(upbeat music) >> Hello everyone, welcome to this special Cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE in theCUBE Studios. We've got a special video here. We love when we have startups that are launching. It's an exclusive video of a hot startup that's launching. Got great reviews so far. You know, word on the street is, they got something different and unique. We're going to' dig into it. Amit Govrin who's the CEO and co-founder of Kubiya, which stands for Cube in Hebrew, and they're headquartered in Bay Area and in Tel Aviv. Amit, congratulations on the startup launch and thanks for coming in and talk to us in theCUBE >> Thank you, John, very nice to be here. >> So, first of all, a little, 'cause we love the Cube, 'cause theCUBE's kind of an open brand. We've never seen the Cube in Hebrew, so is that true? Kubiya is? >> Kubiya literally means cube. You know, clearly there's some additional meanings that we can discuss. Obviously we're also launching a KubCon, so there's a dual meaning to this event. >> KubCon, not to be confused with CubeCon. Which is an event we might have someday and compete. No, I'm only kidding, good stuff. I want to get into the startup because I'm intrigued by your story. One, you know, conversational AI's been around, been a category. We've seen chat bots be all the rage and you know, I kind of don't mind chat bots on some sites. I can interact with some, you know, form based knowledge graph, whatever, knowledge database and get basic stuff self served. So I can see that, but it never really scaled or took off. And now with Cloud Native kind of going to the next level, we're starting to see a lot more open source and a lot more automation, in what I call AI as code or you know, AI as a service, machine learning, developer focused action. I think you guys might have an answer there. So if you don't mind, could you take a minute to explain what you guys are doing, what's different about Kubiya, what's happening? >> Certainly. So thank you for that. Kubiya is what we would consider the first, or one of the first, advanced virtual assitants with a domain specific expertise in DevOps. So, we respect all of the DevOps concepts, GitOps, workflow automation, of those categories you've mentioned, but also the added value of the conversational AI. That's really one of the few elements that we can really bring to the table to extract what we call intent based operations. And we can get into what that means in a little bit. I'll save that maybe for the next question. >> So the market you're going after is kind of, it's, I love to hear starters when they, they don't have a Gartner Magic quadrant, they can fit nicely, it means they're onto something. What is the market you're going after? Because you're seeing a lot of developers driving a lot of the key successes in DevOps. DevOps has evolved to the point where, and DevSecOps, where developers are driving the change. And so having something that's developer focused is key. Are you guys targeting the developers, IT buyers, cloud architects? Who are you looking to serve with this new opportunity? >> So essentially self-service in the world of DevOps, the end user typically would be a developer, but not only, and obviously the operators, those are the folks that we're actually looking to help augment a lot of their efforts, a lot of the toil that they're experiencing in a day to day. So there's subcategories within that. We can talk about the different internal developer tools, or platforms, shared services platforms, service catalogs are tangential categories that this kind of comes on. But on top of that, we're adding the element of conversational AI. Which, as I mentioned, that's really the "got you". >> I think you're starting to see a lot of autonomous stuff going on, autonomous pen testing. There's a company out there doing I've seen autonomous AI. Automation is a big theme of it. And I got to ask, are you guys on the business side purely in the cloud? Are you born in the cloud, is it a cloud service? What's the product choice there? It's a service, right? >> Software is a service. We have the classic, Multi-Tenancy SAAS, but we also have a hybrid SAAS solution, which allows our customers to run workflows using remote runners, essentially hosted at their own location. >> So primary cloud, but you're agnostic on where they could consume, how they want to' consume the product. >> Technology agnostic. >> Okay, so that's cool. So let's get into the problem you're solving. So take me through, this will drive a lot of value here, when you guys did the company, what problems did you hone in on and what are you guys seeing as the core problem that you solve? >> So we, this is a unique, I don't know how unique, but this is a interesting proposition because I come from the business side, so call it the top down. I've been in enterprise sales, I've been in a CRO, VP sales hat. My co-founder comes from the bottom up, right? He ran DevOps teams and SRE teams in his previous company. That's actually what he did. So, we met each other halfway, essentially with me seeing a lot of these problems of self-service not being so self-service after all, platforms hitting walls with adoption. And he actually created his own self-service platform, within his last company, to address his own personal pains. So we essentially kind of met with both perspectives. >> So you're absolutely hardcore on self-service. >> We're enabling self-service. >> And that basically is what everybody wants. I mean, the developers want self-service. I mean, that's kind of like, you know, that's the nirvana. So take us through what you guys are offering, give us an example of use cases and who's buying your product, why, and take us through that whole piece. >> Do you mind if I take a step back and say why we believe self-service has somewhat failed or not gotten off. >> Yeah, absolutely. >> So look, this is essentially how we're looking at it. All the analysts and the industry insiders are talking about self-service platforms as being what's going to' remove the dependency of the operator in the loop the entire time, right? Because the operator, that scarce resource, it's hard to hire, hard to train, hard to retain those folks, Developers are obviously dependent on them for productivity. So the operators in this case could be a DevOps, could be a SecOps, it could be a platform engineer. It comes in different flavors. But the common denominator, somebody needs an access request, provisioning a new environment, you name it, right? They go to somebody, that person is operator. The operator typically has a few things on their plate. It's not just attending and babysitting platforms, but it's also innovating, spinning up, and scaling services. So they see this typically as kind of, we don't really want to be here, we're going to' go and do this because we're on call. We have to take it on a chin, if you may, for this. >> It's their child, they got to' do it. >> Right, but it's KTLOs, right, keep the lights on, this is maintenance of a platform. It's not what they're born and bred to do, which is innovate. That's essentially what we're seeing, we're seeing that a lot of these platforms, once they finally hit the point of maturity, they're rolled out to the team. People come to serve themselves in platform, and low and behold, it's not as self-service as it may seem. >> We've seen that certainly with Kubernetes adoption being, I won't say slow, it's been fast, but it's been good. But I think this is kind of the promise of what SRE was supposed to be. You know, do it once and then babysit in the sense of it's working and automated. Nothing's broken yet. Don't call me unless you need something, I see that. So the question, you're trying to make it easier then, you're trying to free up the talent. >> Talent to operate and have essentially a human, like in the loop, essentially augment that person and give the end users all of the answers they require, as if they're talking to a person. >> I mean it's basically, you're taking the virtual assistant concept, or chat bot, to a level of expertise where there's intelligence, jargon, experience into the workflows that's known. Not just talking to chat bot, get a support number to rebook a hotel room. >> We're converting operational workflows into conversations. >> Give me an example, take me through an example. >> Sure, let's take a simple example. I mean, not everyone provisions EC2's with two days (indistinct). But let's say you want to go and provision new EC2 instances, okay? If you wanted to do it, you could go and talk to the assistant and say, "I want to spin up a new server". If it was a human in the loop, they would ask you the following questions: what type of environment? what are we attributing this to? what type of instance? security groups, machine images, you name it. So, these are the questions that typically somebody needs to be armed with before they can go and provision themselves, serve themselves. Now the problem is users don't always have these questions. So imagine the following scenario. Somebody comes in, they're in Jira ticket queue, they finally, their turn is up and the next question they don't have the answer to. So now they have to go and tap on a friend, or they have to go essentially and get that answer. By the time they get back, they lost their turn in queue. And then that happens again. So, they lose a context, they lose essentially the momentum. And a simple access request, or a simple provision request, can easily become a couple days of ping pong back and forth. This won't happen with the virtual assistant. >> You know, I think, you know, and you mentioned chat bots, but also RPA is out there, you've seen a lot of that growth. One of the hard things, and you brought this up, I want to get your reaction to, is contextualizing the workflow. It might not be apparent, but the answer might be there, it disrupts the entire experience at that point. RPA and chat bots don't have that contextualization. Is that what you guys do differently? Is that the unique flavor here? Is that difference between current chat bots and RPA? >> The way we see it, I alluded to the intent based operations. Let me give a tangible experience. Even not from our own world, this will be easy. It's a bidirectional feedback loop 'cause that's actually what feeds the context and the intent. We all know Waze, right, in the world of navigation. They didn't bring navigation systems to the world. What they did is they took the concept of navigation systems that are typically satellite guided and said it's not just enough to drive down the 280, which typically have no traffic, right, and to come across traffic and say, oh, why didn't my satellite pick that up? So they said, have the end users, the end nodes, feed that direction back, that feedback, right. There has to be a bidirectional feedback loop that the end nodes help educate the system, make the system be better, more customized. And that's essentially what we're allowing the end users. So the maintenance of the system isn't entirely in the hands of the operators, right? 'Cause that's the part that they dread. And the maintenance of the system is democratized across all the users that they can teach the system, give input to the system, hone in the system in order to make it more of the DNA of the organization. >> You and I were talking before you came on this camera interview, you said playfully that the Siri for DevOps, which kind of implies, hey infrastructure, do something for me. You know, we all know Siri, so we get that. So that kind of illustrates kind of where the direction is. Explain why you say that, what does that mean? Is that like a NorthStar vision that you guys are approaching? You want to' have a state where everything's automated in it's conversational deployments, that kind of thing. And take us through why that Siri for DevOps is. >> I think it helps anchor people to what a virtual assistant is. Because when you hear virtual assistant, that can mean any one of various connotations. So the Siri is actually a conversational assistant, but it's not necessarily a virtual assistant. So what we're saying is we're anchoring people to that thought and saying, we're actually allowing it to be operational, turning complex operations into simple conversations. >> I mean basically they take the automate with voice Google search or a query, what's the score of the game? And, it also, and talking to the guy who invented Siri, I actually interviewed on theCUBE, it's a learning system. It actually learns as it gets more usage, it learns. How do you guys see that evolving in DevOps? There's a lot of jargon in DevOps, a lot of configurations, a lot of different use cases, a lot of new technologies. What's the secret sauce behind what you guys do? Is it the conversational AI, is it the machine learning, is it the data, is it the model? Take us through the secret sauce. >> In fact, it's all the above. And I don't think we're bringing any one element to the table that hasn't been explored before, hasn't been done. It's a recipe, right? You give two people the same ingredients, they can have complete different results in terms of what they come out with. We, because of our domain expertise in DevOps, because of our familiarity with developer workflows with operators, we know how to give a very well suited recipe. Five course meal, hopefully with Michelin stars as part of that. So a few things, maybe a few of the secret sauce element, conversational AI, the ability to essentially go and extract the intent of the user, so that if we're missing context, the system is smart enough to go and to get that feedback and to essentially feed itself into that model. >> Someone might say, hey, you know, conversational AI, that was yesterday's trend, it never happened. It was kind of weak, chat bots were lame. What's different now and with you guys, and the market, that makes a redo or a second shot at this, a second bite at the apple, as they say. What do you guys see? 'Cause you know, I would argue that it's, you know, it's still early, real early. >> Certainly. >> How do you guys view that? How would you handle that objection? >> It's a fair question. I wasn't around the first time around to tell you what didn't work. I'm not afraid to share that the feedback that we're getting is phenomenal. People understand that we're actually customizing the workflows, the intent based operations to really help hone in on the dark spots. We call it last mile, you know, bottlenecks. And that's really where we're helping. We're helping in a way tribalize internal knowledge that typically hasn't been documented because it's painful enough to where people care about it but not painful enough to where you're going to' go and sit down an entire day and document it. And that's essentially what the virtual assistant can do. It can go and get into those crevices and help document, and operationalize all of those toils. And into workflows. >> Yeah, I mean some will call it grunt work, or low level work. And I think the automation is interesting. I think we're seeing this in a lot of these high scale situations where the talented hard to hire person is hired to do, say, things that were hard to do, but now harder things are coming around the corner. So, you know, serverless is great and all this is good, but it doesn't make the complexity go away. As these inflection points continue to drive more scale, the complexity kind of grows, but at the same time so is the ability to abstract away the complexity. So you're starting to see the smart, hired guns move to higher, bigger problems. And the automation seems to take the low level kind of like capabilities or the toil, or the grunt work, or the low level tasks that, you know, you don't want a high salaried person doing. Or I mean it's not so much that they don't want to' do it, they'll take one for the team, as you said, or take it on the chin, but there's other things to work on. >> I want to add one more thing, 'cause this goes into essentially what you just said. Think about it's not the virtual system, what it gives you is not just the intent and that's one element of it, is the ability to carry your operations with you to the place where you're not breaking your workflows, you're actually comfortable operating. So the virtual assistant lives inside of a command line interface, it lives inside of chat like Slack, and Teams, and Mattermost, and so forth. It also lives within a low-code editor. So we're not forcing anyone to use uncomfortable language or operations if they're not comfortable with. It's almost like Siri, it travels in your mobile phone, it's on your laptop, it's with you everywhere. >> It makes total sense. And the reason why I like this, and I want to' get your reaction on this because we've done a lot of interviews with DevOps, we've met at every CubeCon since it started, and Kubernetes kind of highlights the value of the containers at the orchestration level. But what's really going on is the DevOps developers, and the CICD pipeline, with infrastructure's code, they're basically have a infrastructure configuration at their disposal all the time. And all the ops challenges have been around that, the repetitive mundane tasks that most people do. There's like six or seven main use cases in DevOps. So the guardrails just need to be set. So it sounds like you guys are going down the road of saying, hey here's the use cases you can bounce around these use cases all day long. And just keep doing your jobs cause they're bolting on infrastructure to every application. >> There's one more element to this that we haven't really touched on. It's not just workflows and use cases, but it's also knowledge, right? Tribal knowledge, like you asked me for an example. You can type or talk to the assistant and ask, "How much am I spending on AWS, on US East 1, on so and so customer environment last week?", and it will know how to give you that information. >> Can I ask, should I buy a reserve instances or not? Can I ask that question? 'Cause there's always good trade offs between buying the reserve instances. I mean that's kind of the thing that. >> This is where our ecosystem actually comes in handy because we're not necessarily going to' go down every single domain and try to be the experts in here. We can tap into the partnerships, API, we have full extensibility in API and the software development kit that goes into. >> It's interesting, opinionated and declarative are buzzwords in developer language. So you started to get into this editorial thing. So I can bring up an example. Hey cube, implement the best service mesh. What answer does it give you? 'Cause there's different choices. >> Well this is actually where the operator, there's clearly guard rails. Like you can go and say, I want to' spin up a machine, and it will give you all of the machines on AWS. Doesn't mean you have to get the X one, that's good for a SAP environment. You could go and have guardrails in place where only the ones that are relevant to your team, ones that have resources and budgetary, you know, guidelines can be. So, the operator still has all the control. >> It was kind of tongue in cheek around the editorialized, but actually the answer seems to be as you're saying, whatever the customer decided their service mesh is. So I think this is where it gets into as an assistant to architecting and operating, that seems to be the real value. >> Now code snippets is a different story because that goes on to the web, that goes onto stock overflow, and that's actually one of the things. So inside the CLI, you could actually go and ask for code snippets and we could actually go and populate that, it's a smart CLI. So that's actually one of the things that are an added value of that. >> I was saying to a friend and we were talking about open source and how when I grew up, there was no open source. If you're a developer now, I mean there's so much code, it's not so much coding anymore as it is connecting and integrating. >> Certainly. >> And writing glue layers, if you will. I mean there's still code, but it's not, you don't have to build it from scratch. There's so much code out there. This low-code notion of a smart system is interesting 'cause it's very matrix like. It can build its own code. >> Yes, but I'm also a little wary with low-code and no code. I think part of the problem is we're so constantly focused on categories and categorizing ourselves, and different categories take on a life of their own. So low-code no code is not necessarily, even though we have the low-code editor, we're not necessarily considering ourselves low-code. >> Serverless, no code, low-code. I was so thrown on a term the other day, architecture-less. As a joke, no we don't need architecture. >> There's a use case around that by the way, yeah, we do. Show me my AWS architecture and it will build the architect diagram for you. >> Again, serverless architect, this is all part of infrastructure's code. At the end of the day, the developer has infrastructure with code. Again, how they deploy it is the neuron. That's what we've been striving for. >> But infrastructure is code. You can destroy, you know, terraform, you can go and create one. It's not necessarily going to' operate it for you. That's kind of where this comes in on top of that. So it's really complimentary to infrastructure. >> So final question, before we get into the origination story, data and security are two hot areas we're seeing fill the IT gap, that has moved into the developer role. IT is essentially provisioned by developers now, but the OP side shifted to large scale SRE like environments, security and data are critical. What's your opinion on those two things? >> I agree. Do you want me to give you the normal data as gravity? >> So you agree that IT is now, is kind of moved into the developer realm, but the new IT is data ops and security ops basically. >> A hundred percent, and the lines are so blurred. Like who's what in today's world. I mean, I can tell you, I have customers who call themselves five different roles in the same day. So it's, you know, at the end of the day I call 'em operators 'cause I don't want to offend anybody because that's just the way it is. >> Architectural-less, we're going to' come back to that. Well, I know we're going to' see you at CubeCon. >> Yes. >> We should catch up there and talk more. I'm looking forward to seeing how you guys get the feedback from the marketplace. It should be interesting to hear, the curious question I have for you is, what was the origination story? Why did you guys come together, was it a shared problem? Was it a big market opportunity? Was it an itch you guys were scratching? Did you feel like you needed to come together and start this company? What was the real vision behind the origination? Take a take a minute to explain the story. >> No, absolutely. So I've been living in Palo Alto for the last couple years. Previous, also a founder. So, you know, from my perspective, I always saw myself getting back in the game. Spent a few years in AWS essentially managing partnerships for tier one DevOps partners, you know, all of the known players. Some in public, some of them not. And really the itch was there, right. I saw what everyone's doing. I started seeing consistency in the pains that I was hearing back, in terms of what hasn't been solved. So I already had an opinion where I wanted to go. And when I was visiting actually Israel with the family, I was introduced by a mutual friend to Shaked, Shaked Askayo, my co-founder and CTO. Amazing guy, unbelievable technologists, probably one the most, you know, impressive folks I've had a chance to work with. And he actually solved a very similar problem, you know, in his own way in a previous company, BlueVine, a FinTech company where he was head of SRE, having to, essentially, oversee 200 developers in a very small team. The ratio was incongruent to what the SRE guideline would tell. >> That's more than 10 x rate developer. >> Oh, absolutely. Sure enough. And just imagine it's four different time zones. He finishes day shift and you already had the US team coming, asking for a question. He said, this is kind of a, >> Got to' clone himself, basically. >> Well, yes. He essentially said to me, I had no day, I had no life, but I had Corona, I had COVID, which meant I could work from home. And I essentially programed myself in the form of a bot. Essentially, when people came to him, he said, "Don't talk to me, talk to the bot". Now that was a different generation. >> Just a trivial example, but the idea was to automate the same queries all the time. There's an answer for that, go here. And that's the benefit of it. >> Yes, so he was able to see how easy it was to solve, I mean, how effective it was solving 70% of the toil in his organization. Scaling his team, froze the headcount and the developer team kept on going. So that meant that he was doing some right. >> When you have a problem, and you need to solve it, the creativity comes out of the woodwork, you know, invention is the mother of necessity. So final question for you, what's next? Got the launch, what are you guys hope to do over the next six months to a year, hiring? Put a plug in for the company. What are you guys looking to do? Take a minute to share the future vision and get a plug in. >> A hundred percent. So, Kubiya, as you can imagine, announcing ourselves at CubeCon, so in a couple weeks. Opening the gates towards the public beta and NGA in the next couple months. Essentially working with dozens of customers, Aston Martin, and business earn in. We have quite a few, our website's full of quotes. You can go ahead. But effectively we're looking to go and to bring the next operator, generation of operators, who value their time, who value the, essentially, the value of tribal knowledge that travels between organizations that could be essentially shared. >> How many customers do you guys have in your pre-launch? >> It's above a dozen. Without saying, because we're actually looking to onboard 10 more next week. So that's just an understatement. It changes from day to day. >> What's the number one thing people are saying about you? >> You got that right. I know it's, I'm trying to be a little bit more, you know. >> It's okay, you can be cocky, startups are good. But I mean they're obviously, they're using the product and you're getting good feedback. Saving time, are they saying this is a dream product? Got it right, what are some of the things? >> I think anybody who doesn't feel the pain won't know, but the folks who are in the trenches, or feeling the pain, or experiencing this toil, who know what this means, they said, "You're doing this different, you're doing this right. You architected it right. You know exactly what the developer workflows," you know, where all the areas, you know, where all the skeletons are hidden within that. And you're attending to that. So we're happy about that. >> Everybody wants to clone themselves, again, the tribal knowledge. I think this is a great example of where we see the world going. Make things autonomous, operationally automated for the use cases you know are lock solid. Why wouldn't you just deploy? >> Exactly, and we have a very generous free tier. People can, you know, there's a plugin, you can sign up for free until the end of the year. We have a generous free tier. Yeah, free forever tier, as well. So we're looking for people to try us out and to give us feedback. >> I think the self-service, I think the point is, we've talked about it on the Cube at our events, everyone says the same thing. Every developer wants self-service, period. Full stop, done. >> What they don't say is they need somebody to help them babysit to make sure they're doing it right. >> The old dashboard, green, yellow, red. >> I know it's an analogy that's not related, but have you been to Whole Foods? Have you gone through their self-service line? That's the beauty of it, right? Having someone in a loop helping you out throughout the time. You don't get confused, if something's not working, someone's helping you out, that's what people want. They want a human in the loop, or a human like in the loop. We're giving that next best thing. >> It's really the ratio, it's scale. It's a scaling. It's force multiplier, for sure. Amit, thanks for coming on, congratulations. >> Thank you so much. >> See you at KubeCon. Thanks for coming in, sharing the story. >> KubiyaCon. >> CubeCon. Cube in Hebrew, Kubiya. Founder, co-founder and CEO here, sharing the story in the launch. Conversational AI for DevOps, the theory of DevOps, really kind of changing the game, bringing efficiency, solving a lot of the pain points of large scale infrastructure. This is theCUBE, CUBE conversation, I'm John Furrier, thanks for watching. (upbeat electronic music)

>>Hey guys, welcome back to the Cube's coverage of IFS Unleashed in Miami. I'm Lisa Martin. Been here half a day so far, having great conversations. It is so great to be back on the show floor and I'm getting that sentiment from the IFS execs, their customers, their partners, and the ecosystem. I'm pleased to welcome Kathy Hall as my next guest, the SVP of experience at ifs. Kathy, welcome to the program. >>Thank you. >>Love talking about the customer experience. Talk to me, but the employee experience is equally important because they're like this, but talk to me about your role as the SVP of experience and what that entails. >>Yeah, so I'm really, really fortunate at IFS to be SVP across experience. So I do a lot of work with the r and d team, but I also have a role that spans sales consulting support so I can really get involved in any part of the organization to enable us to deliver moments of service. So I'm really, really fortunate. I've got such a broad remit and really work on everything from the user experience and what the product looks like, feels like, how it interacts, how it moves, how we put our partner, the technologies in there, everything to their customer experience. So how people find it if they have to engage with support or what it's like in presales. And we are really trying to wrap that up into a total experience so that we bring all of those parts together and really productize our experience so that every customer gets a fantastic experience and the best moments of service. So yeah, it's like a short job title and it's a really kind of big role. It's fantastic. >>It is. It's very, it's very encompassing. You have so much visibility across the entire organization that impacts the customer in many different ways. I can't only imagine that having that visibility in that role really helps to create not only a great customer experience but a, a great experience for the employees. And those two things I always think of them as like this, like inextricably linked. >>Yeah, exactly. And we've done a lot over the last couple of years of really trying to make sure we've got the data so we understand both from a product point of view and a service point of view, what our users and our customers think about that moment of service. Where the friction points are, you know, what's really good and, and we can use that to coach our employees to celebrate success, to give people kudos for the fantastic work they do. And that really enables us to create a hype around the customer within, within ifs. And just last week we were celebrating CX day and we did a whole week and had our own sort of internal hashtag of CX days every day. And that was fantastic to really galvanize that spirit of those ifss, you know, Team Purple, really being at the forefront of how we deliver that, that customer experience. And it's fantastic for our customers, but it's also brilliant for our people because it's motivating and, and it empowers people to, to be able to do a great job, which is what we all want to do. >>Absolutely. Employees need to be empowered because if that's not there, then the customer experience inextricably linked will suffer. Talk to me a little bit about the evolution of the role. Has it been something that's been the, a focus of ifs? Cuz there's, you guys have so many unique differentiators for, for a company that isn't widely known, but talk to me about how that came about going, you know, what we need to be able to take to really look at the customer experience through many different lenses, take their feedback and really deliver a product experience that is seamless so that they can deliver those moments of service. >>Yeah, exactly. And I think, you know, when, when Darren took over a ceo, we've been on this really kind of passionate journey to bring service to our customers, bring value to our customers, you know, we really value is at the heart of, of everything for our customers. And, and so it's our ethos too. And so we've, we've sort of woven this value into everything that we do with that focus on the customer. So my role started off sort of more in the come in and then try and understand it from a very product point of view, but in today's kind of world products and service lines emerging things need to be unified. You know, if you go back 20 years a product was built and it got shipped out and somebody picks it up and they implemented it and then there was a support and there were sort of these walls in between, but now of course it is a cloud company and those walls don't exist anymore. >>Product features are coming out regularly. The code sort of flows through the system out to customers. The way that we service has to be different. And so we're thinking all the time, how do we get that to be a seamless process and how do we enable, for example, data within a customer system to identify opportunities to create more value for that customer using technology like AI for example, and then being able to highlight that value back. But then maybe you say to the presales person, okay, this is the precise demonstration and capability that the customer needs to see because this is what the, the system's telling us is the business case. And that then flows through to the scope and it enables us to, to deliver that value. So it's really changing the way that we think about these things and unifying together that product and that service into this kind of bigger total experience and this end to end experience. >>So we're really looking at what are all the friction points along our journeys with the customers, How does it stop them getting value? How do we prioritize that value and, and therefore how do we reimagine an end to end experience? So as that thinking's evolved, my role's also evolved from being quite product centric to being very much across the organization. And I'm lucky I come from a commercial and operational background, so I've got a vast amount of experience in delivering these types of solutions. So that's really helped as well because I'm able to see that that full end to end and, and I've got a, you know, brilliant team of people and, and it comes back to the point where we said before, the people ifs are so engaged to want to deliver value, to want to deliver the moments of service that, that it's kind of easy, you know, just got to kind of focus people in the right way and, and the s comes together. >>That's nice to hear. And that's actually the vibe and the sentiment that we're getting from this. You know, talking about the end to end experience. It's so critical because people used to tolerate fragmented experiences. We don't anymore. One of the things that went away, I think or is in massively short supply during Covid and may not come back as patience and tolerance, right? So being able to deliver that end to end experience to your customers through what you're doing internally is critical for differentiation, for competitive advantage, and of course for your customers to be successful with their customers. >>Yeah, and there's so many parts of that that you could un pick. We, we could spend hours talking about it and as consumers our expectations are huge and we carry those expectations into the workplace. And in the same way, you know, at IFS we want our team to be motivated and, you know, proud and excited about the moments of service they're delivering. Our customers want the same thing from their teams and that also means they want a system where it's easy to train, easy to use, you can pick up, it looks great, you know, it gives users love ifs and it kind of gives them a tool that helps 'em get the job done, doesn't stand in their way. So, you know, all the kind of things we think about internally and how we're measuring customer experience also translates and resonates with our customers. Everything we think about how, you know, our people need to be empowered to deliver a customer experience. That's the same messages that, you know, we hear back time and time again from our customers. So there's so many parallels and we're really able to work with our customers to kind of do both at the same time, which is fantastic. >>Talk about measurement. What are some of the key indicators of success cus success in in from an experienced lens internally and with your customers? >>Yeah, so I mean there's all the obvious ones about, you know, MPS and CSAT and customer effort score. We also put a lot of value into the qualitative feedback. So we use customer A avail, which is an IFS product to collect data on our own moments of service. And you know, the numbers are great and they tell a story, but I also get really sucked into reading the comments back from the customers and there's kind of text analytics and sentiment analytics and for me that's becoming the more powerful kind of piece of data to look at because a story conveys much more than a simple number and it's also something that goes global as well. You know, different countries score in different ways. There's different kind of, you know, there's a lot of gaming that can go on with a score. It can be quite difficult to really interpret, but a but a story and understanding the sentiment behind that customer, that's gold. And if you can put those together and have a way of on scale being able to interpret that analysis, which we can do, you know, that becomes something quite special. So for me it's about a shift to understanding more of those stories as well as keeping, you know, the kind of traditional, traditional measures across the, the learning across the journey points, >>Right? The, the value, I always think the value of the voice of the customer is probably invaluable to organizations because it's honest. >>It absolutely it's honest. And I think once you've got those stories and you've got those metrics and then you're looking at your operational metrics, so what does that mean then in terms of, you know, recur revenue and what does that mean in terms of margins and the costs? And being able to put those three things together so that you couldn't understand the levers that you've got and the, and the results of those levers, that becomes really powerful. And that's really what's driving our, our customers for, for them to deliver in their moments of service as well, which ties back into when we're working within customers and engaging with customers and looking at that value story, doing the value assessments more able to use the, the evidence from industry and previous customers and, and the data sources available to help them also project, you know, an operational efficiency here will have this c CX benefit but actually also has this value benefit >>Oh, a value back to the business. I mean a a good experience is transformative. Yeah, >>Really powerful. >>Any industry. >>Yeah. Yeah. It's, it's so powerful and you know, that really resonates with our customers and that's what they're trying to, to achieve all the time. And so when they're looking at IFS cloud in particular, they're looking at how, you know, the, the software can help them achieve those moments of service and perfect those moments of service and all the technology that comes into play that can enable people to improve those moments of service at the same time as getting those operational benefits. And that enables organizations to then invest more in the customer experience, more advocacy and, and really, you know, feels growth. There's, there's no denying that now you have to have that experience and, and at your point before the expectation from as others consumers, we won't tolerate a bad experience anymore, which is a good thing. >>It is. We, we've all had met plenty of those throughout the last two and a half years. Last question for you, you, what are some of the things that are next for experience at ifs? I know you mentioned before we went live that you started during the pandemic, so you go, go get to meet your team finally, but what are some of the things that excite you about the momentum that you guys are carrying through the rest of the second half? >>Yeah, so our focus now is really bringing the component parts together. So we have several tools across our whole experience that leverage from our IFFs cloud platform in order to deliver those moments of service to our customers. But those tools have grown up in different areas of the business because there's been a specific need in that area of the business. So tools at the pre-sale stage, tools that enable us to deliver scope, more frictionlessly tools that enable us to, to identify and capture value. The next stage is bringing those all together. So this week I announced our vision for experience and the experience hub and that really being a place where you get that thread of value throughout the whole experience where everything is tied into one place and it makes it really frictionless for our customers to get the value from ifs. >>And that's critical. You guys have north of 10,000 customers, it's only growing. Kathy, thank you so much for joining me on the program, talking about the end to end experience that IFS delivers internally and externally to its customers. We appreciate your insights. >>Thank you for having >>Me. My pleasure. For Kathy Hall, I'm Lisa Martin, you're watching The Cube live on the show floor of IFS Unleashed from Miami. Stick around. My next guest joins me in just a minute. I have been in the software and technology industry for over 12 years now, so I've had the.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

