>> John: Hello and welcome to this cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great conversation on making machine learning easier and more affordable in an era where everybody wants more machine learning and AI. We're featuring Neural Magic with the CEO is also Cube alumni, Brian Steve. CEO, Great to see you Brian. Thanks for coming on this cube conversation. Talk about machine learning. >> Brian: Hey John, happy to be here again. >> John: What a buzz that's going on right now? Machine learning, one of the hottest topics, AI front and center, kind of going mainstream. We're seeing the success of the, of the kind of NextGen capabilities in the enterprise and in apps. It's a really exciting time. So perfect timing. Great, great to have this conversation. Let's start with taking a minute to explain what you guys are doing over there at Neural Magic. I know there's some history there, neural networks, MIT. But the, the convergence of what's going on, this big wave hitting, it's an exciting time for you guys. Take a minute to explain the company and your mission. >> Brian: Sure, sure, sure. So, as you said, the company's Neural Magic and spun out at MIT four plus years ago, along with some people and, and some intellectual property. And you summarize it better than I can cause you said, we're just trying to make, you know, AI that much easier. And so, but like another level of specificity around it is. You know, in the world you have a lot of like data scientists really focusing on making AI work for whatever their use case is. And then the next phase of that, then they're looking at optimizing the models that they built. And then it's not good enough just to work on models. You got to put 'em into production. So, what we do is we make it easier to optimize the models that have been developed and trained and then trying to make it super simple when it comes time to deploying those in production and managing them. >> Brian: You know, we've seen this movie before with the cloud. You start to see abstractions come out. Data science we saw like was like the, the secret art of being like a data scientist now democratization of data. You're kind of seeing a similar wave with machine learning models, foundational models, some call it developers are getting involved. Model complexity's still there, but, but it's getting easier. There's almost like the democratization happening. You got complexity, you got deployment, it's challenges, cost, you got developers involved. So it's like how do you grow it? How do you get more horsepower? And then how do you make developers productive, right? So like, this seems to be the thread. So, so where, where do you see this going? Because there's going to be a massive demand for, I want to do more with my machine learning. But what's the data source? What's the formatting? This kind of a stack develop, what, what are you guys doing to address this? Can you take us through and demystify this, this wave that's hitting, that everyone's seeing? >> Brian: Yeah. Now like you said, like, you know, the democratization of all of it. And that brings me all the way back to like the roots of open source, right? When you think about like, like back in the day you had to build your own tech stack yourself. A lot of people probably probably don't remember that. And then you went, you're building, you're always starting on a body of code or a module that was out there with open source. And I think that's what I equate to where AI has gotten to with what you were talking about the foundational models that didn't really exist years ago. So you really were like putting the layers of your models together in the formulas and it was a lot of heavy lifting. And so there was so much time spent on development. With far too few success cases, you know, to get into production to solve like a business stereo technical need. But as these, what's happening is as these models are becoming foundational. It's meaning people don't have to start from scratch. They're actually able to, you know, the avant-garde now is start with existing model that almost does what you want, but then applying your data set to it. So it's, you know, it's really the industry moving forward. And then we, you know, and, and the best thing about it is open source plays a new dimension, but this time, you know, in the, in the realm of AI. And so to us though, like, you know, I've been like, I spent a career focusing on, I think on like the, not just the technical side, but the consumption of the technology and how it's still way too hard for somebody to actually like, operationalize technology that all those vendors throw at them. So I've always been like empathetic the user around like, you know what their job is once you give them great technology. And so it's still too difficult even with the foundational models because what happens is there's really this impedance mismatch between the development of the model and then where, where the model has to live and run and be deployed and the life cycle of the model, if you will. And so what we've done in our research is we've developed techniques to introduce what's known as sparsity into a machine learning model. It's already been developed and trained. And what that sparsity does is that unlocks by making that model so much smaller. So in many cases we can make a model 90 to 95% smaller, even smaller than that in research. So, and, and so by doing that, we do that in a way that preserves all the accuracy out of the foundational model as you talked about. So now all of a sudden you get this much smaller model just as accurate. And then the even more exciting part about it is we developed a software-based engine called Deep Source. And what that, what the Inference Runtime does is takes that now sparsified model and it runs it, but because you sparsified it, it only needs a fraction of the compute that it, that it would've needed otherwise. So what we've done is make these models much faster, much smaller, and then by pairing that with an inference runtime, you now can actually deploy that model anywhere you want on commodity hardware, right? So X 86 in the cloud, X 86 in the data center arm at the edge, it's like this massive unlock that happens because you get the, the state-of-the-art models, but you get 'em, you know, on the IT assets and the commodity infrastructure. That is where all the applications are running today. >> John: I want to get into the inference piece and the deep sparse you mentioned, but I first have to ask, you mentioned open source, Dave and I with some fellow cube alumnis. We're having a chat about, you know, the iPhone and Android moment where you got proprietary versus open source. You got a similar thing happening with some of these machine learning modules where there's a lot of proprietary things happening and there's open source movement is growing. So is there a balance there? Are they all trying to do the same thing? Is it more like a chip, you know, silicons involved, all kinds of things going on that are really fascinating from a science. What's your, what's your reaction to that? >> Brian: I think it's like anything that, you know, the way we talk about AI you think had been around for decades, but the reality is it's been some of the deep learning models. When we first, when we first started taking models that the brain team was working on at Google and billing APIs around them on Google Cloud where the first cloud to even have AI services was 2015, 2016. So when you think about it, it's really been what, 6 years since like this thing is even getting lift off. So I think with that, everybody's throwing everything at it. You know, there's tons of funded hardware thrown at specialty for training or inference new companies. There's legacy companies that are getting into like AI now and whether it's a, you know, a CPU company that's now building specialized ASEX for training. There's new tech stacks proprietary software and there's a ton of asset service. So it really is, you know, what's gone from nascent 8 years ago is the wild, wild west out there. So there's a, there's a little bit of everything right now and I think that makes sense because at the early part of any industry it really becomes really specialized. And that's the, you know, showing my age of like, you know, the early pilot of the two thousands, you know, red Hat people weren't running X 86 in enterprise back then and they thought it was a toy and they certainly weren't running open source, but you really, and it made sense that they weren't because it didn't deliver what they needed to at that time. So they needed specialty stacks, they needed expensive, they needed expensive hardware that did what an Oracle database needed to do. They needed proprietary software. But what happens is that commoditizes through both hardware and through open source and the same thing's really just starting with with AI. >> John: Yeah. And I think that's a great point before we to call that out because in any industry timing's everything, right? I mean I remember back in the 80s, late 80s and 90s, AI, you know, stuff was going on and it just wasn't, there wasn't enough horsepower, there wasn't enough tech. >> Brian: Yep. >> John: You mentioned some of the processing. So AI is this industry that has all these experts who have been itch scratching that itch for decades. And now with cloud and custom silicon. The tech fundamental at the lower end of the stack, if you will, on the performance side is significantly more performant. It's there you got more capabilities. >> Brian: Yeah. >> John: Now you're kicking into more software, faster software. So it just seems like we're at a tipping point where finally it's here, like that AI moment or machine learning and now data is, is involved. So this is where organizations I see really jumping in with the CEO mandate. Hey team, make ML work for us. Go figure it out. It's got to be an advantage for us. >> Brian: Yeah. >> John: So now they go, okay boss, we will. So what, what do they do? What's the steps does an enterprise take to get machine learning into their organizations? Cause you know, it's coming down from the boards, you know, how does this work for rob? >> Brian: Yeah. Like the, you know, the, what we're seeing is it's like anything, like it's, whether that was source adoption or whether that was cloud adoption, it always starts usually with one person. And increasingly it is the CEO, which realizes they're getting further behind the competition because they're not leaning in, you know, faster. But typically it really comes down to like a really strong practitioner that's inside the organization, right? And, that realizes that the number one goal isn't doing more and just training more models and and necessarily being proprietary about it. It's really around understanding the art of the possible. Something that's grounded in the art of the possible, what, what deep learning can do today and what business outcomes you can deliver, you know, if you can employ. And then there's well proven paths through that. It's just that because of where it's been, it's not that industrialized today. It's very much, you know, you see ML project by ML project is very snowflakey, right? And that was kind of the early days of open source as well. And so, we're just starting to get to the point where it's getting easier, it's getting more industrialized, there's less steps, there's less burdensome on developers, there's less burdensome on, on the deployment side. And we're trying to bring that, that whole last mile by saying, you know what? Deploying deep learning and AI models should be as easy as the as to deploy your application, right? You shouldn't have to take an extra step to deploy an AI model. It shouldn't have to require a new hardware, it shouldn't require a new process, a new DevOps model. It should be as simple as what you're already doing. >> John: What is the best practice for companies to effectively bring an acceptable level of machine learning and performance into their organizations? >> Brian: Yeah, I think like the, the number one start is like what you hinted at before is they, they have to know the use case. They have to, in most cases, you're going to find across every industry you know, that that problem's been tackled by some company, right? And then you have to have the best practice around fine-tuning the models already exist. So fine tuning that existing model. That foundational model on your unique dataset. You, you know, if you are in medical instruments, it's not good enough to identify that it's a medical instrument in the picture. You got to know what type of medical instrument. So there's always a fine tuning step. And so we've created open source tools that make it easy for you to do two things at once. You can fine tune that existing foundational model, whether that's in the language space or whether that's in the vision space. You can fine tune that on your dataset. And at the same time you get an optimized model that comes out the other end. So you get kind of both things. So you, you no longer have to worry about you're, we're freeing you from worrying about the complexity of that transfer learning, if you will. And we're freeing you from worrying about, well where am I going to deploy the model? Where does it need to be? Does it need to be on a device, an edge, a data center, a cloud edge? What kind of hardware is it? Is there enough hardware there? We're liberating you from all of that. Because what you want, what you can count on is there'll always be commodity capability, commodity CPUs where you want to deploy in abundance cause that's where your application is. And so all of a sudden we're just freeing you of that, of that whole step. >> John: Okay. Let's get into deep sparse because you mentioned that earlier. What inspired the creation of deep sparse and how does it differ from any other solutions in the market that are out there? >> Brian: Sure. So, so where unique is it? It starts by, by two things. One is what the industry's pretty good at from the optimization side is they're good at like this thing called quantization, which turns like, you know, big numbers into small numbers, lower precision. So a 32 bit representation of a, of AI weight into a bit. And they're good at like cutting out layers, which also takes away accuracy. What we've figured out is to take those, the industry techniques for those that are best practice, but we combined it with unstructured varsity. So by reducing that model by 90 to 95% in size, that's great because it's made it smaller. But we've taken that when it's the deep sparse engine, when you deploy it that looks at that model and says, because it's so much smaller, I no longer have to run the part of the model that's been essentially sparsified. So what that's done is, it's meant that you no longer need a supercomputer to run models because there's not nearly as much math and processing as there was before the model was optimized. So now what happens is, every CPU platform out there has, has an enormous amount of compute because we've sparsified the rest of it away. So you can pick a, you can pick your, your laptop and you have enough compute to run state-of-the-art models. The second thing that, and you need a software engine to do that cause it ignores the parts of the models. It doesn't need to run, which is what like specialized hardware can't do. The second part is it's then turned into a memory efficiency problem. So it's really around just getting memory, getting the models loaded into the cash of the computer and keeping it there. Never having to go back out to memory. So, so our techniques are both, we reduce the model size and then we only run the part of the model that matters and then we keep it all in cash. And so what that does is it gets us to like these, these low, low latency faster and we're able to increase, you know, the CPU processing by an order magnitude. >> John: Yeah. That low latency is key. And you got developers, you know, co coding super fast. We'll get to the developer angle in a second. I want to just follow up on this, this motivation behind the, the deep sparse because you know, as we were talking earlier before we came on camera about the old days, I mean, not too long ago, virtualization and VMware abstracted away the os from, from the hardware rights and the server virtualization changed the game. >> Brian: Yeah. >> John: And that basically invented cloud computing as we know it today. So, so we see that abstraction. >> Brian: Yeah. >> John: There seems to be a motivation behind abstracting the way the machine learning models away from the hardware. And that seems to be bringing advantages to the AI growth. Can you elaborate on, is that true? And it's, what's your comment? >> Brian: It's true. I think it's true for us. I don't think the industry's there yet, honestly. Cause I think the industry still is of that mindset that if I took, if it took these expensive GPUs to train my model, then I want to run my model on those same expensive GPUs. Because there's often like not a separation between the people that are developing AI and the people that have to manage and deploy at where you need it. So the reality is, is that that's everything that we're after. Like, do we decrease the cost? Yes. Do we make the models smaller? Yes. Do we make them faster? A yes. But I think the most amazing power is that we've turned AI into a docker based microservice. And so like who in the industry wants to deploy their apps the old way on a os without virtualization, without docker, without Kubernetes, without microservices, without service mesh without serverless. You want all those tools for your apps by converting AI models. So they can be run inside a docker container with no apologies around latency and performance cause it's faster. You get the best of that whole world that you just talked about, which is, you know, what we're calling, you know, software delivered AI. So now the AI lives in the same world. Organizations that have gone through that digital cloud transformation with their app infrastructure. AI fits into that world. >> John: And this is where the abstraction concepts matter. When you have these inflection points, the convergence of compute data, machine learning that powers AI, it really becomes a developer opportunity. Because now applications and businesses, when they actually go through the digital transformation, their businesses are completely transformed. There is no IT. Developers are the application. They are the company, right? So AI will be part of whatever business or app will be out there. So there is a application developer angle here. Brian, can you explain >> Brian: Oh completely. >> John: how they're going to use this? Because you mentioned docker container microservice, I mean this really is an insane flipping of the script for developers. >> Brian: Yeah. >> John: So what's that look like? >> Brian: Well speak, it's because like AI's kind of, I mean, again, like it's come so fast. So you figure there's my app team and here's my AI team, right? And they're in different places and the AI team is dragging in specialized infrastructure in support of that as well. And that's not how app developers think. Like they've ran on fungible infrastructure that subtracted and virtualized forever, right? And so what we've done is we've, in addition to fitting into that world that they, that they like, we've also made it simple for them for they don't have to be a machine learning engineer to be able to experiment with these foundational models and transfer learning 'em. We've done that. So they can do that in a couple of commands and it has a simple API that they can either link to their application directly as a library to make difference calls or they can stand it up as a standalone, you know, scale up, scale out inference server. They get two choices. But it really fits into that, you know, you know that world that the modern developer, whether they're just using Python or C or otherwise, we made it just simple. So as opposed to like Go learn something else, they kind of don't have to. So in a way though, it's made it. It's almost made it hard because people expect when we talk to 'em for the first time to be the old way. Like, how do you look like a piece of hardware? Are you compatible with my existing hardware that runs ML? Like, no, we're, we're not. Because you don't need that stack anymore. All you need is a library called to make your prediction and that's it. That's it. >> John: Well, I mean, we were joking on Twitter the other day with someone saying, is AI a pet or a cattle? Right? Because they love their, their AI bots right now. So, so I'd say pet there. But you look at a lot of, there's going to be a lot of AI. So on a more serious note, you mentioned in microservices, will deep sparse have an API for developers? And how does that look like? What do I do? >> Brian: Yeah. >> John: tell me what my, as a developer, what's the roadmap look like? What's the >> Brian: Yeah, it, it really looks, it really can go in both modes. It can go in a standalone server mode where it handles, you know, rest API and it can scale out with ES as the workload comes up and scale back and like try to make hardware do that. Hardware may scale back, but it's just sitting there dormant, you know, so with this, it scales the same way your application needs to. And then for a developer, they basically just, they just, the PIP install de sparse, you know, has one commanded to do an install, and then they do two calls, really. The first call is a library call that the app makes to create the model. And models really already trained, but they, it's called a model create call. And the second command they do is they make a call to do a prediction. And it's as simple as that. So it's, it's AI's as simple as using any other library that the developers are already using, which I, which sounds hard to fathom because it is just so simplified. >> John: Software delivered AI. Okay, that's a cool thing. I believe in it personally. I think that's the way to go. I think there's going to be plenty of hardware options if you look at the advances of cloud players that got more silicon coming out. Yeah. More GPU. I mean, there's more instance, I mean, everything's out there right now. So the question is how does that evolve in your mind? Because that's seems to be key. You have open source projects emerging. What, what path does this take? Is there a parallel mental model that you see, Brian, that is similar? You mentioned open source earlier. Is it more like a VMware virtualization thing or is it more of a cloud thing? Is there Yeah. Is it going to evolve in a, in a trajectory that looks similar to what we might've seen in the past? >> Brian: Yeah, we're, you know, when I, when when I got involved with the company, what I, when I thought about it and I was reasoning about it, like, do you, you know, you want to, like, we all do when you want to join something full-time. I thought about it and said, where will the industry eventually get to? Right? To fully realize the value of, of deep learning and what's plausible as it evolves. And to me, like I, I know it's the old adage of, you know, you know, software, its hardware, cloudy software. But it truly was like, you know, we can solve these problems in software. Like there's nothing special that's happening at the hardware layer and the processing AI. The reality is that it's just early in the industry. So the view that that we had was like, this is eventually the best place where the industry will be, is the liberation of being able to run AI anywhere. Like you're really not democratizing, you democratize the model. But if you can't run the model anywhere you want because these models are getting bigger and bigger with these large language models, then you're kind of not democratizing. And if you got to go and like by a cluster to run this thing on. So the democratization comes by if all of a sudden that model can be consumed anywhere on demand without planning, without provisioning, wherever infrastructure is. And so I think that's with or without Neural Magic, that's where the industry will go and will get to. I think we're the leaders, leaders in getting it there. It's right because we're more advanced on these techniques. >> John: Yeah. And your background too. You've seen OpenStack, pre-cloud, you saw open source grow and still exponentially growing. And so you have the same similar dynamic with machine learning models growing. And they're also segmenting into almost a, an ML stack or foundational model as we talk about. So you're starting to see the formation of tooling inference. So a lot of components coming. It's almost a stack, it's almost a, it literally is like an operating system problem space, you know? How do you run things, how do you link things? How do you bring things together? Is that what's going on here? Is this like a data modeling operating environment kind of red hat type thing going on? Like. >> Brian: Yeah. Yeah. Like I think there is, you know, I thought about that too. And I think there is the role of like distribution, because the industrialization not happening fast enough of this. Like, can I go back to like every customers, every, every user does it in their own kind of way. Like it's not, everyone's a little bit of a snowflake. And I think that's okay. There's definitely plenty of companies that want to come in and say, well, this is the way it's going to be and we industrialize it as long as you do it our way. The reality is technology doesn't get industrialized by one company just saying, do it our way. And so that's why like we've taken the approach through open source by saying like, Hey, you haven't really industrialized it if you said. We made it simple, but you always got to run AI here. Yeah, right. You only like really industrialize it if you break it down into components that are simple to use and they work integrated in the stack the way you want them to. And so to me, that first principles was getting thing into microservices and dockers that could be run on VMware, OpenShare on the cloud in the edge. And so that's the, that's the real part that we're happening with. The other part, like I do agree, like I think it's going to quickly move into less about the model. Less about the training of the model and the transfer learning, you know, the data set of the model. We're taking away the complexity of optimization. Giving liberating deployment to be anywhere. And I think the last mile, John is going to be around the ML ops around that. Because it's easy to think of like soft now that it's just a software problem, we've turned it into a software problem. So it's easy to think of software as like kind of a point release, but that's not the reality, right? It's a life cycle. And it's, and so I think ML very much brings in the what is the lifecycle of that deployment? And, you know, you get into more interesting conversations, to be honest than like, once you've deployed in a docking container is around like model drift and accuracy and the dataset changes and the user changes is how do you become from an ML perspective of where of that sending signal back retraining. And, and that's where I think a lot of the, in more of the innovation's going to start to move there. >> John: Yeah. And software also, the software problem, the software opportunity as well is developer focused. And if you look at the cloud native landscape now, similar stacks developing a lot of components. A lot of things to, to stitch together a lot of things that are automating under the hood. A lot of developer productivity conversations. I think this is going to go down that same road. I want to get your thoughts because developers will set the pace. And this is something that's clear in this next wave developer productivity. They're the defacto standards bodies. They will decide what microservices check, API check. Now, skill gap is going to be a problem because it's relatively new. So model sprawl, model sizes, proprietary versus open. There has to be a way to kind of crunch that down into a, like a DevOps, like just make it, get the developer out of the, the muck. So what's your view? Are we early days like that? Or what's the young kid in college studying CS or whatever degree who comes into this with, with both feet? What are they doing? >> Brian: I'll probably say like the, the non-popular answer to that. A little bit is it's happening so fast that it's going to get kind of boring fast. Meaning like, yeah, you could go to school and go to MIT, right? Sorry. Like, and you could get a hold through end like becoming a model architect, like inventing the next model, right? And the layers and combining 'em and et cetera, et cetera. And then what operators and, and building a model that's bigger than the last one and trains faster, right? And there will be those people, right? That actually, like they're building the engines the same way. You know, I grew up as an infrastructure software developer. There's not a lot of companies that hire those anymore because they're all sitting inside of three big clouds. Yeah. Right? So you better be a good app developer, but I think what you're going to see is before you had to be everything, you had to be the, if you were going to use infrastructure, you had to know how to build infrastructure. And I think the same thing's true around is quickly exiting ML is to be able to use ML in your company, you better be like, great at every aspect of ML, including every intricacy inside of the model and every operation's doing, that's quickly changing. Like, you're going to start with a starting point. You know, in the future you're not going to be like cracking open these GPT models, you're going to just be pulling them off the shelf, fine tuning 'em and go. You don't have to invent it. You don't have to understand it. And I think that's going to be a pivot point, you know, in the industry between, you know, what's the future? What's, what's the future of a, a data scientist? ML engineer researcher look like? >> John: I think that's, the outcome's going to be determined. I mean, you mentioned, you know, doing it yourself what an SRE is for a Google with the servers scale's huge. So yeah, it might have to, at the beginning get boring, you get obsolete quickly, but that means it's progressing. So, The scale becomes huge. And that's where I think it's going to be interesting when we see that scale. >> Brian: Yep. Yeah, I think that's right. I think that's right. And we always, and, and what I've always said, and much the, again, the distribute into my ML team is that I want every developer to be as adept at being able take advantage of ML as non ML engineer, right? It's got to be that simple. And I think, I think it's getting there. I really do. >> John: Well, Brian, great, great to have you on theCUBE here on this cube conversation. As part of the startup showcase that's coming up. You're going to be featured. Or your company would featured on the upcoming ABRA startup showcase on making machine learning easier and more affordable as more machine learning models come in. You guys got deep sparse and some great technology. We're going to dig into that next time. I'll give you the final word right now. What do you see for the company? What are you guys looking for? Give a plug for the company right now. >> Brian: Oh, give a plug that I haven't already doubled in as the plug. >> John: You're hiring engineers, I assume from MIT and other places. >> Brian: Yep. I think like the, the biggest thing is like, like we're on the developer side. We're here to make this easy. The majority of inference today is, is on CPUs already, believe it or not, as much as kind of, we like to talk about hardware and specialized hardware. The majority is already on CPUs. We're basically bringing 95% cost savings to CPUs through this acceleration. So, but we're trying to do it in a way that makes it community first. So I think the, the shout out would be come find the Neural Magic community and engage with us and you'll find, you know, a thousand other like-minded people in Slack that are willing to help you as well as our engineers. And, and let's, let's go take on some successful AI deployments. >> John: Exciting times. This is, I think one of the pivotal moments, NextGen data, machine learning, and now starting to see AI not be that chat bot, just, you know, customer support or some basic natural language processing thing. You're starting to see real innovation. Brian Stevens, CEO of Neural Magic, bringing the magic here. Thanks for the time. Great conversation. >> Brian: Thanks John. >> John: Thanks for joining me. >> Brian: Cheers. Thank you. >> John: Okay. I'm John Furrier, host of theCUBE here in Palo Alto, California for this cube conversation with Brian Stevens. Thanks for watching.

