>> Good afternoon everyone, and welcome back to theCUBE where we're coming to you live from Detroit, Michigan at KubeCon and Cloud Native Con. We're going to keep theCUBE puns coming this afternoon because we have the pleasure of being joined by not one but two guests from Kubiya. John Furrier, my wonderful co-host. You're familiar with these guys. You just chatted with them last week. >> We broke the story of their launch and featured them on theCUBE in our studio conversation. This is a great segment. Real innovative company with lofty goals, and they're really good ones. Looking forward to it. >> If that's not a tease to keep watching I don't know what is. (John laughs) Without further ado, on that note, allow me to introduce Amit and Shaked who are here to tell us all about Kubiya. And I'm going to blow the pitch for you a little bit just because this gets me excited. (all laugh) They're essentially the Siri of DevOps, but that means you can, you can create using voice or chat or any medium. Am I right? Is this? Yeah? >> You're hired. >> Excellent. (all laugh) >> Okay. >> We'll take it. >> Who knows what I'll tell the chat to do or what I'll, what I will control with my voice, but I love where you're. >> Absolutely. I'll just give the high level. Conversational AI for the world of DevOps. Kind of redefining how self-service DevOps is supposed to be essentially accessed, right? As opposed to just having siloed information. You know, having different platforms that require an operator or somebody who's using it to know exactly how they're accessing what they're doing and so forth. Essentially, the ability to express your intent in natural language, English, or any language I use. >> It's quite literally the language barrier sometimes. >> Precisely. >> Both from the spoken as well as code language. And it sounds like you're eliminating that as an obstacle. >> We're essentially saying, turn simple, complex cast into simple conversations. That's, that's really what we're saying here. >> So let's get into the launch. You just launched a fresh startup. >> Yeah, yeah, yeah. >> Yeah. >> So you guys are going to take on the world. Lofty goals if that. I had the briefing. Where's the origination story come from? What, how did you guys get here? Was it a problem that you saw, you were experiencing, an itch you were scratching? What was the motivation and what's the origination story? >> Shaked: So. >> Amit: Okay, go first please. >> Essentially everything started with my experience as being an operator. I used to be a DevOps engineer for a few years for a large (indistinct) company. On later stages I even managed an SRE team. So all of these access requires Q and A staff is something that I experience nonstop on Slack or Teams, all of these communication channels. And usually I find out that everything happens from the chat. So essentially back then I created a chat bot. I connect this chat bot to the different organizational tools, and instead of the developers approaching to me or the team using the on call channel or directly they will just approach the bot. But essentially the bot was very naive, and they still needed to know what they, they want to do inside the bot. But it's still managed to solve 70% of the complexity and the toil on us as a team so we could focus on innovation. So Kubiya's a more advanced version of it. Basically with Kubiya you can define what we call workflows, and we convert all of these complexity of access request into simple conversations that the end users, which could be developers, but not only, are having with a DevOps team. So that's essentially how it works, and we're very excited about it. >> So you were up all night answering the same question over and over again. (all laugh) And you said, Hey, screw it. I'm going to just create a bot, bot myself up. (Shaked laughs) But it gets at something important. I mean, I'm not just joking. It probably happened, right? That was probably the case. You were up all night telling. >> Yeah, I mean it was usually stuff that we didn't need to maintain. It was training requests and questions that just keep on repeating themselves. And actually we were in Israel, but we sell three different time zones of developers. So all of these developers, as soon as the day finishes in Israel, the day in the US started. So they will approach us from the US. So we didn't really sleep. (all laugh) As with these requests non-stop. >> It's that 24 hour. >> Yeah, yeah. 24 hours for a single team. >> So the world clock global (indistinct) catches you a little sometimes. Yeah. >> Yeah, exactly. >> So you basically take all the things that you know that are common and then make a chat bot answering as if you're you. But this brings up the whole question of chat bot utilization. There's been a lot of debate in the AI circles that chat bots really haven't made it. They're not, they haven't been good enough. So 'cause NLP and other trivial, >> Amit: Sure. or things that haven't really clicked. What's different now? How do you guys see your approach cracking the code to go that kind of reasoning level? Bots can reason? Now we're in business. >> Yeah. Most of the chat bots are general purpose, right? We're coming with the domain expertise. We know the pain from the inside. We know how the operators want to define such conversations that users might have with the virtual assistant. So we combined all of the technical tools that are needed in order to get it going. So we have a a DSL, domain specific language, where the operators can define these easy conversations and combine all of the different organizational tools which can be done using DSDK. Besides this fact, we have a no code, for less technical people to create such workflows even with no code interface. And we have a CLI, which you could use to leverage the power of the virtual assist even right from your terminal. So that's how I see the domain expertise coming in that we have different communication channels for everyone that needs to be inside the loop. >> That's awesome. >> And I, and I can add to that. So that's one element, which is the domain expertise. The other one is really our huge differentiator, the ability to let the end users influence the system itself. So essentially. >> John: Like how? Give me an example. >> Sure. We call it teach me feature, but essentially if you have any type of a request and the system hasn't created an automation or hasn't, doesn't recognize it, you can go ahead and bind that into your intent and next time, and you can define the scope for yourself only, for the team, or even for the entire organization that actually has to have permission to access the request and control and so on. >> Savannah: That's something. Yeah, I love that as a knowledge base. I mean a custom tool kit. >> Absolutely. >> And I like that you just said for the individual. So let's say I have some crazy workflows that I don't need anybody else to know about. >> 100 percent. >> I can customize my experience. >> Mm hmm. >> Do you see your, this is really interesting, and I'm, it's surprising to me we haven't seen a lot of players in this space before because what you're doing makes a lot of sense to me, especially as someone who is less technical. >> Yeah. >> Do you view yourselves as a gateway tool for more folks to be involved in more complex technology? >> So, so I'll take that. It's not that we haven't seen advanced virtual assistants. They've existed in different worlds. >> Savannah: Right. >> Up until now they've existed more in CRM tools. >> Savannah: Right. >> Call centers, right? >> Shaked: Yeah. >> You go on to Ralph Lauren, Calvin Klein, you go and chat with. Now imagine you can bring that into a world of dev tools that has high domain expertise, high technical amplitude, and now you can go and combine the domain expertise with the accessibility of conversational AI. That's, that's a unique feature here. >> What's the biggest thing that's surprised you with the launch so far? The reaction to the name, Kubiya, which is Cube in Hebrew. >> Amit: Yes. >> Apparently. >> Savannah: Which I love. >> Which by the way, you know, we have a TM and R on our Cube. (all laugh) So we can talk, you know, license rights. >> Let's drop the trademark rules today, John, here. We're here to share information. Confuse the audience. Sorry about that, by the way. (all laugh) >> We're an open source, inclusive culture. We'll let it slide this time. >> The KubeCon, theCUBE, and Kubiya. (John laughs) In the Hebrew we have this saying, third time we all have ice cream. So. (all laugh) >> I think there's some ice cream over there actually. >> There is. >> Yeah, yeah. There you go. >> All kidding aside, all fun. What's, what's been the reaction? Got some press coverage. We had the launch. You guys launched with theCUBE in here, big reception. What's been the common feedback? >> And really, I think I expected this, but I didn't expect this much. Really, the fact that people really believe in our thesis, really expect great things from us, right? We've starting to working with. >> Savannah: Now the pressure's on. >> Rolling out dozens of POCs, but even that requires obviously a lot of attention to the detail, which we're rolling out. This is effectively what we're seeing. People love the fact that you have a unique and fresh way to approaching the self-service which really has been stalled for a while. And we've recognized that. I think our thesis is where we. >> Okay, so as a startup you have lofty goals, you have investors now. >> Amit: Yeah. >> Congratulations. >> Amit: Thank you. >> They're going to want to keep the traction going, but as a north star, what's your, what are you going to, what are you going to take? What territory are you going to take? Is it new territory? Are you eating someone's lunch? Who are you going to be competing with? What's the target? What's the, what's the? >> Sure, sure. >> I'm sure you guys have it. Who are you takin' over? >> I think the gateway, the entry point to every organization is a bottleneck. You solve the hard problem first. That's where you can go into other directions, and you can imagine where other operational workflows and pains that we can help solve once we have essentially the DevOps. >> John: So you see this as greenfield, new opportunity? >> I believe so. >> Is there any incumbent you see out there? An old stodgy? >> Today we're on internal developer platform service catalog type of, you know, use cases. >> John: Yeah. >> But that's kind of where we can grow from there and have the ecosystem essentially embrace us. >> John: How about the technology platform? >> Amit: Yeah. >> What's the vision for the innovation? >> Essentially want to be able to integrate with all of the different cloud providers, cloud solutions, SaaS platforms, and take the atlas approach that they were using right to the chats from everywhere to anywhere. So essentially we want in the end that users will be able to do anything that they need inside all of these complicated platforms, which some of them are totally complicated, with plain English. >> So what's the biggest challenge for you then on that front leading the technology side of the team? >> So I would say that the conversational AI part is truly complicated because it requires to extract many types of intentions from different types of users and also integrate with so many tools and solutions. >> Savannah: Yeah. So it requires a lot of thinking, a lot of architecture, but we are doing it just fine. >> Awesome. What do you guys think about KubeCon this week? What's, what's the top story that you see emerging out of this? Just generally as an industry observer, what's the most important? >> Savannah: Maybe it's them. Announcement halo. >> What's the cover story that you see? (all laugh) I mean you guys are in the innovation intent-based infrastructure. I get that. >> So obviously everyone's looking to diversify their engineering, diversify their platforms to make sure they're as decoupled from the main CSPs as possible. So being able to build their own, and we're really helping enable a lot of that in there. We're really helping improve upon that open source together with managed platforms can really play a very nice game together. So. >> Awesome. So are you guys hiring, recruiting? Tell us about the team DNA. Now you're in Tel Aviv. You're in the bay. >> Shaked: Check our openings on LinkedIn. (all laugh) >> We have a dozen job postings on our website. Obviously engineering and sales then go to market. >> So when theCUBE comes to Tel Aviv, and we have a location there. >> Yeah. >> Will you be, share some space? >> Savannah: Is this our Tel Aviv office happening right now? I love this. >> Amit: We will be hosting you. >> John: theCube with a C and Kube with a K over there. >> Yeah. >> All one happy family. >> Would love that. >> Get some ice cream. >> Would love that. >> All right, so last question for you all. You just had a very big exciting announcement. It's a bit of a coming out party for you. What do you hope to be able to say in a year that you can't currently say right now? When you join us on theCUBE next time? >> No, no, it's absolutely. I think our thesis that you can turn conversations into operations. It's, it sounds obvious to you when you think about it, but it's not trivial when you look into the workflows, into the operations. The fact that we can actually go a year from today and say we got hundreds of customers, happy customers who've proven the thesis or sharing knowledge between themselves, that would be euphoric for us. >> All right. >> You really are about helping people. >> Absolutely. >> It doesn't seem like it's a lip service from both of you. >> No. (all laugh) >> Is there going to be levels of bot, like level one bot level two, level three, and then finally the SRE gets on the phone? Is that like some point? >> Is there going to be bot singularity? Is that, is that what we're exploring right now? (overlapping chatter) >> Some kind of escalation bot. >> Enlightenment with bots. >> We actually planning a feature we want to call a handoff where a human in the loop is required, which often is needed. Machine cannot do it alone. We'll just. >> Yeah, I think it makes total sense for geos, ops at the same. >> Shaked: Yeah. >> But not exactly the same. Really good, good solution. I love the direction. Congratulations on the launch. >> Shaked: Thank you so much. >> Amit: Thank you very much. >> Yeah, that's very exciting. You can obviously look, check out that news on Silicon Angle since we had the pleasure of breaking it. >> Absolutely. >> If people would like to say hi, stalk you on the internet, where's the best place for them to do that? >> Be on our Twitter and LinkedIn handles of course. So we have kubiya.ai. We also have a free trial until the end of the year, and we also have free forever tier, that people can sign up, play, and come say hi. I mean, we'd love to chat. >> I love it. Well, Amit, Shaked, thank you so much for being with us. >> Shaked: Thank you so much. >> John, thanks for sitting to my left for the entire day. I sincerely appreciate it. >> Just glad I can help out. >> And thank you all for tuning in to this wonderful edition of theCUBE Live from Detroit at KubeCon. Who knows what my voice will be controlling next, but either way, I hope you are there to find out. >> Amit: Love it.

(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)

(upbeat music) >> Hello and welcome to theCUBE as we present AWS Startup Showcase, The Next Big Thing in AI, Security & Life Sciences, the hottest startups. And today's session is really the next big thing in AI Security & Life Sciences. As to the AI track is really a big one most important. And we have a feature in company, fiddler.ai. I'm your host, John Furrier with theCUBE. And we're joined by the founders, Krishna Gade, founder and CEO, and Amit Paka, founder and Chief Product Officer. Great to have the founders on. Gentlemen, thank you for coming on this Cube segment for the AWS Startup Showcase. >> Thanks, John... >> Good to be here. >> So the topic of this session is staying compliant and accelerating AI adoption and model performance monitoring. Basically, bottom line is how to be innovative with AI and stay (John laughs) within the rules of the road, if you will. So, super important topic. Everyone knows the benefits of what AI can do. Everyone sees machine learning being embedded in every single application, but the business drivers of compliance and all kinds of new kinds of regulations are popping up. So we don't. The question is how do you stay compliant? Which is essentially how do you not foreclose the future opportunities? That's really the question on everyone's mind these days. So let's get into it. But before we start let's take a minute to explain what you guys do. Krishna, we'll start with you first. What does fiddler.ai do? >> Absolutely, yeah. Fiddler is a model performance management platform company. We help, you know, enterprises, mid-market companies to build responsible AI by helping them continuously monitoring their AI, analyzing it, explaining it, so that they know what's going on with their AI solutions at any given point of time. And they can be like, ensuring that, you know businesses are intact and they're compliant with all the regulations that they have in their industry. >> Everyone thinks AI is a secret sauce. It's magic beans and automatically will just change over the company. (John laughs) So it's kind of like this almost like it's a hope. But the reality is there is some value there but there's something that has to be done first. So let's get into what this model performance management is because it's a concept that needs to be understood well but also you got to implement it properly. There's some foundational things you've got to you know, walk, crawl before you walk and walk before you run kind of thing. So let's get into it. What is model performance management? >> Yeah, that's a great question. So the core software artifact most an AI system is called an AI model. So it essentially represents the patterns inside data accessing manner so that it can actually predict the future. Now, for example, let's say I'm trying to build an AI based credit underwriting system. What I would do is I would look at the historical you know, loans data. You know, good loans and bad loans. And then, I will build it a model that can capture those patterns so that when a new customer comes in I can actually predict, you know, how likely they are going to default on the loan much more activity. And this helps me as a bank or center company to produce more good loans for my company and ensure that my customer is not, you know, getting the right customer service. Now, the problem though is this AI model is a black box. Unlike regular software code you cannot really open up and read its code and read its patterns and how it is doing. And so that's where the risks around the AI models come along. And so you need a ways to innovate to actually explain it. You need to understand it and you need to monitor it. And this is where the model performance management system like Fiddler can help you look into that black box. Understand how it's doing it, monitor its predictions continuously so that you know what these models are doing at any given point of time. >> I mean, I'd love to get your thoughts on this because on the product side I could, first of all, totally awesome concept. No one debates that. But now you've got more and more companies integrating with each other more data's being shared. And so the, you know, everyone knows what an app sec review is, right? But now they're thinking about this concept of how do you do review of models, right? So understanding what's inside the black box is a huge thing. How do you do this? What does it mean? >> Yeah, so typically what you would do is it's just like software where you would validate software code going through QA and like analysis. In case of models you would try to prove the model in like different granularities to really understand how the model is behaving. This could be at a model prediction like level in case of the loans example, Krishna just gave. Why is my model saying high-risk to in particular loan? Or it might be in case of explaining groups of loans. For example, why is my model making high-risk predictions to loans made in California or loans made to all men? Was it loans made to all women? And it could also be at the global level. What are the key data factors important to my model? So the ability to prove the model deeper and really opening up the black box and then using that knowledge to explain how the model is working to non-technical folks in compliance. Or to folks who are regulators, who just want to ensure that they know how the model works to make sure that it's keeping up with kind of lending regulations to ensure that it's not biased and so on. So that's typically the way you would do it with the machine learning model. >> Krishna, talk about the potential embarrassments that could happen. You just mentioned some of the use cases you heard from a mid-saying you know, female, male. I mean, machines, aren't that smart. (John laughs) >> Yeah. >> If they don't have the data. >> Yeah. >> And data is fragmented you've got silos with all kinds of challenges just on the data problem, right? >> Yeah. >> So nevermind the machine learning problems. So, this is huge. I mean, the embarrassment opportunities. >> Yeah. >> And the risk management on whether it's a hack or something else. So you've got public embarrassment by doing something really went wrong. And then, you've got the real business impact that could be damaging. >> Absolutely. You know, AI has come forward a lot, right? I mean, you know, you have lots of data these days. You have a lot of computing power an amazing algorithms that you can actually build really sophisticated models. Some of these models were known to beat humans in image recognition and whatnot. However, the problem is there are risks in using AI, you know, without properly testing it, without properly monitoring it. For example, a couple of years ago, Apple and Goldman Sachs launched a credit card, right? And for their users where they were using algorithms presumably AI or machine learning algorithms to set credit limits. What happened was within the same household husband and wife got 10 times difference in the credit limits being set for them. And some of these people had similar FICO scores, similar salary ranges. And some of them went online and complained about it and that included the likes of Steve Wozniak as well. >> Yeah. >> So this was, these kind of stories are usually embarrassing when you could lose customer trust overnight, right? And, you know, you have to do a lot of PR damage. Eventually, there was a regulatory probate with Goldman Sachs. So there are these problems if you're not properly monitoring area systems, properly validating and testing them before you launch to the users. And that is why tools like Fiddler are coming forward so that you know, enterprises can do this. So that they can ensure responsible AI for both their organization as well as their customers. >> That's a great point, I want to get into this. What it kind of means and the kind of the industry side of it? And then, how that impacts customers? If you guys don't mind, machine learning opposite a term MLOps has been coined in the industry as you know. Basically, operations around machine learning, which kind of gets into the workflows and development life cycles. But ultimately, as you mentioned, this black box and this model being made. There's a heavy reliance on data. So Amit, what does this mean? Because now is it becomes operational with MLOps. There is now internal workflows and activities and roles and responsibilities. How is this changing organizations, you know separate the embarrassment, which is totally true. Now I've got an internal operational aspect and there's dev involved. What's the issue? >> Yeah, so typically, so if you look at the whole life cycle of machine learning ops, in some ways mirrors the traditional life cycle of kind of DevOps but in some ways it introduces new complexities. Specifically, because the models can be a black box. That's one thing to kind of watch out for. And secondly, because these models are probabilistic artifact, which means they are trained on data to grab relationships for what kind of potentially making high accuracy predictions. But the data that they see in life might actually differ and that might hurt their performance especially because machine learning is applied towards these high ROI use cases. So this process of MLOps needs to change to incorporate the fact that machine learning models can be black boxes and machine learning models can decay. And so the second part I think that's also relevant is because machine learning models can decay. You don't just create one model you create multiple versions of these models. And so you have to constantly stay on top of how your model is deviating from your reality and actual reality and kind of bring it back to that representation of reality. >> So this is interesting, I like this. So now there's a model for the model. So this is interesting. You guys have innovated on this model performance management idea. Can you explain the framework and how you guys solve that regulatory compliance piece? Because if you can be a model of the model, if you will. >> Then. >> Then you can then have some stability around maintaining the code basis or the integrity of the model. >> Okay. >> How does that? What do you guys offer? Take us through the framework and how it works and then how it ties to that regulatory piece? >> So the MPM system or the model performance management system really sits at the heart of the machine learning workflow. Keeping track of the data that is flowing through your ML life cycle, keeping track of the models that are going, you know, we're getting created and getting deployed and how they're performing. Keeping track of the whole parts of the models. So it gives you a centralized way of managing all of these information in one place, right? It gives you an oversight from a compliance standpoint from an operational standpoint of what's going on with your models in production. Imagine you're a bank you're probably creating hundreds of these models, but a variety of use cases, credit risk, fraud, anti-money laundering. How are you going to know which models are actually working very well? Which models are stale? Which models are expired? How do you know which models are underperforming? You know, are you getting alerts? So this is what this kind of governance, this performance management is what the system offers. It's a visual interface, lots of dashboards, the developers, operations folks, compliance folks can go and look into. And then they would get alerts when things go wrong with respect to their models. In terms of how it can be helpful to meet in compliance regulations. For example, let's say I'm starting to create a new credit risk model in a bank. Now I'm innovating on different AI algorithms here immediately before I even deploy that model I have to validate it. I have to explain it and create a report so that I can submit to my internal risk management team which can then review it, you know, understand all kinds of risks around it. And then potentially share it with the audit team and then keep a log of these reports so that when a regulator comes visits them, you know they can share these reports. These are the model reports. Is that how the model was created? Fiddler helps them create these reports, keep all of these reports in one place. And then once the model is deployed, you know, it basically can help them monitor these models continuously. So that they don't just have one ad hoc report when it was created upfront, they can a continuous monitoring continuous dashboard in terms of what it was doing in the last one whatever number of months it was running for. >> You know what? >> Historically, if you were to regulate it like all AI applications in the U.S. the legacy regulations are the ones that today are applied as to the equal credit opportunity or the Fed guidelines of like SR 11-7 that kind of comment that's applicable to all banks. So there is no purpose-built AI regulation but the EU released a proposed regulation just about three weeks back. That classifies risk within applications, and specifically for high-risk applications. They propose new oversight and the ads mandating explainability helping teams understand how the models are working and monitoring to ensure that when a model is trained for high accuracy, it maintains that. So now those two mandatory needs of high risk application, those are the ones that are solved by Fiddler. >> Yeah, this is, you mentioned explainable AI. Could you just quickly define that for the audience? Because this is a trend we're seeing a lot more of. Take a minute to explain what is explainable AI? >> Yeah, as I said in the beginning, you know AI model is a new software artifact that is being created. It is the core of an AI system. It's what represents all the patterns in the data and coach them and then uses that knowledge to predict the future. Now how it encodes all of these patterns is black magic, right? >> Yeah. >> You really don't know how the model is working. And so explainable AI is a set of technologies that can help you unlock that black box. You know, quote-unquote debug that model, looking to the model is introspected inspected, probate, whatever you want to call it, to understand how it works. For example, let's say I created an AI model, that again, predicts, you know, loan risk. Now let's say some person, a person comes to my bank and applies for a $10,000 loan, and the bank rejects the loan or the model rejects the loan. Now, why did it do it, right? That's a question that can explain the way I can answer. They can answer, hey, you know, the person's, you know salary range, you know, is contributing to 20% of the loan risk or this person's previous debt is contributing to 30% of the loan risk. So you can get a detailed set of dashboards in terms of attribution of taking the loan risk, the composite loan risk, and then attributing it to all the inputs that the model is observing. And so therefore, you now know how the moral is treating each of these inputs. And so now you have an idea of like where the person is getting effected by this loaner's mark. So now as a human, as an underwriter or a loan officer lending officer, I have knowledge about how the model is working. I can then have my human