(upbeat music) >> Hello, and welcome to this Cube conversation. I'm Dave Nicholson, and this is part of our continuing coverage of Google Cloud Next 2021. We have a very interesting subject to discuss. I have two special guests from Google to join me in a conversation about the financial services space. I'm joined by Yolande Piazza, vice president of financial services sales for Google Cloud and Zac Mauf, managing director for global financial services solutions for Google Cloud. Yolande and Zac, welcome to the Cube. >> Thank you for having us. Looking forward to it. >> Well it's great to have you here. You know, financial services is really an interesting area when you talk about cloud because I'm sure you both remember a time, not that long ago, when we could ask a financial services organization, what their plans for cloud or what their cloud strategy was, and they would give a one word answer and that answer was, never. (laughing) So Zac, let's start out with you, what has changed? Are you and Yolande going to tell us that in fact, financial services organizations are leveraging cloud now? >> Yeah, it's a very exciting time to be in the cloud space, in financial services, because you're exactly right David. People are starting to make the transition to cloud in a real way. And a lot has gone into that, as you know, it's a highly regulated space and so there were a lot of legitimate reasons around getting kind of the regulatory frameworks in place and making sure that the risk and compliance pieces were addressed. But then there was also, as you know, technology is a major backbone for financial services. And so there's also this question of, how do we transition? And a lot of work and time has gone into moving workloads, thinking about like, what is the sort of the right migration strategy for you to get from the current situation to a more cloud native world. And to your point, we're really early, we're really early, but we're very excited and we've been investing heavily on our side to get those foundational pieces in place. But we also realized that we have to think about what are the business cases, that we want to build on top of cloud. It's not just a kind of IT modernization, which is a big part of the story, but the other part of the story is once you get all of this, technology onto the cloud platform, there are things that you can do that you couldn't do in on-prem situations. And a lot of that for us is around the data, AI and ML space. And we really see that being the way to really unlock huge amounts of value. Both of them require massive amounts of compute and breaking down all of these silos that have really developed over time within financial institutions. And really moving to the cloud is the way to unlock a lot of that. So we're really excited about a lot of those use cases that are starting to come to life now. >> Yeah. So I want to dig a little deeper on some of that Zac, but before we do, Yolande make this real for us. Give me some examples of actual real-life financial services organizations and what they're doing with Google Cloud now. >> Yeah, absolutely. And I think we're really proud to be able to announce, a number of new partnerships across the industry. You think about Wells Fargo, you think about Scotia Bank, you think about what we're doing with HSBC. They really are starting to bring to life and recognized that it's not just internally, you have to look at that transformation to cloud, it's really, how do you use this platform to help you go on the journey with your customers? I think a move to a multi-cloud common approach for our customers and our clients, is exactly what we need to be focused on. And the other- >> Hold on, hold on, Yolande. I'm sorry. Did the Google person just say multi-cloud? Because multi- cloud doesn't sound like, only Google Cloud to me. Can you- >> No, and I think Wells, absolutely, and I think Wells announced it's taking a multi-cloud approach to its digital infrastructure strategy, leveraging both Google Cloud and Microsoft Azure. And the reason being is they've openly communicated that a locked in and preparatory systems, isn't the way to go for them. They want that open flexibility. They want the ability to be able to move workloads across the different industries. And I think it's well known that this aligns completely with our principles and at Google we've always said that we support open multi and hybrid cloud strategies because we believe our customers should be able to run what they want, where they want it. And that was exactly the philosophy that that Wells took. So, and if you look at what they were trying to do is they're looking to be able to serve their customers in a different way. I think that it's true now that customers are looking for personalized services, instant gratification, the ability to interact, where they want and when they want. So we're walking with the Wells teams to really bring to life through AI, our complex AI and data solutions to really enable them to move at speed and serve their customers in a rapidly changing world. >> So Yolande, part of the move to cloud includes the fact that we're all human beings and perception can become reality. Issues like security, which are always at the forefront of someone's mind in financial services space, there is the perception, and then there is the reality. Walk us through today where perception is in the financial services space. And then Zac, I'm going to go back to you to tell us what's the reality. And is there a disconnect? Because often technology in this space has been ahead of people's comfort level for rational reasons. So Yolande, can you talk about from a perception perspective where people are. >> So I have to tell you, we are having conversations with both the incumbents and traditional organizations, as well as, the uprising, the fintechs, and the neobanks around how does technology really unlock and unleash a new business model. So we're talking about things like how does technology and help them grow that organization. How does it take out costs in that organization? How do you use all cloud platform to think about managing risks, whether that's operational, whether it's reputational, industry or regulatory type risk? And then how do we enable our partners and our customers to be able to move at speed? So all of those conversations are now on the table. And I think a big shift from when Zac and I both were sitting on the other side of the table in those financial services industries is a recognition that this couldn't and shouldn't be done alone, that it's going to require a partnership, it's going to require, really shifting to put technology at the forefront. And I think when you talk about perception, I would say a couple of years ago, I think it was more of a perception that they were really technology companies. And I think now we're really starting to see the shifts that these are technology companies serving their customers in a banking environment. >> So Zac, can you give us some- Yeah. Yeah. Zac, can you give us some examples of how that plays out from a solutions perspective? What are some of the things that you and Yolande are having conversations with these folks in? >> Yeah. - I mean, absolutely. I think there's three major trends that we're seeing, where I think we can bring the power of sort of the Google ecosystem to really change business models and change how things are done. The first is really this massive change that's been happening for like over 10 years now, but it's really this change in customers, expecting financial institutions to meet them where they are. And that started with information being delivered to them through mobile devices and online banking. And then it went to payments, and now it's going into lending and it's going into insurance. But it changes the way that financial services companies need to operate because now they need to figure out how to deliver everything digitally, embedded into the experience that their customers are having in all of these digital ecosystems. So there's lot that we're doing in that space. The second is really around modernizing the technology environment. There is still a massive amount of paper in these organizations. Most of it has been transferred to digital paper, but the workflows and the processes that are still needing to be streamlined. And there's a lot that we can do with our AI model and technology to be able to basically take unstructured data and create structured data. Thank Google Photos, you can now search for your photo library and find, pictures of you on bridges. The same thing we can now do with documents and routine interactions with chat bot. People are expecting 24/7 service. And a lot of people want to be able to interact through chat versus through voice. And the final part of this that we're seeing a lot of use cases in is in the kind of risk and regulatory space. Coming out of the financial crisis, there was this need to massively upgrade everybody's data capabilities and control and risk environments, because so much it was very manual, and a lot of the data to do a lot of the risk and control work was kind of glued together. So everybody went off and built data lakes and figured out that that was actually a really difficult challenge and they quickly became data swamps. And so really how do you unlock the value of those things? Those three use cases, and there's lots of things underneath those, are areas that we're working with customers on. And it's, like you said, it's really exciting because the perception has changed. The perception has changed that now cloud is the sort of future, and everybody is kind of now realized they have to figure out how to engage. And I think a lot of the partnership things that Yolande was talking about is absolutely true. They're looking for a strategic relationship versus a vendor relationship, and those are really exciting changes for us. >> So I just imagined a scenario where a Dave, Zac, and Yolande are at the cloud pub talking after hours over a few pints, and Dave says, "Wow, you know, 75%, 80% of IT is still on-premises." And Yolande looks at me and says, "On-premises? We're dealing with on-paper still." Such as the life of a financial services expert in this space. So Yolande, what would you consider sort of the final frontier or at least the next frontier in cloud meets financial services? What are the challenges that we have yet to overcome? I just mentioned, the large amount of stuff that's still on premises, the friction associated with legacy applications and infrastructure. That's one whole thing. But is there one thing that in a calendar year, 2022, if you guys could solve this for the financial services industry, what would it be? And if I'm putting you on the spot, so be it. >> No, no. I'm not going to hold it to just one thing. I think the shift, I think the shift to personalization and how does the power of, you know, AI and machine learning really start to change and get into way more predictive technologies. As I mentioned, customers want to be a segmentation of one. They don't want to be forced fit into the traditional banking ecosystems. There's a reason that customers have on average 14 different financial services apps on their phones. Yep. Less than three to 5% of their screen time is actually spent on them. It's because something is missing in that environment. There's a reason that you could go to any social media site and in no time at all, be able to pull up over 200 different communities of people trying to find out financial services information in layman's terms that is relevant to them. So the ability and where we're really doubling down is on this personalization. Being way more predictive, understanding where a customer is on their journey and being able to meet them at that point, whether that's the bright offers, whether that's recognizing, to Zac's point, that they've come in on one channel but they now want to switch to another channel. And how do they not have to start again every time? So these are some of the basics things, so we really doubled down on how do we start to solve in those areas. I think also the shift, I think in many cases, especially in the risk space, it's been very much what I would call, a people process technology approach, start to imagine what happens if you turn that around and think about how technology can help you be more predictive internally in your business and create better outcomes. So I think there's so many areas of opportunities, and what's really exciting is we're not restricted, we're having conversations that are titled, the art of the possible, or the future of, or help us come in and reinvent. So I think you're going to see a lot of shift probably in the next 12 to 18 months, I would say, and the capabilities and the ability to service the customer differently and meet them on their journey. >> Well, it sounds like the life of a cloud financial services person is much more pleasurable than back when it consisted of primarily running into brick walls constantly. This conversation five or 10 years ago would have been more like, please trust us, please. Just give us a shot. >> I think Zac and I both reminisce that we couldn't have joined at a more exciting time. It's the locker or whatever you want to call it, but it is a completely different world and the conversations are fun and refreshing, and you can really start to see how we have the ability to partner to change the landscape, across all of the different financial services industries. And I think that's what keeps Zac and I going every day. >> And you said earlier that you alluded to the idea that you used to be on the other side of the table, in other words, in the financial services industry on the customer side. So you pick the right time to come across. >> Without a doubt, without a doubt. Yes. >> Well, with that, I want to thank both of you for joining me today. This is really fascinating. Financial services is something that touches all of us individually in our daily lives. It's something that everyone can relate to at some level. And it also represents, that tip of the spear, the cutting edge of cloud. So very interesting. Thank you both again, pleasure to meet you both. Next time, hopefully it will be in-person and we can compare our steps that we've taken during the conference. With that I'll sign off. This has been a fantastic Cube conversation, part of our continuing coverage of Google Cloud Next 2021. I'm Dave Nicholson, Thanks again for joining us. >> Thank you. (upbeat music)

>>Hey, welcome to the live panel. My name is Brett. I am your host, and indeed we are live. In fact, if you're curious about that, if you don't believe us, um, let's just show a little bit of the browser real quick to see. Yup. There you go. We're live. So, all right. So how this is going to work is I'm going to bring in some guests and, uh, in one second, and we're going to basically take your questions on the topic designer of the day, that continuous integration testing. Uh, thank you so much to my guests welcoming into the panel. I've got Carlos, Nico and Mandy. Hello everyone. >>Hello? All right, >>Let's go. Let's go around the room and all pretend we don't know each other and that the internet didn't read below the video who we are. Uh, hi, my name is Brett. I am a Docker captain, which means I'm supposed to know something about Docker. I'm coming from Virginia Beach. I'm streaming here from Virginia Beach, Virginia, and, uh, I make videos on the internet and courses on you to me, Carlos. Hey, >>Hey, what's up? I'm Carlos Nunez. I am a solutions architect, VMware. I do solution things with computers. It's fun. I live in Dallas when I'm moving to Houston in a month, which is where I'm currently streaming. I've been all over the Northeast this whole week. So, um, it's been fun and I'm excited to meet with all of you and talk about CIA and Docker. Sure. >>Yeah. Hey everyone. Uh, Nico, Khobar here. I'm a solution engineer at HashiCorp. Uh, I am streaming to you from, uh, the beautiful Austin, Texas. Uh, ignore, ignore the golden gate bridge here. This is from my old apartment in San Francisco. Uh, just, uh, you know, keeping that, to remember all the good days, um, that that lived at. But, uh, anyway, I work at Patrick Corp and I work on all things, automation, um, and cloud and dev ops. Um, and I'm excited to be here and Mandy, >>Hi. Yeah, Mandy Hubbard. I am streaming from Austin, Texas. I am, uh, currently a DX engineer at ship engine. Um, I've worked in QA and that's kind of where I got my, uh, my Docker experience and, um, uh, moving into DX to try and help developers better understand and use our products and be an advocate for them. >>Nice. Well, thank you all for joining me. Uh, I really appreciate you taking the time out of your busy schedule to be here. And so for those of you in chat, the reason we're doing this live, because it's always harder to do things live. The reason we're here is to answer a question. So we didn't come with a bunch of slides and demos or anything like that. We're here to talk amongst ourselves about ideas and really here for you. So we've, we obviously, this is about easy CII, so we're, we're going to try to keep the conversation around testing and continuous integration and all the things that that entails with containers. But we may, we may go down rabbit holes. We may go veer off and start talking about other things, and that's totally fine if it's in the realm of dev ops and containers and developer and ops workflows, like, Hey, it's, it's kinda game. >>And, uh, these people have a wide variety of expertise. They haven't done just testing, right? We, we live in a world where you all kind of have to wear many hats. So feel free to, um, ask what you think is on the top of your mind. And we'll do our best to answer. It may, might not be the best answer or the correct answer, but we're going to do our best. Um, well, let's get it start off. Uh, let's, let's get a couple of topics to start off with. Uh, th the, the easy CGI was my, one of my three ideas. Cause he's the, one of the things that I'm most excited about is the innovation we're seeing around easier testing, faster testing, automated testing, uh, because as much as we've all been doing this stuff for, you know, 15 years, since 20 years since the sort of Jenkins early days, um, it it's, it seems like it's still really hard and it's still a lot of work. >>So, um, let's go around the room real quick, and everybody can just kind of talk for a minute about like your experience with testing and maybe some of your pain points, like what you don't like about our testing world. Um, and we can talk about some pains, cause I think that will lead us to kind of talk about what, what are the things we're seeing now that might be better, uh, ideas about how to do this. I know for me, uh, testing, obviously there's the code part, but just getting it automated, but mostly getting it in the hands of developers so that they can control their own testing. And don't have to go talk to a person to run that test again, or the mysterious Jenkins platform somewhere. I keep mentioning Jenkins cause it's, it is still the dominant player out there. Um, so for me, I'm, I'm, I, I don't like it when I'm walking into a room and there's, there's only one or two people that know how the testing works or know how to make the new tests go into the testing platform and stuff like that. So I'm always trying to free those things so that any of the developers are enabled and empowered to do that stuff. So someone else, Carlos, anybody, um, >>Oh, I have a lot of opinions on that. Having been a QA engineer for most of my career. Um, the shift that we're saying is everyone is dev ops and everyone is QA. Th the issue I see is no one asked developers if they wanted to be QA. Um, and so being the former QA on the team, when there's a problem, even though I'm a developer and we're all running QA, they always tend to come to the one of the former QA engineers. And they're not really owning that responsibility and, um, and digging in. So that's kind of what I'm saying is that we're all expected to test now. And some people, well, some people don't know how it's, uh, for me it was kind of an intuitive skill. It just kind of fit with my personality, but not knowing what to look for, not knowing what to automate, not even understanding how your API end points are used by your front end to know what to test when a change is made. It's really overwhelming for developers. And, um, we're going to need to streamline that and, and hold their hands a little bit until they get their feet wet with also being QA. >>Right. Right. So, um, uh, Carlos, >>Yeah, uh, testing is like, Tesla is one of my favorite subjects to talk about when I'm baring with developers. And a lot of it is because of what Mandy said, right? Like a lot of developers now who used to write a test and say, Hey, QA, go. Um, I wrote my unit tests. Now write the rest of the test. Essentially. Now developers are expected to be able to understand how testing, uh, testing methodologies work, um, in their local environments, right? Like they're supposed to understand how to write an integration tasks federate into and tasks, a component test. And of course, how to write unit tests that aren't just, you know, assert true is true, right? Like more comprehensive, more comprehensive, um, more high touch unit tests, which include things like mocking and stubbing and spine and all that stuff. And, you know, it's not so much getting those tests. Well, I've had a lot of challenges with developers getting those tests to run in Docker because of usually because of dependency hell, but, um, getting developers to understand how to write tests that matter and mean something. Um, it's, it's, it can be difficult, but it's also where I find a lot of the enjoyment of my work comes into play. So yeah. I mean, that's the difficulty I've seen around testing. Um, big subject though. Lots to talk about there. >>Yeah. We've got, we've already got so many questions coming in. You already got an hour's worth of stuff. So, uh, Nico 81st thoughts on that? >>Yeah, I think I definitely agree with, with other folks here on the panel, I think from a, um, the shift from a skillset perspective that's needed to adopt the new technologies, but I think from even from, uh, aside from the organizational, um, and kind of key responsibilities that, that the new developers have to kinda adapt to and, and kind of inherit now, um, there's also from a technical perspective as there's, you know, um, more developers are owning the full stack, including the infrastructure piece. So that adds a lot more to the plate in Tim's oaf, also testing that component that they were not even, uh, responsible for before. Um, and, um, also the second challenge that, you know, I'm seeing is that on, you know, the long list of added, um, uh, tooling and, you know, there's new tool every other day. Um, and, um, that kind of requires more customization to the testing, uh, that each individual team, um, any individual developer Y by extension has to learn. Uh, so the customization, uh, as well as the, kind of the scope that had, uh, you know, now in conferences, the infrastructure piece, um, uh, both of act to the, to the challenges that we're seeing right now for, um, for CGI and overall testing, um, uh, the developers are saying, uh, in, in the market today. >>Yeah. We've got a lot of questions, um, about all the, all the different parts of this. So, uh, let me just go straight to them. Cause that's why we're here is for the people, uh, a lot of people asking about your favorite tools and in one of this is one of the challenges with integration, right? Is, um, there is no, there are dominant players, but there, there is such a variety. I mean, every one of my customers seems like they're using a different workflow and a different set of tools. So, and Hey, we're all here to just talk about what we're, what we're using, uh, you know, whether your favorite tools. So like a lot of the repeated questions are, what are your favorite tools? Like if you could create it from scratch, uh, what would you use? Pierre's asking, you know, GitHub actions sounds like they're a fan of GitHub actions, uh, w you know, mentioning, pushing the ECR and Docker hub and, uh, using vs code pipeline, I guess there may be talking about Azure pipelines. Um, what, what's your preferred way? So, does anyone have any, uh, thoughts on that anyone want to throw out there? Their preferred pipeline of tooling? >>Well, I have to throw out mine. I might as Jenkins, um, like kind of a honorary cloud be at this point, having spoken a couple of times there, um, all of the plugins just make the functionality. I don't love the UI, but I love that it's been around so long. It has so much community support, and there are so many plugins so that if you want to do something, you don't have to write the code it's already been tested. Um, unfortunately I haven't been able to use Jenkins in, uh, since I joined ship engine, we, most of our, um, our, our monolithic core application is, is team city. It's a dotnet application and TeamCity plays really well with.net. Um, didn't love it, uh, Ms. Jenkins. And I'm just, we're just starting some new initiatives that are using GitHub actions, and I'm really excited to learn, to learn those. I think they have a lot of the same functionality that you're looking for, but, um, much more simplified in is right there and get hubs. So, um, the integration is a lot more seamless, but I do have to go on record that my favorite CICT tools Jenkins. >>All right. You heard it here first people. All right. Anyone else? You're muted? I'm muted. Carlin says muted. Oh, Carla says, guest has muted themselves to Carlos. You got to unmute. >>Yes. I did mute myself because I was typing a lot, trying to, you know, try to answer stuff in the chat. And there's a lot of really dark stuff in there. That's okay. Two more times today. So yeah, it's fine. Yeah, no problem. So totally. And it's the best way to start a play more. So I'm just going to go ahead and light it up. Um, for enterprise environments, I actually am a huge fan of Jenkins. Um, it's a tool that people really understand. Um, it has stood the test of time, right? I mean, people were using Hudson, but 15 years ago, maybe longer. And, you know, the way it works, hasn't really changed very much. I mean, Jenkins X is a little different, but, um, the UI and the way it works internally is pretty familiar to a lot of enterprise environments, which is great. >>And also in me, the plugin ecosystem is amazing. There's so many plugins for everything, and you can make your own if you know, Java groovy. I'm sure there's a perfect Kotlin in there, but I haven't tried myself, but it's really great. It's also really easy to write, um, CIS code, which is something I'm a big fan of. So Jenkins files have been, have worked really well for me. I, I know that I can get a little bit more complex as you start to build your own models and such, but, you know, for enterprise enterprise CIO CD, if you want, especially if you want to roll your own or own it yourself, um, Jenkins is the bellwether and for very good reason now for my personal projects. And I see a lot on the chat here, I think y'all, y'all been agreed with me get hub actions 100%, my favorite tool right now. >>Um, I love GitHub actions. It's, it's customizable, it's modular. There's a lot of plugins already. I started using getting that back maybe a week after when GA and there was no documentation or anything. And I still, it was still my favorite CIA tool even then. Um, and you know, the API is really great. There's a lot to love about GitHub actions and, um, and I, and I use it as much as I can from my personal project. So I still have a soft spot for Travis CAI. Um, you know, they got acquired and they're a little different now trying to see, I, I can't, I can't let it go. I just love it. But, um, yeah, I mean, when it comes to Seattle, those are my tools. So light me up in the comments I will respond. Yeah. >>I mean, I, I feel with you on the Travis, the, I think, cause I think that was my first time experiencing, you know, early days get hub open source and like a free CIA tool that I could describe. I think it was the ammo back then. I don't actually remember, but yeah, it was kind of an exciting time from my experience. There was like, oh, this is, this is just there as a service. And I could just use it. It doesn't, it's like get hub it's free from my open source stuff. And so it does have a soft spot in my heart too. So yeah. >>All right. We've got questions around, um, cam, so I'm going to ask some questions. We don't have to have these answers because sometimes they're going to be specific, but I want to call them out because people in chat may have missed that question. And there's probably, you know, that we have smart people in chat too. So there's probably someone that knows the answer to these things. If, if it's not us, um, they're asking about building Docker images in Kubernetes, which to me is always a sore spot because it's Kubernetes does not build images by default. It's not meant for that out of the gate. And, uh, what is the best way to do this without having to use privileged containers, which privileged containers just implying that yeah, you, you, it probably has more privileges than by default as a container in Kubernetes. And that is a hard thing because, uh, I don't, I think Docker doesn't lie to do that out of the gate. So I don't know if anyone has an immediate answer to that. That's a pretty technical one, but if you, if you know the answer to that in chat, call it out. >>Um, >>I had done this, uh, but I'm pretty sure I had to use a privileged, um, container and install the Docker Damon on the Kubernetes cluster. And I CA I can't give you a better solution. Um, I've done the same. So, >>Yeah, uh, Chavonne asks, um, back to the Jenkins thing, what's the easiest way to integrate Docker into a Jenkins CICB pipeline. And that's one of the challenges I find with Jenkins because I don't claim to be the expert on Jenkins. Is there are so many plugins because of this, of this such a huge ecosystem. Um, when you go searching for Docker, there's a lot that comes back, right. So I, I don't actually have a preferred way because every team I find uses it differently. Um, I don't know, is there a, do you know if there's a Jenkins preferred, a default plugin? I don't even know for Docker. Oh, go ahead. Yeah. Sorry for Docker. And jacon sorry, Docker plugins for Jenkins. Uh, as someone's asking like the preferred or easy way to do that. Um, and I don't, I don't know the back into Jenkins that well, so, >>Well, th the new, the new way that they're doing, uh, Docker builds with the pipeline, which is more declarative versus the groovy. It's really simple, and their documentation is really good. They, um, they make it really easy to say, run this in this image. So you can pull down, you know, public images and add your own layers. Um, so I don't know the name of that plugin, uh, but I can certainly take a minute after this session and going and get that. Um, but if you really are overwhelmed by the plugins, you can just write your, you know, your shell command in Jenkins. You could just by, you know, doing everything in bash, calling the Docker, um, Damon directly, and then getting it working just to see that end to end, and then start browsing for plugins to see if you even want to use those. >>The plugins will allow more integration from end to end. Some of the things that you input might be available later on in the process for having to manage that yourself. But, you know, you don't have to use any of the plugins. You can literally just, you know, do a block where you write your shell command and get it working, and then decide if, for plugins for you. Um, I think it's always under important to understand what is going on under the hood before you, before you adopt the magic of a plugin, because, um, once you have a problem, if you're, if it's all a lockbox to you, it's going to be more difficult to troubleshoot. It's kind of like learning, get command line versus like get cracking or something. Once, once you get in a bind, if you don't understand the underlying steps, it's really hard to get yourself out of a bind, versus if you understand what the plugin or the app is doing, then, um, you can get out of situations a lot easier. That's a good place. That's, that's where I'd start. >>Yeah. Thank you. Um, Camden asks better to build test environment images, every commit in CII. So this is like one of those opinions of we're all gonna have some different, uh, or build on build images on every commit, leveraging the cash, or build them once outside the test pile