(upbeat music) >> Welcome back to Supercloud 2 this is Dave Vellante. We're here exploring the intersection of data and analytics and the future of cloud. And in this segment, we're going to look at the evolution of cloud, and try to test some of the Supercloud concepts and assumptions with Brian Gracely, is the founder and co-host along with Aaron Delp of the popular Cloudcast program. Amazing series, if you're not already familiar with it. The Cloudcast is one of the best ways to keep up with so many things going on in our industry. Enterprise tech, platform engineering, business models, obviously, cloud developer trends, crypto, Web 3.0. Sorry Brian, I know that's a sore spot, but Brian, thanks for coming >> That's okay. >> on the program, really appreciate it. >> Yeah, great to be with you, Dave. Happy New Year, and great to be back with everybody with SiliconANGLE again this year. >> Yeah, we love having you on. We miss working with you day-to-day, but I want to start with Gracely's theorem, which basically says, I'm going to paraphrase. For the most part, nothing new gets introduced in the enterprise tech business, patterns repeat themselves, maybe get applied in new ways. And you know this industry well, when something comes out that's new, if you take virtualization, for example, been around forever with mainframes, but then VMware applied it, solve a real problem in the client service system. And then it's like, "Okay, this is awesome." We get really excited and then after a while we pushed the architecture, we break things, introduce new things to fix the things that are broken and start adding new features. And oftentimes you do that through acquisitions. So, you know, has the cloud become that sort of thing? And is Supercloud sort of same wine, new bottle, following Gracely's theorem? >> Yeah, I think there's some of both of it. I hate to be the sort of, it depends sort of answer but, I think to a certain extent, you know, obviously Cloud in and of itself was, kind of revolutionary in that, you know, it wasn't that you couldn't rent things in the past, it was just being able to do it at scale, being able to do it with such amazing self-service. And then, you know, kind of proliferation of like, look at how many services I can get from, from one cloud, whether it was Amazon or Azure or Google. And then, you know, we, we slip back into the things that we know, we go, "Oh, well, okay, now I can get computing on demand, but, now it's just computing." Or I can get database on demand and it's, you know, it's got some of the same limitations of, of say, of database, right? It's still, you know, I have to think about IOPS and I have to think about caching, and other stuff. So, I think we do go through that and then we, you know, we have these sort of next paradigms that come along. So, you know, serverless was another one of those where it was like, okay, it seems sort of new. I don't have to, again, it was another level of like, I don't have to think about anything. And I was able to do that because, you know, there was either greater bandwidth available to me, or compute got cheaper. And what's been interesting is not the sort of, that specific thing, serverless in and of itself is just another way of doing compute, but the fact that it now gets applied as, sort of a no-ops model to, you know, again, like how do I provision a database? How do I think about, you know, do I have to think about the location of a service? Does that just get taken care of for me? So I think the Supercloud concept, and I did a thing and, and you and I have talked about it, you know, behind the scenes that maybe the, maybe a better name is Super app for something like Snowflake or other, but I think we're, seeing these these sort of evolutions over and over again of what were the big bottlenecks? How do we, how do we solve those bottlenecks? And I think the big thing here is, it's never, it's very rarely that you can take the old paradigm of what the thing was, the concept was, and apply it to the new model. So, I'll just give you an example. So, you know, something like VMware, which we all know, wildly popular, wildly used, but when we apply like a Supercloud concept of VMware, the concept of VMware has always been around a cluster, right? It's some finite number of servers, you sort of manage it as a cluster. And when you apply that to the cloud and you say, okay, there's, you know, for example, VMware in the cloud, it's still the same concept of a cluster of VMware. But yet when you look at some of these other services that would fit more into the, you know, Supercloud kind of paradigm, whether it's a Snowflake or a MongoDB Atlas or maybe what CloudFlare is doing at the edge, those things get rid of some of those old paradigms. And I think that's where stuff, you start to go, "Oh, okay, this is very different than before." Yes, it's still computing or storage, or data access, but there's a whole nother level of something that we didn't carry forward from the previous days. And that really kind of breaks the paradigm. And so that's the way I think I've started to think about, are these things really brand new? Yes and no, but I think it's when you can see that big, that thing that you didn't leave behind isn't there anymore, you start to get some really interesting new innovation come out of it. >> Yeah. And that's why, you know, lift and shift is okay, when you talk to practitioners, they'll say, "You know, I really didn't change my operating model. And so I just kind of moved it into the cloud. there were some benefits, but it was maybe one zero not three zeros that I was looking for." >> Right. >> You know, we always talk about what's great about cloud, the agility, and all the other wonderful stuff that we know, what's not working in cloud, you know, tie it into multi-cloud, you know, in terms of, you hear people talk about multi-cloud by accident, okay, that's true. >> Yep. >> What's not great about cloud. And then I want to get into, you know, is multi-cloud really a problem or is it just sort of vendor hype? But, but what's not working in cloud? I mean, you mentioned serverless and serverless is kind of narrow, right, for a lot of stateless apps, right? But, what's not great about cloud? >> Well, I think there's a few things that if you ask most people they don't love about cloud. I think, we can argue whether or not sort of this consolidation around a few cloud providers has been a good thing or a bad thing. I think, regardless of that, you know, we are seeing, we are hearing more and more people that say, look, you know, the experience I used to have with cloud when I went to, for example, an Amazon and there was, you know, a dozen services, it was easy to figure out what was going on. It was easy to figure out what my billing looked like. You know, now they've become so widespread, the number of services they have, you know, the number of stories you just hear of people who went, "Oh, I started a service over in US West and I can't find it anymore 'cause it's on a different screen. And I, you know, I just got billed for it." Like, so I think the sprawl of some of the clouds has gotten, has created a user experience that a lot of people are frustrated with. I think that's one thing. And we, you know, we see people like Digital Ocean and we see others who are saying, "Hey, we're going to be that simplified version." So, there's always that yin and yang. I think people are super frustrated at network costs, right? So, you know, and that's kind of at a lot of, at the center of maybe why we do or don't see more of these Supercloud services is just, you know, in the data center as an application owner, I didn't have to think about, well where, where does this go to? Where are my users? Yes, somebody took care of it, but when those things become front and center, that's super frustrating. That's the one area that we've seen absolutely no cost savings, cost reduction. So I think that frustrates people a lot. And then I think the third piece is just, you know, we're, we went from super centralized IT organizations, which, you know, for decades was how it worked. It was part of the reason why the cloud expanded and became a thing, right? Sort of shadow IT and I can't get things done. And then, now what we've seen is sort of this proliferation of little pockets of groups that are your IT, for lack of a better thing, whether they're called platform engineering or SRE or DevOps. But we have this, expansion, explosion if you will, of groups that, if I'm an app dev team, I go, "Hey, you helped me make this stuff run, but then the team next to you has another group and they have another group." And so you see this explosion of, you know, we don't have any standards in the company anymore. And, so sort of self-service has created its own nightmare to a certain extent for a lot of larger companies. >> Yeah. Thank you for that. So, you know, I want, I want to explore this multi-cloud, you know, by accident thing and is a real problem. You hear that a lot from vendors and we've been talking about Supercloud as this unifying layer across cloud. You know, but when you talk to customers, a lot of them are saying, "Yes, we have multiple clouds in our organization, but my group, we have mono cloud, we know the security, edicts, we know how to, you know, deal with the primitives, whether it's, you know, S3 or Azure Blob or whatever it is. And we're very comfortable with this." It's, that's how we're simplifying. So, do you think this is really a problem? Does it have merit that we need that unifying layer across clouds, or is it just too early for that? >> I think, yeah, I think what you, what you've laid out is basically how the world has played out. People have picked a cloud for a specific application or a series of applications. Yeah, and I think if you talk to most companies, they would tell you, you know, holistically, yes, we're multi-cloud, not, maybe not necessarily on, I don't necessarily love the phrase where people say like, well it happened by accident. I think it happened on purpose, but we got to multi-cloud, not in the way that maybe that vendors, you know, perceived, you know, kind of laid out a map for. So it was, it was, well you will lay out this sort of Supercloud framework. We didn't call it that back then, we just called it sort of multi-cloud. Maybe it was Kubernetes or maybe it was whatever. And different groups, because central IT kind of got disbanded or got fragmented. It turned into, go pick the best cloud for your application, for what you need to do for the business. And then, you know, multiple years later it was like, "Oh, hold on, I've got 20% in Google and 50% in AWS and I've got 30% in Azure. And, you know, it's, yeah, it's been evolution. I don't know that it's, I don't know if it's a mistake. I think it's now groups trying to figure out like, should I make sense of it? You know, should I try and standardize and I backwards standardize some stuff? I think that's going to be a hard thing for, for companies to do. 'cause I think they feel okay with where the applications are. They just happen to be in multiple clouds. >> I want to run something by you, and you guys, you and Aaron have talked about this. You know, still depending on who, which keynote you listen to, small percentage of the workloads are actually in cloud. And when you were with us at Wikibon, I think we called it true private cloud, and we looked at things like Nutanix and there were a lot of other examples of companies that were trying to replicate the hyperscale experience on Prem. >> Yeah. >> And, we would evaluate that, you know, beyond virtualization, and so we sort of defined that and, but I think what's, maybe what's more interesting than Supercloud across clouds is if you include that, that on Prem estate, because that's where most of the work is being done, that's where a lot of the proprietary tools have been built, a lot of data, a lot of software. So maybe there's this concept of sending that true private cloud to true hybrid cloud. So I actually think hybrid cloud in some cases is the more interesting use case for so-called Supercloud. What are your thoughts on that? >> Yeah, I think there's a couple aspects too. I think, you know, if we were to go back five or six years even, maybe even a little further and look at like what a data center looked like, even if it was just, "Hey we're a data center that runs primarily on VMware. We use some of their automation". Versus what you can, even what you can do in your data center today. The, you know, the games that people have seen through new types of automation through Kubernetes, through get ops, and a number of these things, like they've gotten significantly further along in terms of I can provision stuff really well, I can do multi-tenancy, I can do self-service. Is it, you know, is it still hard? Yeah. Because those things are hard to do, but there's been significant progress there. I don't, you know, I still look for kind of that, that killer application, that sort of, you know, lighthouse use case of, hybrid applications, you know, between data center and between cloud. I think, you know, we see some stuff where, you know, backup is a part of it. So you use the cloud for storage, maybe you use the cloud for certain kinds of resiliency, especially on maybe front end load balancing and stuff. But I think, you know, I think what we get into is, this being hung up on hybrid cloud or multi-cloud as a term and go like, "Look, what are you trying to measure? Are you trying to measure, you know, efficiency of of of IT usage? Are you trying to measure how quickly can I give these business, you know, these application teams that are part of a line of business resources that they need?" I think if we start measuring that way, we would look at, you know, you'd go, "Wow, it used to be weeks and months. Now we got rid of these boards that have to review everything every time I want to do a change management type of thing." We've seen a lot more self-service. I think those are the things we want to measure on. And then to your point of, you know, where does, where do these Supercloud applications fit in? I think there are a bunch of instances where you go, "Look, I have a, you know, global application, I have a thing that has to span multiple regions." That's where the Supercloud concept really comes into play. We used to do it in the data center, right? We'd had all sorts of technologies to help with that, I think you can now start to do it in the cloud. >> You know, one of the other things, trying to understand, your thoughts on this, do you think that you, you again have talked about this, like I'm with you. It's like, how is it that Google's losing, you know, 3 billion dollars a year, whatever. I mean, because when you go back and look at Amazon, when they were at that level of revenue where Google is today, they were making money, you know, and they were actually growing faster, by the way. So it's kind of interesting what's happened with Google. But, the reason I bring that up is, trying to understand if you think the hyperscalers will ever be motivated to create standards across clouds, and that may be a play for Google. I mean, obviously with Kubernetes it was like a Hail Mary and kind of made them relevant. Where would Google be without Kubernetes? But then did it achieve the objectives? We could have that conversation some other time, but do you think the hyperscalers will actually say, "Okay, we're going to lean in and create these standards across clouds." Because customers would love that, I would think, but it would sub-optimize their competitive advantage. What are your thoughts? >> I think, you know, on the surface, I would say they, they probably aren't. I think if you asked 'em the question, they would say, "Well, you know, first and foremost, you know, we do deliver standards, so we deliver a, you know, standard SQL interface or a SQL you know, or a standard Kubernetes API or whatever. So, in that, from that perspective, you know, we're not locking you into, you know, an Amazon specific database, or a Google specific database." You, you can argue about that, but I think to a certain extent, like they've been very good about, "Hey, we're going to adopt the standards that people want." A lot of times the open source standards. I think the problem is, let's say they did come up with a standard for it. I think you still have the problem of the costs of migration and you know, the longer you've, I think their bet is basically the longer you've been in some cloud. And again, the more data you sort of compile there, the data gravity concept, there's just going to be a natural thing that says, okay, the hurdle to get over to say, "Look, we want to move this to another cloud", becomes so cost prohibitive that they don't really have to worry about, you know, oh, I'm going to get into a war of standards. And so far I think they sort of realize like that's the flywheel that the cloud creates. And you know, unless they want to get into a world where they just cut bandwidth costs, like it just kind of won't happen. You know, I think we've even seen, and you know, the one example I'll use, and I forget the name of it off the top of my head, but there's a, there's a Google service. I think it's like BigQuery external or something along those lines, that allows you to say, "Look, you can use BigQuery against like S3 buckets and against other stuff." And so I think the cloud providers have kind of figured out, I'm never going to get the application out of that other guy's cloud or you know, the other cloud. But maybe I'm going to have to figure out some interesting ways to sort of work with it. And, you know, it's a little bit, it's a little janky, but that might be, you know, a moderate step that sort of gets customers where they want to be. >> Yeah. Or you know, it'd be interesting if you ever see AWS for example, running its database in other clouds, you started, even Oracle is doing that with, with with Azure, which is a form of Supercloud. My last question for you is, I want to get you thinking about sort of how the future plays out. You know, think about some of the companies that we've put forth this Supercloud, and by the way, this has been a criticism of the concept. Charles Fitzer, "Everything is Supercloud!" Which if true would defeat the purpose of course. >> Right. >> And so right with the community effort, we really tried to put some guardrails down on the essential characteristics, the deployment models, you know, so for example, running across multiple clouds with a purpose build pass, creating a common experience, metadata intelligence that solves a specific problem. I mean, the example I often use is Snowflake's governed data sharing. But yeah, Snowflake, Databricks, CloudFlare, Cohesity, you know, I just mentioned Oracle and Azure, these and others, they certainly claim to have that common experience across clouds. But my question is, again, I come back to, do customers need this capability? You know, is Mono Cloud the way to solve that problem? What's your, what are your thoughts on how this plays out in the future of, I guess, PAs, apps and cloud? >> Yeah, I think a couple of things. So, from a technology perspective, I think, you know, the companies you name, the services you've named, have sort of proven that the concept is viable and it's viable at a reasonable size, right? These aren't completely niche businesses, right? They're multi-billion dollar businesses. So, I think there's a subset of applications that, you know, maybe a a bigger than a niche set of applications that are going to use these types of things. A lot of what you talked about is very data centric, and that's, that's fine. That's that layer is, figuring that out. I think we'll see messaging types of services, so like Derek Hallison's, Caya Company runs a, sort of a Supercloud for messaging applications. So I think there'll be places where it makes a ton of sense. I think, the thing that I'm not sure about, and because again, we've been now 10 plus years of sort of super low, you know, interest rates in terms of being able to do things, is a lot of these things come out of research that have been done previously. Then they get turned into maybe somewhat of an open source project, and then they can become something. You know, will we see as much investment into the next Snowflake if, you know, the interest rates are three or four times that they used to be, do we, do we see VCs doing it? So that's the part that worries me a little bit, is I think we've seen what's possible. I think, you know, we've seen companies like what those services are. I think I read yesterday Snowflake was saying like, their biggest customers are growing at 30, like 50 or 60%. Like the, value they get out of it is becoming exponential. And it's just a matter of like, will the economics allow the next big thing to happen? Because some of these things are pretty, pretty costly, you know, expensive to get started. So I'm bullish on the idea. I don't know that it becomes, I think it's okay that it's still sort of, you know, niche plus, plus in terms of the size of it. Because, you know, if we think about all of IT it's still, you know, even microservices is a small part of bigger things. But I'm still really bullish on the idea. I like that it's been proven. I'm a little wary, like a lot of people have the economics of, you know, what might slow things down a little bit. But yeah, I, think the future is going to involve Supercloud somewhere, whatever people end up calling it. And you and I discussed that. (laughs) But I don't, I don't think it goes away. I don't think it's, I don't think it's a fad. I think it is something that people see tremendous value and it's just, it's got to be, you know, for what you're trying to do, your application specific thing. >> You're making a great point on the funding of innovation and we're entering a new era of public policy as well. R and D tax credit is now is shifting. >> Yeah. >> You know, you're going to have to capitalize that over five years now. And that's something that goes back to the 1950s and many people would argue that's at least in part what has helped the United States be so, you know, competitive in tech. But Brian, always great to talk to you. Thanks so much for participating in the program. Great to see you. >> Thanks Dave, appreciate it. Good luck with the rest of the show. >> Thank you. All right, this is Dave Vellante for John Furrier, the entire Cube community. Stay tuned for more content from Supercloud2.

(techno intro music) >> Hey everyone, good afternoon from sin city. This is Lisa Martin with Dave Vellante. We are in full swing of theCUBE's four days of coverage of AWS re:invent 2022. North of 50,000 people are here. We're nearing hundreds of thousands online. Dave, this has been, this is a great event. We've had great conversations. We're going to be having more conversations. One of the things we love talking about on theCUBE is AWS and its ecosystem of partners, and we are going to do just that right now. Brian Henderson joins us, Director of Marketing at Dell Technologies. Marc Trimuschat, Director of Worldwide Storage Specialists at AWS is also here. Guys, it's great to have you. >> Great to be here. >> Great to be here, yeah. Feeling the energy of the show. >> Isn't it great? >> Mark: I know, amazing. >> It's amazing. It started out high and it has not dropped since Monday night. Brian, talk a little bit about Dell, what you're doing with customers on their Cloud journeys. Every customer, every industry is on one at different points in their journey, but what's Dell helping out with there? >> What we're here to talk about is the progression that we've seen, right, Cloud has changed a lot over the years and Dell has really put out a strategy to help people with their Cloud journey, kind of wherever they are. So a lot of people have moved full shift. A lot of people see that as another location, and what we're showing at the booth is the idea of taking these enterprise capabilities that people know and trust from Dell, courting them to the Cloud. In some cases not courting, but just delivering that software in the Cloud, as well as taking some of the Kubernetes integrations, EKS Anywhere, bringing that on-prem. So we've got some storage, data protection, and our Kubernetes integration to talk about at the show. >> Awesome, Mark, talk about the role from Amazon's point of view that third party vendors like Dell Technologies plays in AWS's expanding vision of Cloud. >> Great, well, we're really excited to be partnering with Dell. What we see that historically is, you know, AWS is focused on builders, people, and really the developer community who are building those components themselves, putting together really resilient infrastructure and applications. What we're seeing today is a shift also to the type of customers that we're seeing, more traditional enterprise customers, who are demanding really performance, the scalability, also the resiliency of what they had on-premises, and they want that on the Cloud as well. So with Dell, and we've got some great solutions that we're partnering on, including Dell PowerFlex that provides that linear scalability and some of the high performance capabilities that customers are demanding. And also, another big trend that we're seeing is customers being affected by things like unfortunately malware events, right, and data protection. So Dell provides some great solutions in both those areas that allow enterprise customers to really experience that mission critical capability and resiliency that they have on-premises in the Cloud. >> You know, Brian, we've been at this a long time. >> Brian: Oh yeah, great to see you again. >> And I've been hearing my whole career that storage is going to get commoditized. And I guess if you're talking about spinning discs or flash drives, it's probably true, but as Mark was just saying if you want resilient storage and things that are recoverable, that don't go down all the time, they're not commodities. >> Brian: Yeah. >> It's real engineering. And you built the stack up, so talk about how that connection, what value you bring to the Cloud and your customers. >> Yeah, so what we see is people are always looking out for enterprise grade capabilities. So there's going to be a set of offerings, and AWS has a fantastic foundation for building on top of with the marketplace. So what we're able to do is really bring, in some cases, decades worth of investment in software engineering and put these advanced capabilities, whether it be PowerFlex with its linear scale. We'll have a file offering very soon. These products have been built from the ground up to do a very unique purpose. Giving that to people in the Cloud is just another location for us, AWS being the market leader. We're the market leader in storage. So us working together for the benefit of customers is really where it's at. >> Can you double click on that, Brian, what Dell and AWS? Give us all those juicy details. >> Sure, sure, sure, so what we've done right before this show is we put a product called PowerFlex, if you go back to 2018 scale IO, and you're taking this really linear scaling software defined architecture, and you're putting that in the Cloud. What that allows you to do is get that really advanced linear scale performance. You can even span clusters across AWS regions, as well as zones. So it's a really unique capability that allows us to be able to check in and do that. And in the data protection space, it's a whole separate category. We've been at this actually quite a while. We've got about 14 exo bytes of data that's already being protected on the AWS Cloud. So we've been at that for quite a while. And the two levels are really, do you want to back that up? Do you want to take a traditional back up application, maybe it's a lift and shift, and I want to back it up the way I used to, and you can do that in the Cloud now. Or we're seeing cyber resiliency come up a lot more, and we were just talking right before, it's a question of when, not if, and so we have to give our customers the option to not only detect that failure event early, but also to separate that copy with a logical air gap. >> The cyber resiliency is a topic we are talking more and more about. It's absolutely critical. We've seen the threat landscape change dramatically in the last couple of years. To your point, Brian, it's no longer, when we think of ransomware, it's no longer are we going to get hit? It's when, it's how often. What's the damage going to be? I think I saw a stat recently that there's one ransomware attack every 11 seconds. The average cost of reaches is in the millions, so what you're doing together on cyber resiliency for businesses in any industry is table stakes. >> Yeah, we just saw a survey that, it was done earlier this year survey, 66% unfortunately of corporations have experienced a malware attack. And that's an 80% increase from last year. >> Lisa: Wow. >> So again, I think that's an opportunity. It's a threat, but an opportunity, and so the partnership with Dell really helps bridge that and helps our customers, our mutual customers, recover from those incidents. >> A lot of people might say, this is interesting. A storage guy from Amazon, a storage guy from Dell, two leaders. And one might think, why didn't they just throw in a dash three, right, but you guys are both customer driven, customer obsessed. In the field, what are customers saying to you in terms of how they want you to work together? >> Well I think there's a place for everything. When you say throw in to S3, so S3 today, one of the big trends when you're looking here is just the amount of data, you know, we hear that rhetoric, you know, we've been in storage for many years, and the data has all increased up and to the right. But, you know, AWSI, S3 today, we have over 280 trillion objects in our, driving a hundred million transactions per second right now, so that's scale. So there's always a place for those really, we have hundreds of thousands of customers running their data links, so that's always going to be that really, you know, highly reliable, highly durable, high available solution for data links. But customers, there's a lot of different applications out there. So where customers are asking are those enterpise. So we have EBS, for example, which is our great, you know, scalable block search, elastic block store. We introduced some new volume types, like GP2, GP2, and IO2VX, which will have that performance. But there's still single availability zone. So what customers have done historically is they maybe the application layer, they put an application layer replication or resiliency across, but customers on-prem, they've relied on storage layers to do that work for them. So, with PowerFlex, that'll stand either using instant storage or EBS, building on that really strong foundation, but provide that additional layer to make it easy for customers to get that resiliency and that scalability that Brian talked about. >> Yep, yep. >> Anything you can add to that? >> Yeah, I mean to your question, how do we work together is really, it's all customer driven. So we see customers that are shifting workloads in the Cloud for the first time. And it might make sense to take an object, like PowerFlex or another storage technology, maybe you want to compress it a little bit before you send it to the Cloud. Maybe you don't want to lift and shift everything. So we have a team of people that works very closely with AWS to be able to determine how are you going to shift that workload out there? Does this make the right sense for you? So it's a very collaborative relationship. And it's all very customer driven because our customers are saying, I've got assets in the public Cloud, and I want them to be managed in a similar fashion to how I'm doing that on-prem. >> So customer obsession is clearly on both sides there. We know that. >> It's where it starts. >> Exactly, exactly. Going back to PowerFlex for a second, Brian, and I'd love to get an example of a joint customer that really is showing the value of what Dell and AWS are doing together. The question for you on PowerFlex, talk about the value that it offers to the public Cloud. And why should customers start there if they are early in this journey? >> All right, yeah, so the two angles are basically, are you coming from PowerFlex or you're coming from Cloud. If you're Cloud native, the advantage would be things like a really, really advanced block file system that has been built from the ground up to be software defined and pretty much Cloud native. What you're getting is that really linear scale up to about 1,000 nodes. You can span that across regions, across availability zones, so it's highly resilient. So if there's a node failure in one site, you're going to rebuild really fast, depending on the size of that cluster. So it's a very advanced architecture that's been built to run, you know, we didn't have to change a single line of code to run this product in the Cloud because it was Cloud native by default, so. >> Well that's the thing. We also see, and you've seen that with some of the other solutions, but customers really want that. Enterprise customers are, they want us to make sure those mission critical applications are working and stay up. So they also want to use the same environment. So we were talking before, we also see use cases where maybe they're using PowerFlex on-premises today and they want to be able to replicate that to PowerFlex that's in the Cloud. So we're seeing those, and the familiarity with that infrastructure really is that easy path, if you will, for those more conservative mission critical customers. >> We've learned a lot over the years from AWS's entry into the marketplace. Two recent teams working backwards. We talk about customer obsession. And also the Cloud experience. It brings me to APEX. >> Oh yeah. >> Dave: How does APEX fit in here? >> Yeah, so APEX is the categorization for all the things that we're doing around a modern Cloud experience for Dell customers. So we're taking them also on a journey, kind of as a service model. There's a do-it-yourself model. And anything that we do that touches Cloud is now being kind of put under that APEX moniker. So everything that we're doing around Project Alpine, enterprise software capabilities in the Cloud. Do you want someone else to manage it for you? Do you want it in a polo? That might be the right fit for you. It's all under that APEX umbrella and journey. So we're kind of still just getting started there, but we're seeing a lot of great traction. People want to pay as they go, you know, it's a very popular model that AWS has pretty much set the foundation for. So pay as you go, utility based pricing, this is all things our customers have been asking for. >> Yeah, so APEX, you basically set a baseline. You can dial it up, dial it down, very much pay by the drink. >> Absolutely. >> And, you know, like you said, it's early days. >> Brian: Yeah. >> But that's, again, AWS has influenced the business in a lot of different ways. >> Again, with the Dell, you know, the trust customers that Dell has built over the years and having those customers come in. We obviously are getting, again, it's an accelerated option for financial services to healthcare and all these customers that have relied on Dell for years, moving to the Cloud, having that trusted name and also that infrastructure that's similar and familiar to them. And then the resilience of the foundation that we have at AWS, I think it works really well together for those customers. >> I think it underscores to the majority of both AWS and in a lot of ways Dell, right. In the early days of Cloud, it was like uh oh, and now it's like oh, actually big market. Customers are demanding this. There's new value that we can create working together. Let's do it. >> Yeah, I mean, it didn't take us that long to get to it, but I'd say we had little fits and starts over the years, and now we've recognized like, this is where the future is. It's going to be Cloud, it's going to be on-prem, it's going to be Edge, it's going to be everything. It's going to be an and world. And so just doing the right thing for customers I think is exactly where we landed. It's a great partnership. >> Do you have a favorite customer story that you think really shines the light on the value of the Dell AWS partnership in terms of the business impact they're making? >> We have several large customers that I can't always like drop the names, but one of them is a very large video game production company. And we do a lot of work together where they're rendering maybe in house, they're sending to a shared location. They're copying data over to S3. They're able to let all their editors access that. They bring it back when it's compressed down a little bit and deliver that. We're also doing a lot of work with, I think I can say this, Amazon Thursday night football games. So what they've done there, it's a partner of ours working with AWS. All the details inside of that roaming truck that they drive around, there's a lot of Dell gear within there, and then everything connects back to AWS for that exact same kind of model. We need to get to the editors on a nightly basis. They're also streaming directly form that truck while they're enabling the editors to access a shared copy of it, so it's really powerful stuff. >> Thursday night prime is pretty cool. You know, some people are complaining cause I can't just switch channels during the commercials. It's like, first of all, you can. Second of all, the stats are unbelievable, right. You can just do your own replay when you want to. There's some cool innovations there. >> Oh yeah, absolutely. >> Very cool innovations. I've got one more question for each of you before we wrap. Marc, a question for you, we're making a fun Instagram reel. So think about a sizzle reel of if you were to summarize the show so far, what is AWS's message to its massive audience this year? >> Well, that's a big question. Because we have such a wide, as we mentioned, such a wide ranging audience. I really see a couple key trends that we're trying to address. One is, again don't forget, I'm a storage guy, so it's going to come from an angle from data, right. So, I think it's just this volume of data and that customers are bringing into the Cloud, either moving in from enterprises today or organically, just growing. You know, a couple years ago, megabytes were a lot, and now, you know, we're talking about petabytes every day. Soon it's going to be exo bytes are going to become the norm. So the big, I'd say, point one is the trend that I see is just the volume of data. And so what we're doing to address that is obviously we talked a little bit about S3 and being able to manage volumes of data, but also things like DataZone that we introduced because customers are looking to make sure that the right governance and controls to be able to access that data. So I think that's one big thing that I see the theme for the show today. The second thing is around, as I said, really these enterprise customers really wanted to move in these mission critical applications into the Cloud, and having that infrastructure to be able to support that easily from what they're doing today and move in quickly. The third area is around data protection, making sure the data protection and malware recovery, that's the theme that we see is really unfortunately that's today. But being able to recover quickly, both having native services and native offerings just built in resiliency into the core platforms, like S3 with object application, et cetera. And also partnering with Dell with cyber recovery and some of the solutions with Dell. >> Excellent, and Brian, last question for you. A bumper sticker that succinctly and powerfully describes why Dell and AWS are such awesome partners for customer issues. >> Best of both worlds, right? >> Lisa: Mic drop. >> Mic drop, done. >> That's awesome. You said that a lot more succinctly. (people laughing) >> Enterprise in Cloud, Cloud comin' to enterprise. >> Yeah, leader meets leader, right? >> Yeah, right. >> Love it, leader meets leader. Guys, it's been a pleasure having you on theCUBE. We appreciate hearing the latest from AWS and Dell from a storage perspective and from a Cloud perspective and how you're helping customers manage the explosion of data that's not going to slow down. We really appreciate you coming by the set. >> Thank you. >> Great, thanks so much, appreciate it. >> My pleasure. For our guests and Dave Vellante, I'm Lisa Martin, you're watching theCUBE, the leader in live enterprise and emerging tech coverage. (techno music)