intuition or lap on it. I can approve the model sometimes I can disapprove the model sometimes. I can use this feedback and deliver it to the data science team, the AI team, so they can actually make the model better over time. So this unlocking black box has several benefits throughout their life cycle. >> That's awesome. Great definition. Great call. I want to grab get that on the record for the audience. Also, we'll make a clip out of that too. One of the things that I meant you brought up I love and want to get into is this MLOps impact. So as we were just talking earlier debugging module models and production, totally cool, relevant, unpacked a black box. But model decay, that's an interesting concept. Can you explain more? Because this to me, I think is potentially a big blind spot for the industry, because, you know, I talked to Swami at Amazon, who runs their AI group and, you know, they want to make AI easier and ML easier with SageMaker and other tools. But you can fall into a trap of thinking everything's done at one and done. It's iterative is you've got leverage here. You got to keep track of the performance of the models, not just debugging them. Are they actually working? Is there new data? This is a whole another practice. Could you explain this concept of model decay? >> Yeah, so let's look at the lending example Krishna was just talking about. If you expect your customers to be your citizens, right? So you will have examples in your training set which might have historical loans made to people that the needs of 40, and let's say 70. And so you will train your model and your model will be trained our highest accuracy in making loans to these type of applicants. But now let's say introduced a new loan product that you're targeting, let's say younger college going folks. So that model is not trained to work well in those kinds of scenarios. Or it could also happen that you could get a lot more older people coming in to apply for these loans. So the data that the model can see in life might not represent the data that you train the model with. And the model has recognized relationships in this data and it might not recognize relationships in this new data. So this is a constant, I would say, it's an ongoing challenge that you would face when you have a live model in ensuring that the reality meets your representation of the reality when you train the model. And so this is something that's unique to machine learning models and it has not been a problem historically in the world of DevOps. But it is a very key problem in the DevOps. >> This is really great topic. And most people who are watching might want to might know of some of these problems when they see the main mainstream press talk about fairness in black versus white skin and bias and algorithms. I mean, that's kind of like the press state that talk about those kinds of like big high level topics. But what it really means is that the data (John laughs) of practiced fairness and bias and skewing and all kinds of new things that come up that the machines just can't handle. This is a big deal. So this is happening to every part of data in an organization. So, great problem statement. I guess the next segue would be, why Fiddler, why now? What are you guys doing? How are you solving these problems? Take us through some use cases. How people engage with you guys? How you solve the problem and how you guys see this evolving? >> Great, so Fiddler is a purpose-built platform to solve for model explainability of modern monitoring and moderate bias detection. This is the only thing that we do, right? So we are super focused on building this tool to be useful across a variety of, you know, AI problems, from financial services to retail, to advertising to human resources, healthcare and so on and so forth. And so we have found a lot of commonalities around how data scientists are solving these problems across these industries. And we've created a system that can be plugged into their workflows. For example, I could be a bank, you know, creating anti-money laundering models on a modern AI platform like TensorFlow. Or I could be like a retail company that is building a recommendation models in, you know, PyTorch, like library. You can bring all of those models into one under one sort of umbrella, like using Fiddler. We can support a variety of heterogeneous types of models. And that is a very very hard technical problem to solve. To be able to ingest and digest all these different types of monotypes and then provide a single pane of glass in terms of how the model is performing. How explaining the model, tracking the model life cycle throughout its existence, right? And so that is the value prop that Fiddler offers, the MLOps team, so they can get this oversight. And so this plugs in nicely with their MLOps so they don't have to change anything and give the additional benefit... >> So, you're basically creating faster outcomes because the teams can work on real problems. >> Right. >> And not have to deal with the maintenance of model management. >> Right. >> Whether it's debugging or decay evaluations, right? >> Right, we take care of all of their model operations from a monitoring standpoint, analysis standpoint, debugability, alerting. So that they can just build the right kind of models for their customers. And we give them all the insights and intelligence to know the problems with behind those models behind their datasets. So that they can actually build more accurate models more responsible models for their customers. >> Okay, Amit, give us the secret sauce. What's going on in the product? How does it all work? What's the secret sauce? >> So there are three key kind of pillars to Fiddler product. One is of course, we leverage the latest research, and we actually productize that in like amazing ways where when you explain models you get the explanation within a second. So this activates new use cases like, let's say counterfactual analysis. You can not only get explanations for your loan, you can also see hypothetically. What if this the loan applicant was, you know, had a higher income? What would the model do? So, that's one part productizing latest research. The second part is infrastructure at scale. So we are not just building something that would work for SMBs. We are building something that works on enterprise scale. So billions and billions of predictions, right? Flowing through the system. We want to make sure that we can handle as larger scale as seamlessly as kind of possible. So we are trying to activate that and making sure we are the best enterprise grade product on the market. And thirdly, user experience. What you'll see when you use Fiddler. Finally, when we do demos to kind of customers what they really see is the product. They don't see that the scale right, right, right then and there. They don't see the deep reason. What they see, what they see are these like beautiful experiences that are very intuitive to them. Where we've merged explainability and monitoring and bias detection in like seamless way. So you get the most intuitive experiences that are not just designed for the technical user, but also for the non-technical user. Who are also stakeholders within AI. >> So the scale thing is a huge point, by the way. I think that's something that you see successful companies. That's a differentiator and frankly, it's the new sustainability. So new lock-in, if you will, not to be in a bad way but in a good way. You do a good job. You get scale, you get leverage. I want to just point out and get your guys' thoughts on your approach on the frame. Where you guys are centralized. >> Right. >> So as decentralization continues to be a wave you guys are taking much more of a centralized approach. Why is that done? Take us through the decision on that. >> Yeah. So, I mean, in terms of, you know decentralization in terms of running models on different you know, containers and, you know, scoring them on multiple number of nodes, that's absolutely makes sense, right? When from a deployment standpoint from a inference standpoint. But when it comes to actually you know, understanding how the models are working. Visualizing them, monitoring them, knowing what's going on with the models. You need a centralized dashboard that a lapsed user can actually use or a head of AI governance inside a bank and use what are all the models that my team is shipping? You know, which models carry risk, you know? How are these models performing last week? This, you need a centralized repository. Otherwise, it'll be very very hard to track these models, right? Because the models are going to grow really really fast. You know, there are so many open source libraries, open source model architecture has been produced. And so many data scientists coming out of grad schools and whatnot. And the number of models in enterprise is just going to grow many many fold in the coming years. Now, how are you going to track all of these things without having a centralized platform? And that's what we envisaged a few years ago that every team will need an oversight tool like Fiddler. Which can keep track of all of their models in one place. And that's what we are finding from our customers. >> As long as you don't get in the way of them creating value, which is the goal, right? >> Right. >> And be frictionless take away the friction. >> Yeah. >> And enable it. Love the concept. I think you guys are on something big there, great products. Great vision. The question I have for you to kind of wrap things up here. Is that this is all new, right? And new, it's all goodness, right? If you've got scale in the Cloud, all these new benefits. Again, more techies coming out of grad school and Computer Science and Engineering, and just data analysis in general is changing. And there's more people to be democratized to be contributing. >> Right. >> How do you operationalize it? How do companies get this going? Because you've got a new thing happening. It's a new wave. >> Okay. >> But it's still the same game, make business run better. >> Right. >> So you've got to deploy something new. What's the operational playbook for companies to get started? >> Absolutely. First step is to, if a company is trying to install AI, incorporate AI into their workflow. You know, most companies I would say, they're in still early stages, right? There a lot of enterprises are still, you know, developing these models. Some of them may have been in labs. ML operationalization is starting to happen and it probably started in a year or two ago, right? So now when it comes to, you know, putting AI into practice, so far, you know, you can have AI models in labs. They're not going to hurt anyone. They're not going to hurt your business. They're not going to hurt your users. But once you operationalize them then you have to do it in a proper manner, in a responsible manner, in a trustworthy manner. And so we actually have a playbook in terms of how you would have to do this, right? How are you going to test these models? How are you going to analyze and validate them before they actually are deployed? How are you going to analyze, you know, look into data bias and training set bias, or test set bias. And once they are deployed to production are you tracking, you know, model performance or time? Are you tracking drifting models? You know, the decay part that we talked about. Do you have alerts in place when model performance goes all over the place? Now, all of a sudden, suddenly you get a lot of false positives in your fraud models. Are you able to track them? We have the personnel in place. You have the data scientists, the ML engineers, the MLOps engineers, the governance teams in place if it's in a regulated industry to use these tools. And then, the tools like Fiddler, will add value, will make them, you know, do their job, institutionalize this process of responsible AI. So that they're not only reaping the benefits of this great technology. There's no doubt about the AI, right? It's actually, it's going to be game changing but then they can also do it in a responsible and trustworthy manner. >> Yeah, it's really get some wins, get some momentum, see it. This is the Cloud way. It gets them some value immediately and grow from there. I was talking to a friend the other day, Amit, about IT the lecture. I don't worry about IT and all the Cloud. I go, there's no longer IT, IT is dead. It's an AI department now. (Amit laughs) So and this is kind of what you guys are getting at. This now it's data now it's AI. It's kind of like what IT used to be enabling organizations to be successful. You guys are looking at it from the perspective of the same way it's enabled success. You put it out that you provision (John laughs) algorithms instead of servers they're algorithms now. This is the new model. >> Yeah, we believe that all companies in the future as it happened to this wave of data are going to be AI companies, right? So it's really just a matter of time. And the companies that are first movers in this are going to have a significant advantage like we're seeing that in like banking already. Where the banks that have made the leap into AI battles are reaping benefits of enabling a lot more models at the same risk profile using deep learning models. As long as you're able to like validate these to ensure that they're meeting kind of like the regulations. But it's going to give significant advantages to a lot of companies as they move faster with respect to others in the same industry. >> Yeah, quickers too, saw a friend too on the compliance side. You mentioned trust and transparency with the whole EU thing. Some are saying that, you know, to be a public company, you're going to have to have AI disclosure soon. You're going to have to have on the disclosure in your public statements around how you're explaining your AI. Again, fantasy today. But pretty plausible. >> Right, absolutely. I mean, the real reality today is, you know less than 10% of the CEOs care about ethical AI, right? And that has to change. And I think, you know, and I think that has to change for the better, because at the end of the day, if you are using AI, if you're not using in a responsible and trustworthy manner then there is like regulation. There is compliance risk, there's operational business risk. You know, customer trust. Losing customers trust can be huge. So I think, you know, we want to provide that you know, insurance, or like, you know like a preventative mechanism. So that, you know, if you have these tools in place then you're less likely to get into those situations. >> Awesome. Great, great conversation, Krishna, Amit. Thank you for sharing both the founders of Fiddler.ai. Great company. On the right side of history in my opinion, the next big thing in AI. AI departments, AI compliance, AI reporting. (John laughs) Explainable AI, ethical AI, all part of this next revolution. Gentlemen, thank you for joining us on theCUBE Amazon Startup Showcase. >> Thanks for having us, John. >> Okay, it's theCUBE coverage. Thank you for watching. (upbeat music)

(upbeat music) >> For years on the queue, we've talked about the benefits of the cloud going beyond IT cost savings. Sure. You can move your workloads into the cloud and minimize the so-called undifferentiated heavy lifting of IT equipment and deployment and management. And of course increased agility is often the number one benefit customers site from the cloud. But increasingly, the value of the cloud is being seen as applying that agility to change an organization's operating model. This drives business value that can be orders of magnitude greater than savings on tech labor costs. And one of the more interesting examples we found, is using the cloud, data and software technology to find, and flexibly source distributed energy resources so that clean energy, can be delivered efficiently. Hello, and welcome to the startup showcase on the cube brought to you by AWS. We're very excited to have two exacts on from AutoGrid. Wait until you hear about the innovations that they're driving and the problems that they're solving around, some of the world's most pressing problems. Amit Narayan is here. He's the CEO of auto-graded Rajeev Singh is the chief technology officer gentlemen, welcome to the program. >> Thank you. >> Thank you for having us. >> You're very welcome. >> Okay, so heard my summary Amit. Maybe you could add some color about AutoGrid. What's your story? >> Yeah, I mean, undoubtedly climate change is one of the defining challenges of our time, and we're already seeing extreme weather events whether these are wildfires in California, are extreme cold events in Texas, last two weeks. As we tackle the climate change through renewables, this whole volatility challenge that we are seeing is only going to become even more pressing. So we at AutoGrid provide software that creates, a virtualization layer, just like you doing in the cloud world, with hardware around all kinds of energy assets, whether these are your EVs in the homes, our batteries are distributed solar panels. And then we apply intelligence from software, to coordinate and orchestrate all of these assets. So you can think of us as a autopilot for the grid, and our technology is called virtual power plants. Which allows us to harness, the power from all these distributed energy resources. >> Yeah. I was going to say you're essentially creating, a virtual power plant. That's amazing of aggregating these distributed resources. I mean, it sounds very logical but it also sounds non-trivial, its a transformative idea. What exactly is a virtual power plant? I mean, how does that all work? >> Yeah. Well, I mean, if you think about how the grid was designed by Edison and Tesla, they really never envisioned a world where you will have a two way flow of power, not just from generator to the consumers, but potentially from the consumers back to the generators. And certainly they didn't really design the grid to incorporate this amount of renewables, which can be intermittent and volatile. So as we are now transitioning to this new energy world, we have to rethink the entire grid architecture, and reinvent how this control system works. But fortunately for us, unlike Edison and Tesla we have some really powerful tools at our disposal namely the internet and the cloud, and these tools do allow us to rethink how we connect all these different assets and we optimize them. And in a way, we are now rebuilding the grid outside in where if you have a battery in your home, not only can it power your own home when power is out, it can actually provide power back to the grid or to your neighbors. And so with this onslaught of DES, we think that we are living in the most exciting times, since Edison and Tesla in terms of how we are going to transition to a sustainable grid. And we think that our software, can play a foundational role in accelerating that transition. >> Lets stick here the bi-directional flow. It's so simple, but genius. Rajeev, maybe you could talk about the tech behind AutoGrid. I mean the secret sauce, lies I think in that whole flexibility management system but there's data involved, probably a fair amount of computer science. Maybe you could explain it more detail. >> Yeah. just as Amit mentioned now, when we started AutoGrid, we had the luxury of, cloud computing a massive scale, at that massive scale and AutoGrid, what we've been able to do is pull together a cloud native computing. They lost the city, the scale, with cutting edge AI and machine learning, as well as all of the dispatch, and command and control technologies, that are all in one platform. And all of them have to come together, to be able to manage and orchestrate, these a massively distributed energy resources. I mean, these could be small, you know batteries or solar panels, et cetera. So gone are the days of large generators that could be managed with smaller compute now because the sheer number of DER's, you need a new paradigm to be able to manage this. And this is really what is under the hood, that constitutes our virtual power plant. >> Rajeev Can you talk a little bit more about your scale model? I mean, how are you able to do this effectively without imploding, or hitting walls? >> Yeah, so obviously, we've been on AWS for about ten years now. And even prior to that, we had the previous company loaded with AWS. So that kind of gave us a glimpse off the sheer scale of compute, that is available to us on tap, if required. So that was quite comforting, because when we did back one of the calculations on the amount of data, that's coming in through IOT industrial IOT from all the distributed energy resources, the amount of processing required for real-time computing as well as, the sheer variety of the other, we have to tackle in in various geographies around the world. AWS made it happen just because having regions, across the globe, we done in, I believe six or seven different AWS regions. We cover a four continents, twelve plus countries. So just because cloud computing was there, we were able to ramp up the solution, very quickly. Now, one thing we are a big believers in is that you only learn by doing, and the only way to learn, is to run production systems. And when we started, of course we didn't do everything right. But we quickly learned we adapted, we scaled, and we kept on scaling. And this is where we are right now. >> Interesting. That's like Andy Jassy says there's no compression algorithm for experience. We know it well. One more for Rajeev, and I want to come back. With AutoGrid tapping, all these energy sources, you got a pretty major threat surface. How are you dealing with security? >> Yeah, we don't talk a lot about our security posture for obvious reasons. Some of the underlying principles are in reducing the blast radius. It should be quite familiar to people who work in security. The use of wide variety of best of the breed security tools, including, and or the past few years. In fact, past five, six years, AWS itself has rolled out a number of security managed services, which are included. But on top of that views, other solutions as well. And it's all designed in layers, with proper segregation, and we have variety of security certifications. One of the most, the one that we're proud of is we are one of the few if not the only NERC solution SAS solution in this domain on AWS. And it's just a culmination of using security by layers. And reducing the blast radius. >> Yeah. Makes sense. And let's turn to some customer use cases. What are some of the main problems, that your customers come to you to solve? How are you approaching them? Maybe you could address that and add some color. >> Yeah, absolutely. I mean, as Rajeev mentioned. There is a lot of deep tech in the platform, and the optimization complexity, grows exponentially with the number of assets. And as you go from a gigawatt scale power plant and you want to get the same power from Tesla power walls. let's say, for every generator you're replacing it with more than two hundred thousand mini generators. And if the complexity grows exponentially. it's far beyond what the current algorithms can handle. So a lot of customers come to us solve their technical challenges. But even beyond that, the whole complexity of transacting, with small generators is very high, and that our business model issues that we help our customers solve. So the whole energy industry, has been designed to have transactions, between very large generators and utilities, but very few of these transactions. And now when you are talking about DER's, you're having millions of transactions with very small entities and maybe even homeowners, back to the utilities. So neither the utilities, have the capability today, to have these transactions, nor the asset owners, and operators, have the capability to go back and have the transactions of the utilities. So we sort of act as an intermediary, and we provide a one-stop shop, for fleet owners and operators. And we say that if you work with us, we will help you monetize your assets, and get more value from these assets, by interfacing with utilities by interfacing with energy markets which can get very complex. >> I love this. I mean, everybody's winning here. Rajeev. I want to come back to the to the cloud a little bit. You talked about, you've been able to AWS for ten years and then even before that, you've got deep experience. I mean. I can't imagine, how you would do this without the cloud. I mean, maybe it could be a really heavy complicated list lift. I mean, you've seen the AWS cloud evolve over time. It's gone way beyond, of course, compute and storage brought in a lot of machine learning capabilities on and on. And I mean, how are you leveraging that evolution? Those zillion features that AWS puts out every year at reinvent. I mean, maybe you could talk about that a little bit. >> Yeah. So of course, when we started, we used it as an infrastructure provider, you know provided us compute networking, security firewalls, et cetera, just on tap. There's very good. Got us started. Then we started leveraging a lot more managed services, that AWS offered, that allowed us to run. For example, variety of databases right to data stores, in a managed fashion, with a very small startup. You're always, running lean. So that helped us with a small team, of system engineers and engineers, back from engineers to be able to put together and run these systems around the globe, just because enablers was responsible, for managing the services. We always keep an eye on. And one thing we love about AWS is the amount of innovation, that they quickly put into production. So, we're always keeping an eye on, what's coming out. And over the years, it has been quite nice to us in some ways, we directly talk to the solution architects, they tell us what's coming, what should be used, what we should not use in what's in production ready What's not. So that level of kind of deep engagement, really helps us. Kind of keep abreast of the innovations that are constantly being rolled out on AWS. And we keep kind of incorporating those into our platform and making it more and more capable. The one thing I also would like to say, is that to be able to aggregate capacity, from all these DER's, it has to be done in a cost effective fashion. So, this is where AWS helps us with running, last a city at the service level. All the microservices can scale independently. So we don't have to have this massive monolith, and across the globe, we don't need to have, fifty of those to be running. And that's going to add up to a massive cost. So we are able to scale, just the portions of the infrastructure just in time when we need it. And that also helps us greatly, in having a cost effective solution, for our customers. >> That's actually great. That's great. So that granularity is important, for you to have fine grain control of your costs. A lot of people sometimes question that granularity that AWS provides, because it does add a level of complexity, but you guys can deal with complexity. You know, one of the things that we haven't talked about I wonder if we could touch, on it is data. I mean, this is the data flow. I'm imagining the data flow, and the metadata and the decisions that you have to make are are quite complex. Can you address that a little bit? I mean, you guys got to be pretty, sharp data walks. >> Yeah. So the people that we have at the company, including myself have come from a billing lodge, high performance and high large enterprise systems, previously from airlines, Ford motor company or pharmaceuticals. In any system, where we are making a lot of decisions. The first thing you have to do is data integration. And again this is something that you just learn by doing and having done it across the globe with a variety of the DER, systems UVS, you name it. We have to pretty much done one of everything, and of course, and be very quickly abstract and learn, if you do something twice, we abstract it and make it into a library. So that the next time around it's just a simple turn-on switch. So it's no secret sauce there you just learn by doing and you kind of constantly abstract and you expand the solution. >> That's great. let's close. The other thing. We really haven't talked much about your company. Maybe you could, add some. whatever you want to to share, metrics. I mean you must be growing, head count, or whatever you're comfortable sharing. If you could just give us, a little glimpse of of the company. >> Yeah, absolutely. We have been around for close to ten years now. We are based in Silicon Valley. We have multiple locations. Our second primary location is in India. Today We are operating in over twelve countries. We have close to five thousand megawatts of distributed energy acids, that we actively control manage. This includes, everything from a thermostat in the home, to very large scale, wind and solar farms, as well as large scale batteries. EVs as a new emerging category. And, we work with a variety of partners. AWS has been one of our founding partners, on day one, you talked about data. We were the first ones to realize how much data we were going to get from all of these assets. And the current systems will not scale. So we made the decision on day one to be on cloud. And that was foundational year. I just want to say that over the last year or so, we have I think collectively as a society realized how individual actions, impact the overall society. And I think we are really at a great inflection point right now, where if we can harness this newly developed consciousness and awareness to accelerate, our transition to new energy, away from fossil fuel, we can really solve what I think is the biggest challenge that we face as a society going forward. >> Yeah. Micro actions that actually have a huge impact. And so I guess, that's kind of of where you see this heading in the future both the general market, your business. I mean presumably, you've been around a while, maybe you'd welcome competition to really solve this problem. Right? >> I think we are in the same fight. We are all working towards the same goal, of having a clean cheap reliable energy. And we would welcome as much support as we get to build momentum for this absolutely >> Its like the Pharma companies cheering each other on for the, for the vaccine. Again, guys super interesting business solving real problems really thanks gentlemen for coming on the program and we wish you well in the years ahead. >> Thank you for having us. >> It's really been our pleasure. Thank you for watching the AWS startup showcase on the cube. I'm Dave Volante.