pipeline. Um, what say you people? >>Uh, well, I I've seen both and generally speaking, my preference is, um, I guess the ant, the it's a consultant answer, right? I think it depends on what you're trying to do, right. So if you have a lot of small changes that are being made and you're creating images for each of those commits, you're going to have a lot of images in your, in your registry, right? And on top of that, if you're building those images, uh, through CAI frequently, if you're using Docker hub or something like that, you might run into rate limiting issues because of Docker's new rate, limiting, uh, rate limits that they put in place. Um, but that might be beneficial if the, if being able to roll back between those small changes while you're testing is important to you. Uh, however, if all you care about is being able to use Docker images, um, or being able to correlate versions to your Docker images, or if you're the type of team that doesn't even use him, uh, does he even use, uh, virgins in your image tags? Then I would think that that might be a little, much you might want to just have in your CIO. You might want to have a stage that builds your Docker images and Docker image and pushes it into your registry, being done first particular branches instead of having to be done on every commit regardless of branch. But again, it really depends on the team. It really depends on what you're building. It really depends on your workflow. It can depend on a number of things like a curse sometimes too. Yeah. Yeah. >>Once had two points here, you know, I've seen, you know, the pattern has been at every, with every, uh, uh, commit, assuming that you have the right set of tests that would kind of, uh, you would benefit from actually seeing, um, the, the, the, the testing workflow go through and can detect any issue within, within the build or whatever you're trying to test against. But if you're just a building without the appropriate set of tests, then you're just basically consuming almond, adding time, as well as all the, the image, uh, stories associated with it without treaty reaping the benefit of, of, of this pattern. Uh, and the second point is, again, I think if you're, if you're going to end up doing a per commit, uh, definitely recommend having some type of, uh, uh, image purging, um, uh, and, and, and garbage collection process to ensure that you're not just wasting, um, all the stories needed and also, um, uh, optimizing your, your bill process, because that will end up being the most time-consuming, um, um, you know, within, within your pipeline. So this is my 2 cents on this. >>Yeah, that's good stuff. I mean, those are both of those are conversations that could lead us into the rabbit hole for the rest of the day on storage management, uh, you know, CP CPU minutes for, uh, you know, your build stuff. I mean, if you're in any size team, more than one or two people, you immediately run into headaches with cost of CIA, because we have now the problem of tools, right? We have so many tools. We can have the CIS system burning CPU cycles all day, every day, if we really wanted to. And so you re very quickly, I think, especially if you're on every commit on every branch, like that gets you into a world of cost mitigation, and you probably are going to have to settle somewhere in the middle on, uh, between the budget, people that are saying you're spending way too much money on the CII platform, uh, because of all these CPU cycles, and then the developers who would love to have everything now, you know, as fast as possible and the biggest, biggest CPU's, and the biggest servers, and have the bills, because the bills can never go fast enough, right. >>There's no end to optimizing your build workflow. Um, we have another question on that. This is another topic that we'll all probably have different takes on is, uh, basically, uh, version tags, right? So on images, we, we have a very established workflow in get for how we make commits. We have commit shots. We have, uh, you know, we know get tags and there's all these things there. And then we go into images and it's just this whole new world that's opened up. Like there's no real consensus. Um, so what, what are your thoughts on the strategy for teams in their image tag? Again, another, another culture thing. Um, commander, >>I mean, I'm a fan of silver when we have no other option. Um, it's just clean and I like the timestamp, you know, exactly when it was built. Um, I don't really see any reason to use another, uh, there's just normal, incremental, um, you know, numbering, but I love the fact that you can pull any tag and know exactly when it was created. So I'm a big fan of bar, if you can make that work for your organization. >>Yep. People are mentioned that in chat, >>So I like as well. Uh, I'm a big fan of it. I think it's easy to be able to just be as easy to be able to signify what a major changes versus a minor change versus just a hot fix or, you know, some or some kind of a bad fix. The problem that I've found with having teams adopt San Bernardo becomes answering these questions and being able to really define what is a major change, what is a minor change? What is a patch, right? And this becomes a bit of an overhead or not so much of an overhead, but, uh, uh, uh, a large concern for teams who have never done versioning before, or they never been responsible for their own versioning. Um, in fact, you know, I'm running into that right now, uh, with, with a client that I'm working with, where a lot, I'm working with a lot of teams, helping them move their applications from a legacy production environment into a new one. >>And in doing so, uh, versioning comes up because Docker images, uh, have tags and usually the tax correlate to versions, but some teams over there, some teams that I'm working with are only maintaining a script and others are maintaining a fully fledged JAK, three tier application, you know, with lots of dependencies. So telling the script, telling the team that maintains a script, Hey, you know, you should use somber and you should start thinking about, you know, what's major, what's my number what's patch. That might be a lot for them. And for someone or a team like that, I might just suggest using commit shots as your versions until you figure that out, or maybe using, um, dates as your version, but for the more for the team, with the larger application, they probably already know the answers to those questions. In which case they're either already using Sember or they, um, or they may be using some other version of the strategy and might be in December, might suit them better. So, um, you're going to hear me say, it depends a lot, and I'm just going to say here, it depends. Cause it really does. Carlos. >>I think you hit on something interesting beyond just how to version, but, um, when to consider it a major release and who makes those decisions, and if you leave it to engineers to version, you're kind of pushing business decisions down the pipe. Um, I think when it's a minor or a major should be a business decision and someone else needs to make that call someone closer to the business should be making that call as to when we want to call it major. >>That's a really good point. And I add some, I actually agree. Um, I absolutely agree with that. And again, it really depends on the team that on the team and the scope of it, it depends on the scope that they're maintaining, right? And so it's a business application. Of course, you're going to have a product manager and you're going to have, you're going to have a product manager who's going to want to make that call because that version is going to be out in marketing. People are going to use it. They're going to refer to and support calls. They're going to need to make those decisions. Sember again, works really, really well for that. Um, but for a team that's maintaining the scripts, you know, I don't know, having them say, okay, you must tell me what a major version is. It's >>A lot, but >>If they want it to use some birds great too, which is why I think going back to what you originally said, Sember in the absence of other options. I think that's a good strategy. >>Yeah. There's a, there's a, um, catching up on chat. I'm not sure if I'm ever going to catch up, but there's a lot of people commenting on their favorite CII systems and it's, and it, it just goes to show for the, the testing and deployment community. Like how many tools there are out there, how many tools there are to support the tools that you're using. Like, uh, it can be a crazy wilderness. And I think that's, that's part of the art of it, uh, is that these things are allowing us to build our workflows to the team's culture. Um, and, uh, but I do think that, you know, getting into like maybe what we hope to be at what's next is I do hope that we get to, to try to figure out some of these harder problems of consistency. Uh, one of the things that led me to Docker at the beginning to begin with was the fact that it wa it created a consistent packaging solution for me to get my code, you know, off of, off of my site of my local system, really, and into the server. >>And that whole workflow would at least the thing that I was making at each step was going to be the same thing used. Right. And that, that was huge. Uh, it was also, it also took us a long time to get there. Right. We all had to, like Docker was one of those ones that decade kind of ideas of let's solidify the, enter, get the consensus of the community around this idea. And we, and it's not perfect. Uh, you know, the Docker Docker file is not the most perfect way to describe how to make your app, but it is there and we're all using it. And now I'm looking for that next piece, right. Then hopefully the next step in that, um, that where we can all arrive at a consensus so that once you hop teams, you know, okay. We all knew Docker. We now, now we're all starting to get to know the manifests, but then there's this big gap in the middle where it's like, it might be one of a dozen things. Um, you know, so >>Yeah, yeah. To that, to that, Brett, um, you know, uh, just maybe more of a shameless plug here and wanting to kind of talk about one of the things that I'm on. So excited, but I work, I work at Tasha Corp. I don't know anyone, or I don't know if many people have heard of, um, you know, we tend to focus a lot on workflows versus technologies, right. Because, you know, as you can see, even just looking at the chat, there's, you know, ton of opinions on the different tooling, right. And, uh, imagine having, you know, I'm working with clients that have 10,000 developers. So imagine taking the folks in the chat and being partnered with one organization or one company and having to make decisions on how to build software. Um, but there's no way you can conversion one or, or one way or one tool, uh, and that's where we're facing in the industry. >>So one of the things that, uh, I'm pretty excited about, and I don't know if it's getting as much traction as you know, we've been focused on it. This is way point, which is a project, an open source project. I believe we got at least, uh, last year, um, which is, it's more of, uh, it's, it is aim to address that really, uh, uh, Brad set on, you know, to come to tool to, uh, make it extremely easy and simple. And, you know, to describe how you want to build, uh, deploy or release your application, uh, in, in a consistent way, regardless of the tools. So similar to how you can think of Terraform and having that pluggability to say Terraform apply or plan against any cloud infrastructure, uh, without really having to know exactly the details of how to do it, uh, this is what wave one is doing. Um, and it can be applied with, you know, for the CIA, uh, framework. So, you know, task plugability into, uh, you know, circle CEI tests to Docker helm, uh, Kubernetes. So that's the, you know, it's, it's a hard problem to solve, but, um, I'm hopeful that that's the path that we're, you know, we'll, we'll eventually get to. So, um, hope, you know, you can, you can, uh, see some of the, you know, information, data on it, on, on HashiCorp site, but I mean, I'm personally excited about it. >>Yeah. Uh I'm to gonna have to check that out. And, um, I told you on my live show, man, we'll talk about it, but talk about it for a whole hour. Uh, so there's another question here around, uh, this, this is actually a little bit more detailed, but it is one that I think a lot of people deal with and I deal with a lot too, is essentially the question is from Cameron, uh, D essentially, do you use compose in your CIO or not Docker compose? Uh, because yes I do. Yeah. Cause it, it, it, it solves so many problems am and not every CGI can, I don't know, there's some problems with a CIO is trying to do it for me. So there are pros and cons and I feel like I'm still on the fence about it because I use it all the time, but also it's not perfect. It's not always meant for CIA. And CIA sometimes tries to do things for you, like starting things up before you start other parts and having that whole order, uh, ordering problem of things anyway. W thoughts and when have thoughts. >>Yes. I love compose. It's one of my favorite tools of all time. Um, and the reason why it's, because what I often find I'm working with teams trying to actually let me walk that back, because Jack on the chat asked a really interesting question about what, what, what the hardest thing about CIS for a lot of teams. And in my experience, the hardest thing is getting teams to build an app that is the same app as what's built in production. A lot of CGI does things that are totally different than what you would do in your local, in your local dev. And as a result of that, you get, you got this application that either doesn't work locally, or it does work, but it's a completely different animal than what you would get in production. Right? So what I've found in trying to get teams to bridge that gap by basically taking their CGI, shifting the CII left, I hate the shift left turn, but I'll use it. >>I'm shifting the CIO left to your local development is trying to say, okay, how do we build an app? How do we, how do we build mot dependencies of that app so that we can build so that we can test our app? How do we run tests, right? How do we build, how do we get test data? And what I found is that trying to get teams to do all this in Docker, which is normally a first for a lot of teams that I'm working with, trying to get them all to do all of this. And Docker means you're running Docker, build a lot running Docker, run a lot. You're running Docker, RM a lot. You ran a lot of Docker, disparate Docker commands. And then on top of that, trying to bridge all of those containers together into a single network can be challenging without compose. >>So I like using a, to be able to really easily categorize and compartmentalize a lot of the things that are going to be done in CII, like building a Docker image, running tests, which is you're, you're going to do it in CII anyway. So running tests, building the image, pushing it to the registry. Well, I wouldn't say pushing it to the registry, but doing all the things that you would do in local dev, but in the same network that you might have a mock database or a mock S3 instance or some of something else. Um, so it's just easy to take all those Docker compose commands and move them into your Yammel file using the hub actions or your dankest Bob using Jenkins, or what have you. Right. It's really, it's really portable that way, but it doesn't work for every team. You know, for example, if you're just a team that, you know, going back to my script example, if it's a really simple script that does one thing on a somewhat routine basis, then that might be a lot of overhead. Um, in that case, you know, you can get away with just Docker commands. It's not a big deal, but the way I looked at it is if I'm, if I'm building, if I build something that's similar to a make bile or rate file, or what have you, then I'm probably gonna want to use Docker compose. If I'm working with Docker, that's, that's a philosophy of values, right? >>So I'm also a fan of Docker compose. And, um, you know, to your point, Carlos, the whole, I mean, I'm also a fan of shifting CEI lift and testing lift, but if you put all that logic in your CTI, um, it changes the L the local development experience from the CGI experience. Versus if you put everything in a compose file so that what you build locally is the same as what you build in CGI. Um, you're going to have a better experience because you're going to be testing something more, that's closer to what you're going to be releasing. And it's also very easy to look at a compose file and kind of, um, understand what the dependencies are and what's happening is very readable. And once you move that stuff to CGI, I think a lot of developers, you know, they're going to be intimidated by the CGI, um, whatever the scripting language is, it's going to be something they're going to have to wrap their head around. >>Um, but they're not gonna be able to use it locally. You're going to have to have another local solution. So I love the idea of a composed file use locally, um, especially if he can Mount the local workspace so that they can do real time development and see their changes in the exact same way as it's going to be built and tested in CGI. It gives developers a high level of confidence. And then, you know, you're less likely to have issues because of discrepancies between how it was built in your local test environment versus how it's built in NCI. And so Docker compose really lets you do all of that in a way that makes your solution more portable, portable between local dev and CGI and reduces the number of CGI cycles to get, you know, the test, the test data that you need. So that's why I like it for really, for local dev. >>It'll be interesting. Um, I don't know if you all were able to see the keynote, but there was a, there was a little bit, not a whole lot, but a little bit talk of the Docker, compose V two, which has now built into the Docker command line. And so now we're shifting from the Python built compose, which was a separate package. You could that one of the challenges was getting it into your CA solution because if you don't have PIP and you got down on the binary and the binary wasn't available for every platform and, uh, it was a PI installer. It gets a little nerdy into how that works, but, uh, and the team is now getting, be able to get unified with it. Now that it's in Golang and it's, and it's plugged right into the Docker command line, it hopefully will be easier to distribute, easier to, to use. >>And you won't have to necessarily have dependencies inside of where you're running it because there'll be a statically compiled binary. Um, so I've been playing with that, uh, this year. And so like training myself to do Docker going from Docker dash compose to Docker space, compose. It is a thing I I'm almost to the point of having to write a shell replacement. Yeah. Alias that thing. Um, but, um, I'm excited to see what that's going, cause there's already new features in it. And it, these built kit by default, like there's all these things. And I, I love build kit. We could make a whole session on build kit. Um, in fact there's actually, um, maybe going on right now, or right around this time, there is a session on, uh, from Solomon hikes, the seat, uh, co-founder of Docker, former CTO, uh, on build kit using, uh, using some other tool on top of build kit or whatever. >>So that, that would be interesting for those of you that are not watching that one. Cause you're here, uh, to do a check that one out later. Um, all right. So another good question was caching. So another one, another area where there is no wrong answers probably, and everyone has a different story. So the question is, what are your thoughts on CII build caching? There's often a debate between security. This is from Quentin. Thank you for this great question. There's often a debate between security reproducibility and build speeds. I haven't found a good answer so far. I will just throw my hat in the ring and say that the more times you want to build, like if you're trying to build every commit or every commit, if you're building many times a day, the more caching you need. So like the more times you're building, the more caching you're gonna likely want. And in most cases caching doesn't bite you in the butt, but that could be, yeah, we, can we get the bit about that? So, yeah. Yeah. >>I'm going to quote Carlos again and say, it depends on, on, you know, how you're talking, you know, what you're trying to build and I'm quoting your colors. Um, yeah, it's, it's got, it's gonna depend because, you know, there are some instances where you definitely want to use, you know, depends on the frequency that you're building and how you're building. Um, it's you would want to actually take advantage of cashing functionalities, um, for the build, uh, itself. Um, but if, um, you know, as you mentioned, there could be some instances where you would want to disable, um, any caching because you actually want to either pull a new packages or, um, you know, there could be some security, um, uh, disadvantages related to security aspects that would, you know, you know, using a cache version of, uh, image layer, for example, could be a problem. And you, you know, if you have a fleet of build, uh, engines, you don't have a good grasp of where they're being cashed. We would have to, um, disable caching in that, in that, um, in those instances. So it, it would depend. >>Yeah, it's, it's funny you have that problem on both sides of cashing. Like there are things that, especially in Docker world, they will cash automatically. And, and then, and then you maybe don't realize that some of that caching could be bad. It's, it's actually using old, uh, old assets, old artifacts, and then there's times where you would expect it to cash, that it doesn't cash. And then you have to do something extra to enable that caching, especially when you're dealing with that cluster of, of CIS servers. Right. And the cloud, the whole clustering problem with caching is even more complex, but yeah, >>But that's, that's when, >>Uh, you know, ever since I asked you to start using build kits and able to build kit, you know, between it's it's it's reader of Boston in, in detecting word, you know, where in, in the bill process needs to cash, as well as, uh, the, the, um, you know, the process. I don't think I've seen any other, uh, approach there that comes close to how efficient, uh, that process can become how much time it can actually save. Uh, but again, I think, I think that's, for me that had been my default approach, unless I actually need something that I would intentionally to disable caching for that purpose, but the benefits, at least for me, the benefits of, um, how bill kit actually been processing my bills, um, from the builds as well as, you know, using the cash up until, you know, how it detects the, the difference in, in, in the assets within the Docker file had been, um, you know, uh, pretty, you know, outweigh the disadvantages that it brings in. So it, you know, take it each case by case. And based on that, determine if you want to use it, but definitely recommend those enabling >>In the absence of a reason not to, um, I definitely think that it's a good approach in terms of speed. Um, yeah, I say you cash until you have a good reason not to personally >>Catch by default. There you go. I think you catch by default. Yeah. Yeah. And, uh, the trick is, well, one, it's not always enabled by default, especially when you're talking about cross server. So that's a, that's a complexity for your SIS admins, or if you're on the cloud, you know, it's usually just an option. Um, I think it also is this, this veers into a little bit of, uh, the more you cash the in a lot of cases with Docker, like the, from like, if you're from images and checked every single time, if you're not pinning every single thing, if you're not painting your app version, you're at your MPN versions to the exact lock file definition. Like there's a lot of these things where I'm I get, I get sort of, I get very grouchy with teams that sort of let it, just let it all be like, yeah, we'll just build two images and they're totally going to have different dependencies because someone happened to update that thing and after whatever or MPM or, or, and so I get grouchy about that, cause I want to lock it all down, but I also know that that's going to create administrative burden. >>Like the team is now going to have to manage versions in a very much more granular way. Like, do we need to version two? Do we need to care about curl? You know, all that stuff. Um, so that's, that's kind of tricky, but when you get to, when you get to certain version problems, uh, sorry, uh, cashing problems, you, you, you don't want those set those caches to happen because it, if you're from image changes and you're not constantly checking for a new image, and if you're not pinning that V that version, then now you, you don't know whether you're getting the latest version of Davion or whatever. Um, so I think that there's, there's an art form to the more you pen, the less you have, the less, you have to be worried about things changing, but the more you pen, the, uh, all your versions of everything all the way down the stack, the more administrative stuff, because you're gonna have to manually change every one of those. >>So I think it's a balancing act for teams. And as you mature, I to find teams, they tend to pin more until they get to a point of being more comfortable with their testing. So the other side of this argument is if you trust your testing, then you, and you have better testing to me, the less likely to the subtle little differences in versions have to be penned because you can get away with those minor or patch level version changes. If you're thoroughly testing your app, because you're trusting your testing. And this gets us into a whole nother rant, but, uh, yeah, but talking >>About penny versions, if you've got a lot of dependencies isn't that when you would want to use the cash the most and not have to rebuild all those layers. Yeah. >>But if you're not, but if you're not painting to the exact patch version and you are caching, then you're not technically getting the latest versions because it's not checking for all the time. It's a weird, there's a lot of this subtle nuance that people don't realize until it's a problem. And that's part of the, the tricky part of allow this stuff, is it, sometimes the Docker can be almost so much magic out of the box that you, you, you get this all and it all works. And then day two happens and you built it a second time and you've got a new version of open SSL in there and suddenly it doesn't work. Um, so anyway, uh, that was a great question. I've done the question on this, on, uh, from heavy. What do you put, where do you put testing in your pipeline? Like, so testing the code cause there's lots of types of testing, uh, because this pipeline gets longer and longer and Docker building images as part of it. And so he says, um, before staging or after staging, but before production, where do you put it? >>Oh man. Okay. So, um, my, my main thought on this is, and of course this is kind of religious flame bait, so sure. You know, people are going to go into the compensation wrong. Carlos, the boy is how I like to think about it. So pretty much in every stage or every environment that you're going to be deploying your app into, or that your application is going to touch. My idea is that there should be a build of a Docker image that has all your applications coded in, along with its dependencies, there's testing that tests your application, and then there's a deployment that happens into whatever infrastructure there is. Right. So the testing, they can get tricky though. And the type of testing you do, I think depends on the environment that you're in. So if you're, let's say for example, your team and you have, you have a main branch and then you have feature branches that merged into the main branch. >>You don't have like a pre-production branch or anything like that. So in those feature branches, whenever I'm doing CGI that way, I know when I freak, when I cut my poll request, that I'm going to merge into main and everything's going to work in my feature branches, I'm going to want to probably just run unit tests and maybe some component tests, which really, which are just, you know, testing that your app can talk to another component or another part, another dependency, like maybe a database doing tests like that, that don't take a lot of time that are fascinating and right. A lot of would be done at the beach branch level and in my opinion, but when you're going to merge that beach branch into main, as part of a release in that activity, you're going to want to be able to do an integration tasks, to make sure that your app can actually talk to all the other dependencies that it talked to. >>You're going to want to do an end to end test or a smoke test, just to make sure that, you know, someone that actually touches the application, if it's like a website can actually use the website as intended and it meets the business cases and all that, and you might even have testing like performance testing, low performance load testing, or security testing, compliance testing that would want to happen in my opinion, when you're about to go into production with a release, because those are gonna take a long time. Those are very expensive. You're going to have to cut new infrastructure, run those tests, and it can become quite arduous. And you're not going to want to run those all the time. You'll have the resources, uh, builds will be slower. Uh, release will be slower. It will just become a mess. So I would want to save those for when I'm about to go into production. Instead of doing those every time I make a commit or every time I'm merging a feature ranch into a non main branch, that's the way I look at it, but everything does a different, um, there's other philosophies around it. Yeah. >>Well, I don't disagree with your build test deploy. I think if you're going to deploy the code, it needs to be tested. Um, at some level, I mean less the same. You've got, I hate the term smoke tests, cause it gives a false sense of security, but you have some mental minimum minimal amount of tests. And I would expect the developer on the feature branch to add new tests that tested that feature. And that would be part of the PR why those tests would need to pass before you can merge it, merge it to master. So I agree that there are tests that you, you want to run at different stages, but the earlier you can run the test before going to production. Um, the fewer issues you have, the easier it is to troubleshoot it. And I kind of agree with what you said, Carlos, about the longer running tests like performance tests and things like that, waiting to the end. >>The only problem is when you wait until the end to run those performance tests, you kind of end up deploying with whatever performance you have. It's, it's almost just an information gathering. So if you don't run your performance test early on, um, and I don't want to go down a rabbit hole, but performance tests can be really useless if you don't have a goal where it's just information gap, uh, this is, this is the performance. Well, what did you expect it to be? Is it good? Is it bad? They can get really nebulous. So if performance is really important, um, you you're gonna need to come up with some expectations, preferably, you know, set up the business level, like what our SLA is, what our response times and have something to shoot for. And then before you're getting to production. If you have targets, you can test before staging and you can tweak the code before staging and move that performance initiative. Sorry, Carlos, a little to the left. Um, but if you don't have a performance targets, then it's just a check box. So those are my thoughts. I like to test before every deployment. Right? >>Yeah. And you know what, I'm glad that you, I'm glad that you brought, I'm glad that you brought up Escalades and performance because, and you know, the definition of performance says to me, because one of the things that I've seen when I work with teams is that oftentimes another team runs a P and L tests and they ended, and the development team doesn't really have too much insight into what's going on there. And usually when I go to the performance team and say, Hey, how do you run your performance test? It's usually just a generic solution for every single application that they support, which may or may not be applicable to the application team that I'm working with specifically. So I think it's a good, I'm not going to dig into it. I'm not going to dig into the rabbit hole SRE, but it is a good bridge into SRE when you start trying to define what does reliability mean, right? >>Because the reason why you test performance, it's test reliability to make sure that when you cut that release, that customers would go to your site or use your application. Aren't going to see regressions in performance and are not going to either go to another website or, you know, lodge in SLA violation or something like that. Um, it does, it does bridge really well with defining reliability and what SRE means. And when you have, when you start talking about that, that's when you started talking about how often do I run? How often do I test my reliability, the reliability of my application, right? Like, do I have nightly tasks in CGI that ensure that my main branch or, you know, some important branch I does not mean is meeting SLA is meeting SLR. So service level objectives, um, or, you know, do I run tasks that ensure that my SLA is being met in production? >>Like whenever, like do I use, do I do things like game days where I test, Hey, if I turn something off or, you know, if I deploy this small broken code to production and like what happens to my performance? What happens to my security and compliance? Um, you can, that you can go really deep into and take creating, um, into creating really robust tests that cover a lot of different domains. But I liked just using build test deploy is the overall answer to that because I find that you're going to have to build your application first. You're going to have to test it out there and build it, and then you're going to want to deploy it after you test it. And that order generally ensures that you're releasing software. That works. >>Right. Right. Um, I was going to ask one last question. Um, it's going to have to be like a sentence answer though, for each one of you. Uh, this is, uh, do you lint? And if you lint, do you lent all the things, if you do, do you fail the linters during your testing? Yes or no? I think it's going to depend on the culture. I really do. Sorry about it. If we >>Have a, you know, a hook, uh, you know, on the get commit, then theoretically the developer can't get code there without running Melinta anyway, >>So, right, right. True. Anyone else? Anyone thoughts on that? Linting >>Nice. I saw an additional question online thing. And in the chat, if you would introduce it in a multi-stage build, um, you know, I was wondering also what others think about that, like typically I've seen, you know, with multi-stage it's the most common use case is just to produce the final, like to minimize the, the, the, the, the, the image size and produce a final, you know, thin, uh, layout or thin, uh, image. Uh, so if it's not for that, like, I, I don't, I haven't seen a lot of, you know, um, teams or individuals who are actually within a multi-stage build. There's nothing really against that, but they think the number one purpose of doing multi-stage had been just producing the minimalist image. Um, so just wanted to kind of combine those two answers in one, uh, for sure. >>Yeah, yeah, sure. Um, and with that, um, thank you all for the great questions. We are going to have to wrap this up and we could go for another hour if we all had the time. And if Dr. Khan was a 24 hour long event and it didn't sadly, it's not. So we've got to make room for the next live panel, which will be Peter coming on and talking about security with some developer ex security experts. And I wanted to thank again, thank you all three of you for being here real quick, go around the room. Um, uh, where can people reach out to you? I am, uh, at Bret Fisher on Twitter. You can find me there. Carlos. >>I'm at dev Mandy with a Y D E N D Y that's me, um, >>Easiest name ever on Twitter, Carlos and DFW on LinkedIn. And I also have a LinkedIn learning course. So if you check me out on my LinkedIn learning, >>Yeah. I'm at Nicola Quebec. Um, one word, I'll put it in the chat as well on, on LinkedIn, as well as, uh, uh, as well as Twitter. Thanks for having us, Brett. Yeah. Thanks for being here. >>Um, and, and you all stay around. So if you're in the room with us chatting, you're gonna, you're gonna, if you want to go to see the next live panel, I've got to go back to the beginning and do that whole thing, uh, and find the next, because this one will end, but we'll still be in chat for a few minutes. I think the chat keeps going. I don't actually know. I haven't tried it yet. So we'll find out here in a minute. Um, but thanks you all for being here, I will be back a little bit later, but, uh, coming up next on the live stuff is Peter Wood security. Ciao. Bye.