>>Hello, fabulous cloud community and welcome to Las Vegas. We are the Cube and we will be broadcasting live from the AWS Reinvent Show floor for the next four days. This is our first opening segment. I am joined by the infamous John Furrier. John, it is your 10th year being here at Reinvent. How does >>It feel? It's been a great to see you. It feels great. I mean, just getting ready for the next four days. It's, this is the marathon of all tech shows. It's, it's busy, it's crowd, it's loud and the content and the people here are really kind of changing the game and the stories are always plentiful and deep and just it's, it really is one of those shows you kind of get intoxicated on the show floor and in the event and after hours people are partying. I mean it is like the big show and 10 years been amazing run People getting bigger. You're seeing the changing ecosystem Next Gen Cloud and you got the Classics Classic still kind of doing its thing. So getting a lot data, a lot of data stories. And our guests here are gonna talk more about that. This is the year the cloud kind of goes next gen and you start to see the success Gen One cloud players go on the next level. It's gonna be really fun. Fun week. >>Yes, I'm absolutely thrilled and you can certainly feel the excitement. The show floor doors just opened, people pouring in the drinks are getting stacked behind us. As you mentioned, it is gonna be a marathon and very exciting. On that note, fantastic interview to kick us off here. We're starting the day with Stripe. Please welcome nor and Brian, how are you both doing today? Excited to be here. >>Really happy to be here. Nice to meet you guys. Yeah, >>Definitely excited to be here. Nice to meet you. >>Yeah, you know, you were mentioning you could feel the temperature and the energy in here. It is hot, it's a hot show. We're a hot crew. Let's just be honest about that. No shame in that. No shame in that game. But I wanna, I wanna open us up. You know, Stripe serving 2 million customers according to the internet. AWS with 1 million customers of their own, both leading companies in your industries. What, just in case there's someone in the audience who hasn't heard of Stripe, what is Stripe and how can companies use it along with AWS nor, why don't you start us off? >>Yeah, so Stripe started back in 2010 originally as a payments company that helped businesses accept and process their payments online. So that was something that traditionally had been really tedious, kind of difficult for web developers to set up. And what Stripe did was actually introduce a couple of lines of code that developers could really easily integrate into their websites and start accepting those payments online. So payments is super core to who Stripe is as a company. It's something that we still focus on a lot today, but we actually like to think of ourselves now as more than just a payments company but rather financial infrastructure for the internet. And that's just because we have expanded into so many different tools and technologies that are beyond payments and actually help businesses with just about anything that they might need to do when it comes to the finances of running an online company. So what I mean by that, couple examples being setting up online marketplaces to accept multi-party payments, running subscriptions and recurring payments, collecting sales tax accurately and compliantly revenue recognition and data and analytics. Importantly on all of those things, which is what Brian and I focus on at Stripe. So yeah, since since 2010 Stripes really grown to serve millions of customers, as you said, from your small startups to your large multinational companies, be able to not only run their payments but also run complex financial operations online. >>Interesting. Even the Cube, the customer of Stripe, it's so easy to integrate. You guys got your roots there, but now as you guys got bigger, I mean you guys have massive traction and people are doing more, you guys are gonna talk here on the data pipeline in front you, the engineering manager. What has it grown to, I mean, what are some of the challenges and opportunities your customers are facing as they look at that data pipeline that you guys are talking about here at Reinvent? >>Yeah, so Stripe Data Pipeline really helps our customers get their data out of Stripe and into, you know, their data warehouse into Amazon Redshift. And that has been something that for our customers it's super important. They have a lot of other data sets that they want to join our Stripe data with to kind of get to more complex, more enriched insights. And Stripe data pipeline is just a really seamless way to do that. It lets you, without any engineering, without any coding, with pretty minimal setup, just connect your Stripe account to your Amazon Redshift data warehouse, really secure. It's encrypted, you know, it's scalable, it's gonna meet all of the needs of kind of a big enterprise and it gets you all of your Stripe data. So anything in our api, a lot of our reports are just like there for you to take and this just overcomes a big hurdle. I mean this is something that would take, you know, multiple engineers months to build if you wanted to do this in house. Yeah, we give it to you, you know, with a couple clicks. So it's kind of a, a step change for getting data out of Stripe into your data work. >>Yeah, the topic of this chat is getting more data outta your data from Stripe with the pipelining, this is kind of an interesting point, I want to get your thoughts. You guys are in the, in the front lines with customers, you know, stripes started out with their roots line of code, get up and running, payment gateway, whatever you wanna call it. Developers just want to get cash on the door. Thank you very much. Now you're kind of turning in growing up and continue to grow. Are you guys like a financial cloud? I mean, would you categorize yourself as a, cuz you're on top of aws? >>Yeah, financial infrastructure of the internet was a, was a claim I definitely wanna touch on from your, earlier today it was >>Powerful. You guys are super financial cloud basically. >>Yeah, super cloud basically the way that AWS kind of is the superstar in cloud computing. That's how we feel at Stripe that we want to put forth as financial infrastructure for the internet. So yeah, a lot of similarities. Actually it's funny, we're, we're really glad to be at aws. I think this is the first time that we've participated in a conference like this. But just to be able to participate and you know, be around AWS where we have a lot of synergies both as companies. Stripe is a customer of AWS and you know, for AWS users they can easily process payments through Stripe. So a lot of synergies there. And yeah, at a company level as well, we find ourselves really aligned with AWS in terms of the goals that we have for our users, helping them scale, expand globally, all of those good things. >>Let's dig in there a little bit more. Sounds like a wonderful collaboration. We love to hear of technology partnerships like that. Brian, talk to us a little bit about the challenges that the data pipeline solves from Stripe for Redshift users. >>Yeah, for sure. So Stripe Data Pipeline uses Amazon RedShift's built in data sharing capabilities, which gives you kind of an instant view into your Stripe data. If you weren't using Stripe data pipeline, you would have to, you know, ingest the state out of our api, kind of pull yourself manually. And yeah, I think that's just like a big part of it really is just the simplicity with what you can pull the data. >>Yeah, absolutely. And I mean the, the complexity of data and the volume of it is only gonna get bigger. So tools like that that can make things a lot easier are what we're all looking for. >>What's the machine learning angle? Cause I know there's lots of big topic here this year. More machine learning, more ai, a lot more solutions on top of the basic building blocks and the primitives at adds, you guys fit right into that. Cause developers doing more, they're either building their own or rolling out solutions. How do you guys see you guys connecting into that with the pipeline? Because, you know, data pipelining people like, they like that's, it feels like a heavy lift. What's the challenge there? Because when people roll their own or try to get in, it's, it's, it could be a lot of muck as they say. Yeah. What's the, what's the real pain point that you guys solve? >>So in terms of, you know, AI and machine learning, what Stripe Data Pipeline is gonna give you is it gives you a lot of signals around your payments that you can incorporate into your models. We actually have a number of customers that use Stripe radar data, so our fraud product and they integrate it with their in-house data that they get from other sources, have a really good understanding of fraud within their whole business. So it's kind of a way to get that data without having to like go through the process of ingesting it. So like, yeah, your, your team doesn't have to think about the ingestion piece. They can just think about, you know, building models, enriching the data, getting insights on top >>And Adam, so let's, we call it etl, the nasty three letter word in my interview with them. And that's what we're getting to where data is actually connecting via APIs and pipelines. Yes. Seamlessly into other data. So the data mashup, it feels like we're back into in the old mashup days now you've got data mashing up together. This integration's now a big practice, it's a becoming an industry standard. What are some of the patterns and matches that you see around how people are integrating their data? Because we all know machine learning works better when there's more data available and people want to connect their data and integrate it without the hassle. What's the, what's some of the use cases that >>Yeah, totally. So as Brian mentioned, there's a ton of use case for engineering teams and being able to get that data reported over efficiently and correctly and that's, you know, something exactly like you touched on that we're seeing nowadays is like simply having access to the data isn't enough. It's all about consolidating it correctly and accurately and effectively so that you can draw the best insights from that. So yeah, we're seeing a lot of use cases for teams across companies, including, a big example is finance teams. We had one of our largest users actually report that they were able to close their books faster than ever from integrating all of their Stripe revenue data for their business with their, the rest of their data in their data warehouse, which was traditionally something that would've taken them days, weeks, you know, having to do the manual aspect. But they were able to, to >>Simplify that, Savannah, you know, we were talking at the last event we were at Supercomputing where it's more speeds and feeds as people get more compute power, right? They can do more at the application level with developers. And one of the things we've been noticing I'd love to get your reaction to is as you guys have customers, millions of customers, are you seeing customers doing more with Stripe that's not just customers where they're more of an ecosystem partner of Stripe as people see that Stripe is not just a, a >>More comprehensive solution. >>Yeah. What's going on with the customer base? I can see the developers embedding it in, but once you get Stripe, you're like a, you're the plumbing, you're the financial bloodline if you will for the all the applications. Are your customers turning into partners, ecosystem partners? How do you see that? >>Yeah, so we definitely, that's what we're hoping to do. We're really hoping to be everything that a user needs when they wanna run an online business, be able to come in and maybe initially they're just using payments or they're just using billing to set up subscriptions but down the line, like as they grow, as they might go public, we wanna be able to scale with them and be able to offer them all of the products that they need to do. So Data Pipeline being a really important one for, you know, if you're a smaller company you might not be needing to leverage all of this big data and making important product decisions that you know, might come down to the very details, but as you scale, it's really something that we've seen a lot of our larger users benefit from. >>Oh and people don't wanna have to factor in too many different variables. There's enough complexity scaling a business, especially if you're headed towards IPO or something like that. Anyway, I love that the Stripe data pipeline is a no code solution as well. So people can do more faster. I wanna talk about it cuz it struck me right away on our lineup that we have engineering and product marketing on the stage with us. Now for those who haven't worked in a very high growth, massive company before, these teams can have a tiny bit of tension only because both teams want a lot of great things for the end user and their community. Tell me a little bit about the culture at Stripe and what it's like collaborating on the data pipeline. >>Yeah, I mean I, I can kick it off, you know, from, from the standpoint like we're on the same team, like we want to grow Stripe data pipeline, that is the goal. So whatever it takes to kind of get that job done is what we're gonna do. And I think that is something that is just really core to all of Stripe is like high collaboration, high trust, you know, this is something where we can all win if we work together. You don't need to, you know, compete with like products for like resourcing or to get your stuff done. It's like no, what's the, what's the, the team goal here, right? Like we're looking for team wins, not, you know, individual wins. >>Awesome. Yeah. And at the end of the day we have the same goal of connecting the product and the user in a way that makes sense and delivering the best product to that target user. So it's, it's really, it's a great collaboration and as Brian mentioned, the culture at Stripe really aligns with that as >>Well. So you got the engineering teams that get value outta that you guys are dealing with, that's your customer. But the security angle really becomes a big, I think catalyst cuz not just engineering, they gotta build stuff in so they're always building, but the security angle's interesting cuz now you got that data feeding security teams, this is becoming very secure security ops oriented. >>Yeah, you know, we are really, really tight partners with our internal security folks. They review everything that we do. We have a really robust security team. But I think, you know, kind of tying back to the Amazon side, like Amazon, Redshift is a very secure product and the way that we share data is really secure. You know, the, the sharing mechanism only works between encrypted clusters. So your data is encrypted at rest, encrypted and transit and excuse me, >>You're allowed to breathe. You also swallow the audience as well as your team at Stripe and all of us here at the Cube would like your survival. First and foremost, the knowledge we'll get to the people. >>Yeah, for sure. Where else was I gonna go? Yeah, so the other thing like you kind of mentioned, you know, there are these ETLs out there, but they, you know that that requires you to trust your data to a third party. So that's another thing here where like your data is only going from stripe to your cluster. There's no one in the middle, no one else has seen what you're doing, there's no other security risks. So security's a big focus and it kind of runs through the whole process both on our side and Amazon side. >>What's the most important story for Stripe at this event? You guys hear? How would you say, how would you say, and if you're on the elevator, what's going on with Stripe? Why now? What's so important at Reinvent for Stripe? >>Yeah, I mean I'm gonna use this as an opportunity to plug data pipelines. That's what we focus on. We're here representing the product, which is the easiest way for any user of aws, a user of Amazon, Redshift and a user of Stripe be able to connect the dots and get their data in the best way possible so that they can draw important business insights from that. >>Right? >>Yeah, I think, you know, I would double what North said, really grow Stripe data pipeline, get it to more customers, get more value for our customers by connecting them with their data and with reporting. I think that's, you know, my goal here is to talk to folks, kind of understand what they want to see out of their data and get them onto Stripe data pipeline. >>And you know, former Mike Mikela, former eight executive now over there at Stripe leading the charge, he knows a lot about Amazon here at aws. The theme tomorrow, Adams Leslie keynote, it's gonna be a lot about data, data integration, data end to end Lifeing, you see more, we call it data as code where engineering infrastructure as code was cloud was starting to see a big trend towards data as code where it's more of an engineering opportunity and solution insights. This data as code is kinda like the next evolution. What do you guys think about that? >>Yeah, definitely there is a ton that you can get out of your data if it's in the right place and you can analyze it in the correct ways. You know, you look at Redshift and you can pull data from Redshift into a ton of other products to like, you know, visualize it to get machine learning insights and you need the data there to be able to do this. So again, Stripe Data Pipeline is a great way to take your data and integrate it into the larger data picture that you're building within your company. >>I love that you are supporting businesses of all sizes and millions of them. No. And Brian, thank you so much for being here and telling us more about the financial infrastructure of the internet. That is Stripe, John Furrier. Thanks as always for your questions and your commentary. And thank you to all of you for tuning in to the Cubes coverage of AWS Reinvent Live here from Las Vegas, Nevada. I'm Savannah Peterson and we look forward to seeing you all week.

(upbeat music) >> We're back at SC22 SuperComputing Conference in Dallas. My name's Paul Gillan, my co-host, John Furrier, SiliconANGLE founder. And huge exhibit floor here. So much activity, so much going on in HPC, and much of it around the chips from AMD, which has been on a roll lately. And in partnership with Dell, our guests are Brian Payne, Dell Technologies, VP of Product Management for ISG mid-range technical solutions, and Raghu Nambiar, corporate vice president of data system, data center ecosystem, and application engineering, that's quite a mouthful, at AMD, And gentlemen, welcome. Thank you. >> Thanks for having us. >> This has been an evolving relationship between you two companies, obviously a growing one, and something Dell was part of the big general rollout, AMD's new chip set last week. Talk about how that relationship has evolved over the last five years. >> Yeah, sure. Well, so it goes back to the advent of the EPIC architecture. So we were there from the beginning, partnering well before the launch five years ago, thinking about, "Hey how can we come up with a way to solve customer problems? address workloads in unique ways?" And that was kind of the origin of the relationship. We came out with some really disruptive and capable platforms. And then it continues, it's continued till then, all the way to the launch of last week, where we've introduced four of the most capable platforms we've ever had in the PowerEdge portfolio. >> Yeah, I'm really excited about the partnership with the Dell. As Brian said, we have been partnering very closely for last five years since we introduced the first generation of EPIC. So we collaborate on, you know, system design, validation, performance benchmarks, and more importantly on software optimizations and solutions to offer out of the box experience to our customers. Whether it is HPC or databases, big data analytics or AI. >> You know, you guys have been on theCUBE, you guys are veterans 2012, 2014 back in the day. So much has changed over the years. Raghu, you were on the founding chair of the TPC for AI. We've talked about the different iterations of power service. So much has changed. Why the focus on these workloads now? What's the inflection point that we're seeing here at SuperComputing? It feels like we've been in this, you know run the ball, get, gain a yard, move the chains, you know, but we feel, I feel like there's a moment where the there's going to be an unleashing of innovation around new use cases. Where's the workloads? Why the performance? What are some of those use cases right now that are front and center? >> Yeah, I mean if you look at today, the enterprise ecosystem has become extremely complex, okay? People are running traditional workloads like Relational Database Management Systems, also new generation of workloads with the AI and HPC and actually like AI actually HPC augmented with some of the AI technologies. So what customers are looking for is, as I said, out of the box experience, or time to value is extremely critical. Unlike in the past, you know, people, the customers don't have the time and resources to run months long of POCs, okay? So that's one idea that we are focusing, you know, working closely with Dell to give out of the box experience. Again, you know, the enterprise applicate ecosystem is, you know, really becoming complex and the, you know, as you mentioned, some of the industry standard benchmark is designed to give the fair comparison of performance, and price performance for the, our end customers. And you know, Brian and my team has been working closely to demonstrate our joint capabilities in the AI space with, in a set of TPCx-AI benchmark cards last week it was the major highlight of our launch last week. >> Brian, you got showing the demo in the booth at Dell here. Not demo, the product, it's available. What are you seeing for your use cases that customers are kind of rallying around now, and what are they doubling down on. >> Yeah, you know, I, so Raghu I think teed it up well. The really data is the currency of business and all organizations today. And that's what's pushing people to figure out, hey, both traditional workloads as well as new workloads. So we've got in the traditional workload space, you still have ERP systems like SAP, et cetera, and we've announced world records there, a hundred plus percent improvements in our single socket system, 70% and dual. We actually posted a 40% advantage over the best Genoa result just this week. So, I mean, we're excited about that in the traditional space. But what's exciting, like why are we here? Why, why are people thinking about HPC and AI? It's about how do we make use of that data, that data being the currency and how do we push in that space? So Raghu mentioned the TPC AI benchmark. We launched, or we announced in collaboration you talk about how do we work together, nine world records in that space. In one case it's a 3x improvement over prior generations. So the workloads that people care about is like how can I process this data more effectively? How can I store it and secure it more effectively? And ultimately, how do I make decisions about where we're going, whether it's a scientific breakthrough, or a commercial application. That's what's really driving the use cases and the demand from our customers today. >> I think one of the interesting trends we've seen over the last couple of years is a resurgence in interest in task specific hardware around AI. In fact venture capital companies invested a $1.8 billion last year in AI hardware startups. I wonder, and these companies are not doing CPUs necessarily, or GPUs, they're doing accelerators, FPGAs, ASICs. But you have to be looking at that activity and what these companies are doing. What are you taking away from that? How does that affect your own product development plans? Both on the chip side and on the system side? >> I think the future of computing is going to be heterogeneous. Okay. I mean a CPU solving certain type of problems like general purpose computing databases big data analytics, GPU solving, you know, problems in AI and visualization and DPUs and FPGA's accelerators solving you know, offloading, you know, some of the tasks from the CPU and providing realtime performance. And of course, you know, the, the software optimizes are going to be critical to stitch everything together, whether it is HPC or AI or other workloads. You know, again, as I said, heterogeneous computing is going to be the future. >> And, and for us as a platform provider, the heterogeneous, you know, solutions mean we have to design systems that are capable of supporting that. So if as you think about the compute power whether it's a GPU or a CPU, continuing to push the envelope in terms of, you know, to do the computations, power consumption, things like that. How do we design a system that can be, you know, incredibly efficient, and also be able to support the scaling, you know, to solve those complex problems. So that gets into challenges around, you know, both liquid cooling, but also making the most out of air cooling. And so we're seeing not only are we we driving up you know, the capability of these systems, we're actually improving the energy efficiency. And those, the most recent systems that we launched around the CPU, which is still kind of at the heart of everything today, you know, are seeing 50% improvement, you know, gen to gen in terms of performance per watt capabilities. So it's, it's about like how do we package these systems in effective ways and make sure that our customers can get, you know, the advertised benefits, so to speak, of the new chip technologies. >> Yeah. To add to that, you know, performance, scalability total cost of ownership, these are the key considerations, but now energy efficiency has become more important than ever, you know, our commitment to sustainability. This is one of the thing that we have demonstrated last week was with our new generation of EPIC Genoa based systems, we can do a one five to one consolidation, significantly reducing the energy requirement. >> Power's huge costs are going up. It's a global issue. >> Raghu: Yeah, it is. >> How do you squeeze more performance too out of it at the same time, I mean, smaller, faster, cheaper. Paul, you wrote a story about, you know, this weekend about hardware and AI making hardware so much more important. You got more power requirements, you got the sustainability, but you need more horsepower, more compute. What's different in the architecture if you guys could share like today versus years ago, what's different in as these generations step function value increases? >> So one of the major drivers from the processor perspective is if you look at the latest generation of processors, the five nanometer technology, bringing efficiency and density. So we are able to pack 96 processor cores, you know, in a two socket system, we are talking about 196 processor cores. And of course, you know, other enhancements like IPC uplift, bringing DDR5 to the market PC (indistinct) for the market, offering overall, you know, performance uplift of more than 2.5x for certain workloads. And of course, you know, significantly reducing the power footprint. >> Also, I was just going to cut, I mean, architecturally speaking, you know, then how do we take the 96 cores and surround it, deliver a balanced ecosystem to make sure that we can get the, the IO out of the system, and make sure we've got the right data storage. So I mean, you'll see 60% improvements and total storage in the system. I think in 2012 we're talking about 10 gig ethernet. Well, you know, now we're on to 100 and 400 on the forefront. So it's like how do we keep up with this increased power, by having, or computing capabilities both offload and core computing and make sure we've got a system that can deliver the desired (indistinct). >> So the little things like the bus, the PCI cards, the NICs, the connectors have to be rethought through. Is that what you're getting at? >> Yeah, absolutely. >> Paul: And the GPUs, which are huge power consumers. >> Yeah, absolutely. So I mean, cooling, we introduce, and we call it smart cooling is a part of our latest generation of servers. I mean, the thermal design inside of a server is a is a complex, you know, complex system, right? And doing that efficiently because of course fans consume power. So I mean, yeah, those are the kind of considerations that we have to put through to make sure that you're not either throttling performance because you don't have you know, keeping the chips at the right temperature. And, and you know, ultimately when you do that, you're hurting the productivity of the investment. So I mean, it's, it's our responsibility to put our thoughts and deliver those systems that are (indistinct) >> You mention data too, if you bring in the data, one of the big discussions going into the big Amazon show coming up, re:Invent is egress costs. Right, So now you've got compute and how you design data latency you know, processing. It's not just contained in a machine. You got to think about outside that machine talking to other machines. Is there an intelligent (chuckles) network developing? I mean, what's the future look like? >> Well, I mean, this is a, is an area that, that's, you know, it's fun and, you know, Dell's in a unique position to work on this problem, right? We have 70% of the mission housed, 70% of the mission critical data that exists in the world. How do we bring that closer to compute? How do we deliver system level solutions? So server compute, so recently we announced innovations around NVMe over Fabrics. So now you've got the NVMe technology and the SAN. How do we connect that more efficiently across the servers? Those are the kinds, and then guide our customers to make use of that. Those are the kinds of challenges that we're trying to unlock the value of the data by making sure we're (indistinct). >> There are a lot of lessons learned from, you know, classic HPC and some of the, you know big data analytics. Like, you know, Hadoops of the world, you know, you know distributor processing for crunching a large amount of amount of data. >> With the growth of the cloud, you see, you know, some pundits saying that data centers will become obsolete in five years, and everything's going to move to the cloud. Obviously data center market that's still growing, and is projected to continue to grow. But what's the argument for captive hardware, for owning a data center these days when the cloud offers such convenience and allegedly cost benefit? >> I would say the reality is that we're, and I think the industry at large has acknowledged this, that we're living in a multicloud world and multicloud methods are going to be necessary to you know, to solve problems and compete. And so, I mean, you know, in some cases, whether it's security or latency, you know, there's a push to have things in your own data center. And then of course growth at the edge, right? I mean, that's, that's really turning, you know, things on their head, if you will, getting data closer to where it's being generated. And so I would say we're going to live in this edge cloud, you know, and core data center environment with multi, you know, different cloud providers providing solutions and services where it makes sense, and it's incumbent on us to figure out how do we stitch together that data platform, that data layer, and help customers, you know, synthesize this data to, to generate, you know, the results they need. >> You know, one of the things I want to get into on the cloud you mentioned that Paul, is that we see the rise of graph databases. And so is that on the radar for the AI? Because a lot of more graph data is being brought in, the database market's incredibly robust. It's one of the key areas that people want performance out of. And as cloud native becomes the modern application development, a lot more infrastructure as code's happening, which means that the internet and the networks and the process should be programmable. So graph database has been one of those things. Have you guys done any work there? What's some data there you can share on that? >> Yeah, actually, you know, we have worked closely with a company called TigerGraph, there in the graph database space. And we have done a couple of case studies, one on the healthcare side, and the other one on the financial side for fraud detection. Yeah, I think they have a, this is an emerging area, and we are able to demonstrate industry leading performance for graph databases. Very excited about it. >> Yeah, it's interesting. It brings up the vertical versus horizontal applications. Where is the AI HPC kind of shining? Is it like horizontal and vertical solutions or what's, what's your vision there. >> Yeah, well, I mean, so this is a case where I'm also a user. So I own our analytics platform internally. We actually, we have a chat box for our product development organization to figure out, hey, what trends are going on with the systems that we sell, whether it's how they're being consumed or what we've sold. And we actually use graph database technology in order to power that chat box. So I'm actually in a position where I'm like, I want to get these new systems into our environment so we can deliver. >> Paul: Graphs under underlie most machine learning models. >> Yeah, Yeah. >> So we could talk about, so much to talk about in this space, so little time. And unfortunately we're out of that. So fascinating discussion. Brian Payne, Dell Technologies, Raghu Nambiar, AMD. Congratulations on the successful launch of your new chip set and the growth of, in your relationship over these past years. Thanks so much for being with us here on theCUBE. >> Super. >> Thank you much. >> It's great to be back. >> We'll be right back from SuperComputing 22 in Dallas. (upbeat music)

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now, in this program, we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program, you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think, like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems. Certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean, commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away. Just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean, we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is, you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and, you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally, I would just say please, like watch in ice in Tim's sessions, Like these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time, really hot area. As Brian said in a moment, I'll be right back with Anna East Dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't want to miss this.