>> Welcome back to Cube On Cloud. My name is Paul Gillin, enterprise editor at SiliconANGLE, and I'm pleased to now have as a guest on the show. Amit Zephyr, excuse me, general manager, vice president of business application platform at Google cloud. Amit is a formerly EVP and corporate officer for product development at Oracle cloud, 24 years at Oracle, and by my account a veteran of seven previous appearances on theCube. Amit welcome, thanks for joining us. >> Thanks for having me Paul, it's always good to be back on theCube. >> Now you are... one of your big focus areas right now is on low-code and no-code. Of course this is a market that seems to be growing explosively. We often hear low code/no code used in the same breath as if they're the same thing. In fact, how are they different? >> I think it's a huge difference, now. I think industry started as low-code mode for many, many years. I mean, there were technologies, or tools provided for kind of helping developers be more productive that's what low-code was doing. It was not really meant, even though it was positioned for citizen developers it was very hard for a non technologist to really build application using low code. No-code is really meant as the word stands, no code. So there's really no coding, there's no understanding required about the underlying technology stack, or knowing how constructs works or how the data is laid out. All that stuff is kind of hidden and abstracted out from you. You are really focused as a citizen developer or a line of business user, in kind of delivering what your business application requirements are, and the business flows are, without having to know anything about writing any code. So you can build applications, you can build your interfaces and not have to learn anything about a single line of code. So that's really no-code and I think they getting to a phase now where the platforms have gotten much stronger and better where you can do very good productive applications without having to write a single line of code. So that's really the goal with no-code, and that's really the future in terms of how we will get more and more line of business users, or citizen developers to build applications they need for their day-to-day work. >> So when would you use one or the other? >> I think since low-code you would probably any developer has been around for eight, 10 years, if not longer where you extract out some of this stuff you can do some of the things in terms of not having to write some code where you have a lot of modules pre-built for you, and then when you want to mix a lot of changes, you go and drop into an ID and write some code or make some changes to a code. So you still get into that, and those are really focused towards semi-professional developers or IT in many cases or even developers who want to reduce the time required to start from, write and building an application. so it makes you much more productive. So if you are a really some semi-professional or you are a developer, you can either use use low-code to improve your productivity and not start from scratch. No-code is really used for folks who are really not interested in learning about coding, don't have any experience in it, and still want to be productive and build applications. And that's really when I would start with.. I would not give a low code to a citizen developer or a line of business user who has no experience with any coding. And that's not really.. It will only productive, They'll get frustrated and not deliver what you need, and not get anything out of it and many cases. >> Well, I've been around this industry long enough to remember fourth-generation languages and visual basic >> Yeah and the predecessors that never really caught on in a big way. I mean, they certainly had big audiences but, right now we're seeing 40, 50% annual market growth. Why is this market suddenly so hot? >> Yeah it's not a difference. I think that as you said, the 4G deal and I think a lot of those tools, even if you look at forms, and PLC and we kind of extracted out the technology and made it easier, but it was not very clear who they were targeting with that. They're still targeting the same developer audience. So the they never expanded the universe of users. It was same user base, just making it simpler for them. So, with those low-code tools, it never landed them getting more and more user base out of that. With no-code platforms, you are now expanding the user community. You are giving this capabilities to more and more users than a low-code tools could provide. That's why I think the growth is much faster. So if you find the right no-code platform, you will see a lot more adoption because you're solving a real problem, you are giving them a lot more capabilities and making the user productive without having to depend on IT in many cases, or having to wait for a lot of those big applications to be built for them even though they need it immediately. So I think that's why I think you're solving a real business problem and giving a lot more capabilities to users and no doubt the users love it and they start expanding the usage. It's very viral adoption in many cases after that. >> Historically the rap on these tools has been that, because they're typically interpreted, the performance is never going to be up to that of application written in C plus plus or something. Is that still the case? Is that a sort of structural weakness of no-code tools or is that changing? >> I think the early days probably not any more. I think if you look at what we are doing at Google Cloud for example, it's not interpreted, I mean, it does do a lot of heavy lifting underneath the covers, but, and you don't have to go into the coding part of it but it brings the whole Cloud platform with it, right? So the scalability, the security the performance, availability all that stuff is built into the platform. So it's not a tool, it's a platform. I think that's thing, the big difference. Most of the early days you will see a lot of these things as a tool, which you can use it, and there's nothing underneath the covers the run kinds are very weak, there's really not the full Cloud platform provided with it, but I think the way we seeing it now and over the last many years, what we have done and what we continue to do, is to bring the power of the Cloud platform with it. So you're not missing out on the scalability, the performance, security, even the compliance and governance is built in. So IT is part of the process even though they might not build an application themselves. And that's where I think the barriers have been lifted. And again, it's not a solution for everything also. I'm not saying that this would go in, if you want to build a full end to end e-commerce site for example, I would not use a no-code platform for it, because you're going to do a lot more heavy lifting, you might want to integrate with a lot of custom stuff, you might build a custom experience. All that kind of stuff might not be that doable, but there are a lot of use cases now, which you can deliver with a platform like what we've been building at Google cloud. >> So, talk about what you're doing at Google cloud. Do you have a play in both the low-code and the no-code market? Do you favor one over the other? >> Yeah no I think we've employed technologies and services across the gamut of different requirements, right? I mean, our goal is not that we will only address one market needs and we'll ignore the rest of the things required for our developer community. So as you know, Google cloud has been very focused for many years delivering capabilities for developer community. With technology we deliver the Kubernetes and containers tend to flow for AI, compute storage all that kind of stuff is really developer centric. We have a lot of developers build applications on it writing code. They have abstracted some of this stuff and provide a lot of low-code technologies like Firebase for building mobile apps, the millions of apps mobile apps built by developers using Firebase today that it does abstract out the technology. And then you don't have to do a lot of heavy lifting yourself. So we do provide a lot of low-code tooling as well. And now, as we see the need for no-code especially kind of empowering the line of business user and citizen developers, we acquired a company called AppSheet, early 2020, and integrated that as part of our Google Cloud Platform as well as the workspace. So the G suite, the Gmail, all the technology all the services we provide for productivity and collaboration. And allowed users to now extend that collaboration capabilities by adding a workflow, and adding another app experience as needed for a particular business user needs. So that's how we looking at it like making sure that we can deliver a platform for spectrum of different use cases. And get that flexibility for the end user in terms of whatever they need to do, we should be able to provide as part of a Google Cloud Platform now. >> So as far as Google Cloud's positioning, I mean you're number three in the market you're growing but not really changing the distance between you and Microsoft for what public information we've been able to see in AWS. In Microsoft you have a company that has a long history with developers and of development tools and really as is that as a core strength do you see your low-code/no-code strategy as being a way to make up ground on them? >> Yeah, I think that the way to look at the market, and again I know the industry analyst and the market loves to do rankings in this world but, I think the Cloud business is probably big enough for a lot of vendors. I mean, this is growing as the amazing pace as you know. And it is becoming, it's a large investment. It takes time for a lot of the vendors to deliver everything they need to. But today, if you look at a lot of the net new growth and lot of net new customers, we seeing a huge percentage of share coming to Google Cloud, right? And we continue to announce some of the public things and the results will come out again every quarter. And we tried to break out the Cloud segment in the Google results more regularly so that people get an idea of how well they're doing in the Cloud business. So we are very comfortable where we are in terms of our growth in terms of our adoption, as well as in terms of how we delivering all the value our customers require, right? So, note out one of the parts we want to do is make sure that we have a end to end offering for all of the different use cases customers require and no-code is one of the parts we want to deliver for our customers as well. We've done very good capabilities and our data analytics. We do a lot of work around AIML, industry solutions. You look at the adoption we've had around a lot of those platform and Hybrid and MultiCloud. It's been growing very, very fast. And this one more additional things we are going to do, so that we can deliver what our customers are asking for. We're not too worried about the rankings we are worried about really making sure we're delivering the value to our customers. And we're seeing that it doesn't end very well. And if you look at the numbers now, I mean the growth rate is higher than any other Cloud vendor as well as be seeing a huge amount of demand been on Google Cloud as well. >> Well, not to belabor the point, but naturally your growth rate is going to be higher if you're a third of the market, I mean, how important is it to you to break into, to surpass the number two? How important are rankings within the Google Cloud team, or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about... we are not focused on ranking, or any of that stuff typically, I think we were worried about making sure customers are satisfied, and the adding more and more customers. So if you look at the volume of customers we're signing up, a lot of the large deals they didn't... do we need to look at the announcement we'd made over the last year, has been tremendous momentum around that. Lot of large banks, lot of large telecommunication companies large enterprises, name them. I think all of them are starting to kind of pick up Google Cloud. So if you follow that, I think that's really what is satisfying for us. And the results are starting to show that growth and the momentum. So we can't cover the gap we had in the previous... Because Google Cloud started late in this market. So if Cloud business grows by accumulating revenue over many years. So I cant look at the history, I'm looking at the future really. And if you look at the growth for the new business and the percentage of the net new business, we're doing better than pretty much any other vendor out there. >> And you said you were stepping up your reference to disclose those numbers. Was that what I heard you say? >> I think every quarter you're seeing that, I think we started announcing our revenue and growth numbers, and we started to do a lot of reporting about our Cloud business and that you will start, you see more and more and more of that regularly from Google now. >> Let's get back just briefly to the low-code/no-code discussion. A lot of companies looking at how to roll this out right now. You've got some big governance issues involved here. If you have a lot of citizen developers you also have the potential for chaos. What advice are you giving customers using your tools for how they should organize around citizen development? >> Yeah, no, I think no doubt. If this needs to be adopted by enterprise you can't make it a completely rogue or a completely shadow based development capabilities. So part of our no-code platform, one thing you want to make sure that this is enterprise ready, it has many aspects required for that. One is compliance making sure you have all the regulatory things delivered for data, privacy, security. Second is governance. A lot of the IT departments want to make sure who's using this platform? How are they accessing it? Are they getting the right security privileges associated with that? Are we giving them the right permissions? So in our a no-code platform we adding all this compliance, and governance regulatory stuff as part of our underlying platform, even though the end user might not have to worry about it the person who's building applications shouldn't have to think about it, but we do want to give controls to IT as needed by the large enterprises. So that is a big part of how we deliver this. We're not thinking about this as like go and build it, and then we write it once you have to do things for your enterprise, and then get it to do it again and again. Because then it just a waste of time and you're not getting the benefit of the platform at all. So we bringing those things together where we have a very easy to use, very powerful no-code platform with the enterprise compliance as well as governance built into that platform as well. And that is really resonating. If you look at a lot of the customers we're working with they do require that and they get excited about it as well as the democratizing of all of their line of business users. They're very happy that they're getting that kind of a platform, which they can scale from and deliver the productivity required. >> Certainly going to make businesses look very different in the future. And speaking of futures, It is January it's time to do predictions. What are your predictions (laughs) for the Cloud for this year? >> No I think that I mean no doubt cloud has become the center for pretty much every company now, I think the digital transformation especially with COVID, has greatly accelerated. We have seen many customers now who are thinking of pieces of their platform, pieces of their workflow or business to be digitized. Now that's trying to do it for all of it. So the one part which we see for this year is the need for more and more of efficiency in the industry are verticalized business workflows. It's not just about providing a plain vanilla Cloud Platform but also providing a lot more content and business details and business workflows by industry segments. So we've been doing a lot of work and we expect a huge amount of that to be becoming more and more core part of our offering as well as what customers are asking for. Where you might need things around say know your customer kind of workflow for financial services, Telehealth for healthcare. I mean, every industry has specific things like demand management and demand forecasting for retail but making that as part of a Cloud service not just saying, hey, I have compute storage network. I have some kind of a platform go add it and go and build what you want for your industry needs, We want to provide them that all those kinds of business processes and content for those industries as well. So we identified six, seven, industries. We see that as a kind of the driving factor for our Cloud growth, as well as helping our customers be much more productive as well as seeing the value of Cloud being much more realistic for them versus just a replacement for the data center. I think that's really the big shift in 21 I think. And I think that will make a big difference for all the companies who are really trying to digitize and be in forefront of the needs as their customers require in the future. >> Of course all of this accelerated by the pandemic and all of the specialized needs that have emerged from that. >> And I think the bond, which is important as well, I think as you know, I mean, everybody talks about AIML as like a big thing. No doubt AIML is an important element of it, but if you make that usable and powerful through this kind of workflows and business processes, as well as particular business applications, I think you see a lot more interest in using it than just a plain manila framework or just technology for the technology sake. So we try to bring the power of AI and ML into this business and industry applications, where we have a lot of good technologists at Google who knows how to use all these things. You wanted to bring that into those applications and platforms >> Exciting times ahead. Amit Zavery thank you so much for joining us. You look just as comfortable as I would expect someone to be who is doing his eighth Cube interview. Thanks for joining us. >> (laughing) Thanks for having me, Paul. >> That's it for this segment of Cube On Cloud, I'm Paul Gillin, stay tuned. (soft music)

>> Welcome back to Cube On Cloud. My name is Paul Gillin, enterprise editor at SiliconANGLE, and I'm pleased to now have as a guest on the show. Amit Zephyr, excuse me, general manager, vice president of business application platform at Google cloud. Amit is a formerly EVP and corporate officer for product development at Oracle cloud, 24 years at Oracle, and by my account a veteran of seven previous appearances on theCube. Amit welcome, thanks for joining us. >> Thanks for having me Paul, it's always good to be back on theCube. >> Now you are... one of your big focus areas right now is on low-code and no-code. Of course this is a market that seems to be growing explosively. We often hear low code/no code used in the same breath as if they're the same thing. In fact, how are they different? >> I think it's a huge difference, now. I think industry started as local mode for many, many years. I mean, there were technologies, or tools provided for kind of helping developers be more productive that's what low-code was doing. It was not really meant, even though it was positioned for citizen developers it was very hard for a non technologist to really build application using low code. No-code is really meant as the word stands, no code. So there's really no coding, there's no understanding required about the underlying technology stack, or knowing how constructs works or how the data is laid out. All that stuff is kind of hidden and abstracted out from you. You are really focused as a citizen developer or a line of business user, in kind of delivering what your business application requirements are, and the business flows are, without having to know anything about writing any code. So you can build applications, you can build your interfaces and not have to learn anything about a single line of code. So that's really no-code and I think they getting to a phase now where the platforms have gotten much stronger and better where you can do very good productive applications without having to write a single line of code. So that's really the goal with no-code, and that's really the future in terms of how we will get more and more line of business users, or citizen developers to build applications they need for their day-to-day work. >> So when would you use one or the other? >> I think since low-code you would probably any developer has been around for eight, 10 years, if not longer where you extract out some of this stuff you can do some of the things in terms of not having to write some code where you have a lot of modules pre-built for you, and then when you want to mix a lot of changes, you go and drop into an ID and write some code or make some changes to a code. So you still get into that, and those are really focused towards semi-professional developers or IT in many cases or even developers who want to reduce the time required to start from, write and building an application. so it makes you much more productive. So if you are a really some semi-professional or you are a developer, you can either use use low-code to improve your productivity and not start from scratch. No-code is really used for folks who are really not interested in learning about coding, don't have any experience in it, and still want to be productive and build applications. And that's really when I would start with.. I would not give a low code to a citizen developer or a line of business user who has no experience with any coding. And that's not really.. It will only productive, They'll get frustrated and not deliver what you need, and not get anything out of it and many cases. >> Well, I've been around this industry long enough to remember fourth-generation languages and visual basic >> Yeah and the predecessors that never really caught on in a big way. I mean, they certainly had big audiences but, right now we're seeing 40, 50% annual market growth. Why is this market suddenly so hot? >> Yeah it's not a difference. I think that as you said, the 4G deal and I think a lot of those tools, even if you look at forms, and PLC and we kind of extracted out the technology and made it easier, but it was not very clear who they were targeting with that. They're still targeting the same developer audience. So the they never expanded the universe of users. It was same user base, just making it simpler for them. So, with those low-code tools, it never landed them getting more and more user base out of that. With no-code platforms, you are now expanding the user community. You are giving this capabilities to more and more users than a low-code tools could provide. That's why I think the growth is much faster. So if you find the right no-code platform, you will see a lot more adoption because you're solving a real problem, you are giving them a lot more capabilities and making the user productive without having to depend on IT in many cases, or having to wait for a lot of those big applications to be built for them even though they need it immediately. So I think that's why I think you're solving a real business problem and giving a lot more capabilities to users and no doubt the users love it and they start expanding the usage. It's very viral adoption in many cases after that. >> Historically the rap on these tools has been that, because they're typically interpreted, the performance is never going to be up to that of application written in C plus plus or something. Is that still the case? Is that a sort of structural weakness of no-code tools or is that changing? >> I think the early days probably not any more. I think if you look at what we are doing at Google Cloud for example, it's not interpreted, I mean, it does do a lot of heavy lifting underneath the covers, but, and you don't have to go into the coding part of it but it brings the whole Cloud platform with it, right? So the scalability, the security the performance, availability all that stuff is built into the platform. So it's not a tool, it's a platform. I think that's thing, the big difference. Most of the early days you will see a lot of these things as a tool, which you can use it, and there's nothing underneath the covers the run kinds are very weak, there's really not the full Cloud platform provided with it, but I think the way we seeing it now and over the last many years, what we have done and what we continue to do, is to bring the power of the Cloud platform with it. So you're not missing out on the scalability, the performance, security, even the compliance and governance is built in. So IT is part of the process even though they might not build an application themselves. And that's where I think the barriers have been lifted. And again, it's not a solution for everything also. I'm not saying that this would go in, if you want to build a full end to end e-commerce site for example, I would not use a no-code platform for it, because you're going to do a lot more heavy lifting, you might want to integrate with a lot of custom stuff, you might build a custom experience. All that kind of stuff might not be that doable, but there are a lot of use cases now, which you can deliver with a platform like what we've been building at Google cloud. >> So, talk about what you're doing at Google cloud. Do you have a play in both the low-code and the no-code market? Do you favor one over the other? >> Yeah no I think we've employed technologies and services across the gamut of different requirements, right? I mean, our goal is not that we will only address one market needs and we'll ignore the rest of the things required for our developer community. So as you know, Google cloud has been very focused for many years delivering capabilities for developer community. With technology we deliver the Kubernetes and containers tend to flow for AI, compute storage all that kind of stuff is really developer centric. We have a lot of developers build applications on it writing code. They have abstracted some of this stuff and provide a lot of low-code technologies like Firebase for building mobile apps, the millions of apps mobile apps built by developers using Firebase today that it does abstract out the technology. And then you don't have to do a lot of heavy lifting yourself. So we do provide a lot of low-code tooling as well. And now, as we see the need for no-code especially kind of empowering the line of business user and