>>Welcome back everyone to Dell Technologies World 2021 the virtual version. You're watching the cubes continuing coverage of the event and we're gonna talk about the Edge, the transformation of telco in the future of our expanding tech universe. With me is Aaron Jason, who's the vice president? Edge and Telkom marketing at Dell Technologies erin great to see you. I love this topic. >>Absolutely. It's it's pretty popular these days. I'm glad to be here with you. Thanks. >>It is popular, you know, cloud was kind of the shiny new toy last decade and it's still growing at double digits but it's kind of mainstream and now the Edge is all the rage. What's the best way to think about? What is the Edge? How do you define that? >>Yeah, you know, that's probably one of the most common questions I get is we start really doubling down on what we're doing it in the Edge world today. Um you know, I tried to basically not overcomplicated too much, you know, last year we really tried to to talk about it as being where you're the physical world, in the virtual world, connect. Um but you know, really it's more about what customers are looking to do with that technology. And so what we're really thinking about it today is the edges really where customers data is being used near point of generation to really define and build the essential value for that customer and that essential value is gonna be different in each vertical in each industry. Right? So in manufacturing, that essential value is created in the factory and retail, it's going to be, you know, at point of sale, whether that's in a store or on your device, in a virtual interaction, um in health care, it's going to be the point of care, Right? So it's gonna be the ambulance or the emergency room or the radiology lab. and of course in farming that essential values created in the field itself. So um, you know, for for many customers, it's really trying to figure out, you know, how do they take technology closer to the point of that value creation to be able to drive new new capabilities for the business, whether it's for what they're trying to accomplish or what they're trying to do in helping their customers. So really that's how we're thinking about the edge today. It's where that value generation occurs for a company. And how do we take technology to that point of generation to deliver value for them? >>Yeah, I like that. I mean to me the edge, I know what it's not, I know the edges, not a mega data center, but but everything else could be the edge. I mean, it's it's to me it's the place that's the most logical, the most logical place to process the data. So as you say, it could be a factory, it could be a hospital, it could be a retail store, it could be, could be a race track, it could be a farm, I mean virtually anything. So the edges, it's always been here, but it's changing. I mean most of the edge data has historically been analog. Everything now is getting instrumented. What are the factors that you think will make this, this industry's vision of the edge real in your opinion? >>Yeah. You know, it's it's really bringing together a handful of technologies that have really started to mature after over the last decade or so. Um the ones that have been around for a little bit, things like IOT have been emerging in the last several years. Um even Ai and machine learning many of those algorithms have been around for decades, but we've only recently been able to bring the compute power required to do that in edge environments in the last decade or so. Um it's so really the two key sort of killer technologies that have matured in the last couple of years is really the mic realization of computing. So being able to put compute almost anywhere on the planet and then the emergence of five G networking, giving us the ability to provide very high performance, low latency and high bandwidth environments to connect all those things together and get the data to those analytics environments. From that computer perspective. I mean, I still like to talk about moore's law as an example of that that ever marched that's been going on for, you know, half a century or more now is continuing to push forward um at a rate that is that that that that just really hasn't slowed down for the most part, you know, the example that I use with people, as, you know, you know, I still remember when I got my first calculator watch as a kid, you know, that Casio calculator watch that so many of us had, And my dad told me the story when he gave it to me, he's like, Hey, look, this has the same amount of compute power as the landing module on the moon, and I didn't know it at the time, but that was my first sort of entry and education around what Moore's law provided. And it's not so much speed. I mean, people think about that as it doubles in speed every 18 months, but it's really more about the density of compute that happens that moore's law drought, pushes along, so I can now squish more and more compute power into a smudge smaller location and I can now take that performance out to the edge in a way that I haven't been able to do before. I mean I think about my history, I joined E M C, that was acquired by Dell Technologies a couple years back. I joined that back in the late nineties when the biggest baddest storage array on the planet was one whole terabyte in size. And now I can fit that in the palm of my hand. In fact, when I walk around, you know, when I used to walk around with my, with my back, my laptop and go into offices, um you know, if I had my laptop and my tablet and my my my smartwatch, I had 12 to 16 cores on me and a couple of terabytes of capacity all connected with the equivalent of tens of T ones. Right? So what was once a small or or a mid sized data center just in the last decade or so? We now all walk around a small data centers and the power that that compute now brings to the edge allows us to take analytics that was really once done in data centers. I may have captured it at the edge, but I had to move it into a data lake. I had to stage it and analyze it. It was more of a historical way of looking at data. Now I can put compute right next to the point of data generation and give insight instantaneously as data is being generated. And that's opening up whole new ways that industries can drive new value for them and for their customers. And that's really what's exciting about it is this combination of these technologies that are all sort of maturing and coming together at the same time. Um, and there's just so much doing, it happened that space and devils really, really excited to be part of bringing that into these environments for our customers. >>I'm gonna give you a stat that a lot of people, I don't, I don't think realize, uh, you talked about moore's law and you're absolutely right. It's really, you know, technically moore's law is about the density, right? But the outcome of being able to do that is performance. And if you do the math, you know, moore's law doubling performance every two years, roughly, The math on that is that means 44 improvement per year in performance. Everybody talks about how moore's laws is dead. It's not, it's just changing. Here's the, here's the stat. If you take a system on a chip, take like for instance apples a 14 and go back five years from 2015 to 2021. If you add up the performance of the CPU the combinatorial factors of the CPU gpu and in the N. P. U. The neural processing unit, just those three, The growth rate has been 118 a year vs 44%. So it's actually accelerating and that doesn't include the accelerators and the DSPS and all the other alternative processors. So, and to your point and by the way that a 14 shipping cost Apple 50 bucks. So and and that fits in the palm of your hand to the point that you were just making So imagine that processing power at the edge most of of of of of ai today is modeling, let's say in the cloud, the vast majority is going to be a i influencing at the edge. So you are right on on that point. >>Yeah, there's no question about it. So, to your point, I mean, moore's law is just of course CPU itself. All right. And it comes out to roughly, on average, it's about 10 x every five years. 100 X every 10 years, 1000 X every 15 years. I mean, it's incredible how much power you can put in a small footprint today. And then if you factor in the accelerators and everything else um, it's actually if anything that innovation is going faster and faster and to your point, um you know, the while the modeling is still going to typically happen in data centers as you pull together lots of different data sets to be able to analyze and create new models. But those models are getting pushed right out to the edge on these compute devices literally feet away at times from the point of data generation to be able to give us really real time analytics and influencing. The other cool thing about this too is you know we're going from sort of more looking backwards and making business analytics based on what has already happened in the past to being able to do that in the very near past. And of course now with modern analytics and models that are being created for ai we're able to do more predictive analytics so we can actually identify errors, identify challenges before they even occur based on pattern matching that they're saying. Um So it's really opening up new doors and new areas that we've never been able to see before that's really all powered by by these capabilities. >>It's insane the amount of data that is coming. We think data is overwhelming today. You ain't seen nothing yet. Um Now erin you cover the edge and the telecom business up. I was beside it when I when I when I found that out because the telecom businesses is ripe for transformation. Um So what do you how is Dell thinking about that? Why are you sort of putting those together? What are the synergies that you see in in the commonalities in those 22 sectors? >>Yeah. I mean at the end of the day it's really all about serving the enterprise customers in the in the organizations of all kinds um that the industry is trying to bring these edge technologies too and that's no different with the telecommunications industry. Right? So you know when when the when the four G world changed about 10 years ago um you know the telecom industry was able to bring the plumbing the network piping out to all the endpoints but they really didn't capture the over the top revenue opportunities that Four G technologies opened up right. That really went to the hyper scholars. It went to you know, a lot of the companies that we all know and love like uh you know, Uber and Airbnb and netflix and others um and that really when the four Gr that was really more about opening up consumer opportunities as we move to five G. And as we move these ultra low latency and high bandwidth capabilities out to the enterprise edge, it's really the B two B opportunities that are opening up and so on the telecom side we're partnering with the telecommunication companies to modernize their network, enroll five G. L. Quickly. But one of the more important things is that we're partnering with them to be able to build services over the top of that that they can then sell into their customer base and their business customer base. So whether that's mech, whether that's private mobility, um delivering data services over the top of those networks, there's a tremendous opportunity for the telecoms to be able to go and capture um Ed revenue opportunities and we're here to help them to partner with them to be able to do that. Now if you put yourself in the shoes of the customer, the enterprise business, a manufacturer or retail, who's looking to be able to leverage these technologies, there's a variety of ways in which they're going to be able to to to consume these technologies. In some cases they'll be getting it direct from vendors direct from Dell Technologies and others. They might be using solutions integrators to be able to combine these technologies together for a particular solution. They may get some of those technologies from their telecom provider and even others, they might get it from the cloud provider. So um Dell wants to make sure that we're being able to help our customers across a variety of ways in which they want to consume those technologies and we have to businesses focused on that. We've got one business focused on edge solutions where we partner with oT vendors closely as well as cloud providers to be able to provide a technology and infrastructure based on which we can consolidate edge workloads To be able to allow customers that want to be able to run those um those services on prem and by those from a direct vendor. Um there's other customers that want to get those through the telecoms. And so we work closely with the telecommunication providers to provide them that modern cloud native disaggregated network that they're looking to build to support 5G. And then help them build those services on the top that they can sell either way whether the customer wants to get that from a vendor like Dell or from a service provider like like uh like an A T and T and Verizon or others. Um Dell looks to partner with them and be a way to provide that underlying infrastructure that connects all of that together for them. >>Well, I mean the beauty of the telco networks is their hardened. But the problem for the telco networks is they're they're hardened and so you've got the over over the top vendors bow guarding their network. The cost per bit is coming down, data is going through the roof and the telcos can't, they can't participate in that over the top and get to those subscribers. But with Five G. And the technologies that you're talking about bringing to the telecoms world, they're they're gonna transform and many are going to start competing directly and this is just a whole new world out there. I wonder Aaron if you could talk about um what you're specifically talking about at Del Tech World this year as it relates to Edge. >>Sure. So the both of the businesses hedge in telecom have a couple announcements this year. This this year, Deltek World, um starting with Edge um as you may recall back in uh in in the fall of last year when we had our last technologies world, we announced our intent to launch an edge business. Um so that that was formulated and stood up over the last couple of months and and we're really focusing on a couple of different areas. How do we look at our overall Dell technologies portfolio and be able to bring particular products and solutions that exist already and be able to apply those uh to edge use cases. We're looking at building a platform which would allow us to be able to consolidate a variety of workloads. And of course we're working on partnerships specifically in the ot space to be able to vertical eyes these offers to help particular uh particular industries. Right now we're focusing on manufacturing and retail but we'll expand that over time. So at Del Tech World this year we're launching our first set of of solutions family which is going to be the Dell Technologies manufacturing edge solutions, the first one that's gonna be launching as a reference architecture with PTC um thing works on top of what we're also proud to be announcing this week, which is our apex private cloud offering. So this is the first example of of of a partnership with an O. T. Provider on top of apex private cloud so that we can bring in as a service platform offering to the Enterprise edge uh for manufacturers. And combined with one of the industry's leading oT software vendors of thing works. So that's one of the solutions were doing um we're also looking to launch a product which is we're taking our existing um streaming data platform from our unified storage team and taking that, which was once running in the data center out to edge these cases as well. And that allows us to be able to capture click stream data in manufacturing and other environments, buffer and cash that in a in an appliance and then be able to move that off to a data like for longer term analytics. While it's in that buffered state though we open provide a P. I. S. So that you can actually do real time influencing against those click stream data as it's flowing through the appliance on its way to the data lake for longer term analytics. So those are two key areas that we're gonna be focusing on from an edge perspective on the telecom side. Um we're really this is going to be a big year from us as we move towards creating a common end end five G platform from quarter Iran and then also start focusing on partnerships and ecosystems on top of that platform. Uh last week at Red hat summit we actually announced a reference architecture for red hat. Open shift on top of Dell technologies infrastructure servers and networking. And here at Dell technologies world. This week we're announcing a reference architecture with VM ware. So running VM ware telecom cloud platform. Also on top of Dell technologies. Power edge servers and power such as um so this allows us to create that foundation that open cloud native. These are container and virtual layers on top of our hard work to give that that cloud native disaggregated uh, network claim to be able to now run and build core edge and ran solutions on top of and you'll be hearing more about what we're doing in this space in the coming months. >>Nice. That's great. The open ran stuff is really exciting now, last question. So mobile world Congress, the biggest telco show is coming up in late june Yeah, still on. According to the G S M, a lot of people have tapped out um, and but the cube is planning to be there with a hybrid presence, both virtual and physical. We'll see um I wonder if there's anything you want to talk about just in terms of what's happening in telco telco transformation, you guys got any get any events coming up, what can you tell us? >>Yeah, so we took a close look at mobile world congress and and uh this has been a challenging year for everybody. Um you know, Dell as well as many other vendors made the decision this year that we would actually not participate, but we look forward to participating uh with full gusto next year when it's back in a physical environment. Um So what we've decided to do is we are going to be having our own virtual launch event on june 9th. Um And in that event, the theme of that is going to be the modern ecosystem in the neighboring leveraging the power of open. Um So we'll be talking a little bit more about what we're doing from that open cloud, native network infrastructure and then also talk a little bit more about what Dell technologies looking to do to bring a broad ecosystem of technology vendors together and deliver that ecosystem platform for the telecom industry. So registration actually opens this week at Dell Technologies World. So if you go to Dell technologies dot com can register for the event. Um we're really excited to be talking to the telecom providers and also other hardware and software vendors that are in that space to see how we can work together to really drive this next generation of five G. >>That's awesome. I'll be looking for that and and look forward to collaborating with you on that, bringing your thought leadership and the cube community we would really love to to partner on that. Aaron, thanks so much for coming to the cube. Really exciting area and best of luck to you. >>Right. Thank you. I appreciate the time. >>All right. And thank you for watching everybody says Dave Volonte for the Cubes, continuous coverage of Del Tech World 2021. The virtual version will be right back right after this short break.

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hello and welcome to this special CUBE Conversation here in the Palo Alto studios, I'm John Furrier, host of theCUBE, for this Leading with Observability series with Under the Hood with Splunk Observability, I'm John Furrier with Arijit Mukherji with Splunk, he's a distinguished engineer, great to have you on. These are my favorite talks. Under the Hood means we're going to get all the details, what's powering observability, thanks for coming on. >> It's my pleasure, John, it's always nice to talk to you. >> Leading with Observability is the series, want to take a deep dive look across the spectrum of the product, the problems that it's solving, but Under the Hood is a challenge, because, people are really looking at coming out of COVID with a growth strategy, looking at cloud-native, Kubernetes, you're starting to see microservices really be a big part of that, in real deployments, in real scale. This has been a theme that's been growing, we've been covering it. But now, architectural decisions start to emerge. Could you share your thoughts on this, because this becomes a big conversation. Do you buy a tool here, how do you think it through, what's the approach? >> Exactly, John. So it's very exciting times in some sense, with observability right now. So as you mentioned and discussed a few times, there's a bunch of trends that are happening in the industry which is causing a renewed interest in observability, and also an appreciation of the importance of it, and observability now as a topic, it's like a huge umbrella topic, it covers many many different things like APM, your infrastructure monitoring, your logging, your real user monitoring, your digital experience management, and so on. So it's quite a set of things that all fall under observability, and so the challenge right now, as you mentioned, is how do we look at this holistically? Because, I think at this point, it is so many different parts to this edifice, to this building, that I think having a non-integrated strategy where you just maybe go buy or build individual pieces, I don't think that's going to get you very far, given the complexity of what we're dealing with. And frankly, that's one of the big challenges that we, as architects within Splunk, we are scratching our heads with, is how do we sort of build all of this in a more coherent fashion? >> You know, one of the things, Arijit, I want to get your thoughts on is because, I've been seeing this trend and, we've been talking about it on theCUBE a lot around systems thinking, and if you look at the distributed computing wave, from just go back 20 years and look at the history of how we got here, a lot of those similar concepts are happening again, with the cloud, but not as simple. You're seeing a lot more network, I won't say network management, but observability is essentially instrumentation of the traffic and looking at all the data, to make sure things like breaches and cybersecurity, and also making systems run effectively, but it's distributed computing at the end of it, so there's a lot of science that's been there, and now new science emerging around, how do you do this all? What's your thoughts on this, because this becomes a key part of the architectural choices that some companies have to make, if they want to be in position to take advantage of cloud-native growth, which is multifold benefits, and your product people talk about faster time to market and all that good stuff, but these technical decisions matter, can you explain? >> Yes, it absolutely does. I think the main thing that I would recommend that everybody do, is understand why observability, what do you want to get out of it? So it is not just a set of parts, as I mentioned earlier, but it brings direct product benefits, as we mentioned, like faster mean time to resolution, understanding what's going on in your environment, having maybe fewer outages at the same time, understanding your causes, so many different benefits. So the point is not that one has the ability to do maybe (indistinct) or ability to do infrastructure (indistinct), the main question is aspirationally, what are my goals that are aligned to what my business wants? So what do I want to achieve, do I want to innovate faster? In that case, how is observability going to help me? And this is sort of how you need to define your strategy in terms of what kind of tools you get and how they work together. And so, if you look at what we're doing at Splunk, you'll notice it's extremely exciting right now, there's a lot of acquisitions happening, a lot of products that we're building, and the question we're asking as architects is, suppose we want to use, that will help us achieve all of this, and at the same time be somewhat future-proofed. And I think any organization that's either investing in it, or building it, or buying it, they all would probably want to think along those lines. Like what are my foundational principles, what are the basic qualities I want to have out of this system? Because technologies and infrastructures will keep on changing, that's sort of the rule of nature right now. The question is how do we best address it in a more future-proofed system? At Splunk, we have come up with a few guiding principles, and I'm sure others will have done the same. >> You know, one of the dynamics I want to get your reaction to is kind of two perspectives, one is, the growth of more teams that are involved in the work, so whether it's from cyber to monitoring, there's more teams with tools out there that are working on the network. And then you have just the impact of the diversity of use cases, not so much data volume, 'cause that's been talked about, lot of, we're having a tsunami of data, that's clear. But different kinds of dynamics, whether it's real-time, bursting, and so when you have this kind of environment, you can have gaps. And solar winds have taught us anything, it's that you have to identify problems and resolve them, this comes up a lot in observability conversations, MTTI, mean time to identify, and then to resolve. These are concepts. If you don't see the data, you can't understand what's going on if you can't measure it. This is like huge. >> Yes, absolutely right, absolutely right. So what we really need now is, as you mentioned, we need an integrated tool set, right? What we mean by that, is the tools must be able to work together, the data must be able to be used across the board. So like by use case it should not be siloed or fragmented, that they should work as one system that users are able to learn, and then sort of be able to use effectively without context switching. Another concept that's quite important is, how flexible are you? Are you digging yourself into a fixed solution, or are you depending on open standards that will then let you change out implementations, or vendors, or what have you, (static crackles) down the line, relatively easily. So understanding how you're collecting the data, how good can open standards and open source you're using is important. But to your point about missing and gaps, I think full fidelity, like understanding every single transaction, if you can pull it off, is a fascinating superpower, because that's where you don't get the gaps, and if you are able to go back and track any bad transaction, any time, that is hugely liberating, right? Because without that, if you're going to do a lot of sampling, you're going to miss a huge percentage of the user interactions, that's probably a recipe for some kind of trouble down the line, as you mentioned. And actually, these are some of those principles that we are using to build the Splunk Observability Suite, is no sample or full fidelity is a core foundational principle, and for us, it's not just isolated to, let's say application performance management, where user gets your API and you're able to track what happened, we are actually taking this upstream, up to the user, so the user is taking actions on the browser, how do we capture and correlate what's happening on the browser, because (indistinct) as you know, there's a huge move towards single-page applications, where half of my logic that my users are using is actually running on the browser, right? And so understanding the whole thing end to end, without any gaps, without any sampling, is extremely powerful. And so yes, so those are some of the things that we're investing in, and I think, again, one should keep in mind, when they're considering observability. >> You know, we were talking the other day, and having a debate around technical debt, and how that applies to observability, and one of the things that you brought up earlier about tools, and tool sprawl, that causes problems, you have operational friction, and we've heard people say "Yeah, I've got too many tools," and just too much, to replatform or refactor, it's just too much pain in the butt for me to do that, so at some point they break, I take on too much technical debt. When is that point of no return, where someone feels the pain on tool sprawl? What are some of the signaling where it's like, "You better move now (indistinct) too late," 'cause this integrated platform, that's what seems to be the way people go, as you mentioned. But this tool sprawl is a big problem. >> It is, and I think it starts hitting you relatively early on, nowadays, if you ask my opinion. So, tool sprawl is I think, if you find yourself, I think using three or four different tools, which are all part of some critical workload together, that's a stink that there's something could be optimized. For example, let's say I'm observing whether my website works fine, and if my alerting tool is different from my data gathering, or whatever, the infrastructure monitoring metrics tool, which is different from my incident management tool, which is different from my logs tool, then if you put the hat on of an engineer, a poor engineer who's dealing with a crisis, the number of times they have to context switch and the amount of friction that adds to the process, the delay that it adds to the process is very very painful. So my thinking is that at some point, especially if we find that core critical workloads are being fragmented, and that's when sort of I'm adding a bunch of friction, it's probably not good for us to sort of make that sort of keep on going for a while, and it would be time to address that problem. And frankly, having these tools integrated, it actually brings a lot of benefit, which is far bigger than the sum of the parts, because think about it, if I'm looking at, say, an incident, and if I'm able to get a cross-tool data, all presented in one screen, one UI, that is hugely powerful because it gives me all the information that I need without having to, again, dig into five different tools, and allows me to make quicker, faster decisions. So I think this is almost an inevitable wave that everybody must and will adopt, and the question is, I think it's important to get on the good program early, because unless you sort of build a lot of practices within an organization, that becomes very very hard to change later, it is just going to be more costly down the line. >> So from an (indistinct) standpoint, under the hood, integrated platform, takes that tool sprawl problem away, helps there. You had open source technology so there's no lock-in, you mentioned full fidelity, not just sampling, full end to end tracing, which is critical, wants to avoid those gaps. And then the other are I want to get your thoughts on, that you didn't bring up yet, that people are talking about is, real time streaming of analytics. What role does that play, is that part of the architecture, what function does that do? >> Right, so to me, it's a question of, how quickly do I find a problem? If you think about it, we are moving to more and more software services, right? So everybody's a software service now, and we all talk to each other in different services. Now, any time you use a dependency, you want to know how available it is, what are my SLAs and SLOs and so on, and three nines is almost a given, that you must provide three nines or better. Ideally four nines of availability, because your overall system stability is going to be less than the one of any single part, and if you go to look at four nines, you have about four or five minutes of total downtime in one whole month. That's a hard thing to be able to control. And if your alerting is going to be in order of five or 10 minutes, there's no chance you're going to be able to promise the kind of high availability that you need to be able to do, and so the fundamental question is you need to understand problems quick, like fast, within seconds, ideally. Now streaming is one way to do it, but that really is the problem definition, how do I find the problems early enough so that I can give my automation or my engineers time to figure out what happened and take corrective action? Because if I can't even know that there's something amiss, then there's no chance I'm going to be able to sort of provide that availability that my solution needs. So in that context, real time is very important, it is much more important now, because we have all these software and service dependencies, than it maybe used to be in the past. And so that's kind of why, again, at Splunk, we invested in real time streaming analytics, with the idea again being, let the problem, how can we address this, how can we provide customers with quick, high level important alerts in seconds, and that sort of real time streaming is probably the best way to achieve that. And then, if I were to, sorry, go ahead. >> No, go on, finish. >> Yeah, I was going to say that it's one thing to get an alert, but the question then is, now what do I do with it? And there's obviously a lot of alert noise that's going out, and people are fatigued, and I have all these alerts, I have this complex environment, understanding what to do, which is sort of reducing the MTTR part of it, is also important, I think environments are so complex now, that without a little bit of help from the tool, you are not going to be able to be very effective, it's going to take you longer, and this is also another reason why integrated tools are better, because they can provide you hints, looking at all the data, not just one type, not just necessarily logs, or not just necessarily traces, but they have access to the whole data set, and they can give you far better hints, and that's again one of the foundational principles, because this is in the emergent field of AIOps, where the idea is that we want to bring the power of data science, the power of machine learning, and to aid the operator in figuring out where a problem might be, so that they can at least take corrective action faster, not necessarily fix it, but at least bypass the problem, or take some kind of corrective action, and that's a theme that sort of goes across our suite of tools is, the question we ask ourselves is, "In every situation, what information could I have provided them, what kind of hints could we have provided them, to short circuit their resolution process?" >> It's funny you mention suite of tools, you have an Observability Suite, which Splunk leads with, as part of the series, it's funny, suite of tools, it's kind of like, you kind of don't want to say it, but it is kind of what's being discussed, it's kind of a platform and tool working together, and I think the trend seems to be, it used to be in the old days, you were a platform player or a tool player, really kind of couldn't do both, but now with cloud-native, as it's distributed computing, with all this importance around observability, you got to start thinking, suite has platform features, could you react to that, and how would you talk about that, because what does it mean to be a platform? Platforms have benefits, tools have benefits, working together implies it's a combination, could you share your thoughts on that reaction to that? >> That's a very interesting question you asked, John, so this is actually, if you asked me how I look at the solution set that we have, I will explain it thus. We are a platform, we are a set of products and tools, and we are an enterprise solution. And let me explain what I mean by that, because I think all of these matter, to somebody or the other. As a platform, you're like "How good am I in dealing with data?" Like ingesting data, analyzing data, alerting you, so those are the core foundational features that everybody has, these are the database-centric aspects of it, right? And if you look at a lot of organizations who have mature practices, they are looking for a platform, maybe it scales better than what they have, or whatnot, and they're looking for a platform, they know what to do, build out on top of that, right? But at the same time, a platform is not a product, 99% of our users, they're not going to make database calls to fetch and query data, they want an end to end, like a thing that they can use to say, "Monitor my Kubernetes," "Monitor my Elasticsearch," "Monitor my," you know, whatever other solution I may have. So then we build a bunch of products that are built on top of the platform, which provide sort of the usability, so where, it's very easy to get on, send the data, have built-in content, dashboard (indistinct), what have you, so that my day to day work is fast, because I'm not a observability engineer, I'm a software engineer working on something, and I want to use observability, make it easy for me, right? So that's sort of the product aspect of it. But then if you look at organizations that a little bit scale up, just a product is also not good enough. Now we're looking at a observability solution that's deployed in an enterprise, and there are many many products, many many teams, many many users, and then how can one be effective there? And if you look at what's important at that level, it's not the database aspect or the platform aspect, it's about how well can I manage it, do I have visibility into what I am sending, what my bill is, can I control against incorrect usage, do I have permissions to sort of control who can mess with my (indistinct) and so on, and so there's a bunch of layer of what we call enterprise capabilities that are important in an organizational setting. So I think in order to build something that's successful in this space, we have to think at all these three levels, right? And all of these are important, because in the end, it's how much value am I getting out of it, it's not just what's theoretically possible, what's really happening, and all of these are important in that context. >> And I think, Arijit, that's amazing masterclass right there, soundbite right there, and I think it's because the data also is important, if you're going to be busting down data silos, you need to have a horizontally scalable data observability space. You have to have access to the data, so I think the trend will be more integrated, clearly, and more versatile from a platform perspective, it has to be. >> Absolutely, absolutely. >> Well, we're certainly going to bring you back on our conversations when we have our events and/or our groups around digital transformation Under the Hood series that we're going to do, but great voice, great commentary, Arijit, thank you for sharing that knowledge with us, appreciate it. >> My pleasure, thank you very much. >> Okay, I'm John Furrier with theCUBE, here, Leading with Observability content series with Splunk, I'm John Furrier with theCUBE, thanks for watching. (calm music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Welcome to the cubes event. Virtual event. Cuban Cloud. I'm John for your host. We're here talking to all the thought leaders getting all the stories around Cloud What's going on this year and next today, Tomorrow and the future. We gotta featured startup here. Jonah Clipper, who is the CEO and founder of Stack Hawks. Developing security software for developers to have them put security baked in from the beginning. Johnny, thanks for coming on and being featured. Start up here is part of our Cuban cloud. Thanks for joining. >>Thanks so much for having me, John. >>So one of our themes this year is obviously Cloud natives gone mainstream. The pandemic has shown that. You know, a lot of things have to be modern. Modern applications, the emerald all they talked about modern applications. Infrastructure is code. Reinvent, um is here. They're talking about the next gen enterprise. Their public cloud. Now you've got hybrid cloud. Now you've got multi cloud. But for developers, you just wanna be building security baked in and they don't care where the infrastructure is. So this is the big trend. Like to get your thoughts on that. But before we jump in, tell us about Stack Hawk What you guys do your founded in 2019. Tell us about your company and what Your mission is >>Awesome. Yeah, our mission is to put application security in the hands of software developers so that they can find and fix upset books before they deployed a production. And we do that through a dynamic application scanning capability. Uh, that's deployable via docker, so engineers can run it locally. They can run it in C I C. D. On every single PR or merge and find bugs in the process of delivering software rather than after it's been production. >>So everyone's talking about shift left, shift left for >>security. What does >>that mean? Uh, these days. And what if some of the hurdles that people are struggling with because all I hear is shift left shift left from, like I mean, what does What does that actually mean? Now, Can you take us through your >>view? Yes, and we use the phrase a lot, and I and I know it can feel a little confusing or overused. Probably. Um, When I think of shift left, I think of that Mobius that we all look at all of the time, Um, and how we deliver and, like, plan, write code, deliver software and then manage it. Monitor it right like that entire Dev ops workflow. And today, when we think about where security lives, it either is a blocker to deploying production. Or most commonly, it lives long after code has been deployed to production. And there's a security team constantly playing catch up, trying to ensure that the development team whose job is to deliver value to their customers quickly, right, deploy as fast as we can, as many great customer facing features, um there, then, looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are. And, um, trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as their writing code or in the CIA CD pipeline long before code has been deployed to production. >>And so you guys attack that problem right there so they don't have to ship the code and then come back and fix it again. Or where we forgot what the hell is going on. That point in time some Q 18 gets it. Is that the kind of problem that that's out there? Is that the main pain point? >>Yeah, absolutely. I mean a lot of the way software, specifically software like ours and dynamic applications scanning works is a security team or a pen tester. Maybe, is assessing applications for security vulnerability these, um, veteran