Sagar, you take us through this. >> No problem. And as you imagine, I think that the big theme here is zero trust. So, a couple of things here. And let me just explain this chart a little bit, because we're going to be going through a couple of these. What you're seeing on the X-axis here, is this is effectively what we're classifying as near term growth opportunity from all customers. The way we measure that effectively is we look at all the evaluations, current evaluations, planned evaluations, we look at people who are evaluated and plan to utilize these vendors. The more indications you get on that the more to the top right you're going to be. The more indications you get around I'm aware of but I don't plan to evaluate, or I'm replacing this early-stage vendor, the further down and on the left you're going to be. So, on the X-axis you have near term growth opportunity from all customers, and on the Y-axis you have near term growth opportunity from, really, the biggest shops in the world, your Global 2000, your Forbes Private 225, like Cargill, as an example, and then, of course, your federal agencies. So you really want to be positioned up and to the right here. So, the big takeaway here is zero trust. So, just a couple of things on this slide when we think about zero trust. As organizations accelerate their Cloud and Saas spend because of COVID-19, and, you know, what we were talking about earlier, Dave, remote work becomes the new normal, that perimeter security approach is losing appeal, because the perimeter's less defined, right? Apps and data are increasingly being stored in the Cloud. That, and employees are working remotely from everywhere, and they're accessing all of these items. And so what we're seeing now is a big move into zero trust. So, if we look at that chart again, what you're going to see in that upper right quadrant are a lot of identity and access management players. And look at the bifurcation in general. This is what we were talking about earlier in terms of the landscape not doing well. Most security vendors are in that red area, you know, in the middle to the bottom. But if you look at the top right, what are you seeing here? Unify ID, Auth0, WSO2, right, all identity and access management players. These are critical in your zero trust approach, and this is one of the few area where we are seeing upticks. You also see here BitSight, Lucideus. So that's going to be security assessment. You're seeing VECTRA and Netskope and Darktrace, and a few others here. And Cloud Security and IDPS, Intrusion Detection and Prevention System. So, very few sectors are seeing an uptick, very few security sectors actually look pretty good, based on opportunities that are coming. But, essentially, all of them are in that work-from-home aligned security stack, so to speak. >> Right, and of course, as we know, as we've been reporting, buyers have options, from both established companies and these emerging companies that are public, Okta, CrowdStrike, Zscaler. We've seen the work-from-home pivot benefit those guys, but even Palo Alto Networks, even CISCO, I asked (other speaker drowns out speech) last week, I said, "Hey, what about this pivot to work from home? "What about this zero trust?" And he said, "Look, the reality is, yes, "a big part of our portfolio is exposed "to that traditional infrastructure, "but we have options for zero trust as well." So, from a buyer's standpoint, that perceived flight to safety, you have a lot of established vendors, and that clearly is showing up in your data. Now, the other sector that we want to talk about is database. We've been reporting a lot on database, data warehouse. So, why don't you take us through the next graphic here, if you would. >> Sagar: No problem. So, our theme here is that Snowflake is really separating itself from the pack, and, again, you can see that here. Private database and data warehousing vendors really continue to impact a lot of their public peers, and Snowflake is leading the way. We expect Snowflake to gain momentum in the next few years. And, look, there's some rumors that IPOing soon. And so when we think about that set-up, we like it, because as organizations transition away from hybrid Cloud architectures to 100% or near-100% public Cloud, Snowflake is really going to benefit. So they look good, their data stacks look pretty good, right, that's resiliency, redundancy across data centers. So we kind of like them as well. Redis Labs bring a DB and they look pretty good here on the opportunity side, but we are seeing a little bit of churn, so I think probably Snowflake and DataStax are probably our two favorites here. And again, when you think about Snowflake, we continue to think more pervasive vendors, like Paradata and Cloudera, and some of the other larger database firms, they're going to continue seeing wallet and market share losses due to some of these emerging providers. >> Yeah. If you could just keep that slide up for a second, I would point out, in many ways Snowflake is kind of a safer bet, you know, we talk about flight to safety, because they're well-funded, they're established. You can go from zero to Snowflake very quickly, that's sort of their mantra, if you will. But I want to point out and recognize that it is somewhat oranges and tangerines here, Snowflake being an analytical database. You take MariaDB, for instance, I look at that, anyway, as relational and operational. And then you mentioned DataStax. I would say Couchbase, Redis Labs, Aerospike. Cockroach is really a... EValue Store. You've got some non-relational databases in there. But we're looking at the entire sector of databases, which has become a really interesting market. But again, some of those established players are going to do very well, and I would put Snowflake on that cusp. As you pointed out, Bloomberg broke the story, I think last week, that they were contemplating an IPO, which we've known for a while. >> Yeah. And just one last thing on that. We do like some of the more pervasive players, right. Obviously, AWS, all their products, Redshift and DynamoDB. Microsoft looks really good. It's just really some of the other legacy ones, like the Teradatas, the Oracles, the Hadoops, right, that we are going to be impacted. And so the claw providers look really good. >> So, the last decade has really brought forth this whole notion of DevOps, infrastructure as code, the whole API