citizen developers, we acquired a company called AppSheet, early 2020, and integrated that as part of our Google Cloud Platform as well as the workspace. So the G suite, the Gmail, all the technology all the services we provide for productivity and collaboration. And allowed users to now extend that collaboration capabilities by adding a workflow, and adding another app experience as needed for a particular business user needs. So that's how we looking at it like making sure that we can deliver a platform for spectrum of different use cases. And get that flexibility for the end user in terms of whatever they need to do, we should be able to provide as part of a Google Cloud Platform now. >> So as far as Google Cloud's positioning, I mean you're number three in the market you're growing but not really changing the distance between you and Microsoft for what public information we've been able to see in AWS. In Microsoft you have a company that has a long history with developers and of development tools and really as is that as a core strength do you see your low-code/no-code strategy as being a way to make up ground on them? >> Yeah, I think that the way to look at the market, and again I know the industry analyst and the market loves to do rankings in this world but, I think the Cloud business is probably big enough for a lot of vendors. I mean, this is growing as the amazing pace as you know. And it is becoming, it's a large investment. It takes time for a lot of the vendors to deliver everything they need to. But today, if you look at a lot of the net new growth and lot of net new customers, we seeing a huge percentage of share coming to Google Cloud, right? And we continue to announce some of the public things and the results will come out again every quarter. And we tried to break out the Cloud segment in the Google results more regularly so that people get an idea of how well they're doing in the Cloud business. So we are very comfortable where we are in terms of our growth in terms of our adoption, as well as in terms of how we delivering all the value our customers require, right? So, note out one of the parts we want to do is make sure that we have a end to end offering for all of the different use cases customers require and no-code is one of the parts we want to deliver for our customers as well. We've done very good capabilities and our data analytics. We do a lot of work around AIML, industry solutions. You look at the adoption we've had around a lot of those platform and Hybrid and MultiCloud. It's been growing very, very fast. And this one more additional things we are going to do, so that we can deliver what our customers are asking for. We're not too worried about the rankings we are worried about really making sure we're delivering the value to our customers. And we're seeing that it doesn't end very well. And if you look at the numbers now, I mean the growth rate is higher than any other Cloud vendor as well as be seeing a huge amount of demand been on Google Cloud as well. >> Well, not to belabor the point, but naturally your growth rate is going to be higher if you're a third of the market, I mean, how important is it to you to break into, to surpass the number two? How important are rankings within the Google Cloud team, or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about... we are not focused on ranking, or any of that stuff typically, I think we were worried about making sure customers are satisfied, and the adding more and more customers. So if you look at the volume of customers we're signing up, a lot of the large deals they didn't... do we need to look at the announcement we'd made over the last year, has been tremendous momentum around that. Lot of large banks, lot of large telecommunication companies large enterprises, name them. I think all of them are starting to kind of pick up Google Cloud. So if you follow that, I think that's really what is satisfying for us. And the results are starting to show that growth and the momentum. So we can't cover the gap we had in the previous... Because Google Cloud started late in this market. So if Cloud business grows by accumulating revenue over many years. So I cant look at the history, I'm looking at the future really. And if you look at the growth for the new business and the percentage of the net new business, we're doing better than pretty much any other vendor out there. >> And you said you were stepping up your reference to disclose those numbers. Was that what I heard you say? >> I think every quarter you're seeing that, I think we started announcing our revenue and growth numbers, and we started to do a lot of reporting about our Cloud business and that you will start, you see more and more and more of that regularly from Google now. >> Let's get back just briefly to the low-code/no-code discussion. A lot of companies looking at how to roll this out right now. You've got some big governance issues involved here. If you have a lot of citizen developers you also have the potential for chaos. What advice are you giving customers using your tools for how they should organize around citizen development? >> Yeah, no, I think no doubt. If this needs to be adopted by enterprise you can't make it a completely rogue or a completely shadow based development capabilities. So part of our no-code platform, one thing you want to make sure that this is enterprise ready, it has many aspects required for that. One is compliance making sure you have all the regulatory things delivered for data, privacy, security. Second is governance. A lot of the IT departments want to make sure who's using this platform? How are they accessing it? Are they getting the right security privileges associated with that? Are we giving them the right permissions? So in our a no-code platform we adding all this compliance, and governance regulatory stuff as part of our underlying platform, even though the end user might not have to worry about it the person who's building applications shouldn't have to think about it, but we do want to give controls to IT as needed by the large enterprises. So that is a big part of how we deliver this. We're not thinking about this as like go and build it, and then we write it once you have to do things for your enterprise, and then get it to do it again and again. Because then it just a waste of time and you're not getting the benefit of the platform at all. So we bringing those things together where we have a very easy to use, very powerful no-code platform with the enterprise compliance as well as governance built into that platform as well. And that is really resonating. If you look at a lot of the customers we're working with they do require that and they get excited about it as well as the democratizing of all of their line of business users. They're very happy that they're getting that kind of a platform, which they can scale from and deliver the productivity required. >> Certainly going to make businesses look very different in the future. And speaking of futures, It is January it's time to do predictions. What are your predictions (laughs) for the Cloud for this year? >> No I think that I mean no doubt cloud has become the center for pretty much every company now, I think the digital transformation especially with COVID, has greatly accelerated. We have seen many customers now who are thinking of pieces of their platform, pieces of their workflow or business to be digitized. Now that's trying to do it for all of it. So the one part which we see for this year is the need for more and more of efficiency in the industry are verticalized business workflows. It's not just about providing a plain vanilla Cloud Platform but also providing a lot more content and business details and business workflows by industry segments. So we've been doing a lot of work and we expect a huge amount of that to be becoming more and more core part of our offering as well as what customers are asking for. Where you might need things around say know your customer kind of workflow for financial services, Telehealth for healthcare. I mean, every industry has specific things like demand management and demand forecasting for retail but making that as part of a Cloud service not just saying, hey, I have compute storage network. I have some kind of a platform go add it and go and build what you want for your industry needs, We want to provide them that all those kinds of business processes and content for those industries as well. So we identified six, seven, industries. We see that as a kind of the driving factor for our Cloud growth, as well as helping our customers be much more productive as well as seeing the value of Cloud being much more realistic for them versus just a replacement for the data center. I think that's really the big shift in 21 I think. And I think that will make a big difference for all the companies who are really trying to digitize and be in forefront of the needs as their customers require in the future. >> Of course all of this accelerated by the pandemic and all of the specialized needs that have emerged from that. >> And I think the bond, which is important as well, I think as you know, I mean, everybody talks about AIML as like a big thing. No doubt AIML is an important element of it, but if you make that usable and powerful through this kind of workflows and business processes, as well as particular business applications, I think you see a lot more interest in using it than just a plain manila framework or just technology for the technology sake. So we try to bring the power of AI and ML into this business and industry applications, where we have a lot of good technologists at Google who knows how to use all these things. You wanted to bring that into those applications and platforms >> Exciting times ahead. Amit Zavery thank you so much for joining us. You look just as comfortable as I would expect someone to be who is doing his eighth Cube interview. Thanks for joining us. >> (laughing) Thanks for having me, Paul. >> That's it for this segment of Cube On Cloud, I'm Paul Gillin, stay tuned. (soft music)

(upbeat music) >> Announcer: From around the globe, it's theCUBE covering Google Cloud Next OnAir '20. >> Hi everybody, welcome back. This is Dave Vellante and you're watching theCUBE's continuous coverage of Google Next OnAir, nine weeks of cloud content. There was just a buffet of content. It started out with sort of industry trends, we got into productivity, infrastructure, deep dive in security analytics, database, app modernization, cloud AI and we're wrapping up the nine weeks with Business Application Platform. And with me is Amit Zavery, who's the general manager and vice president of the Business Application Platform at Google cloud. Amit, always a pleasure. Thanks for coming on. >> Definitely, Thanks for having me Dave. You're welcome. So tell me more about this role and kind of your swim lane, if you will. >> Definitely. I think as you can imagine with especially all this digital transformation getting accelerated due to COVID, that's a huge amount of demand and interest from customers to be able to build applications, integrate them and modernize systems and automate all of them very quickly and easily in a cost effective manner. So that has been driving a lot of the thinking at Google for quite a few of years already. But I think that a little more accelerated with some of the work we've been doing previously with our stack around API management, no code app development, automation capabilities in our platform as well and we're bringing a lot of these things together in an offering so that customers can take advantage of a lot of the innovation in this space and improve the digital transformation and innovate quickly as well. So that's what we've done with Business Application Platform. We're providing capabilities for any kind of developers, be it the technical user who has a lot of programming experience as well as the other spectrum, which are the system developers who don't really have any kind of a software engineering background, but want be able to build applications and automate and there're processes very quickly and easily. So we want to provide them all the tooling and capabilities so that they can do that and be more effective than they would otherwise be. >> I want to ask you about digital transformation. I mean, obviously it's a word that's thrown around, a phrase that's thrown around a lot and there's a spectrum of what it means to people. I was talking to somebody the other day, and this obviously will resonate with you, with your background in enterprise apps but they were talking about an ERP system that was put in 15 years ago before Iphone, before cloud and it just says you know those systems are fossilized and the business has changed dramatically but the ERP system hasn't. To them, digital transformation was basically upgrading the system. And so, but obviously to Google and your role, it means something much different, doesn't it? >> I saw a lot more, right? I think no doubt having a digital application. No doubt is important, it's a good starting point. But you said some of the systems are pretty old and they're not connected together between different parts of the business. And this is huge amount of manual processes. and there's a lot of, I would say disparate pieces which never come together if you don't really put a well thought out digital transformation project or intimidation around it. So a lot of times all these businesses, when they're connecting things together, they do need a platform to kind of bring their business processes, their workflows, their applications, and the interaction between different users, be it external and internal into a more automated system. And that's really where digital transformation really shines and improves a lot of the ability for customers to compete as well as meet their customer demands and be more effective than otherwise they would be. >> And cloud is critical there but it's connecting to an ecosystem. So I want to ask you about your strategy of the Business Application Platform. And of course, Google is known for great tech. It's very open, a lot of downstream contributions, you think about Kubernetes and Anthos. So how would you describe your group strategy and how does it dovetail with Google cloud overall? >> Yeah no doubt, I think the cloud is kind of the central team underneath the covers, right? So it does run on a multicloud and hybrid mechanism. So that is available anywhere as well as you have choice of and flexibility of deployment. It's also a platform on top of Anthos so you have the advantage of multicloud as well as support for all the different systems. You might have both on-prem as well as in various other cloud providers as well. And the other things we are doing is we're taking advantage a lot of the AIML capabilities, a lot of our data analytics capabilities and bringing a lot of those underlying technologies and extracting it out to a SaaS based offering on Business Application Platform. So the customer's perspective, they want to build an application, They use, we recently acquired a company called AppSheet at the start of this year. So they can easily now use AppSheet to build those applications without writing a single line of code. And then if you create that application, it provides connectivity to also a lot of other systems out there be it applications like SAP, salesforce.com. But also a lot of legacy systems in house or custom systems you might have built and put connectors to that. And then allows you to now monetize and take systems and provide API so then you can now extend it and bring it out into the partner community, as well as customers to be able to build applications around that as well. So it connects all those things together, takes advantage of the Google cloud and the ecosystem we have built and provides customers and users a much easier way to kind of build and deliver applications and automation on it. >> Okay, so that makes sense in terms of why you acquired, made that acquisition. But I want to talk about no code development. It's something that you've been talking about quite a bit lately. Tell the audience, what is no code development? Why do we need it? >> Yeah, I think if you look at some of these report nowadays, there's a limited amount of capacity and capabilities IT can provide. And for complicated and very large systems, you of course need IT to kind of make your business efficient and implement a lot of the systems together. But there a lot of other applications which departments and line of business users want to use and build and they can't wait around for IT. And there, I think you look at some of the reports from Gartner, for example, they're going to be four times more developers outside IT than they are going to be in IT. And those folks are not going to be software engineers, they're not professional programmers but still they need efficiency and automation and application development tools. This is where no code really brings a lot of value. So tools like AppSheet, which we acquired, as market leading no code development platform makes it very easy for anybody without any experience writing any code and building applications. They can point click and start building an application and be effectively produce something which they can collaborate and use between different users inside the company or outside without spending a lot of money and time to deliver that. And that's why the no-code application platforms are becoming very popular because it does make your business more efficient, makes your business more automated, it's cost effective and it's very productive, right? So that has been the trend now more and more, and we speak a lot of, especially nowadays, if you look at telehealth, you look at say, if you want to do mortgage lending, you want to build an app easily quickly without having to wait around for it. You are interacting with a lot of people through digital mediums now and instead of people using a lot of digital tools. And that's why I think there's no-code a platforms become much more important, powerful and usable in this mechanism as well. >> Okay, I think it's important to point out. We're talking about no-code here, not low-code, no-code, there's a difference. >> There's a big difference. I think the low-code was kind of the interim stage where tools, which are coming out into the market were available to make it a little easier for development but not enough to kind of democratize it for everybody. With no-code, you are now allowing and opening it up to a lot more vaster community of users who can multiple build applications and take advantage of a lot of technology innovation happening in the platform like cloud and other things as well. Media reporting is another good example where you want to be able to build dashboards quickly and easily without again writing codes. So the no-code becomes a lot more important and usable for this kind of needs. >> So I wonder if we could stay on this for a minute. You've used the example of programming a VCR, many of us remember how difficult that was early on and now it's just you talk to it and it works. You used that as an example of what no code is like. Can you explain that a little bit more? >> I think, basically it should be natural, right? I think when we used to program a VCR, you'd read some manuals, you'd read some code, you have to kind of go through the whole process. I don't even know how many of our audience nowadays even know about that or even think about it anymore. makes us all very dated. But it was a very cumbersome process and then you would worry about whether you recorded it or not, and that you got it on the right time and did you get the right show? And then you'd up deleting the wrong things or whatever it may be the case. A Lot of those things are now getting extracted and simpler in terms of the no-code development where if you are looking for a particular application interface, if you're looking to build say a mortgage lending app, a lot of those building blocks are already available to you. You kind of making it specific to your need, but really using a lot of the building blocks and get you the final solution versus learning about wiring, everything yourself with a lot of pieces of code in there, right? So that's becoming a straightforward. We have customers like Solvay, for example, which is a large chemical automation company. And they are being able to build multiple applications with 400 plus users inside the company and deliver a lot more automation inside the organization than they would otherwise be. >> So you kind of touched on this with the different modules and capabilities and functions within an organization. But when I think about that VCR analogy, I mean, it's doing one thing and that's pretty simple. How does that apply? And again, you kind of touched on it, but it seems like IT is much or business is much more complicated but so this actually works? >> Yeah I think it's a works. We provide a lot of our kind of templates and system examples in the no-code tooling, as well as the a lot of complexity, which is built underneath the cover which is completely hidden from the user perspective, right? So when I'm building an application, I'm still getting the power of the cloud, I'm getting the power of our underlying platform, the scalability, reliability, the security, the integration, all that kind of stuff is brought into this tooling without you having to learn any of those things. And that really is where the power comes in and it's flexible enough that you can kind of pretty much do any kind of application deployment. I will not build a full blown eCommerce site with it, but I can do a lot of typical day to day kind of applications like vacation approval or things you might want to do for mortgage lending, understanding a telehealth app for doctors. And so we're seeing a lot of the, we had customers who were doing this for hospital bed tracking during the COVID current crisis going on, right? Where they want to know what kind of PPE is available? How many beds are empty? So tracking that at the hospital level, at the health care departments, all that kind of stuff we're done very quickly and powerfully than they otherwise would have. >> Is there a concern amongst your customers about privacy, governance, compliance, security with all these citizen developers? How do you ensure that those fundamental edicts of the organization are preserved? >> Yeah, I think this is a similar thing than any other system we will make available to our customers in the cloud. We guarantee that all the data is only available to the people who are allowed to based on the privileges and the security profiles and everything else. So there's no really any kind of fear from the system perspective that you will get access to something which you're not allowed to. You do log in, you do have to have an account, you do have to have all the relevant credentials before you get access to it. Same thing with privacy. We make sure that nothing is shared with anybody who's not allowed to. So we apply the same tenant, same kind of rules to any kind of data or information we keep in the cloud for any other application development. All we're doing is abstracting it out and making it easier so that everybody who wants to build things don't have to learn 20 other things to kind of get going. So the ability to do this in faster and quickly is there but all the underlying philosophy and principles still remain intact into our products as well. >> Right, makes sense. You guys obviously you have this API first mentality. I've heard about things like API gateway, Apogee, data capabilities, automating AppSheets. Can you bring us up to date on some of those innovations? >> You will see a lot of updates in this area. So we've been innovating very aggressively. Of course, we have a product called Apogee which is a market leading API management product in the industry today. It does the full life cycle of APIs, including testing, development, publishing, monetization, security, all that kind of stuff for API. And we have thousands of customers using it today. Beyond that, what we've done is we've added a lot of ability from that Stack to kind of expose APIs and consume them through AppSheet. So we have an API data source for AppSheet. So it's easy for you to find APIs and build an app is one. Second, we also released something called API gateway, which is a very high performance, low latency cloud native gateway running on serverless. So a lot of applications are built on serverless platform nowadays. And if you want to now manage that to an API layer, we provide a gateway on top of Google cloud. So anybody can also use it very quickly and easily as well. So that's another area which we added. And the third thing which we are announcing is something called actually AppSheet automation. So as I talked about AppSheet for app development, we're also now adding a lot of workflow and business process automation underneath the covers as part of AppSheet. That's something we're making available to our customers so they can automate a business process and connect things together very quickly but also get the value of the automation in their application as well. So those are new innovations, new releases we're adding to our platform as part of business application offering so that anybody can take advantage of it. >> I mean, I love this trend because to the extent you've been able, I mean, this is the Holy grail. If you can enable business users, they're closer obviously to what's going on, closer to the customer and they can respond much more quickly. Are you seeing, for instance a user builds an app using an AppSheet, are you seeing because of the API richness, are you seeing other innovation around those occurring? Are we at that point yet? Or are they still kind of islands of- >> No, i think The scope of usage is growing very fast, right? We have more than 400,000 users on AppSheet are building applications. Thousands of thousands of applications been built on it, millions of users kind of using it at the end from the logging in and using those applications as well. So I think the innovation is happening very fast, where they're connecting different things, as well as now building an ecosystem, even in Solvay as example, I was giving you. The multiple apps are built by multiple departments, and they're kind of bringing those ecosystem together into a reuse, be able to kind of find new use cases around it, those kinds of things as well. >> Are organization's coming back to say, hey, we love this? But remember when we first started spinning up VMs, it was so