prod that's normally where these tools are run and they throw them back over the wall, you know, interrupting sprints and interrupting the developer workflow. So there's a ton of context switching, which is super expensive, and it's very disruptive to the business to not know about those issues before they're in prod. And they're also higher risk issues because they're in fraud s. So you have to be able to see a >>wrong flywheel. Basically, it's like you have a penetration test is okay. I want to do ship this app. Pen test comes back, okay? We gotta fix the bug, interrupts the cycle. They're not coding there in fire drill mode. And then it's a chaotic death spiral at that point, >>right? Or nothing gets done. God, how did >>you What was the vision? How did you get here? What? How did you start? The company's woke up one morning. Seven started a security company. And how did what was the journey? What got you here? >>Sure. Thanks. I've been building software for software engineers since 2010. So the first startup I worked for was very much about making it easy for software engineers to deploy and manage applications super efficiently on any cloud provider. And we did programmatic updates to those applications and could even move them from cloud to cloud. And so that was sort of cutting my teeth and technology and really understanding the developer experience. Then I was a VP of product at a company called Victor Ops. We were purchased by spunk in 2018. But that product was really about empowering software engineers to manage their own code in production. So instead of having a network operations center right who sat in front of screens and was waiting for something to go wrong and would then just end up dialing there, you know, just this middle man trying to dial to find the person who wrote the software so that they can fix it. We made that way more efficient and could just route issues to software engineers. And so that was a very dev ops focused company in terms of, um, improving meantime to know and meantime to resolve by putting up time in the hands of software engineers where it didn't used to live there before it lived in a more traditional operations type of role. But we deploy software way too quickly and way too frequently to production to assume that another human can just sit there and know how to fix it, because the problems aren't repeatable, right? So So I've been living in the space for a long time, and I would go to conferences and people would say, Well, I love for, you know, we have these digital transformation initiatives and I'm in the security team and I don't feel like I'm part of this. I don't know. I don't know how to insert myself in this process. And so I started doing a lot of research about, um, how we can shift this left. And I was actually doing some research about penetration testing at the time, Um, and found just a ton of opportunity, a ton of problems, right that exist with security and how we do it today. So I really think of this company as a Dev Ops first Company, and it just so happens to be that we're taking security, and we're making it, um, just part of the the application testing framework, right? We're testing for security bugs, just like we would test for any other kind of bucks. >>That's an awesome vision of other great great history there. And thanks for sharing that. I think one of the things that I think this ties into that we have been reporting aggressively on is the movement to Dev Stack Up, Dev, Ops Dev SEC Ops. And you know, just doing an interview with the guy who stood up space force and big space conversation and were essentially riffing on the idea that they have to get modern. It's government, but they got to do more commercial. They're using open source. But the key thing was everything. Software defined. And so, as you move into suffer defined, then they say we want security baked in from the beginning and This is the big kind of like sea level conversation. Bake it in from the beginning, but it's not that easy. And this is where I think it's interesting where you start to think, uh, Dev ops for security because security is broken. So this is a huge trend. It sounds easy to say it baked security in whether it's an i o T edge or multi cloud. There's >>a lot >>of work there. What should people understand when they hear that kind of platitude of? I just baked security and it's really easy. It's not. It's not trivial. What's your thoughts on >>that? It isn't trivial. And in my opinion, there aren't a lot of tools on the market that actually make that very easy. You know, there are some you've had sneak on this program and they're doing an excellent job, really speaking to the developer and being part of that modern software delivery workflow. Um, but because a lot of tools were built to run in production, it makes it really difficult to bake them in from the beginning. And so, you know, I think there are several goals here. One is you make the tooling work so that it works for the software engineer and their workflow. And and there's some different values that we have to consider when its foreign engineer versus when it's for a security person, right? Limit the noise, make it as easy as possible. Um, make sure that we only show the most critical things that are worth an engineer. Stopping what they're doing in terms of building business value and going back and fixing that bugs and then create a way to discuss in triage other issues later outside of the development. Workflow. So you really have to have a lot of empathy and understanding for how software is built and how software engineers behave, I think, in order to get this right. So it's not easy. Um, but we're here and other tools air here. Thio support companies in doing that. >>What's the competitive strategy for you guys going forward? Because there's a big sea change. Now I see an inflection point. Obviously, Cove it highlights. It's not the main reason, but Cloud native has proven it's now gone mainstream kubernetes. You're seeing the big movement there. You're seeing scale be a huge issue. Software defined operations are now being discussed. So I think it's It's a simple moment for this kind of solution. How are you guys going to compete? What's what's the winning strategy? How are you guys gonna compete to win? >>Yeah, so there's two pieces to that one is getting the technology right and making sure that it is a product that developers love. And we put a ton of effort into that because when a software engineer says, Hey, I'd love to use the security product, right? CSOs around the world are going to be like, Yes, please. Did a software engineer just ask me, You have the security product. Thank you, Right. We're here to make it so easy for them and get the tech right. And then the other piece, in terms of being competitive, is the business model. There were something like, I don't You would know better than me, but I think the data point I last saw was like 1300 venture backed security companies since 2012 focused on selling to see SOS and Fortune 2000 companies. It is a mess. It's so noisy, nobody can figure out what anybody actually does. What we have done is said no, we're going to take a modern business model approach to security. So you know, it's a SAS platform that makes it super easy for a software engineer or anybody on the team to try and buy the software. So 14 day trial. You don't have to talk to anybody if you don't want Thio Awesome support to make sure that people can get on boarded and with our on boarding flow, we've seen that our customers go from signing up to first successful scan of their platform or whatever app they chose to scan in a knave ridge of about 10 minutes. The fastest is eight, right? So it's about delivering value to our customers really quickly. And there aren't many companies insecurity on the market today. That do that? >>You know, you mentioned pen test earlier. I I hear that word. Nice shit. And, like, pen test penetration test, as it's called, um, Sock reports. I mean, these are things that are kind of like I got to do that again. I know these people are doing things that are gonna be automated, but one of the things that cloud native has proven as be killer app is integrations because when you build a modern app, it has to integrate with someone else. So there you need these kind of pen tests. You gotta have this kind of code review. And as code, um, is part of, say, a purpose built device where it's an I o T. Edge updates have toe happen. So you need mawr automation. You need more scale around both updating software to, ah, purpose built device or for integration. What's your thoughts in reaction to that? Because this is a riel software challenge from a customer standpoint, because there are too many tools out there and every see so that I talk to says, I just want to get rid of half the tools consolidate down around my clouds that I'm working through my environment and b'more developer oriented, not just purchasing stuff. So you have all this going on? What's your reaction to that? You got the you know, the integration and you've got the software updates on purpose built devices. >>Yeah, I mean, we I make a joke a little bit. That security land is like, you know, acronyms. Dio there are so many types of security that you could choose to implement. And they all have a home and different use cases that are certainly valuable toe organizations. Um, what we like to focus on and what we think is interesting and dynamic application scanning is because it's been hard toe automate dynamic application for especially for modern applications. I think a lot of companies have ignored theon pertuan ity Thio really invest in this capability and what's cool about dynamic. And you were mentioning pen testing. Is that because it's actively attacking your app? It when you get a successful test, it's like a It's like a successful negative test. It's that the test executed, which means that bug is present in your code. And so there's a lot less false positives than in other types of scanning or assessment technologies. Not to say there isn't a home for them. There's a lot of we could we could spend a whole hour kind of breaking down all the different types of bugs that the different tools confined. Um, but we think that if you want to get started developer first, you know there's a lot of great technologies. Pick a couple or one right pick stack hawk pick, sneak and just get started and put it in your developer workflow. So integrations are super important. Um, we have integrations with every C I C. D provider, making it easy to scan your code on every merge or release. And then we also have workflow integrations for software engineers associated with where they want to be doing work and how they want to be interrupted or told about an issue. So, you know, we're very early to market, but right out of the gate, we made sure that we had a slack integration so that scans are running. Or as we're finding new things, it's populating in a specific slack channel for those engineers who work on that part of the app and you're a integration right. If we find issues, we can quickly make tickets and route them and make sure that the right people are working on those issues. Eso That's how I think about sort of the integration piece and just getting started. It's like you can't tackle the whole like every accurate, um, at once like pick something that helps you get started and then continue to build out your program, as you have success. >>A lot of these tools can they get in the hands of developers, and then you kind of win their trust by having functionality. Uh, certainly a winning strategy we've seen. You know, Splunk, you mentioned where you worked for Data Dog and very other tools out there just get started easily. If it's good, it will be used. So I love that strategy. Question. I wanna ask you mentioned Dr earlier. Um, they got a real popular environment, but that speaks to the open source area. How do you see the role of open source playing with you guys? Is that gonna be part of your community outreach? Does the feed into the product? Could you share your vision on how stack hawks engaging and playing an open source? >>Yeah, absolutely. Um So when we started this company, my co founders and I, we sat down and said here, What are the problems? Okay, the world doesn't need a better scanner, right? If you walk the floor of, ah, security, uh, conference. It's like our tool finds a million things and someone else is. My tool finds a million and five things. Right, And that's how they're competing on value. It's really about making it easy to use and put in the pipeline. So we decided not to roll. Our own scanner were based on an open source capability called Zap the Set Attack Proxy. Uh, it is the most the world's most downloaded application scanner. And, uh, actually we just hired the founder of Zap to join the Stack Hawk team, and we're really excited to continue to invest in the open source community. There is a ton of opportunity to grow and sort of galvanize that community. And then the work that we do with our customers and the feedback that we get about the bugs we find if there, ah, false positive or this one's commonly risk accepted, we can go back to the community, which were already doing and saying, Hey, ditch this rule, Nobody likes it or we need to improve this test. Um, so it's a really nice relationship that we have, and we are looking forward to continuing to grow that >>great stuff. You guys are hot. Start of love. The software on security angle again def sec. Cox is gonna be It's gonna be really popular. Can you talk about some of the customer success is What's the What's the feedback from customers? Can you share some of the use cases that you guys are participating in where you're winning? You mentioned developers love it and try It can just give us a couple of use cases and examples. >>Yeah. Ah, few things. Um ah, lot of our customers are already selling on the notion. Like before we even went to G A right. They told all of their customers that they scan for security bugs with every single release. So in really critical, uh, industry is like fintech, right. It's really important that their customers trust that they're taking security seriously, which everybody says they dio. But they show it to their customers by saying here, every single deploy I can show you if there were any new security bugs released with that deploy. So that's really awesome. Other things We've heard our, uh, people being able to deploy really quickly thio the Salesforce marketplace, right? Like if they have toe have a scan to prove that that they can sell on Salesforce, they do that really rapidly. Eso all of that's going really well with our customers. >>How would I wanna How would I be a customer if I was interested in, um, using Stack Hawks say we have some software we wanna stand up, and, uh, it's super grade. And so Amazon Microsoft Marketplace Stairs Force They'll have requirements or say I want to do a deal with an integration they don't want. They want to make sure there's no nothing wrong with the code. This seems to be a common use case. How doe I if I was a customer, get involved or just download software? Um, what's the What's the procurement? What's the consumption side of it looked like, >>Yeah, you just go to Stockholm dot com and you create an account. If you'd like to get started that way so you can have a 14 day free trial. We have extremely extensive documentation, so it's really easy to get set up that way. You should have some familiarity. Or grab a software engineer who has familiarity with a couple of things. So one is how to use Docker, right? So Docker is, ah, deployment mechanism for the scanner. We do that so you can run it anywhere that you would like to, and we don't have to do things like pierce firewalls or other protective measures that you've instrumented on your production environment. You just run it, um, wherever you like in your system. So locally, C I c d So docker is an important thing to understand the way we configure our scanner is through a, um, a file. So if you are getting a scan today, either your security team is doing it or you have a pen tester doing it. Um, the whole like getting ready for that engagement takes a lot of time because the people who are running the tests don't know how the software was built. So the way we think about this is, just ask them. So you just fill out a Yamil file with parameters that tell the scanner what to dio tell it how to authenticate and not log out. Um, feed us an A p. I speak if you want, so weaken super efficiently, scan your app and you can be up and running really quickly, and then that's it. You can work with our team at any time if you need help, and then we have a really efficient procurement process >>in my experience some of the pen tests of firms out there, is it? It's like the house keeping seal of approval. You get it once and then you gotta go back again. Software change, new things come in. And it's like, Wait a minute, what's the new pen test? And then you to write a check or engaged to have enough meeting? I mean, this is the problem. I mean, too many meetings. Do you >>guys solve that problem? Do >>you solve that problem? >>We solve a piece of that problem. So I think you know, part of how I talk about our company is this idea that we live in a world where we deploy software every single day. Yet it seems reasonable that once a year or twice a year, we go get a pen test where human runs readily available, open source software on our product and gives us a like, quite literal. Pdf of issues on. It's like this is so intellectually dishonest, like we deploy all of the time. So here's the thing. Pen tests are important and everybody should do them. But that should not be the introduction to these issues that are also easy to automate and find in your system. So the way we think about how we work with pen testers is, um, run, stack hawk or zapped right in an automated fashion on your system, and then give that, give the configuration and give the most recent results to your pen tester and say, Go find the hard stuff. You shouldn't be cutting checks for $30,000 to a pen tester or something that you could easily meet in your flare up. Klein. You could write the checks for finding finding the hard stuff that's much more difficult to automate. >>I totally agree. Final question. Business model Once I get in, is it a service software and services? A monthly fee? How do you guys make money? >>Yep, it is software as a service, it is. A monthly fee were early to market. So I'm not going to pretend that we have perfectly cracked the pricing. Um, but the way that we think about this is this is a team product for software engineers and for, you know, informed constituents, right? You want a product person in the product. You want a security person in the product? Um, and we also want to incent you to scan your APS And the most modern fashion, which is scanning the smallest amount of http that lives in your app, like in a micro services architecture because it makes a lot easier, is easy to isolate the problems where they live and to fix those issues really quickly. So we bundle team and for a UPS and then we scale within, uh, companies as they add more team. So pen users. 10 APS is 3 99 a month. And as you add software engineers and more applications, we scale within your company that way. >>Awesome. So if you're successful, you pay more, but doesn't matter. You already succeeded, and that's the benefit of by As you go Great stuff. Final question. One more thing. Your vision of the future. What are the biggest challenges you see in the next 24 months? Plus beyond, um, that you're trying to attack? That's a preferred future that you see evolving. What's the vision? >>Yeah, you've touched on this a couple of times in this interview with uh being remote, and the way that we need to build software already has been modernizing, and I feel like every company has a digital transformation initiative, but it has toe happen faster. And along with that, we have to figure out how Thio protect and secure these Moderna Gail. The most important thing that we do the hearts and minds of our support engineers and make it really easy for them to use security capabilities and then continue to growth in the organization. And that's not an easy thing tied off. It's easy change, a different way of being security. But I think we have to get their, uh, in order to prepare the security, uh, in these rapidly deployed and developed applications that our customers expect. >>Awesome. Jodi Clippers, CEO and founder of Stack Hawk. Thank you for coming on. I really appreciate it. Thanks for spending the time featured Startup is part of our Cuban cloud. I'm Sean for your host with silicon angle to Cube. Thanks for watching

>> From around the globe, it's theCUBE, with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We are celebrating Exascale Day. 10, 18, I think it's the second year of celebrating Exascale Day, and we're really excited to have our next guest and talk about kind of what this type of compute scale enables, and really look a little bit further down the road at some big issues, big problems and big opportunities that this is going to open up. And I'm really excited to get in this conversation with our next guest. He is Bryan Pijanowski the Professor of Landscape and Soundscape Ecology at Purdue University. Bryan, great to meet you. >> Great to be here. >> So, in getting ready for this conversation, I just watched your TED Talk, and I just loved one of the quotes. I actually got one of quote from it that's basically saying you are exploring the world through sound. I just would love to get a little deeper perspective on that, because that's such a unique way to think about things and you really dig into it and explain why this is such an important way to enjoy the world, to absorb the world and think about the world. >> Yeah, that's right Jeff. So the way I see it, sound is kind of like a universal variable. It exists all around us. And you can't even find a place on earth where there's no sound, where it's completely silent. Sound is a signal of something that's happening. And we can use that information in ways to allow us to understand the earth. Just thinking about all the different kinds of sounds that exist around us on a daily basis. I hear the birds, I hear the insects, but there's just a lot more than that. It's mammals and some cases, a lot of reptiles. And then when you begin thinking outside the biological system, you begin to hear rain, wind, thunder. And then there's the sounds that we make, sounds of traffic, the sounds of church bells. All of this is information, some of it's symbolic, some of it's telling me something about change. As an ecologist that's what I'm interested in, how is the earth changing? >> That's great and then you guys set up at Purdue, the Purdue Center for Global Soundscapes. Tell us a little bit about the mission and some of the work that you guys do. >> Well, our mission is really to use sound as a lens to study the earth, but to capture it in ways that are meaningful and to bring that back to the public to tell them a story about how the earth kind of exists. There's an incredible awe of nature that we all experience when we go out and listen into to the wild spaces of the earth. I've gone to the Eastern Steppes of Mongolian, I've climbed towers in the Paleotropics of Borneo and listened at night. And ask the question, how are these sounds different? And what is a grassland really supposed to sound like, without humans around? So we use that information and bring it back and analyze it as a means to understand how the earth is changing and really what the biological community is all about, and how things like climate change are altering our spaces, our wild spaces. I'm also interested in the role that people play and producing sound and also using sound. So getting back to Mongolia, we have a new NSF funded project where we're going to be studying herders and the ways in which they use sonic practices. They use a lot of sounds as information sources about how the environment is changing, but also how they relate back to place and to heritage a special sounds that resonate, the sounds of a river, for example, are the resonance patterns that they tune their throat to that pay homage to their parents that were born at the side of that river. There's these special connections that people have with place through sound. And so that's another thing that we're trying to do. In really simple terms, I want to go out and, what I call it sounds rather simple, record the earth-- >> Right. >> What does that mean? I want to go to every major biome and conduct a research study there. I want to know what does a grassland sound like? What is a coral reef sound like? A kelp forest and the oceans, a desert, and then capture that as baseline and use that information-- >> Yeah. >> For scientific purposes >> Now, there's so much to unpack there Bryan. First off is just kind of the foundational role that sound plays in our lives that you've outlined in great detail and you talked about it's the first sense that's really activated as we get consciousness, even before we're born right? We hear the sounds of our mother's heartbeat and her voice. And even the last sense that goes at the end a lot of times, in this really intimate relationship, as you just said, that the sounds represent in terms of our history. We don't have to look any further than a favorite song that can instantly transport you, almost like a time machine to a particular place in time. Very, very cool. Now, it's really interesting that what you're doing now is taking advantage of new technology and just kind of a new angle to capture sound in a way that we haven't done before. I think you said you have sound listening devices oftentimes in a single location for a year. You're not only capturing sound, the right sound is changes in air pressure, so that you're getting changes in air pressure, you're getting vibration, which is kind of a whole different level of data. And then to be able to collect that for a whole year and then start to try to figure out a baseline which is pretty simple to understand, but you're talking about this chorus. I love your phrase, a chorus, because that sound is made up of a bunch of individual inputs. And now trying to kind of go under the covers to figure out what is that baseline actually composed of. And you talk about a bunch of really interesting particular animals and species that combine to create this chorus that now you know is a baseline. How did you use to do that before? I think it's funny one of your research papers, you reach out to the great bird followers and bird listeners, 'cause as you said, that's the easiest way or the most prolific way for people to identify birds. So please help us in a crowdsource way try to identify all the pieces that make this beautiful chorus, that is the soundscape for a particular area. >> Right, yeah, that's right. It really does take a team of scientists and engineers and even folks in the social sciences and the humanities to really begin to put all of these pieces together. Experts in many fields are extremely valuable. They've got great ears because that's the tools that they use to go out and identify birds or insects or amphibians. What we don't have are generalists that go out and can tell you what everything sounds like. And I'll tell you that will probably never ever happen. That's just way too much, we have millions of species that exist on this planet. And we just don't have a specific catalog of what everything sounds like, it's just not possible or doable. So I need to go out and discover and bring those discoveries back that help us to understand nature and understand how the earth is changing. I can't wait for us to eventually develop that catalog. So we're trying to develop techniques and tools and approaches that allow us to develop this electronic catalog. Like you're saying this chorus, and it doesn't necessarily have to be a species specific chorus, it can be a chorus of all these different kind of sounds that we think relate back to this kind of animal or that kind of animal based upon the animals instrument-- >> Right, great. >> And this is the sound. >> Now again, you know, keep it to the exascale theme, right? You're collecting a lot of data and you mentioned in one of the pieces I've dug up, that your longest study in a single location is 17 years. You've got over 4 million recordings. And I think you said over 230 years if you wanted to listen to them all back to back. I mean, this is a huge, a big data problem in terms of the massive amount of data that you have and need to run through an analysis. >> Yeah, that's right. We're collecting 48,000 data points per second. So that's 48 kilohertz. And then so you multiply everything and then you have a sense of how many data points you actually have to put them all together. When you're listening to a sound file over 10 minutes, you have hundreds of sounds that exist in them. Oftentimes you just don't know what they are, but you can more or less put some kind of measure on all of them and then begin to summarize them over space and time and try to understand it from a perspective of really science. >> Right, right. And then I just love to get your take as you progress down this kind of identification road, we're all very familiar with copyright infringement hits on YouTube or social media or whatever, when it picks up on some sound and the technology is actually really sophisticated to pick up some of those sound signatures. But to your point, it's a lot easier to compare against the known and to search for that known. Then when you've got this kind of undefined chorus that said we do know that there can be great analysis done that we've seen AI and ML applied, especially in the surveillance side on the video-- >> Right. >> With video that it can actually do a lot of computation and a lot of extracting signal from the noise, if you will. As you look down the road on the compute side for the algorithms that you guys are trying to build with the human input of people that know what you're listening to, what kind of opportunities do you see and where are we on that journey where you can get more leverage out of some of these technology tools? >> Well, I think what we're doing right now is developing the methodological needs, kind of describe what it is we need to move into that new space, which is going to require these computational, that computational infrastructure. So, for example, we have a study right now where we're trying to identify certain kinds of mosquitoes (chuckling) a vector-borne mosquitoes, and our estimates is that we need about maybe 900 to 1200 specific recordings per species to be able to put it into something like a convolutional neural network to be able to extract out the information, and look at the patterns and data, to be able to say indeed this is the species that we're interested in. So what we're going to need and in the future here is really a lot of information that allow us to kind of train these neural networks and help us identify what's in the sound files. As you can imagine the computational infrastructure needed to do that for data storage and CPU, GPU is going to be truly amazing. >> Right, right. So I want to get your take on another topic. And again the basis of your research is really all bound around the biodiversity crisis right? That's from the kind of-- >> Yeah. >> The thing that's started it and now you're using sound as a way to measure baseline and talk about loss of species, reduced abundancies and rampant expansion of invasive species as part of your report. But I'd love to get your take on cities. And how do you think cities fit the future? Clearly, it's an efficient way to get a lot of people together. There's a huge migration of people-- >> Right. >> To cities, but one of your themes in your Ted Talk is reconnecting with nature-- >> Yeah. >> Because we're in cities, but there's this paradox right? Because you don't want people living in nature can be a little bit disruptive. So is it better to kind of get them all in a tip of a peninsula in San Francisco or-- >> Yeah. >> But then do they lose that connection that's so important. >> Yeah. >> I just love to get your take on cities and the impacts that they're have on your core research. >> Yeah, I mean, it truly is a paradox as you just described it. We're living in a concrete jungle surrounded by not a lot of nature, really, honestly, occasional bird species that tend to be fairly limited, selected for limited environments. So many people just don't get out into the wild. But visiting national parks certainly is one of those kinds of experience that people oftentimes have. But I'll just say that it's getting out there and truly listening and feeling this emotional feeling, psychological feeling that wraps around you, it's a solitude. It's just you and nature and there's just no one around. >> Right. >> And that's when it really truly sinks in, that you're a part of this place, this marvelous place called earth. And so there are very few people that have had that experience. And so as I've gone to some of these places, I say to myself I need to bring this back. I need to tell the story, tell the story of the awe of nature, because it truly is an amazing place. Even if you just close your eyes and listen. >> Right, right. >> And it, the dawn chorus in the morning in every place tells me so much about that place. It tells me about all the animals that exist there. The nighttime tells me so much too. As a scientist that's spent most of his career kind of going out and working during the day, there's so much happening at night. Matter of fact-- >> Right. >> There's more sounds at night than there were during the day. So there is a need for us to experience nature and we don't do that. And we're not aware of these crises that are happening all over the planet. I do go to places and I listen, and I can tell you I'm listening for things that I think should be there. You can listen and you can hear the gaps, the gaps and that in that chorus, and you think what should be there-- >> Right. >> And then why isn't it there? And that's where I really want to be able to dig deep into my sound files and start to explore that more fully. >> It's great, it's great, I mean, I just love the whole concept of, and you identified it in the moment you're in the tent, the thunderstorm came by, it's really just kind of changing your lens. It's really twisting your lens, changing your focus, because that sound is there, right? It's been there all along, it's just, do you tune it in or do you tune it out? Do you pay attention? Do not pay attention is an active process or a passive process and like-- >> Right. >> I love that perspective. And I want to shift gears a little bit, 'cause another big environmental thing, and you mentioned it quite frequently is feeding the world's growing population and feeding it-- >> Yeah. >> In an efficient way. And anytime you see kind of factory farming applied to a lot of things you wonder is it sustainable, and then all the issues that come from kind of single output production whether that's pigs or coffee or whatever and the susceptibility to disease and this and that. So I wonder if you could share your thoughts on, based on your research, what needs to change to successfully and without too much destruction feed this ever increasing population? >> Yeah, I mean, that's one of the grand challenges. I mean, society is facing so many at the moment. In the next 20 years or so, 30 years, we're going to add another 2 billion people to the planet, and how do we feed all of them? How do we feed them well and equitably across the globe? I don't know how to do that. But I'll tell you that our crops and the ecosystem that supports the food production needs the animals and the trees and the microbes for the ecosystem to function. We have many of our crops that are pollinated by birds and insects and other animals, seeds need to be dispersed. And so we need the rest of life to exist and thrive for us to thrive too. It's not an either, it's not them or us, it has to be all of us together on this planet working together. We have to find solutions. And again, it's me going out to some of these places and bringing it back and saying, you have to listen, you have to listen to these places-- >> Right. >> They're truly a marvelous. >> So I know most of your listening devices are in remote areas and not necessarily in urban areas, but I'm curious, do you have any in urban areas? And if so, how has that signature changed since COVID? I just got to ask, (Bryan chuckling) because we went to this-- >> Yeah. >> Light switch moment in the middle of March, human activity slowed down-- >> Yeah. >> In a way that no one could have forecast ever on a single event, globally which is just fascinating. And you think of the amount of airplanes that were not flying and trains that we're not moving and people not moving. Did you have any any data or have you been able to collect data or see data as the impact of that? Not only directly in wherever the sensors are, but a kind of a second order impact because of the lack of pollution and the other kind of human activity that just went down. I mean, certainly a lot of memes (Bryan chuckling) on social media of all the animals-- >> Yeah. >> Come back into the city. But I'm just curious if you have any data in the observation? >> Yeah, we're part of actually a global study, there's couple of hundred of us that are contributing our data to what we call the Silent Cities project. It's being coordinated out of Europe right now. So we placed our sensors out in different areas, actually around West Lafayette area here in Indiana, near road crossings and that sort of thing to be able to kind of capture that information. We have had in this area here now, the 17 year study. So we do have studies that get into areas that tend to be fairly urban. So we do have a lot of information. I tell you, I don't need my sensors to tell me something that I already know and you suspect is true. Our cities were quiet, much quieter during the COVID situation. And it's continued to kind of get a little bit louder, as we've kind of released some of the policies that put us into our homes. And so yes, there is a major change. Now there have been a couple of studies that just come out that are pretty interesting. One, which was in San Francisco looking at the white-crowned sparrow. And they looked at historical data that went back something like 20 years. And they found that the birds in the cities were singing a much softer, 30% softer. >> Really? >> And they, yeah, and they would lower their frequencies. So the way sound works is that if you lower your frequencies that sound can travel farther. And so the males can now hear themselves twice as far just due to the fact that our cities are quieter. So it does have an impact on animals, truly it does. There was some studies back in 2001, during  the September, the 9/11 crisis as well, where people are going out and kind of looking at data, acoustic data, and discovering that things were much quieter. I'd be very interested to look at some of the data we have in our oceans, to what extent are oceans quieter. Our oceans sadly are the loudest part of this planet. It's really noisy, sound travels, five times farther. Generally the noise is lower frequencies, and we have lots of ships that are all over the planet and in our oceans. So I'd really be interested in those kinds of studies as well, to what extent is it impacting and helping our friends in the oceans. >> Right, right, well, I was just going to ask you that question because I think a lot of people clearly understand sound in the air that surrounds us, but you talk a lot about sound in ocean, and sound as an indicator of ocean health, and again, this concept of a chorus. And I think everybody's probably familiar with the sounds of the humpback whale right? He got very popular and we've all seen and heard that. But you're doing a lot of research, as you said, in oceans and in water. And I wonder if you can, again, kind of provide a little bit more color around that, because I don't think you people, maybe we're just not that tuned into it, think of the ocean or water as a rich sound environment especially to the degree as you're talking about where you can actually start to really understand what's going on. >> Yeah, I mean, some of us think that sound in the oceans is probably more important to animals than on land, on the terrestrial side. Sound helps animals to navigate through complex waterways and find food resources. You can only use site so far underwater especially when it gets to be kind of dark, once you get down to certain levels. So there many of us think that sound is probably going to be an important component to measuring the status of health in our oceans. >> It's great. Well, Bryan, I really enjoyed this conversation. I've really enjoyed your Ted Talk, and now I've got a bunch of research papers I want to dig into a little bit more as well. >> Okay.(chuckling) >> It's a fascinating topic, but I think the most important thing that you talked about extensively in your Ted Talk is really just taking a minute to take a step back from the individual perspective, appreciate what's around us, hear, that information and I think there's a real direct correlation to the power of exascale, to the power of hearing this data, processing this data, and putting intelligence on that data, understanding that data in a good way, in a positive way, in a delightful way, spiritual way, even that we couldn't do before, or we just weren't paying attention like with what you know is on your phone please-- >> Yeah, really. >> It's all around you. It's been there a whole time. >> Yeah. (both chuckling) >> Yeah, Jeff, I really encourage your viewers to count it, just go out and listen. As we say, go out and listen and join the mission. >> I love it, and you can get started by going to the Center for Global Soundscapes and you have a beautiful landscape. I had it going earlier this morning while I was digging through some of the research of Bryan. (Bryan chuckling) Thank you very much (Bryan murmurs) and really enjoyed the conversation best to you-- >> Okay. >> And your team and your continued success. >> Alright, thank you. >> Alright, thank you. All right, he's Bryan-- >> Goodbye. >> I'm Jeff, you're watching theCUBE. (Bryan chuckling) for continuing coverage of Exascale Day. Thanks for watching. We'll see you next time. (calm ambient music)