(bright upbeat music) >> Welcome back to Detroit guys and girls. Lisa Martin here with John Furrier. We've been on the floor at KubeCon + CloudNativeCon North America for about two days now. We've been breaking news, we would have a great conversations, John. We love talking with CUBE alumni whose companies are just taking off. And we get to do that next again. >> Well, this next segment's awesome. We have former CUBE host, Brian Gracely, here who's an executive in this company. And then the entrepreneur who we're going to talk with. She was on theCUBE when it just started now they're extremely successful. It's going to be a great conversation. >> It is, Idit Levine is here, the founder and CEO of solo.io. And as John mentioned, Brian Gracely. You know Brian. He's the VP of Product Marketing and Product Strategy now at solo.io. Guys, welcome to theCUBE, great to have you here. >> Thanks for having us. >> Idit: Thank so much for having us. >> Talk about what's going on. This is a rocket ship that you're riding. I was looking at your webpage, you have some amazing customers. T-Mobile, BMW, Amex, for a marketing guy it must be like, this is just- >> Brian: Yeah, you can't beat it. >> Kid in a candy store. >> Brian: Can't beat it. >> You can't beat it. >> For giant companies like that, giant brands, global, to trust a company of our size it's trust, it's great engineering, it's trust, it's fantastic. >> Idit, talk about the fast trajectory of this company and how you've been able to garner trust with such mass organizations in such a short time period. >> Yes, I think that mainly is just being the best. Honestly, that's the best approach I can say. The team that we build, honestly, and this is a great example of one of them, right? And we're basically getting the best people in the industry. So that's helpful a lot. We are very, very active on the open source community. So basically it building it, anyway, and by doing this they see us everywhere. They see our success. You're starting with a few customers, they're extremely successful and then you're just creating this amazing partnership with them. So we have a very, very unique way we're working with them. >> So hard work, good code. >> Yes. >> Smart people, experience. >> That's all you need. >> It's simple, why doesn't everyone do it? >> It's really easy. (all laughing) >> All good, congratulations. It's been fun to watch you guys grow. Brian, great to see you kicking butt in this great company. I got to ask about the landscape because I love the ServiceMeshCon you guys had on a co-located event on day zero here as part of that program, pretty packed house. >> Brian: Yep. >> A lot of great feedback. This whole ServiceMesh and where it fits in. You got Kubernetes. What's the update? Because everything's kind of coming together- >> Brian: Right. >> It's like jello in the refrigerator it kind of comes together at the same time. Where are we? >> I think the easiest way to think about it is, and it kind of mirrors this event perfectly. So the last four or five years, all about Kubernetes, built Kubernetes. So every one of our customers are the ones who have said, look, for the last two or three years, we've been building Kubernetes, we've had a certain amount of success with it, they're building applications faster, they're deploying and then that success leads to new challenges, right? So we sort of call that first Kubernetes part sort of CloudNative 1.0, this and this show is really CloudNative 2.0. What happens after Kubernetes service mesh? Is that what happens after Kubernetes? And for us, Istio now being part of the CNCF, huge, standardized, people are excited about it. And then we think we are the best at doing Istio from a service mesh perspective. So it's kind of perfect, perfect equation. >> Well, I'll turn it on, listen to your great Cloud cast podcast, plug there for you. You always say what is it and what isn't it? >> Brian: Yeah. >> What is your product and what isn't it? >> Yeah, so our product is, from a purely product perspective it's service mesh and API gateway. We integrate them in a way that nobody else does. So we make it easier to deploy, easier to manage, easier to secure. I mean, those two things ultimately are, if it's an internal API or it's an external API, we secure it, we route it, we can observe it. So if anybody's, you're building modern applications, you need this stuff in order to be able to go to market, deploy at scale all those sort of things. >> Idit, talk about some of your customer conversations. What are the big barriers that they've had, or the challenges, that solo.io comes in and just wipes off the table? >> Yeah, so I think that a lot of them, as Brian described it, very, rarely they had a success with Kubernetes, maybe a few clusters, but then they basically started to on-ramp more application on those clusters. They need more cluster maybe they want multi-class, multi-cloud. And they mainly wanted to enable the team, right? This is why we all here, right? What we wanted to eventually is to take a piece of the infrastructure and delegate it to our customers which is basically the application team. So I think that that's where they started to see the problem because it's one thing to take some open source project and deploy it very little bit but the scale, it's all about the scale. How do you enable all those millions of developers basically working on your platform? How do you scale multi-cloud? What's going on if one of them is down, how do you fill over? So that's exactly the problem that they have >> Lisa: Which is critical for- >> As bad as COVID was as a global thing, it was an amazing enabler for us because so many companies had to say... If you're a retail company, your front door was closed, but you still wanted to do business. So you had to figure out, how do I do mobile? How do I be agile? If you were a company that was dealing with like used cars your number of hits were through the roof because regular cars weren't available. So we have all these examples of companies who literally overnight, COVID was their digital transformation enabler. >> Lisa: Yes. Yes. >> And the scale that they had to deal with, the agility they had to deal with, and we sort of fit perfectly in that. They re-looked at what's our infrastructure look like? What's our security look like? We just happened to be right place in the right time. >> And they had skillset issues- >> Skillsets. >> Yeah. >> And the remote work- >> Right, right. >> Combined with- >> Exactly. >> Modern upgrade gun-to-the-head, almost, kind of mentality. >> And we're really an interesting company. Most of the interactions we do with customers is through Slack, obviously it was remote. We would probably be a great Slack case study in terms of how to do business because our customers engage with us, with engineers all over the world, they look like one team. But we can get them up and running in a POC, in a demo, get them through their things really, really fast. It's almost like going to the public cloud, but at whatever complexity they want. >> John: Nice workflow. >> So a lot of momentum for you guys silver linings during COVID, which is awesome we do hear a lot of those stories of positive things, the acceleration of digital transformation, and how much, as consumers, we've all benefited from that. Do you have one example, Brian, as the VP of product marketing, of a customer that you really think in the last two years just is solo.io's value proposition on a platter? >> I'll give you one that I think everybody can understand. So most people, at least in the United States, you've heard of Chick-fil-A, retail, everybody likes the chicken. 2,600 stores in the US, they all shut down and their business model, it's good food but great personal customer experience. That customer experience went away literally overnight. So they went from barely anybody using the mobile application, and hence APIs in the backend, half their business now goes through that to the point where, A, they shifted their business, they shifted their customer experience, and they physically rebuilt 2,600 stores. They have two drive-throughs now that instead of one, because now they have an entire one dedicated to that mobile experience. So something like that happening overnight, you could never do the ROI for it, but it's changed who they are. >> Lisa: Absolutely transformative. >> So, things like that, that's an example I think everybody can kind of relate to. Stuff like that happened. >> Yeah. >> And I think that's also what's special is, honestly, you're probably using a product every day. You just don't know that, right? When you're swiping your credit card or when you are ordering food, or when you using your phone, honestly the amount of customer they were having, the space, it's like so, every industry- >> John: How many customers do you have? >> I think close to 200 right now. >> Brian: Yeah. >> Yeah. >> How many employees, can you gimme some stats? Funding, employees? What's the latest statistics? >> We recently found a year ago $135 million for a billion dollar valuation. >> Nice. >> So we are a unicorn. I think when you took it we were around like 50 ish people. Right now we probably around 180, and we are growing, we probably be 200 really, really quick. And I think that what's really, really special as I said the interaction that we're doing with our customers, we're basically extending their team. So for each customer is basically a Slack channel. And then there is a lot of people, we are totally global. So we have people in APAC, in Australia, New Zealand, in Singapore we have in AMEA, in UK and in Spain and Paris, and other places, and of course all over US. >> So your use case on how to run a startup, scale up, during the pandemic, complete clean sheet of paper. >> Idit: We had to. >> And what happens, you got Slack channels as your customer service collaboration slash productivity. What else did you guys do differently that you could point to that's, I would call, a modern technique for an entrepreneurial scale? >> So I think that there's a few things that we are doing different. So first of all, in Solo, honestly, there is a few things that differentiated from, in my opinion, most of the companies here. Number one is look, you see this, this is a lot, a lot of new technology and one of the things that the customer is nervous the most is choosing the wrong one because we saw what happened, right? I don't know the orchestration world, right? >> John: So choosing and also integrating multiple things at the same time. >> Idit: Exactly. >> It's hard. >> And this is, I think, where Solo is expeditious coming to place. So I mean we have one team that is dedicated like open source contribution and working with all the open source community and I think we're really good at picking the right product and basically we're usually right, which is great. So if you're looking at Kubernetes, we went there for the beginning. If you're looking at something like service mesh Istio, we were all envoy proxy and out of process. So I think that by choosing these things, and now Cilium is something that we're also focusing on. I think that by using the right technology, first of all you know that it's very expensive to migrate from one to the other if you get it wrong. So I think that's one thing that is always really good at. But then once we actually getting those portal we basically very good at going and leading those community. So we are basically bringing the customers to the community itself. So we are leading this by being in the TOC members, right? The Technical Oversight Committee. And we are leading by actually contributing a lot. So if the customer needs something immediately, we will patch it for him and walk upstream. So that's kind of like the second thing. And the third one is innovation. And that's really important to us. So we pushing the boundaries. Ambient, that we announced a month ago with Google- >> And STO, the book that's out. >> Yes, the Ambient, it's basically a modern STO which is the future of SDL. We worked on it with Google and their NDA and we were listed last month. This is exactly an example of us basically saying we can do it better. We learn from our customers, which is huge. And now we know that we can do better. So this is the third thing, and the last one is the partnership. I mean honestly we are the extension team of the customer. We are there on Slack if they need something. Honestly, there is a reason why our renewal rate is 98.9 and our net extension is 135%. I mean customers are very, very happy. >> You deploy it, you make it right. >> Idit: Exactly, exactly. >> The other thing we did, and again this was during COVID, we didn't want to be a shell-for company. We didn't want to drop stuff off and you didn't know what to do with it. We trained nearly 10,000 people. We have something called Solo Academy, which is free, online workshops, they run all the time, people can come and get hands on training. So we're building an army of people that are those specialists that have that skill set. So we don't have to walk into shops and go like, well okay, I hope six months from now you guys can figure this stuff out. They're like, they've been doing that. >> And if their friends sees their friend, sees their friend. >> The other thing, and I got to figure out as a marketing person how to do this, we have more than a few handfuls of people that they've got promoted, they got promoted, they got promoted. We keep seeing people who deploy our technologies, who, because of this stuff they're doing- >> John: That's a good sign. They're doing it at at scale, >> John: That promoter score. >> They keep getting promoted. >> Yeah, that's amazing. >> That's a powerful sort of side benefit. >> Absolutely, that's a great thing to have for marketing. Last question before we ran out of time. You and I, Idit, were talking before we went live, your sessions here are overflowing. What's your overall sentiment of KubeCon 2022 and what feedback have you gotten from all the customers bursting at the seam to come talk to you guys? >> I think first of all, there was the pre-event which we had and it was a lot of fun. We talked to a lot of customer, most of them is 500, global successful company. So I think that people definitely... I will say that much. We definitely have the market feed, people interested in this. Brian described very well what we see here which is people try to figure out the CloudNative 2.0. So that's number one. The second thing is that there is a consolidation, which I like, I mean STO becoming right now a CNCF project I think it's a huge, huge thing for all the community. I mean, we're talking about all the big tweak cloud, we partner with them. I mean I think this is a big sign of we agree which I think is extremely important in this community. >> Congratulations on all your success. >> Thank you so much. >> And where can customers go to get their hands on this, solo.io? >> Solo.io? Yeah, absolutely. >> Awesome guys, this has been great. Congratulations on the momentum. >> Thank you. >> The rocket ship that you're riding. We know you got to get to the airport we're going to let you go. But we appreciate your insights and your time so much, thank you. >> Thank you so much. >> Thanks guys, we appreciate it. >> A pleasure. >> Thanks. >> For our guests and John Furrier, This is Lisa Martin live in Detroit, had to think about that for a second, at KubeCon 2022 CloudNativeCon. We'll be right back with our final guests of the day and then the show wraps, so stick around. (gentle music)

(soft upbeat music) >> Okay, we're kicking things off with Brian Gilmore. He's the director of IoT, an emerging technology at InfluxData. Brian, welcome to the program. Thanks for coming on. >> Thanks, Dave, great to be here. I appreciate the time. >> Hey, explain why InfluxDB, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >> No, no, not at all. I mean, I think, for us it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like SQL query support, things like that, we have to figure out a way to execute those for them in a way that will scale long term. And then we also want to make sure we're innovating, we're sort of staying ahead of the market as well, and sort of anticipating those future needs. So, you know, this is really a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine. But, you know, initially, the customers who are using us are going to see just great improvements in performance, you know, especially those that are working at the top end of the workload scale, you know, the massive data volumes and things like that. >> Yeah, and we're going to get into that today and the architecture and the like. But what was the catalyst for the enhancements? I mean, when and how did this all come about? >> Well, I mean, like three years ago, we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product. And sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was a long journey. (chuckles) I guess, you know, phase one was, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to optimize for like multi-tenant, multi-cloud, be able to host it in a truly like SAS manner where we could use, you know, some type of customer activity or consumption as the pricing vector. And that was sort of the birth of the real first InfluxDB cloud, you know, which has been really successful. We've seen, I think, like 60,000 people sign up. And we've got tons and tons of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a daily basis. And having that sort of big pool of very diverse and varied customers to chat with as they're using the product, as they're giving us feedback, et cetera, has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that, and then also making these big leaps as we're doing with this new engine. >> All right, so you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really want to understand how much of a pivot this is, and what does it take to make that shift from, you know, time series specialist to real time analytics and being able to support both? >> Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. Time series data is always going to be fundamental in sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. The time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics. If we're being honest though, I think our user base is well aware that the way we were architected was much more towards those sort of like backwards-looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a time to response on the queries, and can we get that to the point where the result sets are coming back so quickly from the time of query that we can like, limit that window down to minutes and then seconds? And now with this new engine, we're really starting to talk about a query window that could be like returning results in, you know, milliseconds of time since it hit the ingest queue. And that's really getting to the point where, as your data is available, you can use it and you can query it, you can visualize it, you can do all those sort of magical things with it. And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the real time queries, the multiple language query support. But, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a limited number of customers, strategic customers and strategic availabilities zones to start, but, you know, everybody over time. >> So you're basically going from what happened to, and you can still do that, obviously, but to what's happening now in the moment? >> Yeah. Yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the underlying data collection, the architecture, the infrastructure, the devices, and you know, the sort of highly distributed nature of all of this. So, yeah, I mean, getting a customer or a user to be able to use the data as soon as it is available, is what we're after here. I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >> Yeah, I mean, it is operationally, or operational real time is different. And that's one of the things that really triggered us to know that we were heading in the right direction is just how many sort of operational customers we have, you know, everything from like aerospace and defense. We've got companies monitoring satellites. We've got tons of industrial users using us as a process historian on the plant floor. And if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're going to do here is we're going to start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their historians and databases. >> Is this available, these innovations to InfluxDB cloud customers, only who can access this capability? >> Yeah, I mean, commercially and today, yes. I think we want to emphasize that for now our goal is to get our latest and greatest and our best to everybody over time of course. You know, one of the things we had to do here was like we doubled down on sort of our commitment to open source and availability. So, like, anybody today can take a look at the libraries on our GitHub and can inspect it and even can try to implement or execute some of it themselves in their own infrastructure. We are committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. And so just, you know, being careful, maybe a little cautious in terms of how big we go with this right away. Just sort of both limits, you know, the risk of any issues that can come with new software roll outs, we haven't seen anything so far. But also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products. But once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's going to be exciting time for the whole ecosystem. >> Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are going to help deliver on this vision. What should we know there? >> Well, I mean, I think, foundationally, we built the new core on Rust. This is a new very sort of popular systems language. It's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well, and if it does find error conditions. I mean, we've loved working with Go, and a lot of our libraries will continue to be sort of implemented in Go, but when it came to this particular new engine, that power performance and stability of Rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parquet for persistence. I think, for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our time series merge trees, this is a big break from that. You know, Arrow on the sort of in mem side and then Parquet in the on disk side. It allows us to present, you know, a unified set of APIs for those really fast real time queries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that Parquet format, which is also cool because there's an entire ecosystem sort of popping up around Parquet in terms of the machine learning community. And getting that all to work, we had to glue it together with Arrow Flight. That's sort of what we're using as our RPC component. It handles the orchestration and the transportation of the columnar data now, we're moving to like a true columnar database model for this version of the engine. You know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like, blurring that line between real time and historical data, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >> Yeah, again, I mean, it's funny. You mentioned Rust. It's been around for a long time but it's popularity is, you know, really starting to hit that steep part of the S-curve. And we're going to dig into more of that, but give us, is there anything else that we should know about, Brian? Give us the last word. >> Well, I mean, I think first, I'd like everybody sort of watching, just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if you want to participate or if you want to work sort of in terms of early access with the new engine, please reach out to the team. I'm sure, you know, there's a lot of communications going out and it'll be highly featured on our website. But reach out to the team. Believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to, because we can flip a lot of stuff on, especially in cloud through feature flags. But if there's something new that you want to try out, we'd just love to hear from you. And then, you know, our goal would be, that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into this vertically-oriented stack of cloud services, and enterprise databases, and edge databases, you know, it's going to be what we all make it together, not just those of us who are employed by InfluxDB. And then finally, I would just say, please, like, watch and Anais' and Tim's sessions. Like, these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer-obsessed, which is amazing. And there's no better takes, like honestly, on the sort of technical details of this than theirs, especially when it comes to the value that these investments will bring to our customers and our communities. So, encourage you to, you know, pay more attention to them than you did to me, for sure. >> Brian Gilmore, great stuff. Really appreciate your time. Thank you. >> Yeah, thanks David, it was awesome. Looking forward to it. >> Yeah, me too. I'm looking forward to see how the community actually applies these new innovations and goes beyond just the historical into the real time. Really hot area. As Brian said, in a moment, I'll be right back with Anais Dotis-Georgiou to dig into the critical aspects of key open source components of the InfluxDB engine, including Rust, Arrow, Parquet, Data Fusion. Keep it right there. You don't want to miss this. (soft upbeat music)

>> Okay, up next in the Lightning Talk Session is Brian Galligan; Mgr, Security and Operations at Brookfield Properties. Brian, great to see you. Thanks for coming on. >> Thanks for having me, John. >> So unified visibility across extended asset surface area is key these days. You can't secure what you can't see. So tell me more about how you were able to centralize your view of network assets with Armis and what impact that had on your business. >> Yeah, that's been a really key component of ours where we've actually owned multiple companies with them and are always acquiring companies from time to time. So it's always a question. What is actually out there and what do we need to be worried about. So from an inventory perspective it's definitely something that we've been looking into. Armis was a great partner in being able to get us the visibility into a lot of the IoT that we have out in the environment. And then also trying to find what we have and what's actually installed on those devices. What's running, who's talking to who. So that's definitely been a key component with our partnership with Armis. >> You know, we interview a lot of practitioners and companies and one things we found is vulnerability Management programs. There's a lot of gaps. You know, vulnerability management comes across more sometimes just IT devices, but not all assets. How has Armis Vulnerability Management made things better for your business? And what can you see now that you couldn't see before? >> Yeah, again, because we own multiple companies and they actually use different tools for vulnerability management. It's been a challenge to be able to compare apples to apples on when we have vulnerability. When we have risk out there, how do you put a single number to it? How do you prioritize different initiatives across those sectors? And being able to use Armis and have that one score, have that one visibility and also that one platform that you can query across all of those different companies, has been huge because we just haven't had the ability to say are we vulnerable to X, Y and Z across the board in these different companies? >> You know, it's interesting when you have a lot of different assets and companies, as you mentioned. It kind of increases the complexity and yeah we love the enterprise. You solve complexity by more complexity but that's not the playbook anymore. We want simplicity. We want to have a better solution. So when you take into account, the criticality of these businesses as you're integrating in, in real time and the assets within those business operations you got to keep focused on the right solutions. What has Armis done for you that's been correct and right for you guys? >> Yeah, so being able to see the different like be able to actually drill down into the nitty gritty on what devices are connecting to what. Being able to enforce policies that way, I think has been a huge win that we've been able to see from Armis. It's one of those things where we were able to see north-south traffic. No problem with our typical SIM tools, firewall tools and different logging sources but we haven't been able to see anything east-west and that's where we're going to be most vulnerable. That's where we've been actually found. We found some gaps in our coverage from a pen test perspective where we've found that where we don't have that visibility. Armis has allowed us to get into that communication to better fine tune the rules that we have across devices across sectors, across the data center to properties. Properties of the data center and then also to the cloud. >> Yeah, visibility into the assets is huge. But as you're in operations you got to operationalize these tools. I mean, some people sound like they've got a great sales pitch and all sounds like, "Wait a minute, I got to re-configure my entire operations." At the end of the day, you want to have an easy to use, but effective capability. So you're not taxed either personnel or operations. How easy has it been with Armis to implement from an ease of use, simplicity, plug and play? In other words, how quickly did you get to the time to value? Can you share your thoughts? >> This honestly is the biggest value that we've seen in Armis. I think a, a big kudos goes to the professional services group for getting us stood up being able to explain the tool, be able to dig into it and then get us to that time to value. Honestly, we've only scratched the surface on what Armis can give us which is great because they've given us so much already. So definitely taking that model of let's crawl, walk, run with what we're able to do. But the professional services team has given us so much assistance in getting from one collector to now many collectors. And we're in that deployment phase where we're able to gather more data and find those anomalies that are out there. I again, big props to the, the professional services team. >> Yeah, you know one of we'd add an old expression when you know when the whole democratization happened on the web here comes all the people, you know social media and whatnot now with IoT here comes all the devices. Here comes all the things- >> Yeah. >> Things >> More things are being attached to the network. So Armis has this global asset knowledge base that crowd-sources the asset intelligence. How has that been a game changer for you? And were you shocked when you discovered how many assets they were able to discover and what impact did that have for you? >> We have a large wifi footprint for guests, vendors, contractors that are working on site along with our corporate side, which has a lot of devices on it as well. And being able to see what devices are using what services on there and then be able to fingerprint them easily has been huge. I would say one of the best stories that I can tell is actually with a pen test that we ran recently. We were able to determine what the pen test device was and how it was acting anomalous and then fingerprint that device within five minutes opposed to getting on the phone with probably four or five different groups to figure out what is this device? It's not one of our normal devices. It's not one of our normal builds or anything. We were able to find that device within probably three to five minutes with Armis and the fingerprinting capability. >> Yeah, nothing's going to get by you with these port scans or any kind of activity, so to speak, jumping on the wifi. Great stuff. Anything else you'd like to share about Armis while I got you here? >> Yeah, I would say that something recently, we actually have an open position on our team currently. And one of the most exciting things is being able to share our journey that we've had with Armis over the last year, year and a half, and their eyes light up when they hear the capabilities of what Armis can do, what Armis can offer. And you see a little bit of jealousy of, you know, "Hey I really wish my current organization had that." And it's one of those selling tools that you're able to give to security engineers, security analysts saying, "Here's what you're going to have on the team to be able to do your job, right." So that you don't have to worry about necessarily the normal mundane things. You get to actually go do the cool hunting stuff, which Armis allows you to do. >> Well. Brian, thanks for the time here on this Lightning Talk, appreciate your insight. I'm John Furrier with theCUBE the leader in enterprise tech coverage. Up next in the Lightning Talk Session is Alex Schuchman. He's the CISO of Colgate-Palmolive Thanks for watching.