easy. Are you seeing organizations say, hey, we need better line of sight on it. It could be in a catalog of what we're doing or marketplace. Are you seeing demand for that? >> Yeah, so we seeing a lot. I think there's a lot of reuse. Like we have partners who also build a build applications and put that into our marketplace as well and then we're also seeing a lot of interest from solution providers who build applications on top of what you might have as modules and deliver to our end customers as well. So now there's a lot of interest in that regards and there's a lot of good examples coming out and we're seeing a lot of ways of bringing some of these things together as well. >> I mean, how does machine intelligence, AI, how does it fit into your whole agenda and strategy? And what does it mean for a customer? >> Yeah, I think as you know, Google has been innovating and has been one of the top AIML vendor out in the marketplace today. And we have definitely taken a lot of advantage of that innovation and experience in that. So for example, when I talked about automation, a lot of the automation in AppSheet is being done using AIML technologies Google has built in terms of predicting the way the customer is going to use the application, how they're going to be able to take a business process and connect them together. A lot of that things have been built using AIML technologies at Google cloud. Beyond that on API management for our operational dashboards and operational monitoring. So make sure that we can give you five nines of availability. We kind of really use lot of AIML technologies to understand anomalies, figure out where the issues might be and predict those things and make sure that we kind of fixing those things in advance before things go down, right? Same thing in security, abuse, usage, make any kind of DDoS kind of things or whatever may be the security issues as well. We use a lot of AIML capabilities to make sure we're monitoring and securing our systems as well. So we're in the middle of everything. >> Right. Has the pandemic, you know, the last 150 days, obviously it's changed things and we've talked about digital transformation being accelerated. How are you thinking about sort of the go forward as a result of the post isolation era? >> Yeah, I think this is probably going to be... I don't think this is good. Once we get out of the COVID situation whenever that happens, some of the way we work and where we operate will definitely change than what it used to be pretty much in a way. So I do expect a lot more of video conferencing, for example I do expect a lot of digitalization. I do expect a lot of automation requirements, everybody trying to be more efficient and sharing things and working remotely. Those kinds of things will continue as a trend. So from our perspective, the work we're doing around API management, around digitalization, around digital transformation, around AppSheet automation, all those things are probably right things for the right kind of future where these technologies and tech offerings we do in Google cloud as well as other things we are doing broadly will make a big difference for everyone. >> Yeah recently, I want to kind of end just to get your industry perspectives. Recently, I wrote a piece that a video just on the enterprise app space, kind of the systems of record. And, you know, these are entrenched companies and even you see some of the new SaaS startups, but they're large companies and done very well. I was trying to sort of noodle on where does the potential of disruption come? Where's the new innovation? And I think some of the things that we're talking about here, this no-code, cloud. I mean, obviously you guys play in the application space but it seems like a part of your strategy is to enable developers to really build new types of applications. And maybe that's where the next wave of disruption comes, perhaps in vertical industries, perhaps with this no code. What are your thoughts on that? >> I know, you're right. I think the productivity in the collaboration space, no doubt is going through a huge transformation and change. I mean, Google being in the forefront of it with G Suite. If you look at some of the numbers and the metrics in terms of video conferencing and this collaboration in general has been going through the roof in terms of usage. AppSheet combination with that, for example, right? So if you're building an application, you're doing video conferencing, I might be able to build a telehealth app very quickly and easily. So that's where the no-code and collaboration, for example and productivity becomes part that story. Similarly, as you said, the industry solutions where you probably heard some of the innovation we're doing in that area by specific industry with business processes. Again, adding an API layer underneath the covers to connect different systems together, and then publishing that to an application through AppSheet becomes, again, a very much a great thought out solution and very easy to kind of provide that to our customers as well. So changes in productivity and collaboration, changes in no code app development, having a platform to connect all these things and make it easy to adopt is really a big part of our story as we move forward. And that's the reason why we're kind of increasing our investment in the Business Application Platform and just kind of pour to a lot of things we're doing. We did an acquisition on Looker, for example, for business intelligence. And that's an important part as part of business application platform, to be able to provide intelligence to what people are doing, what data you have to be able to do self service reporting, and then publish that to on a dashboard as well, which might be created through AppSheet or custom doesn't matter. But we provide you that whole end to end onto it. And then technology like Anthos ties it together to give you multicloud as well as a hybrid kind of delivery mechanism. So you have flexibility of choice how you deliver and run those systems. >> Yeah, I love that Looker example for sure. We're basically seeing the democratization of business apps. Amit, thanks so much for coming back in theCUBE. It's great to see you. Hopefully sometime soon we can see each other face to face. >> Yeah. I look forward to it and thank you again for having me. >> And thank you for watching our continuous coverage on theCUBE with Google's Next OnAir nine weeks of coverage. Keep it right there. Be right back after this short break. (upbeat music)

>> Presenter: From theCUBE Studios in Palo Alto and Boston, connecting with Dot leaders all around the world. This is a CUBE conversation. >> Everyone welcome to theCUBE studio here in Palo Alto. I'm John Furrier, host of theCUBE. We're here with our quarantine crew. We've been here for three months quarantining but we're getting the stories out. We're talking to all of our favorite guests and most important stories in technologies here remotely and we have a great conversation in store for you today with Amit Walia CEO of Informatica. Cube alumni, frequent guest of theCUBE, now, the CEO of Informatica. Amit, great to see you. Thank you for coming on this CUBE conversation. >> Good to see you John. It's different to be doing this like this versus being in the studio with you but I'm glad that we could leverage technology to still talk to each other. >> You're usually right here, right next to me, but I'm glad to get you remotely at least and I really appreciate you. You always have some great commentary and insights. And Amit, before we get into the real meaty stuff that I'd love around the data, I want to get your thoughts on this COVID-19 crisis. It's a new reality, it's highlighted as we've been reporting on SiliconANGLE for the past few months. The at scale problems that people are facing but it's also an opportunity. People are sheltered in place, there's a lot of anxiety on what their work environment is going to look like but the world still runs. Your thoughts on the current crisis and how you're looking at it, how you're navigating it as a leader. >> No doubt, it is a very unique situation we all live in. We've never all faced something like this. So I think first of all, I'll begin by expressing my prayers for anyone out there who has been impacted by it and of course, a huge round of thank you to all the heroes out there at the front lines. The healthcare workers, the doctors, the nurses (mumbles) so we can't forget that. These are very unique situations but as you said, let's not forget that this is a health crisis first and then it becomes an economic crisis. And then, as you said there is a tremendous amount of disruption and (mumbles) I think all of them will go through some phases and I think you can see already while there is disruption in front of us, you see the digital contents of organizations who are ready for that have definitely faced it lot better but as obviously the ones that have been somewhat in the previous generations, let's just say business models or technologies models are struggling through it. So there is a lot data chain. I think they're still learning. We're absolutely still learning and we will continue to learn til the end of this year and we'll come out very different for the next decade for sure. >> If anyone who's watching goes to YouTube on the SiliconANGLE CUBE and look at your videos over the years, we've been talking about big data and these transformational things. It's been an inside the industry kind of discussion. Board room for your clients and your business and Informatica but I think this is now showing the world this digital transformation. The future has been pulled forward faster than people have been expecting it and innovation strategy has been on paper, maybe some execution but now I think it's apparent to everyone that the innovation strategy needs to start now because of this business model impact, the economic crisis is exposed. The scale of opportunities and challenges, there will be winners and losers and projects still need to get done or reset or reinvented to come out of this with growth. So this is going to be the number one conversation. What are your thoughts around this? >> No, so I've talked to hundreds of customers across the globe and we see the same thing. In fact actually, in some ways as we went through this, something very profound dawned on me. We, John, talked about digital transformation for the last few years and clearly digital transformation will accelerate but as I was talking to customers, I came to this realization that we actually haven't digitally transformed. To be honest, what happened in the last three to four years is that it was more digital modernization. A few apps got tweaked, a few front-ends got tweaked but if you realize, it was more digital modernization, not transformation because in my opinion, there are four aspects to digital transformation. You think of new products and services, you think of new models of engaging with your customers, you think of absolutely new operating models and you think of fundamentally new business models. That's a whole rewrite of an organization, which is not just creating a new application out there, fundamental end to end transformation. My belief is, our belief is that, now starts a whole new era of transformation, digital transformation. We've just gone through digital modernization. >> Well, that's a great point and the business model impacts create... And in times of these inflection points, and again, you're a student of history in the tech industry, PC revolution, TCP IP. These are big points in time. They're not transitions. The big players tend to win the transitions. When you have a transformation, it's a Cambrian explosion of new kinds of capabilities. This is really, I would agree with your point but I think it's going to be a Cambrian explosion because the business model forcing function is there. How do you see it play, 'cause you're in the middle of all this, 'cause you guys are the control plane for data in the industry as a company. You enable these new apps. Could you share your-- >> So, we see a lot of that and I think the way to think about it, I think first of all, you said it right. This is a step function changing orbit. This is a whole new... You get to a new curve, you go to a different model. It's a whole new equation you're hiking for the curve you're going to be on. It's not just changing the gradient of the curve you've been on, this is going to be a whole journey. And when we think of the new world of digital transformation, there are four elements that are taught. First of all, it has to be strategic. It has to be Board, CEO, executive topped down, fundamentally across the whole organization, across every function of an organization. Second one you talked about scale. I believe this is all about innovating at scale. It's not about, hey, let me go put a new application in some far plans of my business. You've got to innovate at scale, end to end change does not happen in bits and pieces. Third one, this is cloud native, absolutely cloud native. If there was any minuscule of doubt, this is taking it away. Cloud nativity is the fundamental differentiator and the last but not the least is digital natives, which is where everybody wants to go become a digitally transformed company that are data-led. You got to make data-led decisions. So for competence, strategic mindset, innovation at scale cloud nativity and being data-led is going to define digital transformation. >> I think that encapsulates absolutely innovation strategy. I agree with you 100%, that's really insightful. I want to also get your thoughts on some things that you're talking about and you have always had some really kind of high level conversations around this and theCUBE has been a very social organization. We'd love to be that social construct between companies and audiences but you use a term, the digital transformation, the soul of digital transformation is data 4.0. This idea of having a soul is interesting because the apps all have personalization built in. You have CLAIRE, you've been doing CLAIRE AI for a while. So this idea of social organizations, a soul is kind of an interesting piece of metadata you're putting out in the messaging. What do you mean by that? How can digital transmission have a soul? >> I think we talked about it a lot and I think it just came to me that, look at the end of the day, any transformation is so fundamental to anything that anybody does and I think if you think about, you can go to a fundamental transformation that is just qualitative, it's qualitative and quantitative. It's about a human body, it's about a human body transforming itself and then something doesn't have a soul, John, it does not have life. It cannot truly move to the next paradigm. So I believe that, any transformation has to have a soul and the digital world is all about data. So obviously, we believe that we're walking into a data-for-data world where, as I said, the four pillars of digital transformation would be data-led and I believe data is the soul of that transformation and data itself is moving into a new paradigm. You've heard us talk about 1.0, 2.0, 3.0, and this is the new world of 4.0, a data 4.0 which basically is all about cloud nativity, intelligent automation, AI powered, focusing on data, trust in data ethics and operations and innovation at scale. When you bring these elements together, then that enables digital transformation to happen on the shoulders of data 4.0, which in my opinion, is the soul of digital transformation. >> All right, so just rewind on data 4.0 for a minute. Pretend I'm a CIO, I'm super busy. I don't have time to read up about it. Give me the bottom line, what is data 4.0? Describe it to me in basic terms, is it just an advancement, acceleration? What's the quick elevator pitch on 4.0, data 4.0? >> Very simple?. We're all walking into a world where we're going to be digital. Digital means that we're basically going to be creating tons of data. By the way, and data is everywhere. It's not just within the four walls of us. It's basically what I call transaction and interaction and with the scale and volume of data increasing, the complexity of it increasing. We want to make decisions. I say, tomorrow's decision, today and with data that is available to us yesterday, so I can be better at that decision. So we need intelligence, we need automation, we need flexibility, which is where AI comes in. These are all very fundamental rewrites of the technology stack to enable a fundamental business transformation. So in that world, data is front and center and you look at the amount of data we are going to collect, the whole concept of data ethics and data trust become very important, not just Goodwill governance, governance is important but data privacy, data trust becomes very important. Then we're going to do things like contact tracing, it's very important for the society but the ethics, trust and privacy of what you and I will give to the government is going to become very much important. So to me, that world that we go in, every enterprise has to think data first, data led, build an infrastructure to support the business in that context and then, as I said, then the soul, which is data will give life to digital transformation. >> That's awesome. Love the personalization and the soul angle on it. I always believe that you guys had that intelligent automation fabric and to me, you said earlier, cloud native is apparent to everyone now. I think out of all this crisis, I think the one thing that's not going to be debated anymore is that cloud native is the operating model. I think that's pretty much a done deal at this point. So having this horizontally scalable data, you know I've been on this rant for years. I think that's the killer app. I think having horizontally scalable data is going to enable a lot, souls and more life. So I got to ask you the real, the billion dollar question. I'm a customer of yours or prospect or a large enterprise. I'm seeing what's happening at scale, provisioning of VPNs for 100% employees at home, except for the most needed workers. I now see all the things I need to either process, I need to cancel and projects that double down on. I still got to go out and build my competitive advantage. I still have to run my business. So I need to really start deploying right out of the gate data centric, data first, virtual first, whatever you want to call it, the new reality first, this inflection point. What do I do? What is the things that you see as projects or playbook recipes that people could implement? >> First of all, we see a very fundamental reevaluation of the entire business model. In fact, we have this term that we're using now that we have to think of business has a business 360 and if I think about it in this new world, that the businesses that stood the test is that had basically what I call, a digital supply chain or in a very digital scalable way of interacting with their customers, being able to engage with their customers. A digital fabric often making sure that they can bring their product and services to the customers very quickly or in some cases, if they were creating new products and services, they had the ability for a whole new supply chain to reach that end customer. And of course, a business model that is flexible so they dont obviously, they can cater to the needs of their customers. So in all of these worlds, customers are a building digital, scalable data platforms and when I say platforms, it's not about some monolithic platform. These are, as you and I have talked about, very modular microservices based platform that reside on what we call metadata. Data has to be the soul of the digital enterprise. Metadata is the nervous system, that makes it all work. That's the left brain, right brain, that makes it all work, which is where we put AI on top. AI that works for the customers and then they leverage it but AI applied to that metadata allows them to be very flexible, nimble and make these decisions very rapidly, whether they are doing analytics for tomorrow's offering to be brought in front of a customer or understanding the customer better to give them something that appeals to them in changing times or to protect the customer's data or to provide governance on top of it. Anything that you would like to do has to ride on top of what I call a, AI led metadata driven platform that can scale horizontally. >> Okay, so I got to go to the next level on this, which is, okay, you got me on that. I hear what you're saying, I agree, great. But I got to put my developers to work and I got insight, I got analytics teams, I got competencies but Amit, my complexities don't go away. I still got compliance at scale, I got governance at scale but I also got, now my developers not just to get analytical insight, there's great dashboards and there's great analytic data out there, you guys do a good job there. I got to get my developers coding so I can get that agility of the data into the apps for visualization in the app or having a key ingredient of the software. How do I do that? What's your answer to that one? >> So, that's a critic use case. If you think about it, for a developer, one of the biggest challenge for analytics project is how do I bring all the data that is in sites across the enterprise so then I can put it in any kind of visualization analytics tool and things are happening at scale. An enterprise is spread across the globe. It's so many different data sources available everywhere. Again, what we've done is that as a part of the data platform when you focus upon the metadata, that allows you to go to one place where you can have full access to all of the data assets that are available across (mumbles). Do you remember at theCUBE years ago, we unveiled the launch of our enterprise data catalog, which as I said, was the Google for enterprise data through metadata. Now, developers don't have to go start wasting their time, trying to find whether data has (mumbles), through the catalog that CLAIRE is in-built, they have access to it. They can start putting that to work and figuring out how do I take different kinds of data? How do I put it in some data times tool? Through which we have the in-built integrations. Do what I call the valuable last mile work, which is where the intelligence is needed from them versus spend their energy trying to figure out where good data, clean data, all kinds of data sets. We have eliminated all of that complexity with the help of metadata data platform, CLAIRE, to let the developers do what I call value-added productive work. >> Amit, final question for you. I know you talk to customers a lot, you're always on the road, you got a great product background, that's where you came from, good mix understanding of the business but now your customers and prospects are trynna put the fires out. The big room that... No one's going to talk about their kitchen appliances when the house is burning down and in some cases on the business model side or if it's a growth strategy, they're going to put all their energies where the action is. So getting mind share with them is going to be very difficult. How are you as a leader and how is Informatica getting in front of these folks and saying, "Look, I know things are tough "but we're an important supplier for you." How do you differentiate? How are you going to get that mind share? What are some of those conversations? 'Cause this is really the psychology of the marketplace right now, the buyer and the customer. >> Well, first of all, obviously we had to adapt to reach our customers in a different way because, virtually based just like you and I are chatting right now and to be candid, our teams were fantastic in being able to do it. We've actually already had multiple pretty big sides of it. In fact, the first week before we started (mumbles), we had set up the MDM and Data Governance Summit up in New York and we expected thousands of customers to come there, ask them (mumbles) virtual and we did it virtually and we had three times more people attend the virtual event. It was much easier for people who attended from the confines of their living room. So we'd gone 100% virtual and good news is, that our customers are heavily engaged. We've actually had more participation of customers coming and attending our events. We've had obviously our customers speaking, talking about how they've created value. In light of that next week, we have the big event which we're calling, CLAIREview named after ClAIRE AI engine. It's basically a beautiful net-filled tech experience. We'll have a keynote, we'll have seasons and episodes, people can do bite-sized viewing at their own leisure. We'll talk about all kinds of transformation. In fact, we have Scott Guthrie who runs all of Azure and Cloud at Microsoft as a part of my Keynote. We have two great customers, CDO at XXL and a CEO of GDR nonprofit that does (mumbles) on diabetes work talk about the data journeys. We have Martin Byer from Gardner. So we've been able to pivot and our customers are heavily engaged because data is a P-zero or a P-one activity for them to invest in. So we haven't seen any drop-off in customer engagement with us and we've been very blessed that we have a very loyal and a very high retention rate customer base. >> Well, I would expect that being the center of the value proposition, where we've always said data has been. One more final question since this just popped in my head. You and I have been talking about the edge for years. Certainly now the edge is exposed, we all know what the edge is, it's working at home. It's the human, it's me, it's my IOT devices. More than ever, the edge is now the new perimeter. It's the edge and now the edges is there. There's something that you've been talking a while. This is another part of data fabric that's important. Your view on this new edge that's now visualized by everybody, realized this immersion. What's your thoughts on the edge? >> Oh, I think the edge is real now. You and me chatted about that almost four years ago and I (mumbles). Look, think of it this way. Think of how security is going to change. There's no more data center to which we route our traffic anymore. It's sitting over there somewhere where no human beings is going to have access. People are connecting to all kinds of cloud application directly from their offices or living rooms or their cultures and the world of security has to change in that context. And people are more going to be more, enterprise (mumbles) are more worried about, hey, how do I make sure that that data centric, privacy and security is there in my device and that connects to the third party cloud vendors versus I can't transfer traffic to mine, everything to my VPN. So the edge is going to become a lot more compute intensive as well as it will require a lot of the elements that are, to be honest, used to be data center centric. We have to lighten them and bring them to the edge so enterprises can feel assured and working because at the end of the day, they have to run a business by the standards that an enterprise is held to. So you will see a ton of innovation, by the way, robotics. Robotics is going to make edge even more interesting in live view. So I see the next couple of years, heavy IOP edge computing, just like the clients that are modeled to mainframe that the PC became like a mainframe in terms of compute capacity. I guarantee at the desktop, compute capacity will go down to the edge and we're going to see that happen in the next five years or so. >> The edge is the new data centers. I always say, it's the land is the way, the way is the land. Amit, great to see you and thanks for sharing and I'm sorry, we can't do it in person but this has been like a fireside chat meets CUBE interview, remote. Thanks for spending the time and sharing your insights and we've always had great interviews at your events, virtual again, this year. We're going to spread it out over time, good call. Thanks for coming on, I appreciate it. >> Thanks, John, take care. >> Okay, Amit, CEO of Informatica, always great to get the conversation updates from him on the industry and what Informatica, as at the center of the value proposition data 4.0. This is really the new transformation, not transition, data science, data, data engineering, all happening. theCUBE with our remote interviews, bringing you all the coverage here from our Palo Alto studios, I'm John Furrier. Thanks for watching. (gentle music)