from around the globe it's thecube covering space and cyber security symposium 2020 hosted by cal poly hello everyone welcome to the space and cyber security symposium 2020 hosted by cal poly where the intersection of space and security are coming together i'm john furrier your host with thecube here in california i want to welcome our featured guest lieutenant general john f thompson with the united states space force approach to cyber security that's the topic of this session and of course he's the commander of the space and missile system center in los angeles air force base also heading up space force general thank you for coming on really appreciate you kicking this off welcome to the symposium hey so uh thank you very much john for that very kind introduction also uh very much thank you to cal poly uh for this opportunity to speak to this audience today also a special shout out to one of the organizers uh dustin brun for all of his work uh helping uh get us uh to this point uh ladies and gentlemen as uh as uh john mentioned uh i'm jt thompson uh i lead the 6 000 men and women of the united states space forces space and missile system center which is headquartered here at los angeles air force base in el segundo if you're not quite sure where that's at it's about a mile and a half from lax this is our main operating location but we do have a number of other operating locations around the country with about 500 people at kirtland air force base in albuquerque new mexico uh and about another 500 people on the front range of the rockies uh between colorado springs and uh and denver plus a smattering of other much smaller operating locations nationwide uh we're responsible for uh acquiring developing and sustaining the united states space force's critical space assets that includes the satellites in the space layer and also on the ground layer our ground segments to operate those satellites and we also are in charge of procuring launch services for the u.s space force and a number of our critical mission partners across the uh department of defense and the intelligence community um just as a couple of examples of some of the things we do if you're unfamiliar with our work we developed and currently sustained the 31 satellite gps constellation that satellite constellation while originally intended to help with global navigation those gps signals have provided trillions of dollars in unanticipated value to the global economy uh over the past three decades i mean gps is everywhere i think everybody realizes that agriculture banking the stock market the airline industry uh separate and distinct navigation systems it's really pervasive across both the capabilities for our department of defense and capabilities for our economy and and individuals billions of individuals across our country and the planet some of the other work we do for instance in the communications sector uh secure communications satellites that we design and build that link america's sons and daughters serving in the military around the world and really enable real-time support and comms for our deployed forces and those of our allies we also acquire uh infrared missile warning satellites uh that monitor the planet for missile launches and provide advanced warning uh to the u.s homeland and to our allies uh in case some of those missile launches are uh nefarious um on a note that's probably a lot closer to home maybe a lot closer to home than many of us want to think about here in the state of california in 2018 smc jumped through a bunch of red tape and bureaucracy uh to partner with the u.s forest service during the two of the largest wildfires in the state's history the camp and woolsey fires in northern california as those fires spread out of control we created processes on the fly to share data from our missile warning satellites those are satellites that are systems that are purpose built to see heat sources from thousands of miles above the planet and we collaborated with the us forest service so that firefighters on the ground uh could track those fires more in real time and better forecast fires and where they were spreading thereby saving lives and and property by identifying hot spots and flare-ups for firefighters that data that we were able to working with our contractors pass to the u.s forest service and authorities here in california was passed in less than an hour as it was collected to get it into the hands of the emergency responders the first responders as quickly as possible and doing that in an hour greatly surpassed what was available from some of the other assets in the airborne and ground-based fire spotters it was really instrumental in fighting those fires and stopping their spread we've continued uh that involvement in recent years using multiple systems to support firefighters across the western u.s this fall as they battled numerous wildfires that unfortunately continue working together with the u.s forest service and with other partners uh we like to make uh we like to think that we made a difference here but there's still a lot more work to go and i think that we should always be asking ourselves uh what else can space data be used for and how can we more rapidly get that space data to uh stakeholders so that they can use it for for purposes of good if you will how else can we protect our nation how else can we protect our friends and allies um i think a major component of the of the discussion that we will have throughout this conference is that the space landscape has changed rapidly and continues to change rapidly um just over the past few years uh john and i were talking before we went live here and 80 nations now have uh space programs 80 nearly 80 space faring nations on the planet um if you just look at one mission area that uh the department of defense is interested in and that's small launch there are currently over a hundred different small launch companies uh within the u.s industrial base vying for commercial dod and civil uh payload capabilities uh mostly to low earth orbit it's it's just truly a remarkable time if you factor in those things like artificial intelligence and machine learning um where we're revolutionary revolutionizing really uh the ways that we generate process and use data i mean it's really remarkable in 2016 so if you think about this four years ago uh nasa estimated that there were 28 terabytes of information transiting their space network each day and that was four years ago um uh obviously we've got a lot of desire to work with a lot of the people in the audience of this congress or in this conference uh we need to work with big thinkers like many of you to answer questions on how best we apply data analytics to extract value and meaning from that data we need new generations of thinkers to help apply cutting edge edge theories of data mining cyber behaviorism and internet of things 2.0 it's just truly a remarkable time uh to be in the space business and the cyber aspects of the states of the space business are truly truly daunting and important to uh to all of us um integrating cyber security into our space systems both commercial and government is a mandate um it's no longer just a nice to have as the us space force and department of the air force leadership has said many times over the past couple of years space is becoming congested and contested and that contested aspect means that we've got to focus on cyber security uh in the same way that the banking industry and cyber commerce focus on uh cyber security day in and day out the value of the data and services provided is really directly tied to the integrity and availability of that data and services from the space layer from the ground control segments associated with it and this value is not just military it's also economic and it's not just american it's also a value for the entire world particularly particularly our allies as we all depend upon space and space systems your neighbors and friends here in california that are employed at the space and missile system center uh work with network defenders we work with our commercial contractors and our systems developers um our international allies and partners to try and build as secure and resilient systems as we can from the ground up that keep the global commons of space free and open for exploration and for commerce um as john and i were talking earlier before we came online there's an aspect of cyber security for space systems especially for some of our legacy systems that's more how do we bolt this on because we fielded those space systems a number of years ago and the the challenges of cyber security in the space domain have grown so we have a part that we have to worry about bolting it on but then we have to worry about building it in as we as we field new systems and build in a flexibility that that realizes that the cyber threat or the cyber security landscape will evolve over time it's not just going to be stagnant there will always be new vulnerabilities and new threat vectors that we always have to look at look uh as secretary barrett who is our secretary of the air force likes to say most americans use space before they have their first cup of coffee in the morning the american way of life really depends on space and as part of the united states space force we work with defense leaders our congress joint and international military teammates and industry to ensure american leadership in space i really thank you for this opportunity to address the audience today john and thanks so much to cal poly for letting me be one of the speakers at this event i really look forward to this for uh several months and so with that i look forward to your questions as we kind of move along here general thank you very much for the awesome uh introductory statement uh for the folks watching on the stream brigadier general carthan is going to be in the chat answering any questions feel free to chat away he's the vice commander of space and missile systems center he'll be available um a couple comments from your keynote before i get to my questions because it just jumped in my head you mentioned the benefits of say space but the fires in california we're living that here that's really real time that's a benefit you also mentioned the ability for more people launching payloads into space and i only imagine moore's law smaller faster cheaper applies to rockets too so i'm imagining you have the benefits of space and you have now more potential objects flying out sanctioned and maybe unsanctioned so you know is it going to be more rules around that i mean this is an interesting question because it's exciting space force but for all the good there is potentially bad out there yeah so i i john i think the uh i think the basics of your question is as space becomes more congested and contested is there a need for more international norms of how satellites fly in space what kind of basic features satellites have to perhaps deorbit themselves what kind of basic protections does do all satellites should all satellites be afforded as part of a peaceful global commons of space i think those are all fantastic questions and i know that u.s and many uh allied policy makers are looking very very hard at those kinds of questions in terms of what are the norms of behavior and how we uh you know how how we field and field is the military term but you know how we uh populate uh using civil or uh commercial terms uh that space layer at different altitudes uh low earth orbit mid mid-earth orbit geosynchronous earth orbit different kinds of orbits uh what the kind of mission areas we accomplish from space that's all things that need to be definitely taken into account as uh as the place gets a little bit not a little bit as the place gets increasingly more popular day in and day out well i'm super excited for space force i know that a new generation of young folks are really interested in it's an emerging changing great space the focus here at this conference is space and cyber security intersection i'd like to get your thoughts on the approach that space force is taking to cyber security and how it impacts our national goals here in the united states yeah yeah so that's a that's a great question john let me let me talk about in two uh two basic ways but number one is and and i know um some people in the audience this might make them a little bit uncomfortable but i have to talk about the threat right um and then relative to that threat i really have to talk about the importance of uh of cyber and specifically cyber security as it relates to that threat um the threats that we face um really represent a new era of warfare and that new era of warfare involves both space and cyber uh we've seen a lot of action in recent months uh from certain countries notably china and russia uh that have threatened what i referred to earlier as the peaceful global commons of space for example uh it through many unclassified sources and media sources everybody should understand that um uh the russians have been testing on orbit uh anti-satellite capabilities it's been very clear if you were following just the week before last the department of defense released its uh 2020 military and security developments involving the people's republic of china um uh and uh it was very clear that china is developing asats electronic jammers directed energy weapons and most relevant to today's discussion offensive cyber uh capabilities there are kinetic threats uh that are very very easy to see but a cyber attack against a critical uh command and control site or against a particular spacecraft could be just as devastating to the system and our war fighters in the case of gps and important to note that that gps system also impacts many civilians who are dependent upon those systems from a first response perspective and emergency services a cyber attack against a ground control site could cause operators to lose control of a spacecraft or an attacker could feed spoofed data to a system to mislead operators so that they send emergency services personnel to the to the wrong address right attacks on spacecraft on orbit whether directly via a network of intrusion or enabled through malware introduced during the systems production uh while we're building the satellite can [ __ ] or corrupt the data denial of service type attacks on our global networks obviously would disrupt our data flow and interfere with ongoing operations and satellite control i mean if gps went down i you know i hesitate to say it this way because we might elicit some screams from the audience but if gps went down a starbucks wouldn't be able to handle your mobile order uber drivers wouldn't be able to find you and domino's certainly certainly wouldn't be able to get there in 30 minutes or less right so with a little bit of tongue-in-cheek there from a military operations perspective it's dead serious um uh we have become accustomed in the commercial world to threats like lance ransomware and malware and those things have unfortunately become commonplace in commercial terrestrial networks and computer systems however what we're seeing is that our adversaries with the increased competition in space these same techniques are being retooled if you will to use against our national security space systems uh day in and day out um as i said during my opening remarks on the importance of cyber the value of these systems is directly tied to their integrity if commanders in the field uh firefighters in california or baristas in in starbucks can't trust the data they see they're receiving then that really harms their decision-making capabilities one of the big trends we've recently seen is the mood move towards proliferated leo uh uh constellations obviously uh spacex's uh starlink uh on the commercial side and on the military side the work that darpa and my organization smc are doing on blackjack and casino as well as some space transport layer constellation work that the space development agency is designing are all really really important types of mesh network systems that will revolutionize how we plan and field warfighting systems and commercial communications and internet providing systems but they're also heavily reliant on cyber security uh we've got to make sure that they are secured to avoid an accident or international damage uh loss of control of these constellations really could be catastrophic from both a mission perspective or from uh you know satellites tumbling out of low earth orbit perspective another trend is introductions in artificial intelligence and machine learning on board spacecraft or at the edge our satellites are really not so much hardware systems with a little software anymore in the commercial sector and in the defense sector they're basically flying boxes full of software right and we need to ensure the data that we're getting out of those flying boxes full of software are helping us base our decisions on accurate data and algorithms govern governing the right actions and that those uh that those systems are impervious to the extent possible uh to nefarious uh modifications so in summation a cyber security is vital element of everything in our national security space goals and i would argue for our national uh goals uh writ large including uh economic and information uh uh dimensions uh the space force leadership at all levels uh from uh some of the brand new second lieutenants that general raymond uh swore into the space force this morning uh ceremonially from the uh air force association's air space and cyberspace conference uh to the various highest levels general raymond uh general d t thompson myself and a number of other senior leaders in this enterprise we've got to make sure that we're all working together to keep cyber security at the forefront of our space systems because it they absolutely depend on it you know you mentioned uh hardware software threats opportunities challenges i want to ask you because you you got me thinking of the minute there around infrastructure i mean we've heard critical infrastructure you know grids here on on earth you're talking about critical infrastructure a redefinition of what critical infrastructure is an extension of what we have so i'd love to get your thoughts about space force's view of that critical infrastructure vis-a-vis the threat vectors because you know the term threat vectors has been kicked around in the cyber space oh yeah threat vectors they're always increasing the surface area well if the surface area is from space it's an unlimited surface area so you got different vectors so you got new critical infrastructure developing real time really fast and you got an expanded threat vector landscape putting that in perspective for the folks that aren't really inside the ropes on these critical issues how would you explain this and how would you talk about those two things well so i tell you um i just like um uh just like uh i'm sure people in the security side or the cyber security side of the business in the banking industry feel they feel like it's uh all possible threat vectors represent a dramatic and protect potentially existential threat to all of the dollars that they have in the banking system to the financial sector on the department of defense side we've got to have sort of the same mindset um that threat vector from to and through space against critical space systems ground segments the launch enterprise or transportation uh to orbit and the various different uh domains within uh within space itself like i mentioned before uh leo mio and geo-based satellites with different orbits all of the different mission areas that are accomplished from space that i mentioned earlier some that i didn't mention like weather tactical or wide band communications uh various new features of space control all of those are things that we have to worry about from a cyber security uh threat perspective and it's a it's a daunting challenge right now right yeah it's awesome and one of the things we've been following on the hardware side here in the on the ground is the supply chain we've seen you know malware being you know really put into really obscure hardware who manufactures it as being outsourced obviously government has restrictions but with the private sector uh you mentioned china and and the us kind of working together across these these peaceful areas but you got to look at the supply chain how does the supply chain the security aspect impact the mission of the u.s space force yeah yeah so so um how about another um just in terms of an example another kind of california-based historical example right um the very first u.s satellite uh explorer one was built by uh the jet propulsion uh laboratory folks uh not far from here in el segundo up in uh up in pasadena um that satellite when it was first built in the late 50s uh weighed a little bit over 30 pounds and i'm sure that each and every part was custom made and definitely made by u.s companies fast forward to today the global supply chain is so tightly coupled and frankly many industries are so specialized almost specialized regionally around the planet we focus every day to guarantee the integrity of every component that we put in our space systems is absolutely critical to the operations of those satellites and we're dependent upon them but it becomes more difficult and more difficult to understand the the heritage if you will of some of the parts that are used the thousands of parts that are used in some of our satellites that are literally school bus sized right the space industry especially uh national security space sector um uh is relatively small compared to other commercial industries and we're moving to towards using more and more parts uh from non-us companies uh cyber security and cyber awareness have to be baked in from the beginning if we're going to be using parts that maybe we don't necessarily um understand 100 percent like an explorer one uh the the lineage of that particular part the environmental difficulties in space are well known the radiation environment the temperature extremes the vacuum those require specialized component and the us military is not the only uh customer in that space in fact we're definitely not the dominant customer uh in space anymore all those factors require us along with our other government partners and many different commercial space organizations to keep a very close eye on our supply chains from a quality perspective a security perspective and availability um there's open source reporting on supply training intrusions from um many different breaches of commercial retailers to the infectious spread of uh you know compromised patches if you will and our adversaries are aware of these techniques as i mentioned earlier with other forms of attack considering our supply chains and development networks really becomes fair game for our adversaries so we have to uh take that threat seriously um between the government and industry sectors here in the u.s we're also working with our industry partners to enact stronger defenses and assess our own vulnerabilities last fall we completed an extensive review of all of our major contracts here at space and missile system center to determine the levels of cyber security requirements we've implemented across our portfolio and it sounds really kind of you know businessy geeky if you will you know hey we looked at our contracts to make sure that we had the right clauses in our contracts to address cyber security as dynamically as we possibly could and so we found ourselves having to add new language to our contracts to require system developers to implement some more advanced uh protective measures in this evolving cyber security environment so that data handling and supply chain perspective uh protections um from contract inception to launch and operations were taken into account uh cyber security really is a key performance parameter for us now it's as important as the the mission performance of the system it's as important as cost it's as important as schedule because if we deliver the perfect system on time and on cost uh it can perform that missile warning or that communications mis mission perfectly but it's not cyber secure if it doesn't have cyber protections built into it or the ability to implement mitigations against cyber uh threats then we've essentially fielded a shoe box in space that doesn't do the k the the war fighter or the nation uh any good um supply chain risk management is a is a major challenge for us uh we're doing a lot to coordinate with our industry partners uh we're all facing it head on uh to try and build secure and trusted components uh that keep our confidence as leaders firefighters and baristas uh as the case may be uh but it is a challenge and we're trying to rise to that challenge you know this so exciting this new area because it really touches everything you know talk about geeking out on on the tech the hardware the systems but also you put your kind of mba hat on you go what's the roi of the extra development and how you how things get built because the always the exciting thing for space geeks is like you're building cool stuff people love it's it's exciting but you still have to build and cyber security has proven that security has to be baked in from the beginning and be thought as a system architecture so you're still building things which means you've got to acquire things you got to acquire parts you got to acquire build software and and sustain it how is security impacting the acquisition and the sustainment of these systems for space yeah from initial development uh through planning for the acquisition design development fielding or production fielding and sustainment it impacts all aspects of of the life cycle john uh we simply especially from the concept of baking in cyber security uh we can't wait until something is built and then try and figure out how to make it cyber secure so we've moved way further uh towards working side by side with our system developers to strengthen cyber security from the very beginning of a system's development cyber security and the resilience associated with it really have to be treated as a key system attribute as i mentioned earlier equivalent with data rates or other metrics of performance we like to talk in uh in the space world about uh mission assurance and mission assurance has always you know sort of taken us as we as we technically geek out right mission assurance has always taken us to the will this system work in space right can it work in a vacuum can it work in you know as it as it uh you know transfers through uh the van allen radiation belt or through the the um the southern hemisphere's electromagnetic anomaly right will it work out in space and now from a resiliency perspective yeah it has to work in space it's got to be functional in space but it's also got to be resistant to these cyber security threats it's it's not just i think uh general dt thompson quoted this term it's not just widget assurance anymore it's mission assurance um uh how does that satellite uh operator that ground control segment operate while under attack so let me break your question a little bit uh just for purposes of discussion into into really two parts uh cyber uh for cyber security for systems that are new and cyber security uh for systems that are in sustainment or kind of old and legacy um obviously there's cyber vulnerabilities that threaten both and we really have to employ different strategies for for defense of of each one for new systems uh we're desperately trying to implement across the department of defense in particular in the space world a kind of a devsecops methodology and practice to delivering software faster and with greater security for our space systems here at smc we have a program called enterprise ground services which is a tool kit basically a collection of tools for common command and control of different satellite systems egs as we call it has an integrated suite for defensive cyber capabilities network operators can use these tools to gain unprecedented insight to data flows and to monitor space network traffic for anomalies or other potential indicators of of bad behavior malicious behavior if you will um uh it's rudimentary at this point but because we're using devsecops and that incremental development approach as we scale it it just becomes more and more capable you know every every product increment that we field here at uh at uh la air force base uh uh we have the united space space forces west coast software factory which we've dubbed kobayashi maru they're using those agile devops uh software development practices uh to deliver uh space awareness software uh to the combined space operations center uh affectionately called the csp that c-spock is just down the road uh from cal poly uh there in san luis obispo at vandenberg air force base they've securely linked the c-spock with other space operation centers around the planet our allies australia canada and the uk uh we're partnering with all of them to enable secure and enhanced combined space operations so lots of new stuff going on as we bake in new development uh capabilities for our our space systems but as i mentioned earlier we've got large constellations on satellite of satellites on orbit right now some of them are well in excess of a decade or more old on orbit and so the design aspects of those satellites are several decades old and so but we still have to worry about them because they're critical to our space capabilities um we've been working with an air force materiel command organization uh called crows which stands for the cyber resiliency office for uh weapon systems to assess all of those legacy platforms from a cyber security perspective and develop defensive strategies and potential hardware and software upgrades to those systems to better enable them to to live through this increasingly cyber security uh concerned era that we currently live in our industry partners have been critical to to both of those different avenues both new systems and legacy systems we're working closely with them to defend and upgrade uh national assets and develop the capabilities to do similar with uh with new national assets coming online the vulnerabilities of our space systems really kind of threaten the way we've done business in the past both militarily and in the case of gps economically the impacts of that cyber security risk are clear in our acquisition and sustainment processes but i've got to tell you it that as the threat vectors change as the vulnerabilities change we've got to be nimble enough agile enough to be able to bounce back and forth we can't just say uh many people in the audience are probably familiar with the rmf or the risk management framework approach to um to reviewing uh the cyber security of a system we can't have program managers and engineers just accomplish an rmf on a system and then hey high five we're all good uh it's a journey not a destination that's cyber security and it's a constant battle rhythm throughout a weapon systems life cycle not just a single event i want to get to this commercial business needs and your needs on the next question but before i go there you mentioned the agile and i see that clearly because when you have accelerated innovation cycles you've got to be faster and we saw this in the computer industry mainframes mini computers and then when you started getting beyond me when the internet hit and pcs came out you saw the big enterprises the banks and and government start to work with startups it used to be a joke in the entrepreneurial circles is that you know there's no way if you're a startup you're ever going to get a contract with a big business enterprise now that used to be for public sector and certainly uh for you guys so as you see startups out there and there's acquisition involved i'm sure would love to love to have a contract with space force there's an roi calculation where if it's in space and you have a sustainment view edit software you might have a new kind of business model that could be attractive to startups could you share your thoughts on the folks who want to be a supplier to you uh whether they're a startup or an existing business that wants to be agile but they might not be that big company we are john that's a fantastic question we are desperately trying to reach out to to those new space advocates to those startups to those um what we sometimes refer to within the department of defense those non-traditional uh defense contractors a couple of things just for uh thinking purposes on some of the things that we're trying to highlight um uh three years ago we created here at uh space and missile system center uh the space enterprise consortium uh to provide a platform uh a contractual vehicle really to enable us to rapidly prototype uh development of space systems and to collaborate uh between the u.s space force uh traditional defense contractors non-traditional vendors like startups and even some academic institutions uh spec as we call it space enterprise consortium uses a specialized contracting tool to get contracts uh awarded quickly many in the audience may be familiar with other transaction agreements and that's what spec is based on and so far in just three years spec has awarded 75 different uh prototyping contracts worth over 800 million dollars with a 36 reduction in time to award and because it's a consortium based competition for um for these kinds of prototyping efforts the barrier to entry for small and non-traditional for startups even for academic institutions to be able to compete for these kinds of prototypings is really lowered right um uh these types of partnerships uh that we've been working through on spec uh have really helped us work with smaller companies who might not have the background or expertise in dealing with the government or in working with cyber security uh for their systems both their developmental systems and the systems that they're designing and trying to build we want to provide ways for companies large and small to partner together and support um uh kind of mutually beneficial uh relationships between all um recently uh at the annual air force association uh conference that i mentioned earlier i moderated a panel with several space industry leaders uh all from big traditional defense contractors by the way and they all stressed the importance of building bridges and partnerships uh between major contractors in the defense industry and new entrants uh and that helps us capture the benefits of speed and agility that come with small companies and startups as well as the expertise and specialized skill sets of some of those uh larger contractors uh that we rely on day in and day out advanced cyber security protections and utilization of secure facilities are just a couple of things that i think we could be prioritizing more so in those collaborations as i mentioned earlier the spec has been very successful in awarding a number of different prototyping contracts and large dollar values and it's just going to get better right there's over 400 members of the space enterprise consortium 80 of them are non-traditional kinds of vendors and we just love working with them another thing that many people in the audience may be familiar with in terms of our outreach to innovators uh if you will and innovators that include uh cyber security experts is our space pitch day events right so we held our first event last november in san francisco uh where we awarded over a two-day period about 46 million dollars to 30 different companies um that had potentially game-changing ideas these were phase two small business innovative research efforts uh that we awarded with cash on the spot uh we're planning on holding our second space pitch day in the spring of 2021. uh we're planning on doing it right here in los angeles uh covent 19 environment permitting um and we think that these are you know fantastic uh uh venues for identifying and working with high-speed startups startups and small businesses who are interested in uh really truly partnering with the us air force it's a as i said before it's a really exciting time to be a part of this business uh and working with the innovation economy uh is something that the department of defense uh really needs to do in that um the innovation that we used to think was ours you know that 80 percent of the industrial-based innovation that came from the department of defense uh the the script has been flipped there and so now more than 70 percent uh particularly in space innovation uh comes from the commercial sector not from uh not from the defense business itself and so um that's a tsunami of uh investment and a tsunami of uh capability and i need to figure out how to get my surfboard out and ride it you know what i mean yeah i mean it's one of those things where the flip the script has been flipped but it's exciting because it's impacting everything are you talking about systems architecture you're talking about software you're talking about a business model you talk about devsecops from a technical perspective but now you have a business model innovation all the theaters of uh are exploding in innovation technical business personnel this brings up the workforce challenge you've got the cyber needs for the u.s space force there's probably a great roi model for new kinds of software development that could be priced into contracts that's a entrepreneurial innovation you got the the business model theater you've got the personnel how does the industry adopt and change you guys are clearly driving this how does the industry adjust to you yeah so um i think a great way to answer that question is to just talk about the kind of people that we're trying to prioritize in the u.s space force from a from an acquisition perspective and in this particular case from a from a cyber security perspective as i mentioned earlier it's the most exciting time to be in space programs uh really since the days of apollo um uh you know just to put it in terms that you know maybe have an impact with the audience uh from 1957 until today approximately 9 000 satellites uh have been launched from the various space faring countries around the planet uh less than two thousand of those nine thousand are still up on orbit and operational and yet in the new space regime um players like spacex have plans to launch you know 12 000 satellites for some of their constellations alone it really is a remarkable time in terms of innovation and fielding of space capabilities and all of those space capabilities whether they're commercial civil or defense are going to require appropriate cyber security uh protections it's just a really exciting time uh to be working in stuff like this and so uh folks like the folks in this audience who have a passion about space and a passion about cyber security are just the kind of people that we want to work with because we need to make sure our systems are are secure and resilient we need folks that have technical and computing expertise engineering skills to be able to design cybersecure systems that can detect and mitigate attacks uh but we also as you alluded to we need people that have that business and um you know business acumen human networking background so that we can launch the startups and work with the non-traditional businesses uh help to bring them on board help to secure both their data and our data and uh and and make sure our processes and systems are are free as much as possible from uh uh from attack um for preparation for for audience members who are young and maybe thinking about getting into this uh trade space um you gotta be smart on digital networking uh you gotta understand basic internet protocols concepts uh programming languages uh database design uh learn what you can from penetration or vulnerability testing and and uh risk assessment i will tell you this and i don't think he will i know he will not mind me telling you this but you've got to be a lifelong learner and so two years ago i'm at home one evening and i get a phone call on my cell phone and it's my boss the commander of air force space command uh general j raymond who is now currently the chief of space operations and he is on temporary duty flying overseas he lands where he's going and he first thing he does when he lands is he calls me and he goes jt um while i was traveling um i noticed that there were e-books available on the commercial airliner i was traveling on and there was an e-book on something called scrumming and agile devsecops and i read it have you read it um and i said no sir but if you tell me what the title of the book is i will read it and so i got to go to my staff meeting um you know the very next week the next time we had a staff meeting and tell everybody in the stab meeting hey if the four star and the three star can read the book about scrumming then i'm pretty sure all of you around this table and all our lieutenants and our captains our gs13s all of our government employees can get smart on uh the scrumming development process and interestingly as another side i had a telephone call with him last year during the holidays where he was trying to take some leave and i said sir what are you up to today are you are you you know making eggnog for the event tonight or whatever and the chief of space operations told me no i'm trying to teach myself python i'm at lesson two and it's not going so well but i'm i'm gonna figure this out and so that kind of thing if the chief of staff or the you know the the the chief of space operations can prioritize scrumming and python language and innovation in his daily schedule then we're definitely looking for other people who can do that and we'll just say lower levels of rank uh throughout our entire space force enterprise um look i i we don't need to need people that can code a satellite from scratch but we need to know we need to have people that have a basic grasp of the programming basics and cyber security requirements and that can turn those things into into meaningful actions obviously in the space domain things like basic physics and orbital mechanics are also important uh space is not an intuitive uh domain so under understanding how things survive uh on orbit is really critical to making the right design and operational decisions and you know i know there's probably a lot because of this conference i know there's a probably a whole lot of high-speed cyber security experts out in the audience and i need those people in the u.s space force the the country is counting on it but i wouldn't discount having people that are just cyber aware or cyber savvy right i have contracting officers and logisticians and program managers and they don't have to be high-end cyber security experts but they have to be aware enough about it to be able to implement cyber security protections um into our space system so the skill set is is really really broad um our adversaries are pouring billions of dollars into uh define designing uh and fielding offensive and destructive space cyber security weapons right they've repeatedly shown really a blatant disregard of safety and international norms for good behavior on orbit and the cyber security aspects of our space systems is really a key battleground going forward so that we can maintain that as i mentioned before peaceful uh global commons of space we really need all hands on deck if you're interested in helping in uniform if you're interested in helping uh not in uniform uh but as a government employee a commercial or civil employee to help us make cyber security more important uh or more cape more able to be developed for our space systems then we'd really love to uh to work with you or have you on the team to build that safe and secure future for our space systems lieutenant general john thompson great insight thank you for sharing all that awesome stories too and motivation for the young next generation the united states space force approach of cyber security really amazing talk thank you for your time final parting question is as you look out and you had your magic wand what's your view for the next few years in terms of things that we could accomplish it's a super exciting time what do you hope for so um um first of all john thanks to you and and thanks to cal poly uh for the invitation and and thanks to everybody for uh for their interest in cyber security especially as it relates to space systems that's here at the conference um uh there's a quote and i'll read it here uh from uh bernard schriever who was the uh the founder if you will uh a legend in uh dod space the founder of the western development division which was a predecessor organization to space and missile systems center general shrever i think captures the essence of what how we see the next couple of years the world has an ample supply of people who can always come up with a dozen good reasons why new ideas will not work and should not be tried but the people who produce progress are breed apart they have the imagination the courage and the persistence to find solutions and so i think if you're hoping that the next few years of space innovation and cyber security innovation are going to be a pony ride at the county fair then perhaps you should look for another line of work because i think the next few years in space and cyber security innovation are going to be more like a rodeo um and a very dynamic rodeo as it goes it is a an awesome privilege to be part of this ecosystem it's really an honor for me to um to be able to play some small role uh in the space ecosystem and trying to improve it uh while i'm trying to improve the chances of uh of the united states of america in a uh in a space war fighting uh uh environment um and so i thank all of you for uh participating today and for this little bit of time that you've allowed me to share with you thank you sir thank you for your leadership and thank you for the for the time for this awesome event space and cyber security symposium 2020 i'm john furrier on behalf of cal poly thanks for watching [Music]