>> Okay, up next in the Lightning Talk Session is Brian Galligan; Mgr, Security and Operations at Brookfield Properties. Brian, great to see you. Thanks for coming on. >> Thanks for having me, John. >> So unified visibility across extended asset surface area is key these days. You can't secure what you can't see. So tell me more about how you were able to centralize your view of network assets with Armis and what impact that had on your business. >> Yeah, that's been a really key component of ours where we've actually owned multiple companies with them and are always acquiring companies from time to time. So it's always a question. What is actually out there and what do we need to be worried about. So from an inventory perspective it's definitely something that we've been looking into. Armis was a great partner in being able to get us the visibility into a lot of the IoT that we have out in the environment. And then also trying to find what we have and what's actually installed on those devices. What's running, who's talking to who. So that's definitely been a key component with our partnership with Armis. >> You know, we interview a lot of practitioners and companies and one things we found is vulnerability Management programs. There's a lot of gaps. You know, vulnerability management comes across more sometimes just IT devices, but not all assets. How has Armis Vulnerability Management made things better for your business? And what can you see now that you couldn't see before? >> Yeah, again, because we own multiple companies and they actually use different tools for vulnerability management. It's been a challenge to be able to compare apples to apples on when we have vulnerability. When we have risk out there, how do you put a single number to it? How do you prioritize different initiatives across those sectors? And being able to use Armis and have that one score, have that one visibility and also that one platform that you can query across all of those different companies, has been huge because we just haven't had the ability to say are we vulnerable to X, Y and Z across the board in these different companies? >> You know, it's interesting when you have a lot of different assets and companies, as you mentioned. It kind of increases the complexity and yeah we love the enterprise. You solve complexity by more complexity but that's not the playbook anymore. We want simplicity. We want to have a better solution. So when you take into account, the criticality of these businesses as you're integrating in, in real time and the assets within those business operations you got to keep focused on the right solutions. What has Armis done for you that's been correct and right for you guys? >> Yeah, so being able to see the different like be able to actually drill down into the nitty gritty on what devices are connecting to what. Being able to enforce policies that way, I think has been a huge win that we've been able to see from Armis. It's one of those things where we were able to see north-south traffic. No problem with our typical SIM tools, firewall tools and different logging sources but we haven't been able to see anything east-west and that's where we're going to be most vulnerable. That's where we've been actually found. We found some gaps in our coverage from a pen test perspective where we've found that where we don't have that visibility. Armis has allowed us to get into that communication to better fine tune the rules that we have across devices across sectors, across the data center to properties. Properties of the data center and then also to the cloud. >> Yeah, visibility into the assets is huge. But as you're in operations you got to operationalize these tools. I mean, some people sound like they've got a great sales pitch and all sounds like, "Wait a minute, I got to re-configure my entire operations." At the end of the day, you want to have an easy to use, but effective capability. So you're not taxed either personnel or operations. How easy has it been with Armis to implement from an ease of use, simplicity, plug and play? In other words, how quickly did you get to the time to value? Can you share your thoughts? >> This honestly is the biggest value that we've seen in Armis. I think a, a big kudos goes to the professional services group for getting us stood up being able to explain the tool, be able to dig into it and then get us to that time to value. Honestly, we've only scratched the surface on what Armis can give us which is great because they've given us so much already. So definitely taking that model of let's crawl, walk, run with what we're able to do. But the professional services team has given us so much assistance in getting from one collector to now many collectors. And we're in that deployment phase where we're able to gather more data and find those anomalies that are out there. I again, big props to the, the professional services team. >> Yeah, you know one of we'd add an old expression when you know when the whole democratization happened on the web here comes all the people, you know social media and whatnot now with IoT here comes all the devices. Here comes all the things- >> Yeah. >> Things >> More things are being attached to the network. So Armis has this global asset knowledge base that crowd-sources the asset intelligence. How has that been a game changer for you? And were you shocked when you discovered how many assets they were able to discover and what impact did that have for you? >> We have a large wifi footprint for guests, vendors, contractors that are working on site along with our corporate side, which has a lot of devices on it as well. And being able to see what devices are using what services on there and then be able to fingerprint them easily has been huge. I would say one of the best stories that I can tell is actually with a pen test that we ran recently. We were able to determine what the pen test device was and how it was acting anomalous and then fingerprint that device within five minutes opposed to getting on the phone with probably four or five different groups to figure out what is this device? It's not one of our normal devices. It's not one of our normal builds or anything. We were able to find that device within probably three to five minutes with Armis and the fingerprinting capability. >> Yeah, nothing's going to get by you with these port scans or any kind of activity, so to speak, jumping on the wifi. Great stuff. Anything else you'd like to share about Armis while I got you here? >> Yeah, I would say that something recently, we actually have an open position on our team currently. And one of the most exciting things is being able to share our journey that we've had with Armis over the last year, year and a half, and their eyes light up when they hear the capabilities of what Armis can do, what Armis can offer. And you see a little bit of jealousy of, you know, "Hey I really wish my current organization had that." And it's one of those selling tools that you're able to give to security engineers, security analysts saying, "Here's what you're going to have on the team to be able to do your job, right." So that you don't have to worry about necessarily the normal mundane things. You get to actually go do the cool hunting stuff, which Armis allows you to do. >> Well. Brian, thanks for the time here on this Lightning Talk, appreciate your insight. I'm John Furrier with theCUBE the leader in enterprise tech coverage. Up next in the Lightning Talk Session is Alex Schuchman. He's the CISO of Colgate-Palmolive Thanks for watching.

>>Okay, welcome everyone to the cube conversation here in Palo Alto, California. I'm John furrier, host of the cube. Got a great guest, Brian Gallagin manager of security and operations at Brookfield properties in the middle of all the sites, CSO action, a lot of security, a lot of operations to secure his environ. Brian, great to come on with you. Appreciate it. >>Thanks John. >>So talk about Brookfield properties. What's your environment look like you're in the middle of the security operations piece of it. You've got a great implementation. You got Armas doing some device management work. We've talked about that in the segment there, but broader speaking, what's your environment look like? What are some of the challenges? What's the scope and scale of the security that you're trying to manage? >>Yeah. Brookfield properties owns it's an asset management company and it owns real estate of all kinds. And we're current, we're constantly buying and selling assets. So the biggest challenge is finding out when we acquire company, what is actually in that environment and how quickly can we actually spin up our policies and protection capabilities in, in those areas? So I think uniquely from our perspective, it's a, a lot about finding what, what has been installed over the last decade, what what's secure, what's not secure, what follows our policies. And then what do we do to actually lock those down? Do, can we use the existing hardware that's in place? So we don't have to buy something new and we can use Armas policies to dictate that, or is it something that needs to be potentially removed? Because it is that critical vulnerability. There's something out there being used in the wild that we might actually have to purge that from our >>Environment, you know, facilities and companies you guys bought, and you're buying companies, you're buying assets, you're buying a lot of internet of things. You've got it, environments, you know, all kinds of challenges from, you know, identity, access management to, you know, what's connecting to your wifi networks all over the place. You got a diverse set of things. And one of the challenges in cyber right now is if you're just behind a little bit, you're gonna be vulnerable. I'm not talking about antiquated old, outdated. It we're talking about like getting it up to speed. If you're just a little bit behind you're behind the hackers and the bad guys. So the, the, the constant, you know, bar raising required is a huge challenge. What's your reaction to that? Can you scope the, the scale of this opportunity and challenge? >>Yeah, so you're, you're absolutely right. It's not only are the vulnerabilities changing, but again, our landscapes constantly changing. So being able to try to keep up with that velocity is, is a challenge with our current tool set. So when we actually onboarded with Armas about a year and a half ago, that's one of those things that we were con we were instantly given the ability to increase our visibility past our normal areas, which typically was, was our firewalls. Now we're able to see beyond the firewalls, to the switch level on, on the access points themselves a lot, a lot of guest traffic. I think we talked about in the previous segment, there's a lot of guest traffic on there trying to figure out who's doing what, and are they following the policies that are there. It also gives us the ability to double check our configurations that we have out there. We assume that we're, we're correct. And we do, you know, our annual pen test that we do, but that's something that's not necessarily enough. We, we, one at one once a year, checkin is not enough to be able to prove that the configurations we have is keeping us secure in the way that we think that, that, that we are. >>Yeah. I love the fact that you got the engineering and your title because engineering, the solutions is almost on a, a constant cadence. You have to have the re-engineering and the refactoring of the, of the, of the technology to match in as the changing landscape comes in, whether it's just physical access or more, more devices coming on the network, do you worry about like ransomware and inheriting previous environments, and, and you mentioned earlier locking things down. That's one step, what's your, what's your posture on all this? >>That that's a hundred percent. The biggest, the biggest problem is, is making sure, especially with ransomware we've, we've seen it before, making sure that when we buy an asset, that they have the capabilities to detect deter and potentially clean up in, in, in those circumstances of, of a, like a ransomware malware attack and that kind of stuff. So it's, it's definitely a, a huge concern of ours. So what we, what we're able to do now with Armas is once we buy that company, before we integrate their services with everything else that we have, we're able to actually have that kind of grace period, where they still are functioning a little bit more autonomously and not hooked into our network. We can do that due diligence that maybe we couldn't do prepurchase to see what's actually out there what's vulnerable. What needs to get changed day one, what needs to get changed six months from now, and what can maybe wait a whole sales cycle of two to three years to actually change >>Out? Yeah, Brian, you hit a really hot topic. That's not talked about much in the press or in the media. And that is, is that a lot of MNA, mergers and acquisitions happen, and there's actually ransomware waiting in the wings to actually lock that down pre post acquisition, then ransom on that asset. And so there's not a lot of due diligence or options to say, Hey, you know, make sure you make sure that they're ransomware free prior to the acquisition, not get stuck with an asset and saying that code's gone, or we are, you know, being held hostage. And this is a huge issue. >>It it's, it's all about trust by verify. You know, also like, you know, we're, we're doing surveys with these guys. We're, we're sitting down with the it teams that end up being our partners. And it it's about figuring out what, what have they done and what, what can you actually transition to? Like, maybe there's some gaps here. Maybe there's some improvements we can do. The, the other, the other key piece is making sure that our, our security tools are out there. So we, if we have an expectation that our security tools are on there before we grant them access to the greater Brookfield network, we're able to do compliance checks on things like that. Obviously vulnerability is another one that we actually haven't gotten too far into, but it's another one of those things where we can actually do compliance checks on here's the CVEs that are out there that we wanna make sure that you meet a minimum bar that you have defense against that. Or if you have devices out there that are banned per, per our policy, we're able to do those checks prior to granting greater visibility to the rest of the network. >>Yeah. You know, there's a lot of industry hype around certain things. We have a con congested market of people trying to sell you stuff right. And gonna really empathize with you there. And, you know, there's a big discussion endpoint protection. And then, you know, you mentioned trust and verify earlier, you know, there's kind of this confluence of zero trust, which kind of comes from me like no perimeter. So we gotta have a word that says zero trust. I get that. And then we look at like security supply chain in software, say open source, the word trust is coming out more and more. So, you know, what is it? Is it more trust or zero trust, trust and verified. So you're starting to see this confluence of, of posture. What's your reaction to this, this, these conversations, because I can see where you want to have trust, cuz you're moving across multiple access systems. I can see zero trust cuz trust and verify. What's your take on that? >>Yeah, I'm I, I think as a security professional, I think most of us I wanna say are in that trust, but verify boat definitely closer to the zero trust where we wanna make sure that we, we have a whole list of good policies out there. But if there's nothing to back it up, there's nothing to double check it. There's nothing to verify that you're, you're basically just hoping that it was done correctly. So that's, that's one of those things that is, is definitely huge. I think on our side is we, we trust our, our friends in it to do the right thing. We trust our customers on the business side to do the right thing. That's, that's huge for us, but having the tools to be able to say, are we actually good again without having an incident or having our pen test or pen test friends find it. That's one of those things where we don't have a lot of tools to do that. And like you said, people are always trying to sell you those tools. So being able to transition to something like ouris where we've actually seen the value right off the bat in being able to have that confidence raised that the risks that we've identified are the risks that are out there is, is huge for us. >>You know, as, as physical assets change, you, you acquire more territory, more companies, topologies change, software changes in the cloud with more iteration. I mean, you could have an always on pen test model, right? You gotta have pen test. You gotta have the slack reports. This is a challenge to move across environments. I wanna get your thoughts on the identity and access management. And the big cloud players are doing the same thing you got Amazon's Amazon, it's different from Azure and you got on-premises. So, you know, inter access. Management's great if you have an environment, but interoperability is a conversation that's happening a lot. What's your view on this because everything is changing, but you wanna lock it down and not restrict the growth and the evolution of change. What's your, what's your reaction to that? >>Yeah. With our, with our main identity management platform, that's actually freed us up to be able to give cross access access to employees that we have that maybe do work in multiple systems, or there's an application that's purchased by one group that is not purchased by another group instead of creating a whole new contract for that, we were, we're actually able to utilize that one system to be able to grant access to things that you otherwise wouldn't have. So I, I think having that in our business model where we are very segmented from an it perspective, we have multiple infrastructure teams. We have multiple development teams, multiple infrastructure or multiple networking teams. Being able to have that collaboration where you can share information a little bit better. It has been huge for us. And then from a security perspective, privilege access management, making sure that we can lock down special access, special permissions applications that shouldn't be used at certain times of the day, certain locations, whatever it might be is, is huge for us. And, and we definitely partner with, with all of our, our vendors for that very closely, for the reason that that's where a lot of these breaches happen, where one account gets hit and that that account has more privileges than it should. That's something that keeps us up at night. And again, our, our vendors that we use for identity management, our, our key partners of ours for that >>All great, great insight there, Brian, I really appreciate that final question for you. You know, as a makeup for people who are insecurity, it's kinda like sports, you know, you want to have an athlete that knows the game, you're playing football. You better know the game, you're playing baseball. You gotta know the game you're in. And cyber's one of those games it's got, got a lot going on. What is the, what is the ideal candidate coming outta school or profile? I mean, if you got a quarterback, they gotta throw the ball. They gotta have agility. If you're running back, you gotta have skills. What does the, the tech athlete look like? If you look at a person you're looking at hiring, what does that person look like? What's the makeup of their skillset. Can you share cuz there's not a lot of degrees out there for cyber. You gotta kind of learn it. It's evolving and it's a huge opportunity. What's your take. >>Yeah. And, and I would even say, you know, beyond somebody coming outta the school, somebody transitioning into the field too practical experience is key. And obviously you can't get your foot in the door before you actually have the opportunity to do that. So what are some things that you can do on your own? There are plenty of resources out there that actually give you tutorials on how to do, hacking how to do directory traversal, how to, how to do some of those small things that you can set up a lab, or maybe even there's a virtual lab that's hosted via. There's a, there's a free platforms out there that you can actually do this where you're doing virtual capture the flags. And you're able to post that. I've seen that on a couple resumes. I haven't seen it on a whole lot, but that shows that like not only do you know, you, you know, your fundamentals of it, but now you also understand the mindset of what a hacker is and whether you're gonna be blue team red team or purple team, being able to understand what a hacker can do is huge. >>So my CSO and I have had plenty of conversations where if we see any sort of practical experience, any hacking experience, any technical experience, that's gonna Trump, a lot of the other certifications that you can get. Yeah. Resumes these days. It's, it's a lot of cert chasers. And from our perspective, like that's, that's not as important. It's great to see the, the effort and, and the desire to up your game via certifications, but being able to show practical experience even on these free websites and being able to kind of link your profile to, to that is, is huge. And it's one of those things that's, you're not necessarily gonna get from a, a, you know, cybersecurity 1 0 1 class. You may actually have to go out and find some of those materials. >>Yeah. And then get, you know, you gotta get in, in the flow, so to speak and, and again, thinking like you gotta think like the enemy to beat the enemy. >>Exactly. >>Right. Brian Galligan, thanks for coming on the cube. Really appreciate your insight manager of security and operations engineering at Brookfield properties, man, you're in the center. We got a lot of things going on. You guys doing a great job. Thanks for sharing your, your insights here in the cube. I really appreciate it. >>Thanks, John. >>Okay. This is a cube conversation. I'm Jennifer with the cube. Thanks for watching.

(soft intro music) >> Welcome back to theCUBE's coverage of VeeamON 2022. Dave Vellante with David Nicholson. Brian Schwarz is here. We're going to stay on cloud. He's the director of product management at Google Cloud. The world's biggest cloud, I contend. Brian, thanks for coming on theCUBE. >> Thanks for having me. Super excited to be here. >> Long time infrastructure as a service background, worked at Pure, worked at Cisco, Silicon Valley guy, techie. So we're going to get into it here. >> I love it. >> I was saying before, off camera. We used to go to Google Cloud Next every year. It was an awesome show. Guys built a big set for us. You joined, right as the pandemic hit. So we've been out of touch a little bit. It's hard to... You know, you got one eye on the virtual event, but give us the update on Google Cloud. What's happening generally and specifically within storage? >> Yeah. So obviously the Cloud got a big boost during the pandemic because a lot of work went online. You know, more things kind of being digitally transformed as people keep trying to innovate. So obviously the growth of Google Cloud, has got a big tailwind to it. So business has been really good, lots of R&D investment. We obviously have an incredible set of technology already but still huge investments in new technologies that we've been bringing out over the past couple of years. It's great to get back out to events to talk to people about 'em. Been a little hard the last couple of years to give people some of the insights. When I think about storage, huge investments, one of the things that some people know but I think it's probably underappreciated is we use the same infrastructure for Google Cloud that is used for Google consumer products. So Search and Photos and all the public kind of things that most people are familiar with, Maps, et cetera. Same infrastructure at the same time is also used for Google Cloud. So we just have this tremendous capability of infrastructure. Google's got nine products that have a billion users most of which many people know. So we're pretty good at storage pretty good at compute, pretty good at networking. Obviously a lot of that kind of shines through on Google Cloud for enterprises to bring their applications, lift and shift and/or modernize, build new stuff in the Cloud with containers and things like that. >> Yeah, hence my contention that Google has the biggest cloud in the world, like I said before. Doesn't have the most IS revenue 'cause that's a different business. You can't comment, but I've got Google Cloud running at $12 billion a year run rate. So a lot of times people go, "Oh yeah, Google they're third place going for the bronze." But that is a huge business. There aren't a lot of 10, $12 billion infrastructure companies. >> In a rapidly growing market. >> And if you do some back of napkin math, whatever, give me 10, 15, let's call it 15% of that, to storage. You've got a big storage business. I know you can't tell us how big, but it's big. And if you add in all the stuff that's not in GCP, you do a lot of storage. So you know storage, you understand the technology. So what is the state of technology? You have a background in Cisco, nearly a networking company, they used to do some storage stuff sort of on the side. We used to say they're going to buy NetApp, of course that never happened. That would've made no sense. Pure Storage, obviously knows storage, but they were a disk array company essentially. Cloud storage, what's different about it? What's different in the technology? How does Google think about it? >> You know, I always like to tell people there's some things that are the same and familiar to you, and there's some things that are different. If I start with some of the differences, object storage in the Cloud, like just fundamentally different. Object storage on-prem, it's been around for a while, often used as kind of like a third tier of storage, maybe a backup target, compliance, something like that. In the cloud, object storage is Tier one storage. Public reference for us, Spotify, okay, use object storage for all the songs out there. And increasingly we see a lot of growth in-- >> Well, how are you defining Tier one storage in that regard? Again, are you thinking streaming service? Okay. Fine. Transactional? >> Spotify goes down and I'm pissed. >> Yeah. This is true. (Dave laughing) >> Not just you, maybe a few million other people too. One is importance, business importance. Tier one applications like critical to the business, like business down type stuff. But even if you look at it for performance, for capabilities, object storage in the cloud, it's a different thing than it was. >> Because of the architecture that you're deploying? >> Yeah. And the applications that we see running on it. Obviously, a huge growth in our business in AI and analytics. Obviously, Google's pretty well known in both spaces, BigQuery, obviously on the analytics side, big massive data warehouses and obviously-- >> Gets very high marks from customers. >> Yeah, very well regarded, super successful, super popular with our customers in Google Cloud. And then obviously AI as well. A lot of AI is about getting structure from unstructured data. Autonomous vehicles getting pictures and videos around the world. Speech recognition, audio is a fundamentally analog signal. You're trying to train computers to basically deal with analog things and it's all stored in object storage, machine learning on top of it, creating all the insights, and frankly things that computers can deal with. Getting structure out of the unstructured data. So you just see performance capabilities, importance as it's really a Tier one storage, much like file and block is where have kind of always been. >> Depending on, right, the importance. Because I mean, it's a fair question, right? Because we're used to thinking, "Oh, you're running your Oracle transaction database on block storage." That's Tier one. But Spotify's pretty important business. And again, on BigQuery, it is a cloud-native born in the cloud database, a lot of the cloud databases aren't, right? And that's one of the reasons why BigQuery is-- >> Google's really had a lot of success taking technologies that were built for some of the consumer services that we build and turning them into cloud-native Google Cloud. Like HDFS, who we were talking about, open source technologies came originally from the Google file system. Now we have a new version of it that we run internally called Colossus, incredible technologies that are cloud scale technologies that you can use to build things like Google Cloud storage. >> I remember one of the early Hadoop worlds, I was talking to a Google engineer and saying, "Well, wow, that's so cool that Hadoop came. You guys were the main spring of that." He goes, "Oh, we're way past Hadoop now." So this is early days of Hadoop (laughs) >> It's funny whenever Google says consumer services, usually consumer indicates just for me. But no, a consumer service for Google is at a scale that almost no business needs at a point in time. So you're not taking something and scaling it up-- >> Yeah. They're Tier one services-- for sure. >> Exactly. You're more often pairing it down so that a fortune 10 company can (laughs) leverage it. >> So let's dig into data protection in the Cloud, disaster recovery in the Cloud, Ransomware protection and then let's get into why Google. Maybe you could give us the trends that you're seeing, how you guys approach it, and why Google. >> Yeah. One of the things I always tell people, there's certain best practices and principles from on-prem that are just still applicable in the Cloud. And one of 'em is just fundamentals around recovery point objective and recovery time objective. You should know, for your apps, what you need, you should tier your apps, get best practice around them and think about those in the Cloud as well. The concept of RPO and RTO don't just magically go away just 'cause you're running in the Cloud. You should think about these things. And it's one of the reasons we're here at the VeeamON event. It's important, obviously, they have a tremendous skill in technology, but helping customers implement the right RPO and RTO for their different applications. And they also help do that in Google Cloud. So we have a great partnership with them, two main offerings that they offer in Google. One is integration for their on-prem things to use, basically Google as a backup target or DR target and then cloud-native backups they have some technologies, Veeam backup for Google. And obviously they also bought Kasten a while ago. 'Cause they also got excited about the container trend and obviously great technologies for those customers to use those in Google Cloud as well. >> So RPO and RTO is kind of IT terms, right? But we think of them as sort of the business requirement. Here's the business language. How much data are you willing to lose? And the business person says, "What? I don't want to lose any data." Oh, how big's your budget, right? Oh, okay. That's RPO. RTO is how fast you want to get it back? "How fast do you want to get it back if there's an outage?" "Instantly." "How much money do you want to spend on that?" "Oh." Okay. And then your application value will determine that. Okay. So that's what RPO and RTO is for those who you may not know that. Sometimes we get into the acronym too much. Okay. Why Google Cloud? >> Yeah. When I think about some of the infrastructure Google has and like why does it matter to a customer of Google Cloud? The first couple things I usually talk about is networking and storage. Compute's awesome, we can talk about containers and Kubernetes in a little bit, but if you just think about core infrastructure, networking, Google's got one of the biggest networks in the world, obviously to service all these consumer applications. Two things that I often tell people about the Google network, one, just tremendous backbone bandwidth across the regions. One of the things to think about with data protection, it's a large data set. When you're going to do recoveries, you're pushing lots of terabytes often and big pipes matter. Like it helps you hit the right recovery time objective 'cause you, "I want to do a restore across the country." You need good networks. And obviously Google has a tremendous network. I think we have like 20 subsea cables that we've built underneath the the world's oceans to connect the world on the internet. >> Awesome. >> The other thing that I think is really underappreciated about the Google network is how quickly you get into it. One of the reasons all the consumer apps have such good response time is there's a local access point to get into the Google network somewhere close to you almost anywhere in the world. I'm sure you can find some obscure place where we don't have an access point, but look Search and Photos and Maps and Workspace, they all work so well because you get in the Google network fast, local access points and then we can control the quality of service. And that underlying substrate is the same substrate we have in Google Cloud. So the network is number one. Second one in storage, we have some really incredible capabilities in cloud storage, particularly around our dual region and multi-region buckets. The multi-region bucket, the way I describe it to people, it's a continent sized bucket. Single bucket name, strongly consistent that basically spans a continent. It's in some senses a little bit of the Nirvana of storage. No more DR failover, right? In a lot of places, traditionally on-prem but even other clouds, two buckets, failover, right? Orchestration, set up. Whenever you do orchestration, the DR is a lot more complicated. You got to do more fire drills, make sure it works. We have this capability to have a single name space that spans regions and it has strong read after write consistency, everything you drop into it you can read back immediately. >> Say I'm on the west coast and I have a little bit of an on-premises data center still and I'm using Veeam to back something up and I'm using storage within GCP. Trace out exactly what you mean by that in terms of a continent sized bucket. Updates going to the recovery volume, for lack of a better term, in GCP. Where is that physically? If I'm on the west coast, what does that look like? >> Two main options. It depends again on what your business goals are. First option is you pick a regional bucket, multiple zones in a Google Cloud region are going to store your data. It's resilient 'cause there's three zones in the region but it's all in one region. And then your second option is this multi-region bucket, where we're basically taking a set of the Google Cloud regions from around North America and storing your data basically in the continent, multiple copies of your data. And that's great because if you want to protect yourself from a regional outage, right? Earthquake, natural disaster of some sort, this multi-region, it basically gives you this DR protection for free and it's... Well, it's not free 'cause you have to pay for it of course, but it's a free from a failover perspective. Single name space, your app doesn't need to know. You restart the app on the east coast, same bucket name. >> Right. That's good. >> Read and write instantly out of the bucket. >> Cool. What are you doing with Veeam? >> So we have this great partnership, obviously for data protection and DR. And I really often segment the conversation into two pieces. One is for traditional on-prem customers who essentially want to use the Cloud as either a backup or a DR target. Traditional Veeam backup and replication supports Google Cloud targets. You can write to cloud storage. Some of these advantages I mentioned. Our archive storage, really cheap. We just actually lowered the price for archive storage quite significantly, roughly a third of what you find in some of the other competitive clouds if you look at the capabilities. Our archive class storage, fast recovery time, right? Fast latency, no hours to kind of rehydrate. >> Good. Storage in the cloud is overpriced. >> Yeah. >> It is. It is historically overpriced despite all the rhetoric. Good. I didn't know that. I'm glad to hear. >> Yeah. So the archive class store, so you essentially read and write into this bucket and restore. So it's often one of the things I joke with people about. I live in Silicon Valley, I still see the tape truck driving around. I really think people can really modernize these environments and use the cloud as a backup target. You get a copy of your data off-prem. >> Don't you guys use tape? >> Well, we don't talk a lot about-- >> No comment. Just checking. >> And just to be clear, when he says cloud storage is overpriced, he thinks that a postage stamp is overpriced, right? >> No. >> If I give you 50 cents, are you going to deliver a letter cross country? No. Cloud storage, it's not overpriced. >> Okay. (David laughing) We're going to have that conversation. I think it's historically overpriced. I think it could be more attractive, relative to the cost of the underlying technology. So good for you guys pushing prices. >> Yeah. So this archive class storage, is one great area. The second area we really work with Veeam is protecting cloud-native workloads. So increasingly customers are running workloads in the Cloud, they run VMware in the Cloud, they run normal VMs, they run containers. Veeam has two offerings in Google that essentially help customers protect that data, hit their RPO, RTO objectives. Another thing that is not different in the Cloud is the need to meet your compliance regulations, right? So having a product like Veeam that is easy to show back to your auditor, to your regulator to make sure that you have copies of your data, that you can hit an appropriate recovery time objective if you're in finance or healthcare, energy. So there's some really good Veeam technologies that work in Google Cloud to protect applications that actually run in Google Cloud all in. >> To your point about the tape truck I was kind of tongue in cheek, but I know you guys use tape. But the point is you shouldn't have to call the tape truck, right, you should go to Google and say, "Okay. I need my data back." Now having said that sometimes the highest bandwidth in the world is putting all this stuff on the truck. Is there an option for that? >> Again, it gets back to this networking capability that I mentioned. Yes. People do like to joke about, okay, trucks and trains and things can have a lot of bandwidth, big networks can push a lot of data around, obviously. >> And you got a big network. >> We got a huge network. So if you want to push... I've seen statistics. You can do terabits a second to a single Google Cloud storage bucket, super computing type performance inside Google Cloud, which from a scale perspective, whether it be network compute, these are things scale. If there's one thing that Google's really, really good at, it's really high scale. >> If your's companies can't afford to. >> Yeah, if you're that sensitive, avoid moving the data altogether. If you're that sensitive, have your recovery capability be in GCP. >> Yeah. Well, and again-- >> So that when you're recovering you're not having to move data. >> It's approximate to, yeah. That's the point. >> Recovering GCV, fail over your VMware cluster. >> Exactly. >> And use the cloud as a DR target. >> We got very little time but can you just give us a rundown of your portfolio in storage? >> Yeah. So storage, cloud storage for object storage got a bunch of regional options and classes of storage, like I mentioned, archive storage. Our first party offerings in the file area, our file store, basic enterprise and high scale, which is really for highly concurrent paralyzed applications. Persistent disk is our block storage offering. We also have a very high performance cash block storage offering and local SSDs. So that's the main kind of food groups of storage, block file object, increasingly doing a lot of work in data protection and in transfer and distributed cloud environments where the edge of the cloud is pushing outside the cloud regions themselves. But those are our products. Also, we spend a lot of time with our partners 'cause Google's really good at building and open sourcing and partnering at the same time hence with Veeam, obviously with file. We partner with NetApp and Dell and a bunch of folks. So there's a lot of partnerships we have that are important to us as well. >> Yeah. You know, we didn't get into Kubernetes, a great example of open source, Istio, Anthos, we didn't talk about the on-prem stuff. So Brian we'll have to have you back and chat about those things. >> I look forward to it. >> To quote my friend Matt baker, it's not a zero sum game out there and it's great to see Google pushing the technology. Thanks so much for coming on. All right. And thank you for watching. Keep it right there. Our next guest will be up shortly. This is Dave Vellante for Dave Nicholson. We're live at VeeamON 2022 and we'll be right back. (soft beats music)