(upbeat music) >> Hello, everyone, welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We're here with a very special guest, Amit Walia CEO of Informatica. Newly appointed CEO, about a month ago, a little bit over a month ago. Head of product before that. Been with Informatica since 2013. Informatica went private in 2015, and has since been at the center of the digital transformation around data, data transformation, data privacy, data everything around data and value and AI. Amit, great to see you, and congratulations on the new CEO role at Informatica. >> Thank you. Always good to be back here, John. >> It's been great to follow you, and for the folks who don't know you, you've been a very product centric CEO. You're a product set CEO, as they call it. But also now you have a company in the middle of the transformation. CloudScale is really mainstream. Enterprise is looking to multicloud, hybrid cloud. This is something that you've been on for many, many years. We've talked about it. So now that you're in charge, you've got the ship, the wheel in your hands. Where are you taking it? What is the update of Informatica? Give us the update. >> Well, thank you. So look, business couldn't be better. I think to give you a little bit of color where we're coming from the last couple of years Informatica went through a huge amount of transformation. All things trying to transform a business model, pivoting to subscription, all things have really been into Cloud, the new workloads as we talked about and all things new like AI. To give a little bit of color, we basically exited last year with a a billion dollars of ARR, not just revenues. So we had a billion dollar ARR company and as we pivoted to subscription, our subscription business for the last couple of years has been growing North of 55%. So that's the scale at which we are running multimillion dollars and if you look at the other two metrics which we keep very clicked near and dear to heart, one is innovation. So we are participating in five Magic Quadrants and we are the leader in all five Magic Quadrants. Five on five as we like to call it Gartner Magic Quadrants, very critical to us because innovation in the tech is very important. Also customer loyalty, very important to us. So we again, we're the number one in customer sat from a TSI survey and Gartner publishes the vendor ratings. We basically have a very strong positioning in that. And lastly, our market share continues to grow. So last IDC survey, our market share continued to grow and with the number one in all our markets. So business couldn't be at a better place where we are right now. >> I want to get into some of the business discussion. We first on the Magic Quadrant front, it's very difficult for the folks that aren't in the Cloud as to understand that to participate in multiple Magic Quadrants, what many do is hard because Clouds horizontally scalable Magic Quadrants used to be old IT kind of categories but to be in multiple Magic Quadrants is the nature of the beast but to be a leader is very difficult because Magic Quarter doesn't truly capture that if you're just a pure play and then try to be Cloud. So you guys are truly that horizontal brand and technology. We've covered this on theCUBE so it's no secret, but I want to get your comments on to be a leader in today, in these quadrants, you have to be on all the right waves. You've got data warehouses are growing and changing, you got the rise of Snowflake. You guys partner with Databricks, again, machine learning and AI, changing very rapidly and there's a huge growth wave behind it as well as the existing enterprises who were transforming analytics and operational workloads. This is really, really challenging. Can you just share your thoughts on why is it so hard? What are some of the key things behind these trends? We've got analytics, I guess you can do if it's just Analytics and Cloud, great, but this is a, this horizontal data play Is not easy. Can you share why? >> No, so yes, first we are actually I would say a very hidden secret. We're the only software company and I'll say that again, the only software company that was the leader in the traditional workloads legacy on premise and via the leader and the Cloud workloads. Not a single software company can say that they were the leader of and they were started 27 years ago and they're still the leader in the Magic Quadrants today. Our Cloud by the way runs at 10 trillion transactions a month scale and obviously we partnered with all the hyperscalers across the board and our goal is to be the Switzerland of data for our customers. And the question you ask is is a critical one, when you think of the key business drivers, what are customers trying to do? One of them is all things Cloud, all things AI is obviously there but one is all data warehouses are going to Cloud, we just talked about that. Moving workloads to Cloud, whether it is analytical, operational, basically we are front and center helping customers do that. Second, a big trend in the world of digital transformation is helping our customers, customer experience and driving that, fueling that is a master data management business, so on and so products behind that, but driving customer experiences, big, big driver of our growth and the third one is no large enterprise can live without data governance, data privacy. Even this is a thing today. You going to make sure that you would deliver a good governance, whether it's compliance oriented or brand oriented, privacy and risk management. And all three of them basically span the business initiatives that featured into those five Magic Quadrants. Our goal is to play across all of them and that's what we do. >> Pat Gelsinger here said a quote on theCUBE, many years ago. He said, "If you're not on the right wave, your could be driftwood," meaning you're going to get crashed over. >> He said very well. >> A lot of people have, we've seen a lot of companies have a good scale and then get washed away, if you will, by a wave. You're seeing like AI and machine learning. We talked a little bit about that. You guys are in there and I want to get your thoughts on this one. Whenever this executive changes, there's always questions around what's happening with the company. So I want you to talk about the state of Informatica because you're now the CEO, there's been some changes. Has there been a pivot? Has there been a sharpening focus? What is going on with Informatica? >> So I think our goal right now is to scale and hyperscale, that's the word. I mean we are in a very strong position. In fact, we use this phrase internally within the company, the next phase of great. We're at a great place and we are chartering the next phase of great for the company. And the goal that is helping our customers, I talked about these three big, big initiatives that companies are investing in, data warehousing and analytics, going to the Cloud, transforming customer experiences and data governance and privacy. And the fourth one that underpins all of them is all things AI. I mean, as we've talked about it before, right? All of these things are complex, hard to do. Look at the volume and complexity of data and what we're investing in is what we call native AI. AI needs, data, data needs AI, as I always said, right? And we had investing in AI to make these things easy for our customers, to make sure that they can scale and grow into the future. And what we've also been very diligent about is partnering. We partnered very well with the hyperscalers, like whether it's AWS, Microsoft, whether it's GCP, Snowflake, great partner of ours, Databricks great partner of ours, Tablo, great partners of ours. We have a variety of these partners and our goal is always customer first. Customers are investing in these technologies. Our goal is to help customers adopt these technologies, not for the sake of technologies, but for the sake of transforming those three business initiatives I talked about. >> You brought up, I was going to ask you the next question about Snowflake and Databricks. Databricks has been on theCUBE, Ali, >> And here's a good friend of ours. And he's got chops, I mean Stanford, Berkeley, he'll kill me with that, he's a cowl at Stanford but Databricks is doing well. They made some good bets and it's paying off for them. Snowflake, a rising star, Frank Slootman's over there now, they are clearly a choice for modern data warehouses as is, inhibits Redshift. How are you working with Snowflake? How do you take advantage of that? Can you just unpack your relationship with Snowflake? >> It's a very deep partnership. Our goal is to help our customers as they pick these technology choices for data warehousing as an example where Snowflake comes into play to make sure that the underlying data infrastructure can work seamlessly for them. See, customers build this complex logic sitting in the old technologies. As they move to anything new, they want to make sure that their transition, migration is seamless, as seamless as it can be. And typically they'll start something new before they retire to something old. With us, they can carry all of that business logic for the last 27 years, their business logic seamlessly and run natively in this case, in the Cloud. So basically we allow them this whole from-to and also the ability to have the best of new technology in the context of data management to power up these new infrastructures where they are going. >> Let me ask you the question around the industry trends, what are the top trends, industry trends that are driving your business and your product direction and customer value? >> Look, digital transformation has been a big trend and digital transformation has fueled all things like customer experiences being transformed, so that remains a big vector of growth. I would say Clouded option is still relatively that an early innings. So now you love baseballs, so we can still say what second, third inning as much as we'd like to believe Cloud has been there. Customers more with that analytical workloads first, still happening. The operational workloads are still in its very, very infancy so that is still a big vector of growth and and a big trend to BC for the next five plus years. >> And you guys are in the middle of that because of data? >> Absolutely. Absolutely because if you're running a large operation workload, it's all about the data at the end of the day because you can change the app, but it's the data that you want to carry, the logic that you've written that you want to carry and we participate in that. >> I've asked you before what I want to ask you again because I want to get the modern update because PureCloud, born in the Cloud startups and whatever, it's easy to say that, do that, everyone knows that. Hybrid is clear now, everyone that sees it as an architectural thing. Multicloud is kind of a state of, I have multiple Clouds but being true multicloud a little bit different maybe downstream conversation but certainly relevant. So as Cloud evolves from public Cloud, hybrid and maybe multi or certainly multi, how do you see those things evolving for Informatica? >> Well, we believe in the word hybrid and I define hybrid exactly as these two things. One is hybrid is multicloud. You're going to have hybrid Clouds. Second is hybrid means you're going to have ground and Cloud inter-operate for a period of time. So to us, we in the center of this hybrid Cloud trail and our goal is to help customers go Cloud native but make sure that they can run whatever was the only business that they were running as much possible in the most seamless way before they can at some point contour. And which is why, as I said, I mean our Cloud native business, our Cloud platform, which we call Informatica Intelligent Cloud Services, runs at scale globally across the globe by the way, on all hyperscalers at 10 plus trillion transactions a month. But yet we've allowed customers to run their on-prem technologies as much as they can because they cannot just rip the bandaid over there, right? So multicloud, ground Cloud, our goal is to help customers, large enterprise customers manage that complexity. Then AI plays a big role because these are all very complex environments and our investment in AI, our AI being called Clare is to help them manage that as in an as automated way, as seamless a way and to be honest, the most important with them is, in the most governed way because that's where the biggest risk or risks come into play. That's when our investments are. >> Let's talk about customers for a second. I want to get your thoughts on this 'cause at Amazon reinvent last year in December, there was a meme going around that we starred on theCUBE called, "If you take the T out of Cloud native, it's Cloud naive," and so the point was is to say, hey, doing Cloud native makes sense in certain cases, but if you'd not really thinking about the overall hybrid and the architecture of what's going on, you kind of could get into a naive situation. So I asked Andy this and I want to ask you any chance and I want to ask you the same question is that, what would be naive for a customer to think about Cloud, so they can be Cloud native or operated in a Cloud, what are some of the things they should avoid so they don't fall into that naive category? Now you've being, hi, I am doing Cloud for Cloud's sake. I mean, so there's kind of this perception of you got to do Cloud right, what's your view on Cloud native and how does people avoid the Cloud naive label? >> It's a good question. I think to me when I talk to customers and hundreds of them across the globe as I meet them in a year, is to really think of their Cloud as a reference architecture for at least the next five years, if not 10. I mean technology changes think of a reference architecture for the next five years. In that, you've got to think of multiple best of breed technologies that can help you. I mean, you've got to think of best of breed as much as possible. Now, you're not going to go have hundreds of different technologies running around because you've got to scale them. But think as much as possible that you are best of breed yet settled to what I call a few platforms as much as possible and then make sure that you basically have the right connection points across different workloads will be optimal for different, let's say Cloud environments, analytical workload and operational workload, financial workload, each one of them will have something that will work best in somewhere else, right? So to me, putting the business focus on what the right business outcome is and working your way back to what Cloud environments are best suited for that and building that reference architecture thoughtfully with a five year goal in mind then jumping to the next most exciting thing, hot thing and trying to experiment your way through it that will not scale would be the right way to go. >> It's not naive to be focusing on the business problems and operating it in a Cloud architecture is specifically what you're saying. Okay so let's talk about the customer journey around AI because this has become a big one. You guys been on the AI wave for many, many years, but now that it's become full mainstream enterprise, how are the applications, software guys looking at this because if I'm an enterprise and I want to go Cloud native, I have to make my apps work. Apps are driving everything these days and you guys play a big role. Data is more important than ever for applicants. What's your view on the app developer DevOps market? >> So to me the big chains that we see, in fact we're going to talk a lot about that in a couple of months when we are at Informatica World, our user conference in May is how data is moving to the next phase. And it's what developers today are doing is that they are building the apps with data in mind first, data first apps. I mean if you're building, let's say a great customer service app, you've got to first figure out what all data do you need to service that customer before you go build an app. So that is a very fundamental shift that has happened. And in that context what happens is that in a Cloud native environment, obviously you have a lot of flexibility to begin with that bring data over there and DevOps is getting complimented by what we see is data Ops, having all kinds of data available for you to make those decisions as you're building an application and in that discussion you and me are having before is that, there is so much data that you would not be able to understand that investing in metadata so you can understand data about the data. I call metadata as the intelligent data. If you're an intelligent enterprise, you've got to invest in metadata. Those are the places where we see developers going first and from there ground up building what we call apps that are more intelligent apps on the future not just business process apps. >> Cloud native versus Cloud naive discussion we were just having it's interesting, you talk about best of breed. I want to get your thoughts on some trends we're seeing you seeing even in cybersecurity with RSA coming up, there's been consolidation. You saw Dell just sold RSA to a private equity company. So you starting to see a lot of these shiny new toy type companies being consolidated in because there's too much for companies to deal with. You're seeing also skills gaps, but also skill shortages. There's not enough people. >> That is true. >> So now you have multiple Clouds, you got Amazon, you got Azure, you got Google GCP, you got Oracle, IBM, VMware, now you have a shortage problem. >> True. So this is putting pressure on the customers. So with that in mind, how are the customers reacting to this and what is best of breed really mean? >> So that is actually a really good one. Look, we all live in Silicon Valley, so we get excited about the latest technology and we have the best of skills here, even though we have a skills problem over here, right? Think about as you move up here from Silicon Valley and you start flying and I fly all over the world and you start seeing that if you're in the middle of nowhere, that is not a whole lot of developers who understand the latest cutting edge technology that happens here. Our goal has been to solve that problem for our customers. Look, our goal is to help the developers but as much as possible provide the customers the ability to have a handful of skilled developers but they can still take our offerings and we abstract away that complexity so that they are dealing only at a higher level. The underlying technology comes and goes and it'll come and go a hundred times. They don't have to worry about that. So our goal is abstract of the underlying changes in technology, focus at the business logically and you could move, you can basically run your business for over the course of 20 years. And that's what we've done for customers. Customers have invested with us, have run their businesses seamlessly for two decades, three decades while so much technology has changed over a period of time. >> And the Cloud is right here scaling up. So I want to get your thoughts on the different Clouds, I'll say Amazon Web Services number one in the Cloud, hyperscaler we're talking pure Cloud, they've got more announcements, more capabilities. Then you've got Azure again, hyperscale trying to catch up to Amazon. More enterprise-focused, they're doing very, very well in the enterprise. I said on Twitter, they're mopping up the enterprise because it's easy, they have an install base there. They've been leveraging it very well. So I think Nadella has done the team, has done a great job with that. You had Google try to specialize and figure out where they're going to fit, Oracle, IBM and everyone else. As you'd have to deal with this, you're kind of an arms dealer in a way with data. >> I would love to say I dance with it, not an arms dealer. >> Not an arms dealer, that's a bad analogy, but you get my point. You have to play well, you have to. It's not like an aspiration, your requirement is you have to play and operate with value in all the Clouds. One, how is that going and what are the different Clouds like? >> Well, look, I always begin with the philosophy that it's customer first. You go where the customers are going and customers choose different technologies for different use cases as deems fit for them. Our job is to make sure our customers are successful. So we begin with the customer in mind and we solve from there. Number two, that's a big market. There is plenty of room for everybody to play. Of course there is competition across the board, but plenty of room for everybody to play and our job is to make sure that we assist all of them to help at the end of the day, our joint customers, we have great success stories with all of them. Again, within mind, the end customer. So that has always been Informatica's philosophy, customer first and we partner with a critical strategic partners in that context and we invest and we've invested with all of them, deep partnerships with all of them. They've all been at Informatica well you've seen them. So again, as I said and I think the easiest way we obviously believe that the subset of data, but keep the customer in mind all the time and everything follows from there. >> What is multicloud mean to your customers if your customer century house, we hear people say, yeah, I use this for that and I get that. When I talk to CIOs and CSOs where there's real dollars and impact on the business, there tends to be a gravitational pull towards one Cloud. Why do people are building their own stacks which is why in-house development is shifted to be very DevOps, Cloud native and then we'll have a secondary Cloud, but they recognize that they have multiple Clouds but they're not spreading their staff around for the reasons around skill shortage. Are you seeing that same trend and two, what do you see is multicloud? >> Well, it is multicloud. I think people sometimes don't realize they're already in a multicloud world. I mean you have so many SaaS applications running around, right? Look around that, so whether you have Workday, whether you have Salesforce and I can keep going on and on and on, right. There are multiple, similarly, multi platform Clouds are there, right? I mean people are using Azure for some use cases. They may want to go AWS for certain other native use cases. So quite naturally customers begin with something to begin with and then the scale from there. But they realize as we, as I talked to customers, I realize, hey look, I have use cases and they're optimally set for some things that are multicloud and they'll end up there, but they all have to begin somewhere before they go somewhere. >> So I have multipleclouds, which I agree with you by the way and talking about this on theCUBE a lot. There's multi multiple Clouds and then this interoperability among Clouds. I mean, remember multi-vendor back in the old days, multicloud, it kind of feels like a multi-vendor kind of value proposition. But if I have Salesforce or Workday and these different Clouds and Amazon where I'm developing or Azure, what is the multi-Cloud interoperability? Is it the data control plane? What problems are the customers facing and the challenge that they want to turn into opportunities around multicloud. >> See a good example, one of the biggest areas of growth for us is helping our customers transform their customer experience. Now if you think about an enterprise company that is thinking about having a great understanding of their customer. Now just think about the number of places that customer data sits. One of our big areas of investment for data is a CRM product called salesforce.com right? Good customer data sits there but there could be where ticketing data sits. There could be where marketing data sits. There could be some legacy applications. The customer data sits in so many places. More often than not we realize when we talked to a customer, it sits in at least 20 places within an enterprise and then there is so much customer data sitting outside of the firewalls of an enterprise. Clickstream data where people are social media data partner data. So in that context, bringing that data together becomes extremely important for you to have a full view of your customer and deliver a better customer experience from there. So it is the customer. >> Is that the problem? >> It's a huge problem right now. Huge problem right now across the board where our customer like, hey, I want to serve my customer better but I need to know my customer better before I can serve them better. So we are squarely in the middle of that helping and we being the Switzerland of data, being fully understanding the application layer and the platform layer, we can bring all that stuff together and through the lens of our customer 360 which is fueled by our master data management product, we allow customers to get to see that full view. And from there you can service them better, give them a next best offer or you can understand the full lifetime value for customer, so on and so forth. So that's how we see the world and that's how we help our customers in this really fragmented Cloud world. >> And that's your primary value proposition. >> A huge value proposition and again as I said, always think customer first. >> I mean you got your big event coming up this Spring, so looking forward to seeing you there. I want to get your take as now that you're looking at the next great chapter of Informatica, what is your vision? How do you see that 20 mile stare out in the marketplace? As you execute, again, your product oriented CEO 'cause your product shops, now you're leading the team. What's your vision? What's the 20 mile stare? >> Well as simple as possible, we're going to double the company. Our goal is to double the company across the board. We have a great foundation of innovation we've put together and we remain paranoid all the time as to where and we always try to look where the world is going, serve our customers and as long as we have great customer loyalty, which we have today, have the foundations of great innovation and a great team and culture at the company, which we fundamentally believe in, we basically right now have the vision of doubling the company. >> That's awesome. Well really appreciate you taking the time. One final question I want to get your thoughts on the Silicon Valley and in the industry, is starting to see Indian-American executives become CEO. You now see you have Informatica. Congratulations. >> Amit: Thank you. >> Arvind over at IBM, Satya Nadella. This has been a culture of the technology for generations 'cause I remember when I broke into the business in the late 80s, 90s, this is the pure love of tech and the meritocracy of technology is at play here. This is a historic moment and it's been written about, but I want to get your thoughts on how you see it evolving and advice for young entrepreneurs out there, future CEOs, what's it take to get there? What's it like? What's your personal thoughts? >> Well, first of all, it's been a humbling moment for me to lead Informatica. It's a great company and a great opportunity. I mean I can say it's the true American dream. I mean I came here in 1998. As a lot of the immigrants didn't have much in my pocket. I went to business school, I was deep in loans and I believed in the opportunity. And I think there is something very special about America. And I would say something really special about Silicon Valley where it's all about at the end of the day value, it's all about meritocracy. The color of your skin and your accent and your, those things don't really matter. And I think we are such an embracing culture typically over here. And, and my advice to anybody is that look, believe, and I genuinely used that word and I've gone through stages in my life where you sometimes doubt it, but you have to believe and stay honest on what you want and look, there is no substitute to hard work. Sometimes luck does play a role, but there is no substitute for hard work. And at the end of the day, good things happen. >> As we say, the for the love of the game, love of tech, your tech athlete, loved it, loved to interview and congratulate, been great to follow your career and get to know you and, and Informatica. It's great to see you at the helm. >> Thank you John, pleasure being here. >> I'm John Furrier here at CUBE conversation at Palo Alto, getting the update on the new CEO from Informatica, Amit Walia, a friend of theCUBE and of course a great tech athlete, and now running a great company. I'm John Furrier. Thanks for watching. (upbeat music)