>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS public sector online. (bright upbeat music) Brought to you by Amazon Web Services. >> Hi, and welcome back to theCUBE's coverage at AWS Public Sector Online, I'm your host Stu Miniman. Really excited always when we come to the AWS public sector show it's not only governments' but you've got nonprofits education, and lots of phenomenal use cases from the practitioners themselves. Really happy to welcome to the program, Dr. Sergio papa. He is a radio diagnostic specialist at the Centro Diagnostico Italiano, of course, in Italy, if you can't tell by the name there, and Dr. Papa, thank you so much for joining us. Why don't you start with a little bit, you know your role at CDI. >> Thank you for your invitation. And I am the director of the diagnostic imaging department and radiotherapy nuclear medicine. We are a very huge institution in the diagnostic area in the laboratory and diagnostic imaging, I think one of the biggest in Europe. >> Excellent. And, of course, one of the very relevant things to talk about, you have a project called Artificial Intelligent or AI for COVID. maybe explain to us a little bit about what led to this and how this what the goals are for this project. >> Yes, we, as you know, is you also are today we were imagining February, March, we're in the middle of the biggest bandemia we have ever experienced it in the past. So we were thinking about some new (mumbles) methodology to give an end for this portrait emergency (mumbles) is working from three or four years around (mumbles) in basic imaging. In particular, we are working very hard in radiomics. After if you need I can talk, I can speak about the radiomics methodology that we are using. So, we had the idea of fine radiomics method on diagnostic imagery and in particular chest X ray and with the with the purpose not to have the diagnosis for this patient, it doesn't matter for us. We were focusing on predicting the clinical outcome of this patient. And, I mean, all these, all the people already diagnosed with COVID-19 viewers where they had an X ray examination, then after we applied over this huge number of X ray examinations radiomic ometers to understand what could be the clear output of this patient. So, I mean, dividing the people going well and then people, otherwise, that we're going to averse of the illness, I mean, to critical therapy even to the death. So we were trying to we are trying to divide two groups of patients. >> Yeah, absolutely. It's so important, of course, one to have that diagnosis, to understand who needs the most treatment, making sure that, you know, hospitals can put the right resources in the right places. So really impressive to do something like machine learning on this on in a relatively short period of time from when this whole pandemic is started. Help us understand a little bit, you know, what are the underlying technologies? How does AWS have been into this whole discussion. >> Well the Support of AWS is in many different areas. The first is that we are really trying to develop a platform without AWS and that's useful for the hospitals, for the institution to store in unique imagery set, all the images can be from different institution, so we don't need any more to send the images in any way. This is the first thing that AWS can give to us. Second is the use of a machine learning all this to analyze this kind of images coming from x ray chest through AWS systems. The third I think could be an aid in the generating the structure of the reports for this patient and moreover, the identification of patterns, different patterns that we can find inside the images. This concerns the radiomics theory, I mean, inside the images, there are many, many more information than what radiologist can can get from. So, I think sector agents can help us, so, AWS can help us to detect all these kind of patterns that we want to collect for our study. And the fourth reason is for the AI that AWS can give us is to share this kind of modality with the other scientific centers of Research Sector and not only for this specific pandemia now but also in the future maybe we will have, we don't hope this but we could have the second wave of the pandemia, there are many signals so about this in China and also in Europe. So these will be useful in the future to find the circle and second wave of pandemia, and also the final reason is that we will share all our results of this study with all the scientific community, I mean, we will improve an open access model together with AWS to share this information with all the scientific community the world. >> It's wonderful that this information can be shared broadly across the community, so important for tackling, you know, this challenging pandemic. I'm curious has your unit or had you used artificial intelligence machine learning before, I'd love to just get a little bit of background on, you know, how much you've used this technology? how accessible it is to be able to leverage it for two use cases like this? >> Absolutely, because, I mean, I'm an radiologist. If I check an image in a CT scan or an X ray, I can see inside that image, the maximum that I can see is 10, 15 to the maximum different patterns, I mean, the volume, the dimension of growth, which is the way of taking contrast media or difference old wishy washout but with my eyes I can see 10, 15 different patterns. And if a machine system examine the same image, it can reach out hundreds of patterns that I cannot see. So, we can detect all these patterns in different images, we can collect this machine learning system can work on these and put together all the similar patterns. So it can divide even in different cluster and then the system has to compare all this difference casts or group or patterns with a very huge database that we built before comparing and try to understand which patterns are linked to different outcome. So we can say, okay, this image has 20, 30, 100 patterns that suggest to us the destiny of the nation will be in one specific while another lesion that for me is exactly the same with my eyes, systems will tell us there are the two lesions are really different, their destiny is really different. This is the radiomics theory and this is what we are applying in our study on X ray chest cavitation. As I said before we select only positive patient. I mean all people that is for sure they have positive to COVID and in the first X ray chest, entering the hospital, we try to evaluate from the first chest X ray what will be the real destiny of the patient better or worse, and then we can also predict, try to predict, obviously, how many intensive care beds are necessity in that institution, we can send the therapy and adjust the therapy for the the different kind, different group of patients, it could be a very big help to an institution, to an hospital especially in periods like in our March or April when every day in every hospital in northern Italy, they were entering 200 person per hospital. It was a dramatic situation. >> Excellent. One of the other things that this pandemic has done is really required some, you know, strong coordination between both public and private entities. If you could speak a little bit to that my understanding is that AWS also help support this with the donation of computational credits. I believe it's the AWS diagnostic development initiative. So help us understand, you know, how the finances and the partnerships between public and private help everyone really, you know, address this current challenge. >> Well, the support from AWS for us is very important because now we, in this way we can use a lot of computing systems much more than what you had in our institution. And moreover, I think that sharing our information without the scientific content at the end of our study, it would be very important thing to do. Now I know we are beginning to appear on our drawn as in our websites, some afford to share information about going. Our study could be really one of the most important of this. >> Great, final question I have for you, Dr. Papa, give us your ideal vision going forward. You talked a little bit about how you know the importance of this to be able to watch and be prepared for a potential wave two where else is this this research relevant and where do you see this project going forward? >> Well, our study is not as focused on pneumonia from COVID-19. But the methodology can be applied in every kind of interstitial pneumonia. I mean, this is one of the first to that, at least, this has made in radiomics to segment at one whole organ, usually in radiomics, we used to studies the single lesions or little areas, I mean, no deals or metastases or primary tumors. This is one of the first, very first important studies where the segmentation is dedicated to the whole organ, I mean, all the lung, both lung. In every patient we segmented to the right or left lung. And in order to study diffuse pathology, in this case, of pneumonia, interstitial pneumonia is very different from bacterial pneumonia. And this methodology at the end of the study will be shared with the scientific community and could be a very interesting advancement our job. >> Dr. Papa, thank you so much for joining us and thank you so much for the very important work that your organization is doing to help attack the global pandemic. >> Thank you too, thank you too. >> I'm Stu Miniman, thank you for watching theCUBE (bright upbeat music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hello and welcome to the cube conversation here in the Palo Alto studio I'm John four host of the cube we are here at the quarantine crew of the cube having the conversations that matter the most now and sharing that with you got a great guest here Phil Quaid was the chief information security officer of Fortinet also the author of book digital bing-bang which I just found out he wrote talking about the difference cybersecurity and the physical worlds coming together and we're living that now with kovat 19 crisis were all sheltering in place Phil thank you for joining me on this cube conversation so I want to get in this quickly that I think the main top thing is that we're all sheltering in place anxiety is high but people are now becoming mainstream aware of what we all in the industry have been known for a long time role of data cybersecurity access to remote tools and we're seeing the work at home the remote situation really putting a lot of pressure on as I've been reporting what I call at scale problems and one of them is security right one of them is bandwidth we're starting to see you know the throttling of the packets people are now living with the reality like wow this is really a different environment but it's been kind of a disruption and has created crimes of opportunity for bad guys so this has been a real thing everyone's aware of it across the world this is something that's now aware on everyone's mind what's your take on this because you guys are fighting the battle and providing solutions and we're doing for a long time around security this highlights a lot of the things in the surface area called the world with what's your take on this carbon 19 orton s been advocating for architectures and strategies that allow you to defend anywhere from the edge through the core all the way up to the cloud boom so with you know high speed and integration and so all the sudden what we're seeing not just you know in the US but the world as well is that that edge is being extended in places that we just hadn't thought about or our CV that people just hadn't planned for before so many people or telecommunication able to move that edge securely out to people's homes and more remote locations and do so providing the right type of security of privacy if those communications that are coming out of those delicate ears I noticed you have a flag in the background and for the folks that might not know you spent a lot of time at the NSA government agency doing a lot of cutting-edge work I mean going back to you know really you know post 9/11 - now you're in the private sector with Fortinet so you don't really speak with the agency but you did live through a time of major transformation around Homeland Security looking at data again different physical thing you know terrorist attacks but it did bring rise to large-scale data to bring to those things so I wanted to kind of point out I saw the flag there nice nice touch there but now that you're in the private sector it's another transformation it's not a transition we're seeing a transformation and people want to do it fast and they don't want to have disruption this is a big problem what's your reaction to that yeah I think what you're reporting out that sometimes sometimes there's catalysts that cause major changes in the way you do things I think we're in one of those right now that we're already in the midst of an evolutionary trend towards more distributed workforces and as I mentioned earlier doing so with the right type of security privacy but I would think what I think the global camp in debt endemic is showing is that we're all going to be accelerating that that thing is like it's gonna be a lot less evolutionary and a little bit more faster that's what happens when you have major world events like this being 911 fortunate tragedies it causes people to think outside the box or accelerate what they're already doing I think wearing that in that world today yeah it pulls forward a lot of things that are usually on the planning side and it makes them reality I want to get your thoughts because not only are CEOs and their employees all thinking about the new work environment but the chief information security officer is people in your role have to be more aware as more things happening what's on the minds of CISOs around the world these days obviously the pandemics there what are you seeing what are some of the conversations what are some of the thought processes what specifically is going on in the of the chief information security officer yeah I think there's probably a there's probably two different two different things there's the there's the emotional side and there's the analytic side on the emotional side you might say that some Caesars are saying finally I get to show how cyber security can be in an abler of business right I can allow you to to to maintain business continuity by allowing your workers to work from home and trying sustain business and allow you to keep paying their salary is very very important to society there's a very important time to step up as the seaso and do what's helpful to sustain mission in on the practical side you say oh my goodness my job's gotten a whole lot harder because I can rely less and less on someone's physical controls that use some of the physical benefits you get from people coming inside the headquarters facility through locked doors and there's personal congress's and personal identification authentication you need to move those those same security strategies and policies and you need to move it out to this broad eggs it's gotten a lot bigger and a lot more distributed so I want to ask you around some of the things they're on cyber screws that have been elevated to the top of the list obviously with the disruption of working at home it's not like an earthquake or a tornado or hurricane or flood you know this backup and recovery for that you know kind of disaster recovery this has been an unmitigated disaster in the sense of it's been unfor casted I was talking to an IT guy he was saying well we provisioned rvv lands to be your VPNs to be 30% and now they need a hundred percent so that disruption is causing I was an under forecast so in cyber as you guys are always planning in and protecting has there been some things that have emerged that are now top of mind that are 100 percent mindshare base or new solutions or new challenges why keep quite done what we're referring to earlier is that yep any good see so or company executive is going to prepare for unexpected things to a certain degree you need it whether it be spare capacity or the ability to recover from something an act of God as you mentioned maybe a flood or tornado or hurricane stuff like that what's different now is that we have a disruption who which doesn't have an end date meaning there's a new temporal component that's been introduced that most companies just can't plan for right even the best of companies that let's say Ronald very large data centers they have backup plans where they have spare fuel to run backup generators to provide electricity to their data centers but the amount of fuel they have might only be limited to 30 days or so it's stored on-site we might think well that's pretty that's a lot of for thinking by storing that much fuel on site for to allow you to sort of work your way through a hurricane or other natural disaster what we have now is a is a worldwide crisis that doesn't have a 30-day window on it right we don't know if it's gonna be 30 days or 120 days or or you know even worse than that so what's different now is that it's not just a matter of surging in doing something with band-aids and twine or an extra 30 days what we need to do is as a community is to prepare solutions that can be enduring solutions you know I have some things that if the absent I might like to provide a little color what those types of solutions are but that that would be my main message that this isn't just a surge for 30 days this is a surge or being agile with no end in sight take a minute explain some of those solutions what are you seeing whatever specific examples and solutions that you can go deeper on there yeah so I talked earlier about the the edge meaning the place where users interact with machines and company data that edge is no longer at the desktop down the hallway it could be 10 miles 450 miles away to where anyone where I'm telling you I'm commuting crumb that means we need to push the data confidentiality things out between the headquarters and the edge you do that with things like a secure secured tunnel it's called VPNs you also need to make sure that the user identification authentication this much is a very very secure very authentic and with high integrity so you do that with multi-factor authentication there's other things that we like that that are very very practical that you do to support this new architecture and the good news is that they're available today in the good news at least with some companies there already had one foot in that world but as I mentioned earlier not all companies had yet embraced the idea of where you're going to have a large percentage of your workforce - until a community so they're not quite so they're there they're reacting quickly to to make sure this edge is better protected by identification and authentication and begins I want to get to some of those edge issues that now translate to kind of physical digital virtualization of of life but first I want to ask you around operational technology and IT OT IT these are kind of examples where you're seeing at scale problem with the pandemic being highlighted so cloud providers etc are all kind of impacted and bring solutions to the table you guys at Foot are doing large scale security is there anything around the automation side of it then you've seen emerge because all the people that are taking care of being a supplier in this new normal or this crisis certainly not normal has leveraged automation and data so this has been a fundamental value proposition that highlights what we call the DevOps movement in the cloud world but automation has become hugely available and a benefit to this can you share your insights into how automation is changing with cyber I think you up a nice question for me is it allowed me to talk about not only automation but convergence so it's let's hit automation first right we all even even pre-crisis we need to be better at leveraging automation to do things that machines do best allow people to do higher-order things whether it's unique analysis or something else with a with a more distributed workforce and perhaps fewer resources automation is more important ever to automatically detect bad things that are about to happen automatically mitigating them before they get or they get to bad you know in the cybersecurity world you use things like agile segmentation and you use like techniques called soar it's a type of security orchestration and you want to eat leverage those things very very highly in order to leverage automation to have machines circum amount of human services but you also brought up on my favorite topics which is ot graceful technology though OTS you know are the things that are used to control for the past almost a hundred years now things in the physical world like electric generators and pipes and valves and things like that often used in our critical infrastructures in my company fort net we provide solutions that secure both the IT world the traditional cyber domain but also the OT systems of the world today where safety and reliability are about most important so what we're seeing with the co19 crisis is that supply chains transportation research things like that a lot of things that depend on OT solutions for safety and reliability are much more forefront of mine so from a cybersecurity strategy perspective what you want to do of course is make sure your solutions in the IT space are well integrated with you solutions in the OT space to the so an adversary or a mistake in cause a working to the crack in causing destruction that convergence is interesting you know we were talking before you came on camera around the fact that all these events are being canceled but that really highlights the fact that the physical spaces are no longer available the so-called ot operational technologies of events is the plumbing the face-to-face conversations but everyone's trying to move to digital or virtual eyes that it's not as easy as just saying we did it here we do it there there is a convergence and some sort of translation this new there's a new roles there's new responsibilities new kinds of behaviors and decision making that goes on in the physical and digital worlds that have to then come together and get reimagined and so what's your take on all this because this is not so much about events but although that's kind of prime time problem zooming it is not the answer that's a streaming video how do you replicate the value of physical into the business value in digital it's not a one-to-one so it's quite possible that that we might look back on this event to cover 19 experience we might look back at it in five or ten years and say that was simply a foreshadowing of our of the importance of making sure that our physical environment is appropriate in private what I mean is that with the with the rapid introduction of Internet of Things technologies into the physical world we're going to have a whole lot of dependencies on the thing inconveniences tendencies inconveniences on things an instrument our physical space our door locks or automobiles paths our temperatures color height lots of things to instrument the physical space and so there's gonna be a whole lot of data that's generated in that cyber in a physical domain increasingly in the future and we're going to become dependent upon it well what happens if for whatever reason in the in the future that's massively disruptive so all of a sudden we have a massive disruption in the physical space just like we're experiencing now with open 19 so again that's why it makes sense now to start your planning now with making sure that your safety and reliability controls in the physical domain are up to the same level security and privacy as the things in your IT delete and it highlights what's the where the value is to and it's a transformation I was just reading an article around spatial economics around distance not being together it's interesting on those points you wrote a book about this I want to get your thoughts because in this cyber internet or digital or virtualization of physical to digital whether it's events or actual equipment is causing people to rethink architectures you mentioned a few of them what's the state of the art thinking around someone who has the plan for this again is in its complex it's not just creating a gateway or a physical abstraction layer of software between two worlds there's almost a blending or convergence here what's your what's your thoughts on what's the state of the art thinking on this area yeah the book that I number of a very esteemed colleagues contribute to what we said is that it's time to start treating cybersecurity like a science let's not pretend it's a dark art that we have to relearn every couple years and what what we said in the in the digital Big Bang is that humankind started flourishing once we admitted our ignorance in ultimately our ignorance in the physical world and discovered or invented you can right word the disciplines of physics and chemistry and once we recognize that our physical world was driven by those scientific disciplines we started flourishing right the scientific age led to lots of things whether it would be transportation health care or lots of other things to improve our quality of life well if you fast forward 14 billion years after that cosmic Big Bang which was driven by physics 50 years ago or so we had a digital Big Bang where there was a massive explosion of bits with the invention of the internet and what we argue in the book is that let's start treating cybersecurity like a science or the scientific principle is that we ought to write down and follow a Rousseau's with you so we can thrive in the in the in a digital Big Bang in the digital age and one more point if you don't mind what we what we noted is that the internet was invented to do two things one connect more people or machines than ever imagined in to do so in speeds that were never imagined so the in the Internet is is optimized around speed in connectivity so if that's the case it may be a fundamental premise of cybersecurity science is make sure that your cyber security solutions are optimized around those same two things that the cyber domains are optimized around speed in integration continue from there you can you can build on more and more complex scientific principles if you focus on those fundamental things and speed and integration yeah that's awesome great insight they're awesome I wanted to throw in while you had the internet history lesson down there also was interesting was a very decentralization concept how does that factor in your opinion to some of the security paradigms is that helped or hurt or is it create opportunities for more secure or does it give the act as an advantage yeah I love your questions is your it's a very informed question and you're in a give me good segue to answer the way you know it should be answer yeah the by definition the distributed nature of the Internet means it's an inherently survivable system which is a wonderful thing to have for a critical infrastructure like that if one piece goes down the hole doesn't go down it's kind of like the power grid the u.s. the u.s. electrical power grid there's too many people who say the grid will go down well that's that's just not a practical thing it's not a reality thing the grades broken up into three major grades and there's AB ulis strategies and implementations of diversification to allow the grid to fail safely so it's not catastrophic Internet's the same thing so like my nipple like I was saying before we ought to de cyber security around a similar principle that a catastrophic failure in one partner to start cybersecurity architecture should result in cascading across your whole architecture so again we need to borrow some lessons from history and I think he bring up a good one that the internet was built on survivability so our cybersecurity strategies need to be the same one of the ways you do that so that's all great theory but one of the ways you do that of course is by making your cybersecurity solutions so that they're very well integrated they connect with each other so that you know speaking in cartoon language you know if one unit can say I'm about to fail help me out and another part of your architecture can pick up a slack and give you some more robust security in that that's what a connected the integrated cyber security architecture do for you yeah it's really fascinating insight and I think resiliency and scale are two things I think are going to be a big wave is going to be added into the transformations that going on now it's it's very interesting you know Phil great conversation I could do a whole hour with you and do a fish lead a virtual panel virtualize that our own event here keynote speech thanks so much for your insight one of things I want to get your thoughts on is something that I've been really thinking a lot lately and gathering perspectives and that is on biosecurity and I say biosecurity I'm referring to covet 19 as a virus because biology involves starting a lab or some people debate all that whether it's true or not but but that's what people work on in the biology world but it spreads virally like malware and has a similar metaphor to cybersecurity so we're seeing conversation starting to happen in Washington DC in Silicon Valley and some of my circles around if biology weapon or it's a tool like open-source software could be a tool for spreading cybersecurity Trojans or other things and techniques like malware spear phishing phishing all these things are techniques that could be deployed metaphorically to viral distribution a biohazard or bio warfare if you will will it look the same and how do you defend against the next covet 19 this is what you know average Americans are seeing the impact of the economy with the shelter in place is that what happens again and how do we prevent it and so a lot of people are thinking about this what is your thoughts because it kind of feels the same way as cybersecurity you got to see it early you got to know what's going on you got to identify it you got to respond to it time to close your contain similar concepts what's your thoughts on with BIOS we don't look with all due respect to the the the bio community let me make a quick analogy to the cyber security strategy right cyber security strategy starts with we start as an attacker so I parts of my previous career I'm an authorized had the opportunity to help develop tools that are very very precisely targeted against foreign adversaries and that's a harder job than you think I mean I think the same is true of anyone of a natural-born or a custom a buyer buyer is that not just any virus has the capability to do a lot of harm to a lot of people selling it so it's it's if that doesn't mean though you can sit back and say since it's hard it'll never happen you need to take proactive measures to look for evidence of a compromise of something whether it's a cyber cyber virus or otherwise you have to actively look for that you have to harm yourself to make sure you're not susceptible to it and once you detect one you need to make sure you have a the ability to do segmentation or quarantine very rapidly very very effectively right so in the cyber security community of course the fundamental strategy is about segmentation you keep different types of things separate that don't need to interact and then if you do have a compromise not everything is compromised and then lastly if you want to gradually say bring things back up to recover you can do some with small chunks I think it's a great analogy segmentation is a good analogy to I think what the nation is trying to do right now by warranty kneeing and gradually reopening up things in in segments in actually mention earlier that some of the other techniques are very very similar you want to have good visibility of where you're at risk and then you can automatically detect and then implement some some mitigations based on that good visibility so I agree with you that it turns out that the cyber security strategies might have a whole lot in common with biohazard I address it's interesting site reliability engineers which is a term that Google coined when they built out their large-scale cloud has become a practice that kind of mindset combined with some of the things that you're saying the cyber security mindset seemed to fit this at scale problem space and I might be an alarmist but I personally believe that we've been having a digital war for many many years now and I think that you know troops aren't landing but it's certainly digital troops and I think that we as a country and a global state and global society have to start thinking about you know these kinds of things where a virus could impact the United States shut down the economy devastating impact so I think Wars can be digital and so I may be an alarmist and a conspirators but I think that you know thinking about it and talking about it might be a good thing so appreciate your insights there Phil appreciated what one other point that might be interesting a few years back I was doing some research with the National Lab and we're looking for novel of cybersecurity analytics and we hired some folks who worked in the biology the bio the biomedical community who were studying a biome fires at the time and it was in recognition that there's a lot of commonality between those who are doing cybersecurity analytics and those reviewing bio biology or biomedical type analytics in you know there was a lot of good cross fertilization between our teams and it kind of helps you bring up one more there's one more point which is what we need to do in cybersecurity in general is have more diversity of workforces right now I don't mean just the traditional but important diversities of sex or color but diversity of experiences right some of the best people I've worked with in the cyber analytics field weren't computer science trained people and that's because they came in problems differently with a different background so one of the things that's really important to our field at large and of course the company my company fort net is to massively increase the amount of cyber security training that's available to people not just the computer scientists the world and the engineers but people in other areas as well the other degree to non-greek people and with that a you know higher level of cyber security training available to a more diverse community not only can we solve the problem of numbers we don't have enough cybersecurity people but we can actually increase our ability to defend against these things I have more greater diversity of thought experience you know that's such a great point I think I just put an exclamation point on that I get that question all the time and the skills gap is should I study computer science and like actually if you can solve problems that's a good thing but really diversity about diversity is a wonderful thing in the age of unlimited compute power because traditionally diversity whether it was protocol diversity or technical diversity or you know human you know makeup that's tend to slow things down but you get higher quality so that's a generalization but you get the point diversity does bring quality and if you're doing a data science you don't want have a blind spot I'm not have enough data so yeah I think a good diverse data set is a wonderful thing you're going to a whole nother level saying bringing diversely skill sets to the table because the problems are diverse is that what you're getting at it is it's one of our I'll say our platforms that we're talking about during the during the covered nineteen crisis which is perhaps there's perhaps we could all make ourselves a little bit better by taking some time out since we're not competing taking some time out and doing a little bit more online training where you can where you can either improve your current set of cybersecurity skills of knowledge or be introduced to them for the first time and so there's one or some wonderful Fortinet training available that can allow both the brand-new folks the field or or the the intermediate level folks with you become higher level experts it's an opportunity for all of us to get better rather than spending that extra hour on the road every day why don't we take at least you know 30 of those 60 minutes or former commute time and usually do some online soccer security treaty feel final question for you great insight great conversation as the world and your friends my friends people we don't know other members of society as they start to realize that the virtualization of life is happening just in your section it's convergence what general advice would you have for someone just from a mental model or mindset standpoint to alleviate any anxiety or change it certainly will be happening so how they can better themselves in their life was it is it thinking more about the the the experiences is it more learning how would you give advice to folks out there who are gonna come out of this post pandemic certainly it's gonna be a different world we're gonna be heightened to digital and virtual but as things become virtualized how can someone take this and make a positive outcome out of all this I I think that the future the future remains bright earlier we talked about sci-fi the integration of the cyber world in the physical world that's gonna provide great opportunities to make us more efficient gives us more free time detect bad things from happening earlier and hopefully mitigating those bad things from happening earlier so a lot of things that some people might use as scare tactics right convergence and Skynet in in robotics and things like that I believe these are things that will make our lives better not worse our responsibilities though is talking about those things making sure people understand that they're coming why they're important and make sure we're putting the right security and privacy to those things as these worlds this physical world and the soccer worlds converged I think the future is bright but we still have some work to do in terms of um making sure we're doing things at very high speeds there's no delay in the cybersecurity we put on top of these applications and make sure we have very very well integrated solutions that don't cause things to become more complex make make things easier to do certainly the winds of change in the big waves with the transformations happening I guess just summarize by saying just make it a head win I mean tailwind not a headwind make it work for you at the time not against it Phil thank you so much for your insights I really appreciate this cube conversation remote interview I'm John Ford with the cube talking about cybersecurity and the fundamentals of understanding what's going on in this new virtual world that we're living in to being virtualized as we get back to work and as things start to to evolve further back to normal the at scale problems and opportunities are there and of course the key was bringing it to you here remotely from our studio I'm John Ferrier thanks for watching [Music]