>>Okay. Now we're joined by Brian Gilmore, director of IOT and emerging technologies at influx data. Welcome to the show. >>Thank you, John. Great to be >>Here. We just spent some time with Evan going through the company and the value proposition, um, with influx DB, what's the momentum. What do see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations, everybody from like the most well funded sort of advanced big technology companies to the smaller academics, the startups and the managing of that sort, sort of data that emits from that technology is time series and us being able to give them a, a platform, a tool that's super easy to use, easy to start. And then of course we'll grow with them is, has been key to us, sort of, you know, riding along with them is they're successful. >>Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now, you go back 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? >>I think it's, it's, it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years. Mm-hmm <affirmative> that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take advantage full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is trusting is, is that there's like a hybrid nature to all of these applications where there is definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified uniform way. Um, that's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the, the outer reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentioned genome too, dig big data is coming to the real world. And I think I, I O T has been kind of like this thing for OT and, and some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, what's the secret sauce because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce. >>Well, I mean, I think part of it is just that the technology is becoming more capable and that's especially on the hardware side, right? I mean, like technology compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, you know, our client libraries and then working hard to make our applications, especially the database as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx DB as sort of an input to automation control the autonomy that people are trying to drive at the edge, but when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallel eyes, AI, and machine learning and all of that. So >>What's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users at I O T market mm-hmm <affirmative>, but you got developers now. Yeah. Kind of together brought that up. How do you see that emerging? How do developers engage? What are some of, as you're seeing that developers are really getting into with influx DB what's >>Yeah. Well, I mean, I think there are the developers who are building companies, right? I mean, these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of, I T there's a lot of that, just those developers, but I think we, you gotta pay attention to those enterprise develop as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for a systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, to optimize inside the business as compared to just building a brand new one. >>You know, it's interesting too, when Evan and I were talking about open source versus closed OT systems, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining opens dozens of data formats out there? A bunch of standards, protocols, new things are emerging, and everyone wants to have a control plane. Everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? >>Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable. It's open source, it's an edge agent. You can run it as close to the edge as you'd like, it speaks dozens of different protocols and its own, right. A couple of which M Q T T UA are very, very, um, applicable to these IOT use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware, into influx DB. These are companies like cap wire and high by who are really experts in those downstream industrial protocols. I mean, that's a business, not everybody wants to be in. It requires some very specialized, very hard work and a lot of support, um, you know, and so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. >>What's some of the customer testimonies that they, that share with you. Can you share some anecdotal, all kind of like, wow, that's the best thing I've ever used. That's really changed my business. Or this is a great tech that didn't helped me in these other areas. What are some of the, um, sound bites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, they are just excited to be able to stick a database onto those tunnel, boring machines, send them in to the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, you have customers who are way far beyond the monitoring use case. >>We're, they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So for predictive maintenance, being able to pull in the telemetry from the machines, but then also all of that external enrichment data, the metadata, the temperatures, the pressures who was operating the machine, those types of things, and being able to of easily integrate with platforms like Jupyter notebooks. Yeah. Or, you know, all of those scientific computing and machine learning libraries to be able to build the models, train the models, and then they can send that information back down to influx TV to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now yeah. It's all about two training, the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. For sure. And then it gets updated the new time. Yeah. So it's like constant data cleansing data iteration, data programming. We're starting to see this new use case emerge in the data feed. Yep. >>Yeah. I mean, I think >>You >>Agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, and we make that really easy for customers like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reformat it, route it, do whatever you need. So at that point you're basically able to, you're playing your data in exactly the way you would wanna do it. You're routing it to D and you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. >>Yeah. And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kind of put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell, he's selling too as well. So you have that whole C your perspective, but he brought up this notion that there's multiple personas involved in kind of the influx DB system architect. You got developers and users. Can you talk about that? Reality as customers start to commercialize and operationalize this from a commercial standpoint, you got a relationship to the cloud. Yep. The edge is there. Yep. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? Yeah. >>I mean, I think edge, you know, edge is you can think of it really as like the local information, right? So it's, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow, allow them to do exactly that. Then what they can do is they can actually down sample that data or they can, you know, detect like the really important metrics or the anomalies. And then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset, and then you can do as things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly >>Detections. So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for influx DB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars. Right. So I don't know. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna become more hybrid. Right. And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. >>And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of, of solution is that we'll be bringing to the customers sort of over the next little bit. Um, but I also think we have to start thinking about like what happens when the edge is actually off the planet, right. I mean, we've got customers, you're gonna talk to two of them, uh, in the panel who are actually working with data that comes from like outside the earth. Like, you know, either in low earth orbit or, you know, all the, you sort of on the other side of the universe and, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, so that we'll be ready for that >>One. I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of new thinking is hyper scale's always been built up full stack developers, even the old OT world that Evan was pointing out, that they built everything. Right. And the world's going into more assembly with core competency and IP and also property being the core of their apple. So faster assembly and building <affirmative>, but also integration. You got all this new stuff happening. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It does. >>So is Tesla and there is the car the same as the data layer. >>I mean, yeah. It's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data in the underlying data platforms so that they don't have to. Right. I mean, I think you talked about it, uh, you know, for them just to be able to adopt the platform quickly, integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately will it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view view that? >>Yeah, I mean, we are APIs, that's the product itself. Like everything people like to think of it is sort of having this nice front end, but the front end is B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now and you know, we, we bring a very sort of useful set of them for managing the time series data. These guys are all challenged with. >>It's interesting. You and I were talking before we came on camera about how, um, data feels gonna have this kind of SRE role that DevOps had site reliability engineers, which managed a bunch of there's so much data out there now. Yeah. >>Yeah. It's like raining data for sure. And I think like that ability to like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler, to be able to understand like what the data sources are, what the data formats are, how to be able to efficiently move that data from point a to point B and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection, storage work >>That's data as code. I mean, data engineering. It is, it is becoming a new discipline it for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean, data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, right? >>Yeah. I mean, I think, you know, it's, it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are. Right. So like we build, we have the sort of client libraries that allow them to just port to us, you know, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point nobody's gonna know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these users and interfaces, which are making all of that data accessible for, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys help take away with APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real, absolutely mainstream enterprises. New got developer attraction too. So congratulations. >>Yeah. It's >>Great. Well, thank you. Any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think once people use it, they try it out. They'll understand very, very >>Quickly awesome open source with developers, enterprise and edge coming together >>All together all together. You're gonna hear more about that in the next segment, too. >>Thanks for coming on. Okay. Thanks. When we return, Dave Lon will lead a panel on edge and data influx DB. You're watching the cube, the leader and high tech enterprise coverage.

(upbeat music) >> Hey everyone. Welcome back to theCUBE's coverage of Coupa Inspire 2022. We are in Las Vegas at the beautiful Cosmopolitan hotel. I'm your host, Lisa Martin. Brian McKillips joins me next, a managing director at Accenture. Brian, it's great to have you on the program. >> Thanks for having me, I'm glad to be here. >> So you have an interesting, you lead a lot of stuff at Accenture and I want to read this off, so I get it right. You lead the intelligent platform services strategy and the industry and functions platform group. Talk to me about those responsibilities. >> Yeah, so the intelligent platform services is the place in the business where we have kind of our large software partners, SAP, Oracle, Microsoft, Workday, Salesforce and Adobe. And we kind of think of ourselves as kind of the engine that powers industry and functional solutions, right? And the way Accenture's gone to market over the last couple of years has been kind of bringing together our breadth of experience all the way from strategy, all the way through operations and these big technology transformations are at the core of that. So that's what we do in intelligent platform services. And we recently launched this what we call the industry and functions platforms group because we realized there's a lot of strategic partners that are critical for us to be have a strong practice around, COUPA being one of them, you know in the supply chain and sourcing and procurement space so that we could create a home to be able to deliver these solutions globally and at scale. So I lead both kind of the strategy across all of IPS and then the new industry and functions platform group. >> Got it. All right. So you're here to talk to me about composable technology. First of all, define that for the audience so they understand what you're talking about. >> Yeah, you bet. So, you know, at Accenture, we're talking a lot about this is the age of compressed transformation, meaning, you know, change is only going to speed up and the need to change and so our clients are really struggling with not only kind of moving fast but that pressure around having to change as dynamics around the world change. So in the age of compressed transformation, we were really talking about how our clients should be kind of reorienting the way they think about their tech stack. And because, you know, historically a lot of us grew up in kind of monolithic implementations with, you know one software provider. But today it's really about composing technology to create new industry, new ways to solve industry problems, functional processes, customer experiences, right? And so composable technology we think about it in three parts. One is a cloud foundation that is, you know, the hyperscalers are a critical part of that. Secondly, our digital core and these are the kind of the historic software packages at the center of a lot of the industry and functional business processes. So you think about SAP and Oracle and Salesforce and things like that. But then around that digital core you have composable elements to be able to plug in. And that could be things like other software packages but it's also kind of industry IP or you know, edge devices, you know think IOT, think smart appliances, think and when you put, pull all those things together you need to be able to not only configure it once but configure and reconfigure as the dynamics of the marketplace change. >> So composable technology isn't necessarily new but has the pandemic been an accelerator of some of the things that you're seeing now in terms of why it's important, what's different about it now as being a foundation for competitive differentiation? >> Yeah, for sure. And it's, you know, I, anybody who's in technology say, you know, you tell them about this idea, they're like, well this isn't new, we've had service oriented architectures for 20 years. >> Right. >> You know, we've been talking about integrating things forever, but the you know, much like we all five to seven years ago we knew that we'd be using our phones to pay for pretty much everything but the tech hadn't caught up, right. Not every restaurant or store that you went to had the point of sale set up, right. So we all kind of knew that was coming. And the same thing has kind of happened around this idea of about composable technology and the three things that are new are one is that the cloud foundation is here, right. >> Yes. >> Where, you know, you now have not only kind of hyperscale high speed compute in at the core you actually have at the edge as well. And the same thing with high speed network, you know you have Starlink, you have 5G rolling out. So you have that cloud foundation that really wasn't there before. The second thing that's happening is the posture of a lot of the ecosystem, major ecosystem players has changed, right. And this started, you know when Satya Nadella took over Microsoft where Microsoft was very much a kind of a closed environment. >> Right. >> Where Satya under his leadership has really kind of changed the posture of being able to integrate into that. And we've seen that really pretty much across the entire landscape. And then lastly, it's become, you know, cheaper and, you know, quicker to be able to integrate with platforms like MuleSoft and others where there's kind of full scale integration platforms. So those are, those are the kind of the things that are new that allows for composable technology to be here in the real world. >> So it's something that's tangible, it's real organizations need to be on this bandwagon I imagine or they're going to be left behind. Gartner had some interesting stats that your team sent over and they were talking about these stats that were very compelling in terms of a seismic shift which always, you hear seismic living in California I think earthquakes, but something substantial. And they said, this seismic shift is going to happen by 2023. And I thought, hang on, that's less than a year away. >> Yeah. >> And they talked about by 2023, organizations that have adopted an intelligent composable approach will outpace competition 80% in the speed of new feature implementation. So if an organization hasn't started on that now is it too late? >> I would say not necessarily too late but they need to look for ways to change their disposition, right. And one of the ways that we've been helping clients do this is through pre-integrated solutions, right. So you know, in the past, the motion would be we would work with a client, they would work with our kind of strategists and consultants and say, what does the the future of supply chain look like for example. And if the client liked it, they would say, okay, I love it, what do I do next? Right. Then there would be another consulting engagement, another consulting engagement and then there would be a blueprint and architecture and at some point there was an implementation and a run. We've actually said we're investing heavily with our ecosystem partners to be able to pre-integrate solutions. So when that supply chain strategist says this is what the post COVID supply chain should look like and the client says, I love it what do I do next, that strategist can turn around and say, well, we've got a pre-integrated solution with SAP at the core sitting on a Microsoft Azure stack integrated with Coupa, wrapped with AI and machine learning and we can drop that and configure it for an environment. So that's how we're working with clients who are in that position that really need to kind of change their disposition is to bring these pre-integrated solutions and drop them in. >> Where are your conversations at the C- Suite level? Because this is, I hear many things in what you just said. Part of it is change management, which is very challenging. There's, people are very resistant to that. >> Brian: Yeah. >> One of the things that we've learned in the last two years is if it's going to come it's going to come but where are your conversations within that executive suite in terms of getting buy-in and going this is the direction we have to go in. >> Brian: Yeah. >> Because our business needs to be not just survive but thrive. >> Yeah. Yeah. These are, I mean, there are certainly of course in kind of traditional channels of tech whether it's, you know, the CIO or the CTO, but increasingly we're seeing this is a CEO discussion and, you know, our CEO Julie Sweet, is very, very market pacing and is having top to top conversations talking about compressed transformation, talking about composable technology because it's no longer just a, you know, a back office function as you know, right. I mean, this is really core to how companies you know, are, change their business models, make money, right. And it's a constant evolution. And that's why we talk about that kind of configuring and reconfiguring, it's not just coming in, implementing once, run it for five years and then when it's time to upgrade, we come back. >> No. >> We really want to be the partner with our clients to basically move in and, you know, across the patch whether it's specific industry processes, specific functional processes, specific customer experiences, we want to be the partner that is constantly tuning and configuring and reconfiguring and composing these solutions from across the ecosystem. >> And helping those businesses in any industry evolve as you talked about this compressed timeline, compressed transformation, such an interesting way of describing it but it's really true, it's what we've been living the last couple of years. >> Brian: Yeah. And so I want to get into Accenture's technology vision. You touched on this a little bit but there was some stats that your team provided that I thought were really, really interesting, a survey that Accenture did, 77% of executives, and we were just talking about the C-suite, state that their tech architecture is becoming critical to the overall success of the organization. So that awareness is there for sure en masse. Another thing that, stat that was interesting was 90% of business and IT execs agree that to be agile we always talk about agility, right, be resilient, organizations need to fast forward this digital transformation at the core. There's that compressed transformation. >> Brian: Yeah. >> Those are very high numbers. >> Brian: Yeah. >> In terms of where organizations say we see where we need to be. What's the vision at Accenture to help organizations get there fast? >> Yeah. Well, I think it's, you know, the thing that came to mind as you were talking is that we have, you know, major clients that have had this had in the, you know consumer packaged goods and apparel space that have had one way that they've done business is directly through retailers, you know, for pretty much their whole existence. Suddenly they need to shift to a direct to consumer model both in terms of marketing, in terms of commerce and that's not, you know, you don't just flip a switch in the back office and, you know, call IT and say hey, hey, can you change around a few things? It's actually shifting the entire core, it touches everything, it touches point of sale, it touches the customer experience, it touches supply chain, it touches employee experience even, right. >> Yeah. >> And so that's why I think it's so important for, you know technology leaders and business leaders to continue to kind of integrate themselves more tightly. >> Yes. >> To be able to make these business model transformations not just, you know, the tech that supports things. >> It's essential. >> Yeah. >> You know, we often in so many shows, Brian, we talk about alignment of business and technology, but it's not trivial. >> Yeah, yeah. >> It's absolutely fundamental to the success of every organization. And they've got to do so and as you said, I'm going to use your, your word, the compressed transformation. >> Yeah. >> A compressed timeframe. So talk to me about some customer examples where you really feel that Accenture and Coupa have helped this organization transform its supply chain to be able to be, use composable technology. >> Brian: Yeah. >> To be a leader in its industry. >> Yeah. Well, one example of that is a major industrial client that we have that has global operations across the world. And they're on a journey to kind of upgrade their digital core ERP that they've been on for a long time. And that's a multi-year journey. But at, you know, today they have needs for sourcing and procurement solutions in specific geographies around the world like Japan, for example. So what we've been able to do and it's a relatively simple example but quickly work with the client and Coupa to identify the right Coupa solution that's born in the cloud that has a great kind of user experience and implement that quickly as well as integrated it into the digital core, right. So they're not separate things. And it becomes part of that architecture, right. It just starts to kind of show the flexibility of when you have, when you come with a kind of composable technology point of view, the way we can help our clients do that. And in some other cases it's even more, you know, more cutting edge. So think about a utilities client, for example that has IOT sensors on their wires and when the, when that wire swings too far they say something's wrong. Automatically it goes back to the digital core cuts a ticket and finds the closest worker. >> Lisa: Okay. >> To then dispatch. The worker then can put on their hollow lens, for example and climb the pole and get directions on how to solve the problem right then and there, right? That's another example of you know, multiple systems, edge devices things coming together in order to create that. And it's only going to get faster, you know, with the metaverse. >> Lisa: Right. >> You know, with web 3.0 coming, with blockchain becoming more and more mainstream, companies need to be thinking about in this age of compressed transformation how to do that composable technology that you can figure and reconfigure. >> Do you think that we're in an age of compressed transformation or is that how it's going to be going forward given the global climate the last two years? >> Yeah. It's definitely going to be that way going forward over the next, you know, probably for the large part of the, the remainder of our career. I mean, we're, our CTO, Paul Daugherty, talks about us being an mega cycle, right? There's so many things changing. And even without these externalities of, you know, political issues and pandemics, you know, the introduction of AI and machine learning, a lot of these technologies I just mentioned, it's, the change is happening in every industry, in every, you know kind of area of the marketplace and in a way that's, you know, that's really exciting, right. And we get to help our clients be able to kind of solve those things not just once, but continually >> There's a tremendous amount of opportunity that's come from compressed transformation, right. A lot of opportunity, a lot of potential. What are some of the things that you're looking forward to say in the next year, as we talked about some of those business and lines of business and IT folks understand we've got to move in this direction. What excites you about the potential that you have to help these organizations really transform? >> Yeah, well, I think, I mean, the, we just came out with our new tech vision which is about the metaverse. And I think that the things that excite me are there's brand new ways like we've lived in a world where transactions take place in a very predictable way with local currencies through a single channel. And that was, that's been sort of fixed for a long time. The fundamentals of the economy or actually in the marketplace are starting to change in terms of how do we transact with things like cryptocurrencies, things like non fungible tokens, you know, all these things that we didn't, you know, they weren't, even the metaverse these were not main line words, even six you know, months ago, 12 months ago. >> Lisa: Right, right. >> Now these things, you know, every it seems like every month there's something new that is, you know, seismic to use your word that is shifting the fundamentals of the marketplace. And I think that's what's really exciting. I mean, that's where, I mean, it's probably one of the most exciting times to be in business, be in the marketplace. It certainly has a lot of challenges. >> Lisa: Yes. >> But, you know, I think we're really about using, you know, the promise of technology to unlock human ingenuity and this is a great time to be able to unlock that human ingenuity. >> And that's such a great alignment with Coupa. I was just in the keynote and there was an Accenture video, Julie Sweet was talking to some other folks about that. Great alignment in the partnership. Brian, thank you for joining me talking about composable technology, what's new, why and the potential that organizations and every business have to use it to unlock competitive advantages. >> Brian: Yeah. >> We appreciate your insights and your time. >> You bet. Pleasure to be here. >> All right. With Brian McKillips, I'm Lisa Martin. You're watching theCUBEe from Coupa Inspire 2022. (upbeat music)