[Music] hello and welcome to this cube conversation here in Palo Alto California I'm John for your host of the cube we're here the very special guest I met while he is CEO of informatica newly appointed CEO about a month ago a little over a month ago had a product before that been with informatics in 2013 informatica went private in 2015 and has since been at the center of the digital transformation around data data transformation data privacy data everything around data and value in AI that made great to see you and congratulations on the new CEO role at informatica so thank you all it's good to be back here John it's been great to follow you and for the folks who don't know you you've been a very product centric CEO your products and CEO as they call it but also now you have a company in the middle of the transformation cloud scale is really mainstream enterprises look at multi cloud hybrid cloud this is something that you've been on for many many years we've talked about it so now that you're in charge you get the ship you get the wheel and you're in your hands were you taking it what is the update of informatica give us the update well thank you solook business couldn't be better I think to give you a little bit of color wavy coming from the last couple of years informatica went through a huge amount of transformation all things trying to transform our business model pivoting to subscription all things heavily bet into cloud the new workloads as we talked about and all things new like AI to give a little bit of color we basically exited it last year with a billion dollars of ARR not just revenue so we're a billion-dollar AR our company and as we pivot it to subscription as subscription business for the last couple of years has been growing north of 55 percent so that's the scale at which we are running multi-billion dollars and if you look at the other two metrics which we keep very click near and dear to hard one is innovation so we are participated in five magic quadrants and we are the leader in all five magic quadrants five one five as we like to call it Gartner Magic Quadrant very very critical to us because innovation in the tech is you know is very important also customer loyalty very important to us so we again were the number one in customer satisfaction continues to grow sore last IDC survey our market share continue to grow and be the number one in all our markets so business couldn't be at a better place where we are and what again some of the business discussed which first method on the Magic Quadrant front it's very difficult the folks that aren't in the club is to understand that to participate in multiple magic quadrants with many many do is hard because clouds horizontally scalable magic partners used to be old IT kind of categories but to be in multiple magic quadrants is the nature of the beast but to be a leader is very difficult because magic question doesn't truly capture that if you just appear play and then try to be cloud so you guys are truly that horizontal brand and and technology we've covered this on the cube so there's no secret but I want to get your comments on to be a leader and today in these quadrants you have to be on all the right waves you've got data warehouses are growing and changing at the rise of snowflake you guys partner with data bricks again machine learning and AI changing very rapidly and there's a huge growth wave behind it as well as the existing enterprises who were you know transforming you know analytics and operational workloads this is really really challenging can you just share your thoughts on why is it so hard what are the some of the key things behind these trends we can analytics I guess you can do if it's just analytics without great but this is a this horizontal data play is not easy can you share why no so yes first we are actually a I would say a very hidden secret we're the only software company and I'll say that again the only software company that was the leader in the traditional world traditional workloads legacy on-premise and via the leader in the cloud workloads not a single software company can say that they were the leader when they were started 27 years ago and there's still the leader in the magic quadrants today our cloud by the way runs at 10 trillion transactions a month scale and obviously we partner with all the hyper scalars across the board and our goal is to be the Switzerland of data for our customers and the question you ask is is a critical one when you think of he business drivers what a customer's trying to do one of them is all all things cloud all things the eye is obviously there but one is all data warehouses are going to cloud we just talked about that moving workloads to cloud whether it is analytical operational basically we have front and center helping customers do that second a big trend in the world of digital transformation is helping our customers customer experience and driving that fueling that is a master data management business so on and so products behind that but driving customer experiences big big driver of our growth and the third one is no large enterprise can live without data governance need a privacy man this is a thing today right you got to make sure that you deliver good governance whether it's compliance oriented or brand oriented privacy and risk management and all three of them basically span the business initiatives that feature into those five magic quadrants our goal is to play across all of them and that's what we do Pat Cal senior had a quote on the cube many years ago he said if you're not on the right wave you could be driftwood its meaning you're gonna get crashed oh sorry well a lot of people have we've seen a lot of companies have a good skill and then get washed away if you will by a wave you're seeing like AI and machine learning we talked a little bit about that you guys are in there I want to get your thoughts on this one is there whenever this executive changes there's always questions around you know what's happening with the company so I want you to talk about the state of informatica because you're now the CEO there's been some changes has there been a pivot has there been a sharpening focus what's going on with informatica so I think I'm cool right now is to scale and hyper scale that's the word I mean we're in a very strong position in fact we use this phrase internally within the company next phase of great we're at a great place and we are chartering the next phase of great for the company and the cool there is helping our customers I talked about these three big big initiatives that companies are investing in data warehousing and analytics going to the cloud transforming customer experiences and data governance and privacy and the fourth one that underpins all of them is all things a I mean as we've talked about before right all of these things are complex hard to do look at the volume and complexity of data and what we're investing in is what we call native ai ai needs data data needs AI as I always said right and we are investing in AI to make these things easy for our customers to make sure that they can scale and grow into the future and what we've also been very diligent about this partnering we partnered very well with the hyper scalars like whether it's AWS Microsoft whether it's GCP snowflake great partner of ours data brick skate part of ours tableau great partner of ours we have a variety of these partners and our cool is always customer first customers are investing in these technologies our goal is to help customers adopt these technologies not for the sake of technologies but for the sake of transforming those three business initiatives I thought you brought up I was gonna ask you the next question but snowflake and data versus data Brooks has been on the cube Holly a great that's a good friend of ours and he's got chops you Stan I'm not Stanford Berkeley he'll kill me with that if it's ow he's but beta Brooks is doing well they made some good bets and it's paying off of them snowflake a rising star Frank's Lubin's over there now they are clearly a choice for modern data warehouses as is any of us redshift how are you working with snowflake how do you take advantage of that can you just unpack your relationship with snowflake it's a it's a very deep partnership our goal is to help our customers you know as they pick these technology choices for data warehousing an example where snowflake comes into play to make sure that the underlying data infrastructure can work seamlessly for them see customers build this complex logic sitting in the old technologies as they move to anything new they want to make sure that that transition migration is seamless as seamless as it can be and typically they'll start something new before they retire something old with us they can carry all of that business logic for the last 27 years their business logic seamlessly and run natively in this case in the cloud so basically we allow them this whole from tool and also the ability to have the best of breed technology in the context of data management to power up these new infrastructures where they are going let me ask you the question around the industry trends what are the top and trends industry trends that are driving your business and your product direction and customer value look digital transformation has been a big trend and digital transformation has fueled all things like customer experiences being transformed so that remains a big vector of growth I would say cloud adoption is still relatively literally inning so no you love these balls we can still say what second third inning as much as we would like to believe cloud has been their customers mode with their analytical workloads first still happening the operational workloads are still in its very very infancy so that is still a big vector of growth and and a big trend that we see for the next five plus years and you guys in the middle of that oh absolutely yeah absolutely because if you're running a large operational workload it's all about the data at the end of the day because you can change the app but it's the data that you want to carry the logic that you've written that you want to carry and we participate in that I have ashes before but I want to ask you again because I want to get the modern update because pure cloud born in the cloud like you know startups and whatever it's easy to say that do that everyone knows that hybrid is clear now everyone that sees that as an architectural thing Multi cloud is kind of a state of I have multiple clouds but being true multi-cloud a little bit different maybe downstream conversation but certainly relevant so as cloud evolves from public cloud hybrid and maybe multi or certainly multi how do you see those things evolving for informatica well we believe in the word hybrid and I define hybrid exactly as these two things one is hybrid is multi cloud you can have hybrid clouds second is hybrid means you're gonna have ground and cloud interoperate for a period of time so to us we sit in the center of this hybrid cloud trend and our goal is to help customers go cloud native but make sure that they can run whatever was the old business that they were running as much possible in the most seamlessly before they can at some point cut over and which is why as I said I've been our cloud native business a cloud platform which we call informatica intelligent cloud services runs at scale globally across the globe by the way on all hyper scalars at ten plus trillion transactions a month but yet we will allowed customers to run their own Prem technologies as much as they can because they cannot just rip the band-aid over there right so multi cloud ground cloud our goal is to help customers large enterprise customers manage that complexity their AI plays a big role because these are all very complex environments and our investment in AI our REI being called Claire is to help them manage that as in an as automated way as seen this away and to be honest the most important thing for them is in the most governed way because that's where the biggest risk risks come into play that's where our investments let's say what customers per second I want to get your thoughts on this because at Amazon reinvent last year in December it was a meme going around on the queue that we that we start on the cube called if you think the tea out of cloud native it's cloud naive and so the the the point was is to say hey doing cloud native makes sense in certain cases but if you're not really thinking about the overall hybrid and the architecture of what's going on you kind of could get into a night naive situation so I asked any of this and I want to ask you any chat so I'll ask you the same question is that what would be naive for a customer to think about cloud so they can be cloud native or operate in a cloud what are some of the things they should avoid so they don't fall into that naive category now you've been you know I hey I'm doing cloud yeah for clouds sake I mean so there's kind of this perception have you got to do cloud right mm-hmm what's your view on cloud native and how does people avoid the cloud naive label it's it's a good question I think to me when I talk to customers and hundreds of them across the globe is I meet them in a year is to really think of their cloud as a reference architecture for at least the next five years if not I'm a technology changes think of a reference architecture for the next five years and in that you got to think of multiple best-of-breed technologies that can help you I mean you got to think best-of-breed as much as possible now you're not going to go have hundreds of different technologies running around because you got to scale them but think as much as possible that you are Best of Breed yet settled to what I call a few platforms as much as possible and then make sure that you basically have the right connection points across different workloads will be optimal for different let's say cloud environments analytical workload and operation workload a financial workload each one of them will have something that will work best in somewhere else right so to me putting the business focus on what the right business outcome is and working you will be back to what cloud environments are best suited for that and building that reference architecture thoughtfully with a five-year goal in mind then jumping to the next most exciting thing hot thing and try to experiment your way through it that will not scale would be the right way to go yeah it's not naive to be focusing on the business problems and operating it in a cloud architecture this is what you're saying okay so let's talk about like the customer journey around AI because this has become a big one you guys been on the AI way for many many years but now that it's become full mainstream enterprise how are the applications software guys looking at this because if I'm an enterprise and I want to go cloud native app to make my apps work yes apps are driving everything these days and you guys play a big role data is more important than ever for applicants what's your view on the app developer DevOps market so to me the big chains of VC in fact we're gonna talk a lot about that in a couple of months when we are at informatica world our user conference in May is how data is moving to the next phase and it's what developers today are doing is that they are building the apps with data in mind first data first apps I mean if you're building let's see a great customer service app you gotta first figure out what all data do you need to service a customer before you go build an app so that is a very fundamental shift that has happened and and in that context what happens is that in a cloud native environment obviously you have a lot of flexibility to begin with that bring data over there and DevOps is getting complemented by what we see is data ops having all kinds of data available for you to make those decisions as you build an application and in that discussion you're near having before is that there is so much data that you will not be able to understand that investing in metadata so you can understand data about the data I called metadata as the intelligent data if you're an intelligent enterprise you gotta invest in metadata those are the places where we see developers going first and from their ground up building what we call apps that are more intelligent apps of the future not just business process apps cloud native versus cloud naive connotation we were just having is interesting you talk about Best of Breed I want to get your thoughts on some trends we're seeing seeing even in cybersecurity with RSA coming up there's been consolidation you saw our Dell Jesolo RSA 2 private equity company so you starting to see a lot of these shiny new toy type companies being consolidated in because there's too much for companies to deal with you're seeing also skills gaps but also skills shortages there's not enough people oh now you have multiple clouds you got Amazon you got Azure you got Google GCP you've got Oracle IBM VMware now you have a shortage problem true so this is putting pressure on the customers so with that in mind how are the customers reacting to this and what is best to breed really mean so that is actually a very good point look we all live in Silicon Valley so we get excited about the latest technology and we have the best of skills here even though we have a skills problem over here right think about as you move away from Silicon Valley and you start flying and I fly all over the world and you start seeing that if you're in the middle of nowhere there is not a whole lot of developers who understand the latest cutting-edge technology that happens here our goal has been to solve that problem for our customers look our goal is to help the developers but as much as possible provide the customers the ability to have a handful of skilled developers but they can still take our offerings and we abstract away that complexity so that they are dealing only at a higher level the underlying technology comes and goes and you know it will come and go 100 times they don't have to worry about that so our goal is abstract away the underlying changes in technology focus at the business logic layer and you can move you can basically run your business for over the course of 20 years and that's what we've done for customers customers were invested with us have run their businesses seamlessly for two decades three decades while so much technology has changed with a period of time and the cloud is right here scaling up so I want to get your thoughts on the different clouds I see Amazon Web Services number one the cloud hyper scalar we're talking pure cloud that gets more announcements more capabilities then you got a sure again hyper scale trying to catch up to Amazon more Enterprise focused are doing very very well on the enterprise I was I said on Twitter they're mopping up the enterprise because it's easy to have an install base there they've been leveraging your very well stuff in atella has done team done a great job that you got Google trying to specialize and figure out where they're gonna fit Oracle IBM everyone else as you'd have to deal with this you're kind of an arms dealer in a way with data I would love to say no hands but not absolute I'm dealing that's the bad analogy but you get my point you have to play well you have to it's not like an aspiration show your requirements you have to play and operate with value in all the clouds one how is that going and what are the different clouds like well I always begin with the philosophy that its customer first you go with the customers a queen and customers choose different technologies for different use cases as deems fit for them our job is to make sure our customers are successful so we begin with the customer in mind and we solve from there number two that's a big market there is plenty of room for everybody to play of course there is competition across the board but plenty of room for everybody to play and our job is to make sure that we assist all of them to help at the end of the day our joint customers we have great success stories with all of them again you get in mind the end customer so that has always been informatic as philosophy customer first and we partner with a critical strategic partners in that context and and we invest and we've invested with all of them deep partnerships of all of them they've all been at informatica well you've seen them so again as I said and I think the easiest way we obviously believe they do this incident of data but keep the customer in mind all the time and everything follows from there what is multi-cloud me to your customers if your customer centric obviously we hear people say yeah I use this for that and I get that when I talk to CIOs and see says with his real dollars and interact on the business there tends to be a gravitational pull towards one cloud a lot of people are building their own stacks in house development has shifted to be very DevOps I'm cloud native and then I'll have a secondary cloud but they recognize that they have multiple clouds but they're not spreading their staff around for the reasons around skill shortage yeah are you seeing that same trend and to what do you see is multi cloud well it is 1d cloud I think I think people sometimes don't realize they're already in a multi cloud world I mean you have so many SAS applications running around right look around that so whether you have work day with your salesforce.com and I can keep going on and on and on right there are multiple similarly multi platform clouds are there right I mean people are using hash or for some use case they may want to go a dime us for certain other negative use cases so quite naturally customers begin with something to begin with and then the scale from there but they realize as we as I talk to customers I realize hey look I have use cases and they're optimally set for some things that are multi-cloud and they'll end up there but they all have to begin somewhere before they go somewhere so I have multiple clouds which I agree with you by the way and talking about this one cube a lot there's multi multiple clouds and then this interoperability among clouds I mean remember multi-vendor back in the old days multi-cloud it kind of feels like a multi vendor kind of value proposition but if I have Salesforce or workday in these different clouds in Amazon where I'm developing or Azure what is the multi cloud interoperability is it the data control plane what problems are the customers facing and the challenge that they want to turn into opportunities do a good example multi-cloud see a good example one of the biggest areas of growth for us is helping a customers transform the customer experience now if you think about an enterprise company that is thinking about having a great understanding of their customer now just think about the