economy. And that's the piece we want to jump into now. And there are some real stand-outs here, you know, despite the early data that we showed you, where CIOs are less prone to look at emerging vendors. There are some, for instance, if you bring up the next chart, guys, like Hashi, that really are standing out, aren't they? >> That's right, Dave. So, again, what you're seeing here is you're seeing that bifurcation that we were talking about earlier. There are a lot of infrastructure software vendors that are not positioned well, but if you look at the ones at the top right that are positioned well... We have two kind of things on here, starting with infrastructure automation. We think a winner here is emerging with Terraform. Look all the way up to the right, how well-positioned they are, how many opportunities they're getting. And for the second straight survey now, Terraform is leading along their peers, Chef, Puppet, SaltStack. And they're leading their peers in so many different categories, notably on allocating more spend, which is obviously very important. For Chef, Puppet and SaltStack, which you can see a little bit below, probably a little bit higher than the middle, we are seeing some elevator churn levels. And so, really, Terraform looks like they're kind of separating themselves. And we've got this great quote from the CIO just a few months ago, on why Terraform is likely pulling away, and I'll read it out here quickly. "The Terraform tool creates "an entire infrastructure in a box. "Unlike vendors that use procedural languages, "like Ants, Bull and Chef, "it will show you the infrastructure "in the way you want it to be. "You don't have to worry about "the things that happen underneath." I know some companies where you can put your entire Amazon infrastructure through Terraform. If Amazon disappears, if your availability drops, load balancers, RDS, everything, you just run Terraform and everything will be created in 10 to 15 minutes. So that shows you the power of Terraform and why we think it's ranked better than some of the other vendors. >> Yeah, I think that really does sum it up. And, actually, guys, if you don't mind bringing that chart back up again. So, a point out, so, Mitchell Hashimoto, Hashi, really, I believe I'm correct, talking to Stu about this a little bit, he sort of led the Terraform project, which is an Open Source project, and, to your point, very easy to deploy. Chef, Puppet, Salt, they were largely disrupted by Cloud, because they're designed to automate deployment largely on-prem and DevOps, and now Terraform sort of packages everything up into a platform. So, Hashi actually makes money, and you'll see it on this slide, and things, Vault, which is kind of their security play. You see GitLab on here. That's really application tooling to deploy code. You see Docker containers, you know, Docker, really all about open source, and they've had great adoption, Docker's challenge has always been monetization. You see Turbonomic on here, which is application resource management. You can't go too deep on these things, but it's pretty deep within this sector. But we are comparing different types of companies, but just to give you a sense as to where the momentum is. All right, let's wrap here. So maybe some final thoughts, Sagar, on the Emerging Technology Study, and then what we can expect in the coming month here, on the update in the Technology Spending Intention Study, please. >> Yeah, no problem. One last thing on the zero trust side that has been a big issue that we didn't get to cover, is VPN spend. Our data is pointing that, yes, even though VPN spend did increase the last few months because of remote work, we actually think that people are going to move away from that as they move onto zero trust. So just one last point on that, just in terms of overall thoughts, you know, again, as we cover it, you can see how bifurcated all these spaces are. Really, if we were to go sector by sector by sector, right, storage and block chain and MLAI and all that stuff, you would see there's a few or maybe one or two vendors doing well, and the majority of vendors are not seeing as many opportunities. And so, again, are you work-from-home aligned? Are you the best vendor of all the other emerging providers? And if you fit those two criteria then you will continue seeing POCs and evaluations. And if you don't fit that criteria, unfortunately, you're going to see less opportunities. So think that's really the big takeaway on that. And then, just in terms of next steps, we're already transitioning now to our next Technology Spending Intention Survey. That launched last week. And so, again, we're going to start getting a feel for how CIOs are spending in 2H-20, right, so, for the back half of the year. And our question changes a little bit. We ask them, "How do you plan on spending in the back half year "versus how you actually spent "in the first half of the year, or 1H-20?" So, we're kind of, tighten the screw, so to speak, and really getting an idea of what's spend going to look like in the back half, and we're also going to get some updates as it relates to budget impacts from COVID-19, as well as how vendor-relationships have changed, as well as business impacts, like layoffs and furloughs, and all that stuff. So we have a tremendous amount of data that's going to be coming in the next few weeks, and it should really prepare us for what to see over the summer and into the fall. >> Yeah, very excited, Sagar, to see that. I just wanted to double down on what you said about changes in networking. We've reported with you guys on NPLS networks, shifting to SD-WAN. But even VPN and SD-WAN are being called into question as the internet becomes the new private network. And so lots of changes there. And again, very excited to see updated data, return of post-COVID, as we exit this isolation economy. Really want to point out to folks that this is not a snapshot survey, right? This is an ongoing exercise that ETR runs, and grateful for our partnership with you guys. Check out ETR.plus, that's the ETR website. I publish weekly on Wikibon.com and SiliconANGLE.com. Sagar, thanks so much for coming on. Once again, great to have you. >> Thank you so much, for having me, Dave. I really appreciate it, as always. >> And thank you for watching this episode of theCube Insights, powered by ETR. This Dave Vellante. We'll see you next time. (gentle music)