(upbeat music) >> Everybody welcome back to theCUBE, continuous coverage of AWS 2021. This is the biggest hybrid event of the year, theCUBEs ninth year covering AWS re:Invent. My name is Dave Vellante. Arwa Kaddoura is here CUBE alumni, chief revenue officer now of InfluxData and Brian Mullen, who's the chief marketing officer. Folks good to see you. >> Thanks for having us. >> Dave: All right, great to see you face to face. >> It's great to meet you in person finally. >> So Brian, tell us about InfluxData. People might not be familiar with the company. >> Sure, yes. InfluxData, we're the company behind a pretty well-known project called Influx DB. And we're a platform for handling time series data. And so what time series data is, is really it's any, we think of it as any data that's stamped in time in some way. That could be every second, every two minutes, every five minutes, every nanosecond, whatever it might be. And typically that data comes from, you know, of course, sources and the sources are, you know, they could be things in the physical world like devices and sensors, you know, temperature gauges, batteries. Also things in the virtual world and, you know, software that you're building and running in the cloud, you know, containers, microservices, virtual machines. So all of these, whether in the physical world or the virtual world are kind of generating a lot of time series data and our platforms are designed specifically to handle that. >> Yeah so, lots to unpack here Arwa, I mean, I've kind of followed you since we met on virtually. Kind of followed your career and I know when you choose to come to a company, you start with the customer that's what your that's your... Those are your peeps. >> Arwa: Absolutely. >> So what was it that drew you to InfluxData, the customers were telling you? >> Yeah, I think what I saw happening from a marketplace is a few paradigm shifts, right? And the first paradigm shift is obviously what the cloud is enabling, right? So everything that we used to take for granted, when you know, Andreessen Horowitz said, "software was eating the world", right? And then we moved into apps are eating the world. And now you look at the cloud infrastructure that, you know, folks like AWS have empowered, they've allowed services like ours and databases, and sort of querying capabilities like Influx DB to basically run at a scale that we never would have been able to do. Just sort of with, you know, you host it yourself type of a situation. And then the other thing that it's enabled is again, if you go back to sort of database history, relational, right? Was humongous, totally transformed what we could do in terms of transactional systems. Then you moved into sort of the big data, the Hadoops, the search, right. The elastic. And now what we're seeing is time series is becoming the new paradigm. That's enabling a whole set of new use cases that have never been enabled before, right? So people that are generating these large volumes of data, like Brian talked about and needing a platform that can ingest millions of points per second. And then the ability to query that in real time in order to take that action and in order to power things like ML and things like sort of, you know, autonomous type capabilities now need this type of capability. So that's all to know >> Okay so, it's the real timeness, right? It's the use cases. Maybe you could talk a little bit more about those use cases and--- >> Sure, sure. So, yeah so we have kind of thinking about things as both the kind of virtual world where people are pulling data off of sources that are in infrastructure, software infrastructure. We have a number like PayPal is a customer of ours, and Apple. They pull a time series data from the infrastructure that runs their payments platform. So you can imagine the volume that they're dealing with. Think about how much data you might have in like a regular relational scenario now multiply every that, every piece of data times however, often you're looking at it. Every one second, every 10 minutes, whatever it might be. You're talking about an order of magnitude, larger volume, higher volume of data. And so the tools that people were using were just not really equipped to handle that kind of volume, which is unique to time series. So we have customers like PayPal in kind of the software infrastructure side. We also have quite a bit of activity among customers on the IOT side. So Tesla is a customer they're pulling telematics and battery data off of the vehicle, pulling that back into their cloud platform. Nest is also our customer. So we're pretty used to seeing, you know, connected thermostats in homes. Think of all the data that's coming from those individual units and their, it's all time series data and they're pulling it into their platform using Influx. >> So, that's interesting. So Tesla take that example they will maybe persist some of the data, maybe not all of it. It's a femoral and end up putting some of it back to the cloud, probably a small portion percentage wise but it's a huge amount of data of data, right? >> Brian: Yeah. >> So, if they might want to track some anomalies okay, capture every time animal runs across, you know, and put that back into the cloud. So where do you guys fit in that analysis and what makes you sort of the best platform for time series data base. >> Yeah, it's interesting you say that because it is a femoral and there are really two parts of it. This is one of the reasons that time series is such a challenge to handle with something that's not really designed to handle it. In a moment, in that minute, in the last hour, you have, you really want to see all the data you want all of what's happening and have full context for what's going on and seeing these fluctuations but then maybe a day later, a week later, you may not care about that level of fidelity. And so you down sample it, you have like a, kind of more of a summarized view of what happened in that moment. So being able to kind of toggle between high fidelity and low fidelity, it's a super hard problem to solve. And so our platform Influx DB really allows you to do that. >> So-- >> And that is different from relational databases, which are great at ingesting, but not great at kicking data out. >> Right. >> And I think what you're pointing to is in order to optimize these platforms, you have to ingest and get rid of data as quickly as you can. And that is not something that a traditional database can do. >> So, who do you sell to? Who's your ideal customer profile? I mean, pretty diverse. >> Yeah, It, so it tends to focus on builders, right? And builders is now obviously a much wider audience, right? We used to say developers, right. Highly technical folks that are building applications. And part of what we love about InfluxData is we're not necessarily trying to only make it for the most sophisticated builders, right? We are trying to allow you to build an application with the minimum amount of code and the greatest amount of integrations, right. So we really power you to do more with less and get rid of unnecessary code or, you know, give you that simplicity. Because for us, it's all about speed to market. You want an application, you have an idea of what it is that you're trying to measure or monitor or instrument, right? We give you the tools, we give you the integrations. We allow you to have to work in the IDE that you prefer. We just launched VS Code Integration, for example. And that then allows these technical audiences that are solving really hard problems, right? With today's technologies to really take our product to market very quickly. >> So, I want to follow up on that. So I like the term builder. It's an AWS kind of popularized that term, but there's sort of two vectors of that. There's the hardcore developers, but there's also increasingly domain experts that are building data products and then more generalists. And I think you're saying you serve both of those, but you do integrations that maybe make it easier for the latter. And of course, if the former wants to go crazy they can. Is that a right understanding? >> Yes absolutely. It is about accessibility and meeting developers where they are. For example, you probably still need a solid technical foundation to use a product like ours, but increasingly we're also investing in education, in videos and templates. Again, integrations that make it easier for people to maybe just bring a visualization layer that they themselves don't have to build. So it is about accessibility, but yes obviously with builders they're a technical foundation is pretty important. But, you know, right now we're at almost 500,000 active instances of Influx DB sort of being out there in the wild. So that to me shows, that it's a pretty wide variety of audiences that are using us. >> So, you're obviously part of the AWS ecosystem, help us understand that partnership they announced today of Serverless for Kinesis. Like, what does that mean to you as you compliment that, is that competitive? Maybe you can address that. >> Yeah, so we're a long-time partner of AWS. We've been in the partner network for several years now. And we think about it now in a couple of ways. First it's an important channel, go to market channel for us with our customers. So as you know, like AWS is an ecosystem unto itself and so many developers, many of these builders are building their applications for their own end users in, on AWS, in that ecosystem. And so it's important for us to number one, have an offering that allows them to put Influx on that bill so we're offered in the marketplace. You can sign up for and purchase and pay for Influx DB cloud using or via AWS marketplace. And then as Arwa mentioned, we have a number of integrations with all the kind of adjacent products and services from Amazon that many of our developers are using. And so when we think about kind of quote and quote, going to where the developer, meeting developers where they are that's an important part of it. If you're an AWS focused developer, then we want to give you not only an easy way to pay for and use our product but also an easy way to integrate it into all the other things that you're using. >> And I think it was 2012, it might've even been 11 on theCUBE, Jerry Chen of Greylock. We were asking him, you think AWS is going to move up the stack and develop applications. He said, no I don't think so. I think they're going to enable developers and builders to do that and then they'll compete with the traditional SaaS vendors. And that's proved to be true, at least thus far. You never say never with AWS. But then recently he wrote a piece called "Castles on the Cloud." And the premise was essentially the ISV's will build on top of clouds. And that seems to be what you're doing with Influx DB. Maybe you could tell us a little bit more about that. We call it super clouds. >> Arwa: That's right. >> you know, leveraging the 100 billion dollars a year that the hyperscalers spend to develop an abstraction layer that solves a particular problem but maybe you could describe what that is from your perspective, Influx DB. >> Yeah, well increasingly we grew up originally as an open source software company. >> Dave: Yeah, right. >> People downloaded the download Influx DB ran it locally on a laptop, put up on the server. And, you know, that's our kind of origin as a company, but increasingly what we recognize is our customers, our developers were building on the building in and on the cloud. And so it was really important for us to kind of meet them there. And so we think about, first of all, offering a product that is easily consumed in the cloud and really just allows them to essentially hit an end point. So with Influx DB cloud, they really have, don't have to worry about any of that kind of deployment and operation of a cluster or anything like that. Really, they just from a usage perspective, just pay for three things. The first is data in, how much data are you putting in? Second is query count. How many queries are you making against? And then third is storage. How much data do you have and how long are you storing it? And really, it's a pretty simple proposition for the developer to kind of see and understand what their costs are going to be as they grow their workload. >> So it's a managed service is that right? >> Brian: It is a managed service. >> Okay and how do you guys price? Is it kind of usage based. >> Total usage based, yeah, again data ingestion. We've got the query count and the storage that Brian talked about, but to your point, back to the sort of what the hyperscalers are doing in terms of creating this global infrastructure that can easily be tapped into. We then extend above that, right? We effectively become a platform as a service builder tool. Many of our customers actually use InfluxData to then power their own products, which they then commercialize into a SaaS application. Right, we've got customers that are doing, you know, Kubernetes monitoring or DevOps monitoring solutions, right? That monitor, you know, people's infrastructure or web applications or any of those things. We've got people building us into, you know, Industrial IoT such as PTC's ThingWorx, right? Where they've developed their own platform >> Dave: Very cool. >> Completely backed up by our time series database, right. Rather than them having to build everything, we become that key ingredient. And then of course the fully cloud managed service means that they could go to market that much quicker. Nobody's for procuring servers, nobody is managing, you know, security patches any of that, it's all fully done for you. And it scales up beautifully, which is the key. And to some of our customers, they also want to scale up or down, right. They know when their peak hours are or peak times they need something that can handle that load. >> So looking ahead to next year, so anyway, I'm glad AWS decided to do re:Invent live. (Arwa mumbling) >> You know, that's weird, right? We thought in June, at Mobile World Congress, we were going to, it was going to be the gateway to returning but who knows? It's like two steps forward, one step back. One step forward, two steps back but we're at least moving in the right direction. So what about for you guys InfluxData? Looking ahead for the coming year, Brian, what can we expect? You know, give us a little view of sharp view of (mumbles) >> Well kind of a keeping in the theme of meeting developers where they are, we want to build out more in the Amazon ecosystem. So more integrations, more kind of ease of use for kind of adjacent products. Another is just availability. So we've been, we're now on actually three clouds. In addition to AWS, we're on Azure and Google cloud, but now expanding horizontally and showing up so we can meet our customers that are working in Europe, expanding into Asia-Pacific which we did earlier this year. And so I think we'll continue to expand the platform globally to bring it closer to where our customers are. >> Arwa: Can I. >> All right go ahead, please. >> And I would say also the hybrid capabilities probably will also be important, right? Some of our customers run certain workloads locally and then other workloads in the cloud. That ability to have that seamless experience regardless, I think is another really critical advancement that we're continuing to invest in. So that as far as the customer is concerned, it's just an API endpoint and it doesn't matter where they're deploying. >> So where do they go, can they download a freebie version? Give us the last word. >> They go to influxdata.com. We do have a free account that anyone can sign up for. It's again, fully cloud hosted and managed. It's a great place to get started. Just learn more about our capabilities and if you're here at AWS re:Invent, we'd love to see you as well. >> Check it out. All right, guys thanks for coming on theCUBEs. >> Thank you. >> Dave: Great to see you. >> All right, thank you. >> Awesome. >> All right, and thank you for watching. Keep it right there. This is Dave Vellante for theCUBEs coverage of AWS re:Invent 2021. You're watching the leader in high-tech coverage. (upbeat music)

>>Hi, welcome to the cubes coverage of splunk.com 21. I need some Martin Brian Berg joins me next director at Accenture leading the EMEA Splunk partnership. Brian, welcome to the program. Talk to me a little bit about the Splunk Accenture partnership. This goes back about five years, I believe. >>Yeah, let me provide a bit of, uh, of a history. Uh, we have been starting with Splunk very intensively more than five years ago. Uh, we have been working very closely together to create something like an incubator approach to really serve the markets as, as best as possible. It was really successful. So exponential growth far beyond the markets, uh, for the last five years. So I'm really proud to be part of that journey. And, uh, the partnership is kind of anchored around three core components. The first component is what we typically call matching up our deep Accentia industry expertise with cloud spend Splunk technology. So it really gives a unique differentiator in the market combining that unique industry understanding and the Splunk technology, which is really capable to have an end to end platform for our clients. I'll give you an example a couple of years ago with, and starting to work in Germany was Dr. >>Bank cargo, which is one of the leading European, uh, freights or companies that, where we really put the Splunk that Phonto to the stretch and are using even IOT data like vegan shots, sensors, or locomotive data to create very fancy, you know, the use cases. So that, that's just an example how the deep industry expertise of Accentia and the blank technology expertise can work together. So the second part, maybe just to mention that is that Accenture in the partnership is developing industrialized solutions and that is, uh, needing to Accentia IP, which very rapidly can serve our customers in creating value and to transforming our clients on their journey. A great example is our supply chain of ring. We have developed a supply chain control tower, which has these days, obviously with the pandemic situation and the supply chain issues, uh, impacting our economic, uh, return. Uh, recovery is a very specific and very strong case. You can really use Splunk as a real time supply chain tour, and that's kind of the industrialized vertical solutions, which we also, uh, did in our partnership at last. Let me comment on that one, the kind of service pillar is really around cloud. So we are focusing heavily on the cloud business. As we see Splunk also an enabler of the cloud journey for our clients >>And both Splunk and Accenture on their own, uh, digital transformation Splunk going to some subscription only back in 2019, Accenture beginning, it's a cloud transformation, 2015. Talk to me about the cloud first initiative. You launched this about a year ago. So during a very challenging time, talk to me about the objectives of the cloud first initiative, how you're working together with Splunk and what some of the value is in it for the customers. >>So Accenture really sings clouds. You see it that we did a very aggressive transformation. The shift we even changed our organization, organizational structure, how we serve our customers within our cloud service to approach. So we combine our expertise from our strategy and consulting experts with implementation and delivery expertise, to have the full end, to end perspective on what we need to transform and transition our clients into the cloud journey. Um, and we are heavily investing into the cloud markets. Uh, we are doing research, uh, in the market to understand also the client needs and the market developments. For example, we recently launched a European, uh, study called cloud continuum where we interviewed more than 4,000 executives around the globe on what are the key priorities along their cloud journey? What is it really that makes it unique and differentiated? Uh, and we see what are the driving factors in the cloud market in Europe, it's a bit special as compared to the us, uh, the key priority driver of our clients moving into the cloud is cost competitive toughness. So they are really moving into the cloud to save costs. The cost play only the second, uh, kind of the answer was like 38% of respondents has been elaborating around increasing customer value. And here you see already the difference between Europe and us, uh, it's, it's much, much lagging behind in terms of understanding the data in your cloud to create new business opportunities and new business value for your customers, which, uh, which is typically, uh, an opportunity, but also challenge >>One of the challenges that organizations often face regardless of where they are in the world is looking at cloud from a price point perspective rather than a transformational journey perspective. But it sounds like you've actually seen the opposite with this survey that you mentioned. >>Yeah. I mean, that's, that's a fair point. So as set, uh, in, in Europe, we are having many clients and customers focusing on the cost competitiveness, but that typically just one key challenge. Uh, another challenge, especially in Europe is around complexity of our data regulation of trust and compliance. So that very often leads to, again, silos in the cloud architecture. So typically something you would want to overcome with the cloud journey and again, in a kind of siloed infrastructure. So we are having, we have seen that more than 60% of our customers have stirred parts of that data and on-premise data stores. They have kind of hybrid cloud environments. We have more than 48% of our customers in kind of a cloud environment. So you will see that the cloud journey again is a very complex task complex journey, and you are ending up very often in a new silos and he explained comes into play because blank can enable you to have the end to end perspective across your full stack, including a multi and hybrid cloud environment. And that's why the reason why we are looking for a strong interlock of our Splunk business into our cloud first approach to really bring that value into our cloud journey of our customers. >>So the, the complexity is, has been increasing. You mentioned a very high percentage of customers in that hybrid multi-cloud environment. How do you Accenture in Splunk, how does this cloud first initiative help address the complexities that cloud that a multi-cloud environment brings and unlock the opportunities in all of this data? >>Yeah, I mean, there's different ways to see that in my perspective, the cloud transformation, the cloud journey always requires a smart data cloud strategy as a core tool. I call it the core to win because without the cloud data strategy, you are losing really the benefit of the cloud journey in terms of the full value potential of your data. Um, I do see like an evolutionary path of the cloud transformation. First of all, is bringing and transitioning our clients into the cloud. And Splunk would be at the first milestone, the end to end perspective of having the cloud transplant, the cloud ops of ability and club monitoring capability. So it's combining the end-to-end picture and mighty cloud hybrid cloud environment in a single pane of glass, which is really unique from a technology perspective. But in the second step, it could even go further and talking about machine learning technologies about AI and bringing that to the next level on that evolutionary path. >>That's what we typically call AI ops. And that again makes a difference in terms of automation, in terms of efficiencies, in terms of prediction capabilities, which is a huge advantage and value potential for our clients. And the third step is coming back a bit to your point in terms of leaving the data value, uh, in the cloud. So if you are getting more and more advanced, you have so much data in your cloud that you could even use it for new business models for new customer service use cases. Uh, and that's kind of the kind of evolutionary past what I call the data to everything cloud, which is very similar to where Splunk is positioning and using all that data and to end for really bringing value and additional value, add to your customers. There's a >>Tremendous amount of value in that data. If it can be analyzed the value unlocked and analyzed and acted on in real time so that organizations can make business decisions on products and services. And obviously from a competitive differentiation perspective, there's a tremendous amount, a tremendous amount of value. And unlocking that data. What are you seeing in the last, in the last year, since there's been so much acceleration where the customers are coming to you saying Accenture Splunk, help us figure out how to migrate to the cloud. We've got to go quickly, we've got these competitive pressures, we've got a very dynamic world market. What's that pathway like? >>Yeah, it's a very interesting time. So typically you see this cloud transformation journeys as a journey of several years. And in the pandemic situation, you have seen that a couple of months for some of our clients, because it's really important to survive in this very disruptive economic situations. So obviously you start first with, uh, getting the basics done with kind of getting the migration done, getting the migration to the cloud and uplifting our client's technology to the next. So the new kind of cloud paradigm, but, uh, assets that kind of next evolutionary path would be increased. Automation would be increased usage of all the cloud data for additional value add and additional business models. Our client use cases. So that's kind of the starting discussion always is how to bring it to the cloud and how to create that flexibility. That also that grows flexibility in terms of being more resilient, being more agile as a customer, but a Splunk can do much, much more. And that's the story we want to, and we want to explain to the market that the basic steps are the right ones and Splunk is getting you there, especially in multi hybrid thought environments. But the very next step is really untapping. The value. >>A lot of organizations have been challenged culturally in the last year and a half with suddenly this distribution of the workforce. And now here we are still in a distributed environment, maybe getting towards a hybrid model, but cultural change is challenging for organizations in any industry where is cultural change as a part of the pathway that Accenture and Splunk help customers to create >>Absolutely spot on sex dealer for the question. And going back to the research research I was talking earlier on, we have also seen that 46% of our clients are really challenged by the complexity of the transition it's complexity of their business, of their business processes, but also the complexity of the operational change. And that really is a major pitfall and th and the major challenge for us. It's not only a technological challenge, but also it's a change and kind of transition management where we also have specified specialized ones items for an hour at, you know, practice our terms and, and change our practice, which are supporting our clients along that transition journey from a cultural perspective, because I mean, you can change your, your it infrastructure. You can create a new architecture in the cloud, but it's really about getting the business into the next level of understanding these complex data situations and processes and leveraging the value of the cloud. So that's a huge business change as well. >>It is a huge business change, which is challenging for a lot of folks again, given the distributed nature with which in which we are still working. Talk to me about an example of a, of a successful customer that, uh, Accenture and Splunk have worked with in the last year. Who's really embraced the cloud first initiative and is transforming their organizations to not only survive these challenging times, but to thrive as well. >>Yeah, one of my favorite examples is a leading hotel chain. Obviously the hotel industry has been heavily impacted by COVID. Uh, so, uh, there was a need to change to a need to get more resilient, more agile and more flexible. You think the cloud transformation story also, again, as a cost transformation play, but also changing the way the business is working. So we started with a typical cloud transformation journey. Uh, we evolved it towards what we, what we call the AI ops scenario in terms of really using machine learning technologies and AI, to get more prediction, more automation, more efficiencies. So we could even reduce, uh, the operational cost by more than 5%, which is a huge baseline and leading a global companies, uh, which frees up a lot of money, which you can then reinvest for kind of new, smarter business use cases in addressing your clients and understanding your clients and ultimately generating new value for your clients. So that's a very nice example of how you could start with an it transformation journey, changing into the cloud architecture, using AI ops, to freeing up resources for new addresses for kind of new addressable cut customer use cases and business benefits. >>What's the go to market like working customers go to learn more and get started. Are they starting with Accenture? And they starting with Splunk? Can I do both? >>We have a very collaborative partnership with Splunk. We have a strong partnership team as we speak. We have more like more than 4,000 people working on Splunk projects globally. So it's a very strong capability. Um, you can reach out to Accentia and, uh, you can reach out to Splunk. It's kind of a collaborator strategic go-to-market approach nursing. That's also a bit the advantage of the Splunk Accenture partnership that we are very closely, very collaboratively going to the market. Yes, exemptions bringing IP and assets, empty industrialized delivery methodology. We are able to really scale up globally across the market and Splunk is bringing their technology and the expertise. I think it's a winning combination >>And winning complication and not collaboration is certainly critical to enable that. Brian last question would be, as we approach the end of calendar year 2021, what are some of the things on the horizon for the cloud first initiative that you're excited about as we enter 2022, >>I think it's really getting traction. Now. We have seen a lot of our clients going into the cloud, but asset, from my perspective, it's just the start of the journey. So once you get that kind of, uh, interesting milestone start, you can create the automation efficiencies. You can create the data value and use the data very for new CRM scenarios, new years use cases. And that's where it really gets interesting and fun and innovative in getting all these data across your company and understanding and being creative, how you can use that to benefit your customer and to bring that customer experience to the next level. And that's what I'm looking really forward to coming from the it transformation, the cloud transformation journey to the customer experience and to improving the customer perspective. >>Improving the customer perspective is key. As, as the customer experience, we're all customers in our daily lives and our personal lives and our business lives. And we have this expectation that any organization we're dealing with is going to be able to give us a stellar experience. Brian, thank you for joining me on the cube today, sharing the latest and greatest and the Splunk Accenture partnership, the value that you're delivering for customers and some of the things that you're excited about as we go forward. We appreciate your time. >>Thanks for >>Having me. My pleasure for Brian Berg. I'm Lisa Martin. You're watching the cubes coverage of splunk.com 21.