>> David: It's the cube covering the virtual Vertica Big Data conference 2020 brought to you by Vertica. Hello, buddy, welcome to this special digital presentation of the cube. We're tracking the Vertica virtual Big Data conferences, the cubes. I think fifth year doing the BDC. We've been to every big data conference that they've held and really excited to be helping with the digital component here in these interesting times. Ron Cormier is here, Principal database engineer at the Trade Desk. Ron, great to see you. Thanks for coming on. >> Hi, David, my pleasure, good to see you as well. >> So we're talking a little bit about your background you got, you're basically a Vertica and database guru, but tell us about your role at Trade Desk and then I want to get into a little bit about what Trade Desk does. >> Sure, so I'm a principal database engineer at the Trade Desk. The Trade Desk was one of my customers when I was working with Hp, at HP, as a member of the Vertica team, and I joined the Trade Desk in early 2016. And since then, I've been working on building out their Vertica capabilities and expanding the data warehouse footprint and as ever growing database technology, data volume environment. >> And the Trade Desk is an ad tech firm and you are specializing in real time ad serving and pricing. And I guess real time you know, people talk about real time a lot we define real time as before you lose the customer. Maybe you can talk a little bit about you know, the Trade Desk in the business and maybe how you define real time. >> Totally, so to give everybody kind of a frame of reference. Anytime you pull up your phone or your laptop and you go to a website or you use some app and you see an ad what's happening behind the scenes is an auction is taking place. And people are bidding on the privilege to show you an ad. And across the open Internet, this happens seven to 13 million times per second. And so the ads, the whole auction dynamic and the display of the ad needs to happen really fast. So that's about as real time as it gets outside of high frequency trading, as far as I'm aware. So we put the Trade Desk participates in those auctions, we bid on behalf of our customers, which are ad agencies, and the agencies represent brands so the agencies are the madman companies of the world and they have brands that under their guidance, and so they give us budget to spend, to place the ads and to display them and once the ads get displayed, so we bid on the hundreds of thousands of auctions per second. Once we make those bids, anytime we do make a bid some data flows into our data platform, which is powered by Vertica. And, so we're getting hundreds of thousands of events per second. We have other events that flow into Vertica as well. And we clean them up, we aggregate them, and then we run reports on the data. And we run about 40,000 reports per day on behalf of our customers. The reports aren't as real time as I was talking about earlier, they're more batch oriented. Our customers like to see big chunks of time, like a whole day or a whole week or a whole month on a single report. So we wait for that time period to complete and then we run the reports on the results. >> So you you have one of the largest commercial infrastructures, in the Big Data sphere. Paint a picture for us. I understand you got a couple of like 320 node clusters we're talking about petabytes of data. But describe what your environment looks like. >> Sure, so like I said, we've been very good customers for a while. And we started out with with a bunch of enterprise clusters. So the Enterprise Mode is the traditional Vertica deployment where the compute and the storage is tightly coupled all raid arrays on the servers. And we had four of those and we're doing okay, but our volumes are ever increasing, we wanted to store more data. And we wanted to run more reports in a shorter period of time, was to keep pushing. And so we had these four clusters and then we started talking with Vertica about Eon mode, and that's Vertica separation of compute and storage where you get the compute and the storage can be scaled independently, we can add storage without adding compute or vice versa or we can add both, like. So that was something that we were very interested in for a couple reasons. One, our enterprise clusters, we're running out of disk, like when adding disk is expensive. In Enterprise Mode, it's kind of a pain, you got to add, compute at the same time, so you kind of end up in an unbalanced place. So beyond mode that problem gets a lot better. We can add disk, infinite disk because it's backed by S3. And we can add compute really easy to scale, the number of things that we run in parallel concurrency, just add a sub cluster. So they are two US East and US west of Amazon, so reasonably diverse. And and the real benefit is that they can, we can stop nodes when we don't need them. Our workload is fairly lumpy, I call it. Like we, after the day completes, we do the ingest, we do the aggregation for ingesting and aggregating all day, but the final hour, so it needs to be completed. And then once that's done, then the number of reports that we need to run spikes up, it goes really high. And we run those reports, we spin up a bunch of extra compute on the fly, run those reports and then spin them down. And we don't have to pay for that, for the rest of the day. So Eon has been a nice Boone for us for both those reasons. >> I'd love to explore you on little bit more. I mean, it's relatively new, I think 2018 Vertica announced Eon mode, so it's only been out there a couple years. So I'm curious for the folks that haven't moved the Eon mode, can you which presumably they want to for the same reasons that you mentioned why by the stories and chunks when you're on Storage if you don't have to, what were some of the challenges that you had to, that you faced in going to Eon mode? What kind of things did you have to prepare for? Were there any out of scope expectations? Can you share that experience with us? >> Sure, so we were an early adopter. We participated in the beta program. I mean, we, I think it's fair to say we actually drove the requirements and a lot of ways because we approached Vertica early on. So the challenges were what you'd expect any early adopter to be going through. The sort of getting things working as expected. I mean, there's a number of cases, which I could touch upon, like, we found an efficiency in the way that it accesses the data on S3 and it was accessing the data too frequently, which ended up was just expensive. So our S3 bill went up pretty significantly for a couple of months. So that was a challenge, but we worked through that another was that we recently made huge strides in with Vertica was the ability to stop and start nodes and not have to start them very quickly. And when they start to not interfere with any running queries, so when we create, when we want to spin up a bunch to compute, there was a point in time when it would break certain queries that were already running. So that that was a challenge. But again, the very good team has been quite responsive to solving these issues and now that's behind us. In terms of those who need to get started, there's or looking to get started. there's a number of things to think about. Off the top of my head there's sort of new configuration items that you'll want to think about, like how instance type. So certainly the Amazon has a variety of instances and its important to consider one of Vertica's architectural advantages in these areas Vertica has this caching layer on the instances themselves. And what that does is if we can keep the data in cache, what we've found is that the performance is basically the same performance of Enterprise Mode. So having a good size cast when needed, can be a little worrying. So we went with the I three instance types, which have a lot of local NVME storage that we can, so we can cache data and get good performance. That's one thing to think about. The number of nodes, the instance type, certainly the number of shards is a sort of technical item that needs to be considered. It's how the data gets, its distributed. It's sort of a layer on top of the segmentation that some Vertica engineers will be familiar with. And probably I mean, the, one of the big things that one needs to consider is how to get data in the database. So if you have an existing database, there's no sort of nice tool yet to suck all the data into an Eon database. And so I think they're working on that. But we're at the point we got there. We had to, we exported all our data out of enterprise cluster as cache dumped it out to S3 and then we had the Eon cluster to suck that data. >> So awesome advice. Thank you for sharing that with the community. So but at the end of the day, so it sounds like you had some learning to do some tweaking to do and obviously how to get the data in. At the end of the day, was it worth it? What was the business impact? >> Yeah, it definitely was worth it for us. I mean, so right now, we have four times the data in our Eon cluster that we have in our enterprise clusters. We still run some enterprise clusters. We started with four at the peak. Now we're down to two. So we have the two young clusters. So it's been, I think our business would say it's been a huge win, like we're doing things that we really never could have done before, like for accessing the data on enterprise would have been really difficult. It would have required non trivial engineering to do things like daisy chaining clusters together, and then how to aggregate data across clusters, which would, again, non trivial. So we have all the data we want, we can continue to grow data, where running reports on seasonality. So our customers can compare their campaigns last year versus this year, which is something we just haven't been able to do in the past. We've expanded that. So we grew the data vertically, we've expanded the data horizontally as well. So we were adding columns to our aggregates. We are, in reaching the data much more than we have in the past. So while we still have enterprise kicking around, I'd say our clusters are doing the majority of the heavy lifting. >> And the cloud was part of the enablement, here, particularly with scale, is that right? And are you running certain... >> Definitely. >> And you are running on prem as well, or are you in a hybrid mode? Or is it all AWS? >> Great question, so yeah. When I've been speaking about enterprise, I've been referring to on prem. So we have a physical machines in data centers. So yeah, we are running a hybrid now and I mean, and so it's really hard to get like an apples to apples direct comparison of enterprise on prem versus Eon in the cloud. One thing that I touched upon in my presentation is it would require, if I try to get apples to apples, And I think about how I would run the entire workload on enterprise or on Eon, I had to run the entire thing, we want both, I tried to think about how many cores, we would need CPU cores to do that. And basically, it would be about the same number of cores, I think, for enterprise on prime versus Eon in the cloud. However, Eon nodes only need to be running half the course only need to be running about six hours out of the day. So the other the other 18 hours I can shut them down and not be paying for them, mostly. >> Interesting, okay, and so, I got to ask you, I mean, notwithstanding the fact that you've got a lot invested in Vertica, and get a lot of experience there. A lot of you know, emerging cloud databases. Did you look, I mean, you know, a lot about database, not just Vertica, your database guru in many areas, you know, traditional RDBMS, as well as MPP new cloud databases. What is it about Vertica that works for you in this specific sweet spot that you've chosen? What's really the difference there? >> Yeah, so I think the key differences is the maturity. There are a number, I am familiar with another, a number of other database platforms in the cloud and otherwise, column stores specifically, that don't have the maturity that we're used to and we need at our scale. So being able to specify alternate projections, so different sort orders on my data is huge. And, there's other platforms where we don't have that capability. And so the, Vertica is, of course, the original column store and they've had time to build up a lead in terms of their maturity and features and I think that other other column stores cloud, otherwise are playing a little bit of catch up in that regard. Of course, Vertica is playing catch up on the cloud side. But if I had to pick whether I wanted to write a column store, first graph from scratch, or use a defined file system, like a cloud file system from scratch, I'd probably think it would be easier to write the cloud file system. The column store is where the real smarts are. >> Interesting, let's talk a little bit about some of the challenges you have in reporting. You have a very dynamic nature of reporting, like I said, your clients want to they want to a time series, they just don't want to snap snapshot of a slice. But at the same time, your reporting is probably pretty lumpy, a very dynamic, you know, demand curve. So first of all, is that accurate? Can you describe that sort of dynamic, dynamism and how are you handling that? >> Yep, that's exactly right. It is lumpy. And that's the exact word that I use. So like, at the end of the UTC day, when UTC midnight rolls around, that's we do the final ingest the final aggregate and then the queue for the number of reports that need to run spikes. So the majority of those 40,000 reports that we run per day are run in the four to six hours after that spikes up. And so that's when we need to have all the compute come online. And that's what helps us answer all those queries as fast as possible. And that's a big reason why Eon is advantage for us because the rest of the day we kind of don't necessarily need all that compute and we can shut it down and not pay for it. >> So Ron, I wonder if you could share with us just sort of the wrap here, where you want to take this you're obviously very close to Vertica. Are you driving them in a heart and Eon mode, you mentioned before you'd like, you'd have the ability to load data into Eon mode would have been nice for you, I guess that you're kind of over that hump. But what are the kinds of things, If Column Mahoney is here in the room, what are you telling him that you want the team, the engineering team at Vertica to work on that would make your life better? >> I think the things that need the most attention sort of near term is just the smoothing out some of the edges in terms of making it a little bit more seamless in terms of the cloud aspects to it. So our goal is to be able to start instances and have them join the cluster in less than five minutes. We're not quite there yet. If you look at some of the other cloud database platforms, they're beating that handle it so I know the team is working on that. Some of the other things are the control. Like I mentioned, while we like control in the column store, we also want control on the cloud side of things in terms of being able to dedicate cluster, some clusters specific. We can pin workloads against a specific sub cluster and take advantage of the cast that's over there. We can say, okay, this resource pool. I mean, the sub cluster is a new concept, relatively new concept for Vertica. So being able to have control of many things at sub cluster level, resource pools, configuration parameters, and so on. >> Yeah, so I mean, I personally have always been impressed with Vertica. And their ability to sort of ride the wave adopt new trends. I mean, they do have a robust stack. It's been, you know, been 10 plus years around. They certainly embraced to do, the embracing machine learning, we've been talking about the cloud. So I actually have a lot of confidence to them, especially when you compare it to other sort of mid last decade MPP column stores that came out, you know, Vertica is one of the few remaining certainly as an independent brand. So I think that speaks the team there and the engineering culture. But give your final word. Just final thoughts on your role the company Vertica wherever you want to take it. >> Yeah, no, I mean, we're really appreciative and we value the partners that we have and so I think it's been a win win, like our volumes are, like I know that we have some data that got pulled into their test suite. So I think it's been a win win for both sides and it'll be a win for other Vertica customers and prospects, knowing that they're working with some of the highest volume, velocity variety data that (mumbles) >> Well, Ron, thanks for coming on. I wish we could have met face to face at the the Encore in Boston. I think next year we'll be able to do that. But I appreciate that technology allows us to have these remote conversations. Stay safe, all the best to you and your family. And thanks again. >> My pleasure, David, good speaking with you. >> And thank you for watching everybody, we're covering this is the Cubes coverage of the Vertica virtual Big Data conference. I'm Dave volante. We'll be right back right after this short break. (soft music)

>> Announcer: From Chicago, it's theCUBE, covering Oracle Transformation Day 2020. Brought to you by Oracle Consulting. >> Welcome back, everybody, to this special digital event coverage that theCUBE is looking into the rebirth of Oracle Consulting. Janet George is here, she's a group VP, autonomous for advanced analytics with machine learning and artificial intelligence at Oracle, and she's joined by Grant Gibson, who's a group VP of growth and strategy at Oracle. Folks, welcome to theCUBE, thanks so much for coming on. >> Thank you. >> Thank you. >> Grant, I want to start with you because you've got strategy in your title. I'd like to start big-picture. What is the strategy with Oracle, specifically as it relates to autonomous, and also consulting? >> Sure, so, I think Oracle has a deep legacy of strength in data, and over the company's successful history, it's evolved what that is from steps along the way. And if you look at the modern enterprise, an Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology, and people know that they need to take advantage of it, it's the how that's really tricky, and that most enterprises, in order to really get an enterprise-level ROI on an AI investment, need to engage in projects of significant scope. And going from realizing there's an opportunity or realizing there's a threat to mobilizing yourself to capitalize on it is a daunting task for enterprise. Certainly one that's, anybody that's got any sort of legacy of success has built-in processes, has built-in systems, has built-in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs, as well as the data science needs to it. So there's-- >> So, wow, there's about five or six things that I want to (Grant chuckles) follow up with you there, so this is a good conversation. Janet, ever since I've been in the industry, when you're talking about AI, it's sort of start-stop, start-stop. We had the AI winter, and now it seems to be here. It almost feels like the technology never lived up to its promise, 'cause we didn't have the horsepower, the compute power, it didn't have enough data, maybe. So we're here today, it feels like we are entering a new era. Why is that, and how will the technology perform this time? >> So for AI to perform, it's very reliant on the data. We entered the age of AI without having the right data for AI. So you can imagine that we just launched into AI without our data being ready to be training sets for AI. So we started with BI data, or we started with data that was already historically transformed, formatted, had logical structures, physical structures. This data was sort of trapped in many different tools, and then, suddenly, AI comes along, and we say, take this data, our historical data, we haven't tested it to see if this has labels in it, this has learning capability in it. We just thrust the data to AI. And that's why we saw the initial wave of AI sort of failing, because it was not ready for AI, ready for the generation of AI, if you will. >> So, to me, this is, I always say this was the contribution that Hadoop left us, right? I mean, Hadoop, everybody was crazy, it turned into big data. Oracle was never that nuts about it, they just kind of watched, sat back and watched, obviously participated. But it gathered all this data, it created cheap data lakes, (laughs) which people always joke, turns into data swamps. But the data is oftentimes now within organizations, at least present, right. >> Yes, yes, yes. >> Like now, it's a matter of what? What's the next step for really good value? >> Well, basically, what Hadoop did to the world of data was Hadoop freed data from being stuck in tools. It basically brought forth this concept of platform. And platform is very essential, because as we enter the age of AI and we enter the petabyte range of data, we can't have tools handling all of this data. The data needs to scale. The data needs to move. The data needs to grow. And so, we need the concept of platform so we can be elastic for the growth of the data. It can be distributed. It can grow based on the growth of the data. And it can learn from that data. So that's the reason why Hadoop sort of brought us into the platform world. And-- >> Right, and a lot of that data ended up in the cloud. I always say for years, we marched to the cadence of Moore's law. That was the innovation engine in this industry. As fast as you could get a chip in, you'd get a little advantage, and then somebody would leapfrog. Today, it's, you've got all this data, you apply machine intelligence, and cloud gives you scale, it gives you agility. Your customers, are they taking advantage of that new innovation cocktail? First of all, do you buy that, and how do you see them taking advantage of this? >> Yeah, I think part of what Janet mentioned makes a lot of sense, is that at the beginning, when you're taking the existing data in an enterprise and trying to do AI to it, you often get things that look a lot like what you already knew, because you're dealing with your existing data set and your existing expertise. And part of, I think, the leap that clients are finding success with now is getting novel data types. You're moving from the zeroes and ones of structured data to image, language, written language, spoken language. You're capturing different data sets in ways that prior tools never could, and so, the classifications that come out of it, the insights that come out of it, the business process transformation that comes out of it is different than what we would have understood under the structured data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. >> So you talked about sort of we're entering the new era, age of AI. A lot of people kind of focus on the cloud as sort of the current era, but it really does feel like we're moving beyond that. The language that we use today, I feel like, is going to change, and you just started to touch on some of it, sensing, our senses, and the visualization, and the auditory, so it's sort of this new experience that customers are seeing, and a lot of this machine intelligence behind that. >> I call it the autonomous enterprise, right? >> Okay. >> The journey to be the autonomous enterprise. And when you're on this journey to be the autonomous enterprise, you need, really, the platform that can help you be. Cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud, or doesn't end with the data lake. These are just infrastructures that are basic, necessary, necessities for being on that autonomous journey. But at the end, it's about, how do you train and scale very large-scale training that needs to happen on this platform for AI to be successful? And if you are an autonomous enterprise, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components, AI and machine learning, to derive business intelligence and business value. >> So I want to get into a little bit of Oracle's role, but to do that, I want to talk a little bit more about the industry. So if you think about the way the industry seems to be restructuring around data, historically, industries had their own stack or value chain, and if you were in the finance industry, you were there for life, you know? >> Yes. >> You had your own sales channel, distribution, et cetera. But today, you see companies traversing industries, which has never happened before. You see Apple getting into content, and music, and there's so many examples, Amazon buying Whole Foods. Data is sort of the enabler there. You have a lot of organizations, your customers, that are incumbents, that they don't want to get disrupted. A big part of your role is to help them become that autonomous enterprise so they don't get disrupted. I wonder if you could maybe comment on how you're doing. >> Yeah, I'll comment, and then, Grant, you can chime in. >> Great. >> So when you think about banking, for example, highly regulated industry, think about agriculture, these are highly regulated industries. It is very difficult to disrupt these industries. But now you're looking at Amazon, and what does an Amazon or any other tech giant like Apple have? They have incredible amounts of data. They understand how people use, or how they want to do, banking. And so, they've come up with Apple Cash, or Amazon Pay, and these things are starting to eat into the market. So you would have never thought an Amazon could be a competition to a banking industry, just because of regulations, but they are not hindered by the regulations because they're starting at a different level, and so, they become an instant threat and an instant disruptor to these highly regulated industries. That's what data does. When you use data as your DNA for your business, and you are sort of born in data, or you've figured out how to be autonomous, if you will, capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be the food industry, it can be the cloud industry, the book industry, you know, different industries. So that's what I see happening with the tech giants. >> So, Grant, this is a really interesting point that Janet is making, that, you mentioned you started off with a couple of industries that are highly regulated and harder to disrupt. You know, music got disrupted, publishing got disrupted, but you've got these regulated businesses, defense. Automotive hasn't been truly disrupted yet, so Tesla maybe is a harbinger. And so, you've got this spectrum of disruption. But is anybody safe from disruption? >> (laughs) I don't think anyone's ever safe from it. It's change and evolution, right? Whether it's swapping horseshoes for cars, or TV for movies, or Netflix, or any sort of evolution of a business, I wouldn't coast on any of it. And I think, to your earlier question around the value that we can help bring to Oracle customers is that we have a rich stack of applications, and I find that the space between the applications, the data that spans more than one of them, is a ripe playground for innovations where the data already exists inside a company but it's trapped from both a technology and a business perspective, and that's where, I think, really, any company can take advantage of knowing its data better and changing itself to take advantage of what's already there. >> The powerful people always throw the bromide out that data is the new oil, and we've said, no, data's far more valuable, 'cause you can use it in a lot of different places. Oil, you can use once and it's all you can do. >> Yeah. >> It has to follow the laws of scarcity. Data, if you can unlock it, and so, a lot of the incumbents, they have built a business around whatever, a factory or process and people. A lot of the trillion-dollar startups, that become trillionaires, you know who I'm talking about, data's at the core, they're data companies. So it seems like a big challenge for your incumbent customers, clients, is to put data at the core, be able to break down those silos. How do they do that? >> Mm, grating down silos is really super critical for any business. If it's okay to operate in a silo, for example, you would think that, "Oh, I could just be payroll and expense reports, "and it wouldn't matter if I get into vendor "performance management or purchasing. "That can operate as a silo." But anymore, we are finding that there are tremendous insights between vendor performance management and expense reports, these things are all connected. So you can't afford to have your data sit in silos. So grating down that silo actually gives the business very good performance, insights that they didn't have before. So that's one way to go. But another phenomena happens. When you start to grate down the silos, you start to recognize what data you don't have to take your business to the next level. That awareness will not happen when you're working with existing data. So that awareness comes into form when you grate the silos and you start to figure out you need to go after a different set of data to get you to new product creation, what would that look like, new test insights, or new capex avoidance, that data is just, you have to go through the iteration to be able to figure that out. >> And then it becomes a business problem, right? If you've got a process now where you can identify 75% of the failures, and you know the value of the other 25% of the failures, it becomes a simple investment. "How much money am I willing to invest "to knock down some portion of that 25%?" And it changes it from simply an IT problem or an expense management problem to the universal cash problem. >> To a business problem. >> But you still need a platform that has APIs, that allows you to bring in-- >> Yes, yes. >> Those data sets that you don't have access to, so it's an enabler. It's not the answer, it's not the outcome, in and of itself, but it enables the outcome. >> Yeah, and-- >> I always say you can't have the best toilet if your plumbing doesn't work, you know what I mean? So you have to have your plumbing. Your plumbing has to be more modern. So you have to bring in modern infrastructure, distributed computing, that, there's no compromise there. You have to have the right ecosystem for you to be able to be technologically advanced and a leader in that space. >> But that's kind of table stakes, is what you're saying. >> Stakes. >> So this notion of the autonomous enterprise, help me here. 'Cause I get kind of autonomous and automation coming into IT, IT ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >> Yeah, this is such a great question. This is what I've been talking about all morning. I think when AI is a technology problem, the company is at a loss. AI has to be a business problem. AI has to inform the business strategy. When companies, the successful companies that have done, so, 90% of our investments are going towards data, we know that, and most of it going towards AI. There's data out there about this. And so, we look at, what are these 90% of the companies' investments, where are these going, and who is doing this right, and who is not doing this right? One of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model. So it's not making a better taxi, but coming up with Uber. So it's not like saying, "Okay, I'm going to be "the drug manufacturing company, "I'm going to put drugs out there in the market," versus, "I'm going to do connected health." And so, how does data serve the business model of being connected health, rather than being a drug company selling drugs to my customers? It's a completely different way of looking at it. And so now, AI's informing drug discovery. AI is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that will help the process of connected care. >> There's a lot of discussion in the press about the ethics of AI, and how far should we take AI, and how far can we take it from a technology standpoint, (laughs) long road map, there. But how far should we take it? Do you feel as though public policy will take care of that, a lot of that narrative is just kind of journalists looking for the negative story? Will that sort itself out? How much time do you spend with your customers talking about that, and what's Oracle's role there? Facebook says, "Hey, the government should figure this out." What's your sort of point of view on that? >> I think everybody has a role, it's a joint role, and none of us can give up our responsibilities. As data scientists, we have heavy responsibility in this area, and we have heavy responsibility to advise the clients on this area also. The data we come from, the past, has to change. That is inherently biased. And we tend to put data science on biased data with a one-dimensional view of the data. So we have to start looking at multiple dimensions of the data. We've got to start examining, I call it irresponsible AI, when you just simply take one variable, we'll start to do machine learning with that, 'cause that's not right. You have to examine the data. You've got to understand how much bias is in the data. Are you training a machine learning model with the bias? Is there diversity in the models? Is there diversity in the data? These are conversations we need to have. And we absolutely need policy around this, because unless our lawmakers start to understand that we need the source of the data to change, and if we look at the source of the data, and the source of the data is inherently biased or the source of the data has only a single representation, we're never going to change that downstream. AI's not going to help us there. So that has to change upstream. That's where the policy makers come into play, the lawmakers come into play. But at the same time, as we're building models, I think we have a responsibility to say, "Can we triangulate? "Can we build with multiple models? "Can we look at the results of these models? "How are these features ranked? "Are they ranked based on biases, sex, age, PII information? "Are we taking the PII information out? "Are we really looking at one variable?" Somebody failed to pay their bill, but they just failed to pay their bill because they were late, versus that they don't have a bank account and we classify them as poor on having no bank account, you know what I mean? So all this becomes part of responsible AI. >> But humans are inherently biased, and so, if humans are building algorithms-- >> That's right, that's right. >> There is the bias. >> So you're saying that through iteration, we can stamp out the bias? Is that realistic? >> We can stamp out the bias, or we can confirm the bias. >> Or at least make it transparent. >> Make it transparent. So I think that even if we can have the trust to be able to have the discussion on, "Is this data "the right data that we are doing the analysis on?" and start the conversation there, we start to see the change. >> Well, wait, so we could make it transparent, then I'm thinking, a lot of AI is black box. Is that a problem? Is the black box syndrome an issue, or are we, how would we deal with it? >> Actually, AI is not a black box. We, in Oracle, we are building our data science platform with an explicit feature called explainability of the model, on how the model came up with the features, what features it picked. We can rearrange the features that the model picked. So I think explainability is very important for ordinary people to trust AI. Because we can't trust AI. Even data scientists can't trust AI, to a large extent. So for us to get to that level where we can really trust what AI's picking, in terms of a model, we need to have explainability. And I think a lot of the companies right now are starting to make that as part of their platform. >> So that's your promise to clients, is that your AI will not be a black box. >> Absolutely, absolutely. >> 'Cause that's not everybody's promise. >> Yes. >> I mean, there's a lot of black box in AI, as you well know. >> Yes, yes, there is. If you go to open source and you start downloading, you'll get a lot of black box. The other advantage to open source is sometimes you can just modify the black box. They can give you access and you can modify the black box. But if you get companies that have released to open source, it's somewhat of a black box, so you have to figure out the balance between. You don't really have to worry too much about the black box if you can see that the model has done a pretty good job as compared to other models. If I triangulate the results of the algorithm, and the triangulation turns out to be reasonable, the accuracy and the r values and the matrixes show reasonable results, then I don't really have to worry if one model is too biased compared to another model. But I worry if there's only one dimension to it. >> Mm-hm, well, ultimately, to much of the data scientists' dismay, somebody on the business side is going to ask about causality. >> That's right. >> "Well, this is what "the model says, why is it saying that?" >> Yeah, right. >> Yeah. >> And, ethical reasons aside, you're going to want to understand why the predictions are what they are, and certainly, as you go in to examine those things, as you look at the factors that are causing the predictions and the outcomes, I think any sort of business should be asking those responsibility questions of everything they do, AI included, for sure. >> So, we're entering a new era, we kind of all agree on that. So I just want to throw a few questions out and have a little fun here, so feel free to answer in any order. So when do you think machines will be able to make better diagnoses than doctors? >> I think they already are making better diagnoses. I mean, there's so much, like, I found out recently that most of the very complicated cancer surgeries are done by machines, doctors just standing by and making sure that the machines are doing it well. And so, I think the machines are taking over in some aspects, I wouldn't say all aspects. And then there's the bedside manners, where you (laughs) really need the human doctor, and you need the comfort of talking to the doctor. >> Smiley face, please! (Janet laughs) >> That's advanced AI, to give it a better bedside manner. >> Okay, when do you think that driving and owning your own vehicle is going to be the exception rather than the rule? >> That, I think, is so far ahead, it's going to be very, very near future, because if you've ever driven in an autonomous car, you'll find that after your initial reservations, you're going to feel a lot more safer in an autonomous car. Because it's got a vision that humans don't. It's got a communication mechanism that humans don't. It's talking to all the fleets of cars. >> It's got a richer sense of data. >> It's got a richer sense of data, it's got a richer sense of vision, it's got a richer sense of ability to (snaps) react when a kid jumps in front of the car. Where a human will be terrified and not able to make quick decisions, the car can. But at the same time, we're going to have some startup problems. We're going to see AI misfire in certain areas, and insurance companies are gearing themselves up for that, 'cause that's just, but the data's showing us that we will have tremendously decreased death rates. That's a pretty good start to have AI driving our cars. >> You're a believer, well, and you're right, there's going to be some startup issues, because this car, the vehicle has to decide, "Do I kill that person who jumped in front of me, "or do I kill the driver?" Not kill, I mean, that's overstating-- >> Yeah. >> But those are some of the startup things, and there will be others. >> And humans, you don't question the judgment system for that. >> Yes. >> There's no-- >> Dave: Right, they're yelling at humans. >> Person that developed, right. It's treated as a one-off. But I think if you look back five years, where were we? You figure, the pace of innovation and the speed and the gaps that we're closing now, where are we going to be in five years? >> Yeah. >> You have to figure it's, I have an eight-year-old son, and I question if he's ever going to drive a car. >> Yeah. >> Yeah. >> How about retail? Do you think retail stores largely will disappear? >> Oh, I think retail, there will be a customer service element to retail, but it will evolve from where it's at in a very, very high-stakes rate, because now, with RFID, you know who's, we used to be invisible as we walked, we still are invisible as you walk into a retail store, even if you spend a lot of money in retail. And now, with buying patterns and knowing who the customer is, and your profile is out there on the Web, just getting a sense of who this person is, what their intent is walking into the store, and doing responsible AI, bringing value to that intent, not irresponsibly, that will gain the trust, and as people gain the trust. And then RFIDs, you're in the location, you're nearby, you'd normally buy the suit, the suit's on sale, bring it all together. So I think there's a lot of connective tissue work that needs to happen, but that's all coming together. >> Yeah, it's about the value-add and what the proposition to the customer is. If it's simply there as a place where you go and pick out something you already know what you're going to get, that store doesn't add value, but if there's something in the human expertise, or in the shared, felt sudden experience of being in the store, that's where you'll see retailers differentiate themselves. >> I like to shop still. (laughs) >> Yeah, yeah. >> You mentioned Apple Pay before. Well, you think traditional banks will lose control of the payment systems? >> They're already losing control of payment systems. If you look at, there was no reason for the banks to create Siri-like assistants. They're all over right now. And we started with Alexa first. So you can see the banks are trying to be a lot more customized, customer service, trying to be personalized, trying to really make you connect to them in a way that you have not connected to the bank before. The way that you connected to the bank is you knew the person at the bank for 20 years, or since when you had your first bank account. That's how you connected with the banks. And then you go to a different branch, and then, all of a sudden, you're invisible. Nobody knows you, nobody knows that you were 20 years with the bank. That's changing. They're keeping track of which location you're going to, and trying to be a more personalized. So I think AI is a forcing function, in some ways, to provide more value, if anything. >> Well, we're definitely entering a new era, the age of AI, the autonomous enterprise. Folks, thanks very much for a great segment, really appreciate it. >> Yeah, our pleasure, thank you for having us. >> Thank you for having us. >> You're welcome, all right, and thank you. And keep it right there, we'll be right back with our next guest right after this short break. You're watching theCUBE's coverage of the rebirth of Oracle Consulting. We'll be right back. (upbeat electronic music)