>> From the Bellagio Hotel in Las Vegas, it's theCUBE, covering UiPath FORWARD IV, brought to you by UiPath. >> Welcome back to theCUBE, live at the Bellagio in Las Vegas, Lisa Martin, with Dave Vellante, we are with UiPath at FORWARD IV. The next topic of conversation is going to be a good one. And that's because it's automation for good. I've got two guests here joining Dave and me. James Droskoski, strategic account exec at UiPath joins us, and Brian Klochkoff, head of automation at Dentsu. Guys, welcome to the program. >> Yeah, thank you. >> Thanks for having us. >> Yeah. Happy to be here. >> We're going to, we're going to dig into automation for good, which is going to be a really feel good conversation. We're going to get into what you're doing. But Brian, I wanted you to give the audience an overview of Dentsu as an organization. Who are you? What do you guys do? >> Sure. So Dentsu is a large network of advertising agencies. We're about 45,000 people large, $10 billion plus in revenue, going across about 125 markets. So we're a large enterprise advertising media, creative CXM type business. We're really focused on helping to elevate our clients' value when it comes to the value proposition around marketing, advertising, and media. >> So you think about that as a, as a, as a, a business that maybe, you know, it's hard to understand where automation might fit in. On the other hand, it's like a lot of moving parts, a lot of arms and legs. >> Brian: Mm-hmm. So how are you applying automation to the business? >> Sure. So when we first started doing proof of concepts level approaches, we approached things in a traditional, Hey, let's go look at the shared services groups. Why are we having invoice processing delays? Things like that. And we started being a bit more prescriptive and proactive about how we were applying the limited POC budget we had to go after these problems. And we started doing some root cause analysis to understand the interaction between the back office functions and the mid-office functions. And what we uncovered was that we could actually be really good custodians of budget and enable people at the same time by solving for problems at a root cause analysis level. So what I mean by that is maybe an invoice is coming down the pipe, and it's not getting processed because it's missing critical information that could be easily added six processes upstream. So what really helped elevate the conversation that we're having around automation for good and be a catalyst for we're going to talk about a bit later is we just started connecting people from the mid-office to the back office, helping them understand, Hey, if we actually follow a process properly, put the right controls in place with RPA to generate critical data elements on those invoices, Shaler in the back office doesn't have to work the weekends because there's not a pipeline back load of invoices for him to process. So we actually connected those mid-office people with the back office people, and it really drove that human connection to drive the change management within our automation journey. And that's kind of been the crux of what we've wanted to do over the past four years, finding ways to elevate our people's potential by integrating automation and AI into their actual day-to-day work. >> Hmm. So tech for good is a theme that you hear a lot and as a, as a media company, that, that, that kind of, we're not gotcha media, you know, we more want to tell the story of tech athletes, and I think we've done a pretty good job of that over the past decade, but so it goes to tech's under fire constantly, especially big tech. We hear the Facebook hearings today and so forth, but so automation kind of early days, oh, you're going to take away my job. I think generally speaking with the fatigue of Zoom and the perpetual workday, people begin to understand that, Hey, maybe automation is a good thing. But automation for good, what, what is that, James? >> Yeah, well, it's, it's not doing technology for the sake of technology. You know? At the end of the day, when we implement solutions with our customers like Dentsu, it's about, what's the impact, what's the change, what's the benefit? And what's unique about Dentsu is because they've grown through acquisition and there are lots of different companies come together, you have to focus on the people first because there is no one process or one system that we can look and just automate that system or process. So automation for good is about focusing on the people, and how do we take the solutions and the programs and the technologies we have and make an impact so that somebody's day is better. Their, their, their job is better. The process they're doing is easier and they can focus on more of the things that make them different. You know? Specifically as we'll uncover in the conversation, you know, we looked at a program that Dentsu is doing around working with different types of people, as far as people with autism, and what was the impact we could do there? And that's uncovered a journey that we've been together for the last two years around seeing how we can make an impact with those types of folks who might not get the same types of opportunities as everybody else. >> Brian, talk about the, the catalyst for that program at Dentsu a couple years ago. >> Sure. So it goes back to that foundational layer of elevating people's potential. So the testimonial that we had from our own employees around applying automation in meaningful ways to progress their day-to-day came from an employee in the mid-office who said, I didn't go $160,000 in student debt to copy paste stuff from Excel into this proprietary platform that we use for media. And that really resonated with us, as leaders in this space, and with our executive leadership, because there was a gap between what our peoples' skills were and what they were actually doing. They wanted to do Mad Men type stuff. They want it to be the Don Drapers and the Peggy Olsons of our industry. And they were losing that opportunity because we weren't tapping into the skills that they had to drive human centric solutions for our clients. So taking that concept, we looked at the partnerships that we have with our outsourcing providers and Autonomy Works, which we're going to doing a session later tomorrow with the CEO, Dave Friedman, we're going to spend a lot of time talking about how the unique skill sets of that company and those people can actually elevate them to do more tech enabled work, but also enabling our own team to focus on building solutions with the skills that we have by allowing them to use the skills that they have to do the machine learning training of models and things like that, which they really excel at from a detail oriented perspective. And that's not only a feel good story, but it's, it's great for our business because the resources on my immediate team are building product, they're building solutions, and we can rely on an excellent partner in them to help us with the maintenance overhead that we're creating through those solutions. And eventually through automation cloud, driving better outcomes through positive, negative reinforcement within machine learning. >> And there are specific examples with individuals with autism. Correct? >> Correct. That's right. >> Yeah. >> Add some color to that. What is that all about? >> Yeah. Let me tell you a little story. So when, when they first brought the conversation to me, I was terrified because I, the type of work that they were outsourcing was very repetitive rule-based. And I'm like, this is perfect for automate. This is exactly what we automate. I was terrified that the program we were going to work on together was going to eliminate the program. And so I was, you know, cautiously, you know, approached it. >> How ironic. >> Yeah. I was like, Hey, that sounds like a great idea. And I hung up. I was like, oh, how are we going to, how am I going to figure out this one? But through the conversation, and we just started, you know, brainstorming and putting our heads together. What was interesting is because of the way that automations work, as far as being very structured and repetitive, it lends itself well to workers with autism. It's exactly the way they think. And what we actually found after kind of coming up with the collaborative ideas, hey, wait a second. We were already doing these kind of botathon hackathon type programs with the Dentsu employees, teaching them the skills, how to build automations for themselves. What if we kind of modified it and adjusted it to cater to these types of individuals who learn differently, and we have to approach it differently. And we went through the program, we adjusted everything. And what was incredible to see was they thrived with the ability to learn how to work this way. They built things that made them more productive, that created more capacity. They could do more with less now, work with more customers, do more work for, for their, for their customers because they had this almost assistant that was kind of like them. And it was, it was just so rewarding. You know, we talk about, again, what's automation for good all about? It's about that personal reward. >> Brian: Yeah. >> I mean, for me, you know, we didn't sell any more licenses or it wasn't about the commercial transaction. It was about, you know, catering to the segment of the workforce that, first of all, it was very educate, enlightening to me to see how many folks are out there that are unemployed. And I got to meet these first 15 individuals that couldn't have been more amazing and more smart and more diligent and hardworking. And the numbers are something in the lines of between 50% and 90% unemployed because they just don't get the same opportunities as people without autism. It's kind of the world's set up for us. So to know that we could do this kind of program together to go have an impact in this community, was the reward in and of itself. And, you know, we've since been working together on how we continue to expand that, how do we, you know, take that forward and, and bring that everywhere. Cause that's, the end of the day, I think beyond, you know, revenue, this is the stuff that really matters, especially in an organization at Dentsu that this is important. >> Yeah. And I think building on the missed opportunity piece around 50% to 90% being unemployed, that's a missed opportunity for business as well. So those skills are so niche and they're so necessary for us to thrive within an environment that's moving as rapidly as we are. Because we just can't keep pace with the change of feature sets that are being released coupled with maintaining existing solutions that we've built. So it's in cross enabling people to really complement each other's unique skills and strengths based off of strong, true partnership. So it really became a beautiful three-way partnership between Dentsu, Autonomy Works and UiPath that we continue to evolve as UiPath makes additional releases with emerging tech that we're officially hearing about right now. So we have a ton of different ideas of how we can bring that into the fold. And what resonates with us the most is hearing different perspectives on how to apply that coming from that working group. So just a different way of thinking about things and the diversity of thought really resonates with, Hey, are we actually applying this thing the right way? Should we be thinking about this differently? Because you get a lot of yes people, you know, when we come and talk to people about how to apply this technology. And when you have somebody with a different perspective, it's able to help us figure out what our long-term strategy is actually going to look like, by taking advantage of the resources and partnerships that we already have in place. >> In terms of that strategic vision, how do you think this three-way partnership that you mentioned is going to influence that percentage of those, these individuals who are unemployed? What are you, any predictions on how much you can bring that down with automation? >> I think that depends on Dave's staffing plan. But, but the goal is to grow, right? So I mean this, this is a, a startup out of Chicago that has, you know, a healthy amount of staff. But finding ways to apply those skills in new ways with technology that's emerging, the horizon is your, is your end point. Right? And I think with the advent of low-code no-code machine learning coming into this type of a platform, it's, it's only opportunistic. There's only, there's only things ahead of us to do that. We just have to make sure that we train people properly and give them that opportunity because they're going to run with it with the right leadership and those skills. >> Yeah. What's exciting also is, is, you know, what started as an idea and a conversation that's now turned into a pilot program and a little bit of expansion of the stuff we're working on together, we've taken some of the excitement and spread it beyond that now. So we've got partners like ENY and PWC and Revature that are saying, and Special Eastern and Automatic, who helped in the initial program saying, how can we help? What can we do? How can we broaden this? And how can we go out to the larger community and make a bigger impact? So, you know, I think it's exciting. We know, we can see how fast RPA and these types of technologies are causing change. And we've got to make sure that people don't get left behind. Especially, you know, someone as this important part of a segment of a workforce. If we can equip them with these skills to be relevant to their current employers or future employers, I think it's, it's critical. You know, another like moment for me during this process was I took for granted, you know, what working actually means, right? It creates independence for us, right? So you get a job, you get paid and generate income. You have the independence now to go live on your own, provide for yourself. A lot of these individuals, I learned, are still living with their parents because they can't get employment. They don't have that independence that we take for granted. So I think, again, that's the essence of what automation for good is all about, is, is being able to go make an impact like that, to that community. And it's, you know, we talk about cultures and brands and you know, it's also great to work with an organization like Dentsu cause they get it, right? Their product is ideas. It's human capital is their, their main ingredient of what they generate value for their customers. And so be able to take that and help people is just, I think what it's all about. >> You're lucky both to be in a business that the incentives are aligned. >> Yeah. >> You're not in businesses that are designed to appropriate data and push ads in front of our face. >> Yeah. >> In a lot of big companies, it's almost like, okay, we got to do this. I don't mean to overstate this, but we have to do this because we're big and we're rich. >> Yeah. >> And so, and if we don't, we're going to get attacked. >> Yeah. Okay. And it's sort of more like a check, check box and to put somebody in charge of it. >> Yep. >> You know, oftentimes a woman or a person of color. And I shouldn't be negative on that. >> Yeah. >> That's fine. That's good to do. But it just seems like there's a nice alignment with automation. AI could be similar because I mean, AI could be used for really bad. Automation. Okay, it maybe takes, the perception is it takes jobs away, but it's a really nice alignment that you can point at a lot of different initiatives. >> Yeah. >> So I think that's really a fortunate dynamic. >> And that's, you know, that's what defines a partnership, right? It's that alignment of long-term interests that, you know, you make the investments now and the sacrifices now to drive that. It's not just commercial. It's not just transactional. >> Dave: Yeah. >> I mean, we were talking about the opportunities for these types of people and for us as a customer and for UiPath. It's it exists within that AI conversation that you were just talking about >> Dave: Yeah. >> Because from a technical perspective, you want to mitigate as much algorithmic bias within your training models. That's what these people are doing. It's helping to train models much more rapidly and effectively and objectively than we could have done otherwise. And that's, having that as part of our extended partnership within our network is going to accelerate the type of work that we want to do within the releases that we're seeing coming out of this conference. Because we don't have to worry about, oh, well, we've got to focus on tax forms and training the models to notice a signature. Because Autonomy Works has us covered there. They're enabling us to do more. We're enabling them to do a little more. And that's, that's the beauty of this intersection between the partners. >> Brian, I presume you talk with prospective customers of UiPath. And I presume also that you probably looked at some of their competitors. If you think about what differentiates this fast moving company, they talked this morning about the cadence of releases. Woo. Very fast. >> Brian: Yeah, it's a lot. >> Why UiPath for Dentsu? >> UiPath has been a tremendous partner for us since about 2017. And we've been able to move on that journey with UiPath. We've been able to help understand the products roadmaps and move at a similar pace as each other. So we're really lucky in that we have the flexibility as an advertising and media company that we're not beholden to internal audits, external audits, and really defined regulatory bodies. So we made a decision, I don't know what, six, seven months ago to collapse six UiPath on-prem instances and migrate to cloud with the sponsorship of our global CTO and our America CTO, just because it was the right thing to do. And because it would enable this type of partnership with external providers. So being able to move at that similar pace from a release cycle, but also from a feature adoption perspective, it's, it just makes the most sense for us. And we have that liberty to go to go do those things as we need to. >> Yeah. So the move to the cloud, you get, you're able to take advantage much faster. >> Yeah. >> Because what did we hear this morning? You release every six months. >> James: Yep. >> Yes. Which is typical for an on-prem. >> James: Yeah. >> And then, but you got to prepare for that. >> James: Yeah. >> I don't know how many N minus ones you support, but it's not infinite. >> James: Yeah. >> You got to move people along, so people have to prep. Whereas now in the cloud, there's the feature. Boom. >> Yeah. >> So being invested in automation for good topic, it's not, it's about automation for good across people in general, within internally to us and externally to us. For our clients, for our employees, and for our partners. The automation cloud enables that to happen much more seamlessly because we don't have the technical debt in place that requires people to VPN into our network and go through the bureaucracy of security, legal, and privacy. Which we've already done by the way, but those conversations bureaucratically still need to happen. With automation cloud, we're able to spin up Autonomy Works employees in real-time and give them the right set of access to go pursue the use cases that they want to, and that we need them to. So that, that technical debt release that we've experienced through the automation cloud is what's enabling us to do this type of good work. >> That makes sense. A bit more, less friction, obviously greater scale. >> Yeah. >> Easier to experiment. >> Yeah. >> Fail fast. >> We went from 12 separate programs to one program in a matter of a couple of months. >> It was wild. >> Yeah. >> And I imagine you're only really scratching the surface here with what you're doing with automation, that really, the horizon is the limit, as you said. Guys, thank you for joining us, talking about automation for good, what you're doing at Dentsu RPA with autistic adults. There's probably so many other great use cases that will come from this. Guys, we appreciate your time. >> Yeah. >> Yeah, thanks for having us. >> Yeah, thank you. >> Thanks, you guys. >> Awesome. >> For Dave Vellante, I'm Lisa Martin coming to you from Vegas UiPath FORWARD IV. (upbeat music plays)

>> Narrator: From the Bellagio hotel in Las Vegas, it's the Cube, covering UiPath Forward IV, brought to you by UiPath. >> Welcome back to the Cube, live at the Bellagio in Las Vegas. Lisa Martin, with Dave Vellante. We are with UiPath at Forward IV. The next topic of conversation is going to be a good one, and that's because it's automation for good. I've got two guests here joining Dave and me, James Droskoski, Strategic Account Exec at UiPath joins us and Brian Khlochkoff, head of automation at Dentsu. Guys, welcome to the program. >> Yeah. Thank you. >> Thanks for having us. >> Yeah. Happy to be here. >> So we're going to, we're going to to dig into automation for good, which is going to be a really feel-good conversation. We're going to get into what you're doing, but Brian, I wanted you to give the audience an overview of Dentsu as an organization. Who are you, what do you guys do? >> Sure. So Dentsu is a large network of advertising agencies. We're about 45,000 people large, 10 billion plus in revenue, going across for 125 markets. So we're a large enterprise advertising media, creative CXM type business. We're really focused on helping to elevate our clients' value when it comes to the value proposition around marketing, advertising, and media. >> So you think about that as a, as a, as a, a business that maybe, you know, it's hard to understand where automation might fit in. On the other hand, it's like a lot of moving parts, a lot of arms and legs. >> Brian: Hmmm. So how are you applying automation to the business? >> Sure. So when we first started doing proof of concepts level approaches, we approach things in a traditional, hey, let's go look at the shared services groups. Why are we having invoice processing delays? Things like that. And we started being a bit more prescriptive and proactive about how we were applying the limited POC budget we had to go after these problems. And we started doing some root cause analysis to understand the interaction between the back office functions and the mid office functions. And what we uncovered was that we could actually be really good custodians of budget and enable people at the same time by solving for problems at a root cause analysis level. So what I mean by that is even the invoices coming down the pipe, and it's not getting processed because it's missing critical information that could be easily added six processes upstream. So what really helped elevate the conversation that we're having around automation for good and be a catalyst for what we're going to talk about a bit later is, we just started connecting people from the mid office to the back office, helping them understand, hey, if we actually follow process properly, put the right controls in place with RPA to generate critical data elements on those invoices, Shaler in the back office doesn't have to work the weekends because there's not a pipeline backload of invoices for them to process. So we actually connected those mid office people with the back office people, and it really drove that human connection to drive the change management and then our automation journey. And that's kind of been the crux of what we've wanted to do over the past four years, finding ways to elevate our people's potential by integrating automation and AI into their actual day-to-day work. >> Hmm. So tech for good is a theme that you hear a lot and as a, as a media company, that, that, that kind of, we're not gotcha media, you know, we've more want to tell the story of tech athletes, and I think we've done a pretty good job of that over the past decade, but so it goes, tech's under fire constantly. It was basically big tech. We hear the Facebook hearings today and so forth, but so automation kind of early days, oh, you're going to take away my job. I think generally speaking with the fatigue of Zoom and the perpetual workday, people begin to understand that, hey, maybe automation is a good thing, but automation for good, what, what is that, James? >> Yeah, well, it's, it's not doing technology for the sake of technology. You know, at the end of the day, when we implement solutions with our customers like Dentsu, it's about, what's the impact? What's the change? What's the benefit? And what's unique about Dentsu is, because they've grown through acquisition and there are lots of different companies come together, you have to focus on the people first cause there is no one process or one system that we can look and just automate that system or process. So automation for good is about focusing on the people and how do we take the solutions and the programs and the technologies we have, make an impact so that somebody's day is better. Their, their, their job is better. That process are doing is easier and they can focus on more of the things that make them different. You know, specifically as we, we'll uncover in the conversation, you know, we looked at a program that Dentsu is doing around working with different types of people, as far as people with autism and what was the impact we could do there. And that's uncovered a journey that we've been together for the last two years around seeing we can have, we can make an impact with those types of folks who might not get the same types of opportunities that everybody else. >> Brian, talk about the, the catalyst for that program at Dentsu, couple years ago. >> Sure, so it goes back to that foundational layer of elevating people's potential. So the testimonial that we had from our own employees around applying automation, meaningful ways to progress their day to day came from an employee in the mid office who said, I didn't go $160,000 in student debt to copy paste stuff from Excel into this proprietary platform that we use for media. And that really resonated with us as leaders in this space and with our executive leadership, because there was a gap between what our people's skills were and what they were actually doing. They wanted to do Mad Men type stuff. They wanted to be the Don Draper's and the Peggy Olsen's of our industry. And they were losing that opportunity because we weren't tapping into the skills that they had to drive human-centric solutions for our clients. So taking that concept, we looked at the partnerships that we have with our outsourcing providers and Autonomy Works, which we're going to be doing a session later tomorrow with the CEO, Dave Friedman, we're going to spend a lot of time talking about how the unique skill sets of that company and those people can actually elevate them to do more tech-enabled work, but also enabling our own team to focus on building solutions with the skills that we have by allowing them to use the skills that they have to do the machine-learning training of models and things like that, which they really Excel at from a detail-oriented perspective. And that's not only a feel good story, but it's, it's great for our business because the resources on my immediate team are building product, they're building solutions, and we can rely on an excellent partner in them to help us with the maintenance overhead that we're creating through those solutions. And eventually through automation cloud, driving better outcomes through positive, negative reinforcement within machine learning. >> And there's specific examples with individuals with autism, correct? >> Correct. That's right. >> Add some color to that. What is that all about? >> Yeah. Let me tell you a little story. So when, when they first brought the conversation to me, I was terrified because I, the type of work that they were outsourcing was very repetitive rule-based and I'm like, this is perfect for automate. This is exactly what we automate. I was terrified that the program we were going to work on together was going to eliminate the program. And so I was, you know, cautiously, you know, approached it- (Dave laughs) >> How ironic. (laughing) >> I was like, hey, that sounds like a great idea. And I hung up. I was like, oh, how are we going to, how am I going to figure out this one? But through the conversation, and we just started, you know, brainstorming and putting our heads together. What was interesting is, because of the way that automations work, as far as being very structured and repetitive, it lends itself well to workers with autism. It's exactly the way they think and what we actually found after kind of coming up with the collaborative ideas, hey, wait a second. We were already doing these kind of bodathon, hackathon type programs with the Dentsu employees, teaching them the skills, how to build automations for themselves. What if we kind of modified it and adjusted it to cater to these types of individuals who learn differently, we have to approach it differently. And we went through the program, we adjusted everything. And what was incredible to see was they thrived with the ability to learn how to work this way. They built things that made them more productive, that created more capacity. They could do more with less now, work with more customers, do more work for, for their, for their customers because they had this almost assistant that was kind of like them. And it was, it was just so rewarding. You know, we talk about, again, what's automation for good all about? It's about that personal reward. >> Brian: Yeah. I mean, for me, you know, we didn't sell any more licenses or it wasn't about the commercial transaction. It was about, you know, catering to the segment of the workforce that, first of all, it was very educate, enlightening to me to see how many folks are out there that are unemployed. And I got to meet these first 15 individuals that couldn't have been more amazing and more smart and more diligent and hardworking, and that the numbers are something in the lines of between 50% and 90% unemployed because they just don't get the same opportunities as people without autism. It's kind of the world's set up for us. So to know that we could do this kind of program together to go have an impact in this community, was the reward in and of itself. And, you know, we've since been working together on how we continue to expand that, how do we, you know, take that forward and bring that everywhere? Cause that's the end of the day, I think beyond, you know, revenue, this is the stuff that really matters, especially in an organization at Dentsu that, this is important. >> Yeah. And I think building on the missed opportunity piece around 50% to 90% being unemployed, that's a missed opportunity for business as well. So those skills are so niche and they're so necessary for us to thrive within an environment that's moving as rapidly as we are, because we just can't keep pace with the change of feature sets that are being released, coupled with maintaining existing solutions that we've built. So it's in cross enabling people to really compliment each other's unique skills and strengths based off of strong, true partnership. So it really became a beautiful three-way partnership between Dentsu, Autonomy Works and UiPath that we continue to evolve as UiPath makes additional releases with emerging tech that we're officially hearing about right now. So we have a ton of different ideas that we can bring that into the fold. And what resonates with us the most is hearing different perspectives on how to apply that coming from that working group. So just a different way of thinking about things and the diversity of thought really resonates with, hey, are we actually applying this thing the right way? Should we be thinking about this differently? Cause you get a lot of, yes, people, you know, when we come and talk to people about how to apply this technology and when you have somebody with a different perspective, it's able to help us figure out what our long-term strategies are actually going to look like, but taking advantage of the resources and partnerships that we already have in place. >> In terms of that strategic vision, how do you think this three-way partnership that you mentioned is going to influence that percentage of those, these individuals who are unemployed? What are you, any predictions on how much you can bring that down with automation? >> I think that depends on Dave's staffing plan. (James laughs) But, but the goal is to grow, right? So I mean this, this is a, a startup out of Chicago that has, you know, a healthy amount of staff, but finding ways to apply those skills in new ways with technology that's emerging, the horizon is your, is your end point. Right? And I think with the advent of low-code no-code machine-learning, coming into this type of a platform, it's, it's only opportunistic, there's only, there's only things ahead of us to do that. We just have to make sure that we train people properly and give them that opportunity cause they're going to run with it with the right leadership and those skills. >> Yeah. What, what's exciting also is, is, you know, what started as an idea and a conversation that's now turned into a pilot program and a little bit of expansion of the stuff we're working on together, we've taken some of the excitement and spread it beyond that now. So we've got partners like ENY and PWC and Revature that are saying, and Specialisterne and Automattic who helped in the initial program saying, how can we help? What can we do? How can we broaden this and how can we go out to the larger community and make a bigger impact? So, you know, I think it's exciting. We know we can see how fast RPA and these types of technologies are causing change. And we got to make sure that people don't get left behind. Especially, you know, someone as this important part of a segment of a workforce. If we can equip them with these skills to be relevant to their current employers or future employers, I think it's, it's critical. You know, another like, moment for me during this process was, I took for granted, you know, what working actually means, right? It creates independence for us, right? So you get a job, you get paid and generate income. You have the independence now to go live on your own, for, provide for yourself. A lot of these individuals, I learned are still living with their parents because they can't get employment. They don't have that independence that we take for granted. So I think, again, that's the essence of what automation for good is all about is, is being able to go and make an impact like that, to that community. And it's, you know, we talk about cultures and brands and, you know, it's also great to work with an organization like Dentsu cause they get it, right? Their product is ideas. It's human capital is their, their main ingredient of what they generate value for their customers. And so be able to take that and help people is just, I think what it's all about. >> You're lucky both to be in a business that the incentives are aligned. >> Yeah. >> You're not in businesses that are designed to appropriate data and push ads in front of our face or- >> James: Yeah. >> And a lot of big companies, It's almost like, okay, we got to do this. I mean, I don't mean to overstate this, but we have to do this because we're big and we're rich. >> James: Yeah. >> And so, and if we don't, we're going to get attacked. >> James: Yeah. >> Okay, and some of it, I can check, check box and to put somebody in charge of it. >> James: Yep. >> You know, often times a woman or a person of color. And I shouldn't be negative on that. >> James: Yeah. That's fine. That's good to do. But it just seems like there's a nice alignment with automation. >> James: Oh. >> AI could be similar because I mean, yeah. It can be used for really bad. Automation, okay, maybe takes, the perception is that it takes jobs away, but it's a really nice alignment that you can point at a lot of different initiatives. >> Yeah. >> So I think that's really a fortune- >> I know that's, that's what defines a partnership, right? It's that alignment of long-term interests that, you know, you make the investments now and the sacrifices now to drive that. It's not just commercial. It's not just transactional. >> Dave: Yeah. >> We were talking about the opportunities for these types of people and for us as a customer and for UiPath, it's, it exists within that AI conversation that you were just talking about. >> Dave: Yeah. >> Because from a technical perspective, you want to mitigate as much algorithmic bias within your training models. That's what these people are doing. It, it's helping to train models much more rapidly and effectively and objectively than we could have done otherwise. And that's, having that as part of our extended partnership within our network is going to accelerate the type of work that we want to do within the releases that we're seeing coming out of this conference because we don't have to worry about oh, well, we got to focus on tax forms and training the models to notice a signature because Autonomy Works has us covered there. They're enabling us to do more. We're enabling them to do a little more. >> Hmmm. And that's, that's the beauty of this intersection between the partners. >> Brian, I presume you talk with prospective customers of UiPaths. And I presume also that you probably looked at some of their competitors. If you think about what differentiates this fast-moving company, they talked this morning about the cadence that releases. Whew, very fast. (laughing) >> Brian: Yeah, that's a lot. >> Why UiPath for Dentsu? >> UiPath has been a tremendous partner for us since about 2017. And we've been able to move on that journey with UiPath. We've been able to help understand the product roadmaps and move at a similar pace as each other. So we're really lucky in that we have the flexibility as an advertising and media company that we're not beholden to internal audits, external audits, and really defined regulatory bodies. So we made a decision, you know, what, six, seven months ago to collapse six UiPath on-prem instances and migrate to cloud with the sponsorship of our global CTO and our Amaris CTO, just because it was the right thing to do. And because it would enable this type of partnership with external providers. So being able to move at that similar pace from a release cycle, but also from a feature adoption perspective, it's, it just makes the most sense for us. And we have that liberty to go to go do those things as we need to. >> Yeah, so the move to the cloud, you get, you're able to take advantage much faster- >> James: Yeah. >> Because what did, what did we hear this morning? You release every six months? >> James: Yep. >> Yes. Which is typical for an on-prem. >> James: Yeah. >> And then, but you got to prepare for that. >> James: Yeah. I don't know how many N minus ones you support, but it's not infinite. >> James: Yeah. >> You got to move people along. So people have to prep, whereas now in the cloud, there's the feature, boom. >> Oh yeah. So being investing automation for good topic, it's not, it's about automation for good across people in general, within internally to us and externally to us, for our clients, for our employees and for our partners. The automation cloud enables that to happen much more seamlessly because we don't have the technical debt in place that requires people to VPN into our network and go through the bureaucracy of security, legal, and privacy, which we've already done by the way, for those conversations, bureaucratically still needs to happen. With automation cloud, we're able to spin up autonomy Works employees in real time and give them the right set of access to go pursue the use cases that they want to, and that we need them to. So that, that technical debt release that we've experienced through the automation cloud is what's enabling us to do this type of good work. >> It makes sense. A bit more, less friction, obviously, greater scale. >> Yeah. >> Easier to experiment. >> Yeah. >> Fail fast. >> We went from 12 separate programs to one program in a matter of a couple of months. >> It was wild. (Brian laughs) >> And I imagine you're only really scratching the surface here with what you're doing with automation. That really the horizon is the limit as you said. Guys, thank you for joining us, talking about automation for good. What you're doing at Dentsu RPA with autistic adults, there's probably so many other great use cases that will come from this. Guys, we appreciate your time. >> Yeah. >> Thanks for having us. Thank you. >> Thanks you guys, awesome. >> For Dave Vellante, I'm Lisa Martin coming to you from Vegas, UiPath forward IV. [light-hearted music plays]