number of places that customer data sets one of the one of the big areas of investment viability the CRM product called salesforce.com right good customer data sits there but there could be where ticketing data sets there could be where marketing data sits there could be some legacy applications the customer data sits in so many places more often than not we realize when we talk to a customer it sits in at least 20 places within an enterprise and then there is so much customer data sitting outside of the firewalls of an enterprise right clickstream data where people had parts or shared a partner data so in that context bringing that data together becomes extremely important for you to have a full view of your customer and deliver a better customer experience from there so it is the cost the customers have the problem it's a huge problem right now huge problem right now across the board where cup a per customer like hey I want to serve my customer better but I need to know my customer better before I can serve them better so we are squarely in the middle of that helping and B being the Switzerland of data being fully understanding the application layer and the platform layer we can bring all that stuff and through the lens of our customer 360 which is fueled by our master data management product we allow customers to get to see that full view and from there you can service them better give them a next best offer or you can understand their lives either full lifetime value for customer so on and so forth so that's how we see the world and that's how we help our customers in this really fragmented cloud world that's your primary value proposition it's a huge value proposition and again as I said always think customer first I met you got your big event coming up this spring so looking forward to seeing you there I want to get your take as now that you're looking at the next great chapter of informatica what is your vision how do you see that twenty miles stare out in the marketplace as you execute again your product oriented CEO because your product chops now you're leading the team what's your vision what's the 20 mile stair well as simple as possible we're gonna double the company our goal is to double the company across the board we have a great foundation of innovation we put together and we remain paranoid all the time as to where and we always start to look where the world is going serve our customers and as long as we have great customer loyalty which we have today have the foundations of great innovation and a great team and culture at the company which we fundamentally believe in we basically right now have the vision of doubling the company that's awesome well really appreciate you taking the time one final question I want to get your thoughts on you know it's looking valley and in the industry starting to see Indian American executives become CEOs you now see you have informatica congratulations Arvind over at IBM sathi natella this has been a culture of the technology for generations I remember when I broken the business in the late 80s 90s this is the pure love of tech and the and the meritocracy of Technology is at play here this is a historic moment it's been written about but I want to get your thoughts on how you see it evolving and advice for young entrepreneurs out there future CEOs what's it take to get there what's it like what's your personal thoughts well first of all it's been a humbling moment for me to lead in from it's a great company and a great opportunity I mean I can say like it's the true Americans dream I mean I came here in 1998 I mean as a lot of immigrants Ted didn't have much in my pocket I went to business school I was deep in loans and and I believed in the opportunity and I think there is something very special about America and I would say something really special about Silicon Valley where it's all about at the end of the day value it's all about meritocracy the color of your skin and your accent and your those things don't really matter and I think we're such an embracing culture typically over here and my advice to anybody is that look believe and I genuinely use that word and I've gone through stages in my life where you sometimes doubt it but you have to believe and stay honest what you want and look there is no substitute to hard work sometimes luck does play a role but there is no substitute artwork and at the end of the day good things happen as we say that for the love of the game love attack your tech athlete love to love to interview and congratulate been great to follow your career get to know you and informatica it's great to see you at the helm thank you John pleasure being here I'm John 4 here is cube conversation in Palo Alto getting the update on the new CEO from informatics at MIT Walia friend of the cube and of course a great tech athlete and now running the great company I'm John forever here thanks for watching [Music] you [Music]

>> From the SiliconAngle media office, in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone, and welcome to this Cube Conversation. You know, the auto industry was a, if not the dominant force in the 20th century economy, and clearly, you see it in the headlines today. I mean all you got to do is look at Tesla. The stock is absolutely on fire, Tesla's market value is actually greater than that of Ford and GM combined. Even though its revenues are about one 12th of those two combined. The macro discussion today is really heating up around ESG, which stands for environmental social governance. So, electric vehicles are really picking up momentum, and maybe that's the tailwind for Tesla, but consumers are pragmatic, the electric is still more expensive than internal combustion-powered vehicles, so we'll see how that plays out. One of the things we talk about a lot on theCUBE is the software content in automobiles. In many ways, these vehicles are code on wheels, so that's part of the hype factor, too. But you know, I've always argued that the incumbent auto makers are actually in a pretty reasonable position to compete. While autonomous vehicles, they may disrupt the incumbents, and even though right now Silicon Valley is ahead of Detroit and Japan and Germany and Korea, there's an ecosystem that is evolving to support traditional auto makers. Now, one of those players is Tactile Mobility. The vast majority of data created around autonomous vehicles today is visual-based with LIDAR as a key enabler. But a human driver, you think about it, they don't just rely on sight, they're able to feel the road, the bumps, the curves, and the impacts of things like weather. In fact, it's estimated that more than 20% of vehicle crashes in the US each year are weather-related. And intelligent cars, they really still can't predict road conditions ahead. Tactile offers software that uses sensors that already live in the vehicles to predict and feel road conditions like black ice and potholes to improve safety. And with me to talk about these trends and his company is Amit Nisenbaum, who's the CEO of Tactile Mobility, Amit, thanks so much for coming on theCUBE. >> Thank you Dave very much for having me. >> Yeah, so really, it was a great opportunity, when I heard you were in town, invited you out, and really appreciate you coming out to our Marlborough studios, but let me start with, why your founders launched Tactile Mobility. >> Well, Dave, it's a very interesting story, I think, for our company, as well for other entrepreneurs to learn from it, because actually, the company's been around for about eight years, and it all started from a conundrum from a question that was posed to our founder, Boaz Mizrachi, which was about how do you take a vehicle from point A to point B at a set speed, with minimum gas consumption, using only the software and data coming off the vehicle sensors that are run of the mill sensors? And that question started this whole company, he believed that it's only an optimization question, meaning all of the data is out there, meaning data about the conditions of the road, the grates, the curvatures, the conditions and the health of the vehicle, meaning engine efficiency, tire health, et cetera et cetera. And what he found out was that actually neither this nor that has existed. So it was way more complicated than a mere optimization question, it's about how do you generate that data about the vehicle and the road? And he launched the company in order to go after those two data sets. He was able to solve that, or to address that question, and to take a vehicle and to show that you can take a vehicle from point A to point B at a set speed while minimizing fuel consumption, up to 10%. By the time that he has done that, gas prices dropped, and the question was what's next, and fortunately enough, the industry and the hype around autonomous vehicles has come around, and that has been the next frontier for our company, and that's what we been focusing on since then, but not only on that but on also other aspects, which I'll be happy to speak about. >> That is an awesome story of a pivot, you see this all the time with startups, it's kind of survive until you can thrive, and then something happens that's a tailwind, great technology that the visionary can see how to reapply it, and a little bit of luck involved, maybe, okay, so you-- >> Stamina. >> Stamina, right, you got to have a strong heart and stomach to be a startup. Okay, and you joined just a couple years ago, what attracted you to Tactile? >> Well I've been in this industry, actually in the cross section of the two industries of automotive and energy for about 12 years now, starting from a company called Better Place that you might have heard of, I was one of the first 10 employees there, and those two industries have been near and dear to my heart ever since. I like big questions, I like big challenges, I like big plays that have the potential to make a real difference, so the fact that the Tactile Mobility, at the time it was called MobiWize, it was in this industry was a big plus, but also the fact that the offering is not really the vanilla flavor offering, everybody's doing LIDAR and radar and cameras, all of a sudden there is someone else that is saying "Wait a minute, there is that "neglected segment, that additional set "of sensors, the sense of tactility that all of us "are using when we're driving, "and computers will need that as well. "How about that, this is something "that nobody pays attention to." And that really caught my attention. >> So I kind of hinted at this in my little narrative up front, the hype was all around autonomous, but let's face it, level five autonomous, it's, we're talking at least 2030, maybe further, but everybody drives some form of autonomous vehicle today, if you purchase a new vehicle, and that's really the space that you play in, so what are the big trends that you see, and what's the problem that you're solving? >> Yeah, so first of all, you're absolutely right, when people speak about autonomous vehicles, they imagine themself a car, a vehicle with big red button and that's it, that's what is called level five. However, there are four levels below that that lead to that, and today most of the vehicles leaving the assembly line are either level two or level three. That's why we're also saying that we're in the business of smart and autonomous vehicles, and the challenges there, if you're looking at the vehicles themself, are challenges of how do we make those vehicles both safer, as well as more enjoyable to ride? And the ability to address both of those together is actually not as simple as one might think, so that's what we're focusing on, and that's the trend, the trend of no compromises, that you go both for safety, as well as a user experience, that's on the vehicle side. Having said that, being a data company that has a proprietary software stack, that allows it to generate that data, the tactile data, the data about the dynamic between the vehicle and the road, allows us also to take that data to the cloud, and in the cloud to split that dynamic into two separate models. One we model independently the vehicle, the vehicle health, and the other one is we're turning each one of the vehicles to become like a probe that feels the road conditions and maps the location of bumps, cracks, oil spills, black ice, et cetera et cetera, and by that we are able to crowd source the data and create new layer of the map, road conditions there. Going back to the question that was posed about how do you take that vehicle from point A to point B, in minimum fuel, here you go, we have those two types of data, and now we can use it in other verticals as well. >> Well that's very interesting, so a lot of people say "Oh, autonomous vehicles, it's all about real time, "you can't do anything in the cloud," and you actually, you're refuting that, because you're building essentially a map of what's happening on the roads, whether it's a pothole or a bump or a curve, et cetera. And so essentially you're doing that in the cloud, modeling that in the cloud and then what, bringing it down in real time, right? >> Yeah, so first of all, the first use case is indeed to bring it back to the vehicles and so the vehicle, and the vehicles around it, will know what's ahead of them. Use cases, there are about preconditioning vehicle systems, for instance, you're approaching a pothole, probably you want, you meaning the vehicle, would like to tune the suspension to become harder or softer. You're approaching black ice, probably you want, you, the vehicle, would like to slow down, so that's one use case, but there are other use cases. Other use cases around, for instance, road authorities and municipalities, we do have customers around the globe, road authorities and municipalities, that are subscribed to our data services, the road condition data services, that allow them to better plan maintenance, as well as dispatch crews to locations of hazards in real time. >> Yeah, so I remember when I was a kid, we had a CB, that's how you communicated what was ahead. "Hey, watch out, there's a pothole up ahead." >> Great technology. >> Now we're doing that, and now does that essentially require some kind of peer to peer network, or? >> So we're agnostic of the technology, we're the data layer behind all of that. These days, everything, or most of the use cases, are still running on vehicle to cloud to vehicle, or to anybody else, but there are companies that are working on vehicle to vehicle. >> So you mentioned a stack, what does your stack look like, can you describe that a little bit? >> Two parts, one is embedded software, that sits on one of the vehicle computers, one of the ECUs, and the other one is the cloud component, the component, the embedded software that sits on one of the vehicle ECUs usually either the gateway, or one of the vehicle dynamics ECUs, or maybe ADAS ECU, et cetera, it takes in real time, mounds of data for multiple existing nonvisual sensors, such as wheel speed from all four wheels, wheel angle, position of the gas pedal, torque of the brake pedal and much much more, ingest all of that, create a unified signal that describes in real time the dynamic between the vehicle and the road, that signal is very very noisy, so we apply signal processing methodologies to clean it, and then we apply on top of it algorithms and AI and all of that in real time, in order to derive insights about the vehicle road dynamics. You probably ask yourself, "Give me a concrete example" or something like that, 'cause it's kind of amorphous. The killer app these days with OEMs, vehicle manufacturers, is what is called available grip level. It's basically a signal to the vehicle computer about how drastically can the vehicle accelerate, decelerate, or change direction, all different types of acceleration, before it will start to skid. Think about it as the performance envelope of the vehicle. Nobody but us can model this using software only in any condition, and this type of data has multiple use cases in the vehicle, happy to tell you more about those, question is if we have time. >> We do, but I want to make a point. The software only, the thing, if I understand it correctly, the OEM doesn't have to change any hardware that, you're using the existing sensors of the vehicle, of which there are certainly dozens if not hundreds, to actually take advantage of this, right, you don't have to do any kind of hardware changes, is that correct? >> We're a data and data analytics and AI company. >> Yeah, so if you wanted to add some color and double click on some examples, that would be great. >> Sure, so going back to the available grip level type of data, of insight, I call it, think about adaptive cruise control, the function that allows a vehicle to drive at a set speed, however, to avoid colliding into the front vehicle. So today, it seems like all of the data is there for ACC, adaptive cruise control, to be effective, you know the distance from the vehicle, probably using a radar, you know the relative velocity between the two vehicles, so you have all of the information, however you don't know, you, again, the vehicle computer, how hard the vehicle can brake given how slippery the road is, given how healthy or worn out the tires are, et cetera et cetera. That means that the vehicle computer needs to err on the safe side and keep the large distance in order to allow safe braking. What's wrong with that? Going back to the question about the trend before, first of all it's not natural to the driver. We keep a certain distance for a certain reason, and when the distance is too large, it just doesn't feel natural to us. That's one thing. However, on the other side, it's also not safe, how is that? You keep too large of a distance, someone at the end will cut you in. And ironically, you kept a large distance to stay safe, all of a sudden you're worse off. So being able to allow the vehicle to know really, what is the tight distance, safe distance to stay from the vehicle, allows that vehicle to be more enjoyable to ride, as well as safe. >> So take that example, because today, I can sort of personalize that adaptive cruise control and say "Okay, I want one bar, two bar, three bar," but that's it, and I sometimes say "Whoa, is three bar right, is two bar right?" And you're right, sometimes I go "Eh, it's too far, "I think I'll cut it down to two bar or one bar." You're saying with your software, the system is intelligent enough to optimize that, to keep me safe, but also keep me having comfortable driving. >> Absolutely true, actually those three bars is kind of a psychological exercise, right? Because the shortest bar is that large distance. When they tell you two bars or three bars, it's kind of like "Do you want to keep a large, "very large, or extra large distance," right? Because they will never allow you to keep shorter distance shorter than what is really really the bare minimum in order to brake at the worst case scenario. >> Even if it's safe. And that's really where your software comes in, okay. Now Porsche is an investor in the company, presumably it's a customer, right? >> No, they actually said publicly that they're a customer as well. >> Okay, great, so talk about how customers are using this, and what the adoption cycle looks like, and maybe give us some examples of how it's being applied. >> So customers, you mean OEMs, car manufacturers. So the way that they use it, I just described it now, the adoption cycle, we in this industry unfortunately cycles are long. We work years to create relationships with the car manufacturers to allow them to learn about our capabilities, to validate the integrity of our software. They also most commonly run RFPs or RFQs in order to choose the right technology, and I'm glad to say that we're winning again and again and again, and then there is the integration cycle, which by itself is a few years in length. So the cycle altogether is long, however, we found that our approach is quite effective, and the approach, not necessarily the technology, yes, but also the way that we approach those OEMs. We are quite, if I may say, humble. We know that we're not the car engineers, the typical car engineers. We actually know very little about cars, what we know, we know data very well and we know AI very well. And when we come to them, we say "We're not trying to replace your engineers, "we're not trying to do what you do, "we're trying to tackle the same problems "that you weren't able to tackle before "from a very different angle," and that works very well. >> So, you talked about the integration cycle of a couple, or maybe even longer, how long is the design cycle for these things, is it also years, or? >> So, the design cycle from our perspective is much much more agile, actually we are working in the Agile framework in terms of the development of the software itself, but you're asking about the design, much faster, but when I said a few years, a couple of years, I meant per OEM to design together, to allow them to feel that we're designing, meaning customizing the software to their needs, as well as implementing it, that's the length. >> But what they get is a competitive advantage, so Porsche as a leader, obviously, and an early adopter, is going to be able to now commercialize this technology, and of course it'll be embedded, but now it'll be a feature that the car salesperson will highlight, and maybe they market it, maybe they don't, but that gives them a competitive differentiation, right? So are you seeing that other OEMs are starting to really get this, and sort of leaning in, or what's your experience? >> Yes, it's the typical technology adoption curve, there are the early adopters, and there are the mainstream and the late adopters, I'm glad to say that these days we're not only working with the early adopters, but also more with the mainstream. I encourage you to stay tuned, I believe that in the coming month or two, we'll have a big announcement about another major OEM that has chosen us commercially for mass production, and we are in quite advanced stages with OEMs both in Europe and North America, starting also to spin out to Asia. >> And is the business model, is it a subscription model, is it a one time payment from the OEM, how's it work? >> That's another thing that made me excited about the company, going back to your question from before, it's quite diverse, I would say. For the OEMs, that's software that we embed in their vehicle, it's software licensing. However, the data that we generate and then upload to the cloud and repurpose it with the OEMs themself, but also as I said before, road authorities, municipalities, fleet managers, insurance companies, I didn't have a chance to touch on all of the verticals. That's a subscription model, so the two models working together, it's actually quite an attractive, valuable position for us and for our investors. >> So there's software license, and then there's data as a service. And so there's also adjacent industries that you can go after, you just mentioned a couple, so when you think about the total available market, which obviously, any CEO is going to do, TAM expansion is part of your job, but so what's that vision, what does that look like? >> So in terms of the size itself, it's measured in the trillions, it's very very big. In terms of the different verticals, the ones that I tapped on are the first ones, but even within those, these days we're really trying to stay razor focused on the OEMs and road authorities and municipalities. We have fleets and fleet managers that are coming to us with requests for the data that we call vehicle DNA, that's the data about the vehicle health, et cetera, and that's the third vertical that we're starting to address these days, but we're only 25 people, growing to 40, we're trying to be very very agile, that's from one end, and from the other end, now that we showed our value to the car manufacturers, we're going for the force multipliers, meaning partnerships with the channels, with the T1s, the suppliers to the OEMs themself. >> And let's see, you've been around eight years, you've been there two years, right, and then I think you did a raise of roughly, what, nine million to date? >> In October 2019, we announced the latest round of nine million dollars from Porsche, as well as some other investors, yes. >> Great, okay, so I mean not a ton of money, but you guys are small, and so, little bit more on the companies, 20, going to 40, you're well capitalized, but today, you see people raising 250 million, what do you sense as your capital needs, I mean you're obviously actively raising money, and doing what a CEO does, but can you share with us your milestones for the next 12, 18 months? >> First of all, we were fortunate, and fortune has something to do with it, I think that being disciplined is another thing, to have revenue already. So our capital needs, we're still not profitable, and we're growing fast, so we need to raise in order to support that growth, but we're quite diligent about that. Also, true, companies have raised tens and hundreds of millions of dollars. First of all, not all companies in this industry are created equal, we're not a hardware company, we're a software and data. We're also not trying to do a fully integrated offering like, let's say Zuks or something like that, which requires way way more money. And actually, I'm quite glad that we're raising as we need, but not more than that, because what you raise, you need to return tenfold, so we have enough in order to support the growth of the company in years to come. >> Well the OEM model is very sales efficient as well, so it's not like in software companies today, are hiring people to do inside sales, outside sales, enterprise sales, and so it's a different business. Well Amit, first of all, congratulations, a really interesting story, really appreciate you coming out to our studios here in Marlborough and sharing your story, and best of luck to you. >> Thank you very much, Dave, it's been a pleasure coming here, and I'm glad that you invited me. >> Great, and thank you everybody for watching, this is Dave Vellante with theCUBE, we'll see you next time. (techno music)

(funk music) (funk music) (funk music) (funk music) >> Hello and welcome to theCUBE studios in Palo Alto, California for another CUBE conversation where we go in-depth with thought leaders driving innovation across the tech industry. I'm your host, Peter Burris. Every enterprise is responding to the opportunities of cloud with significant changes in people, process, how they think about technology, how they're going to align technology overall with their business and with their business strategies. Now those changes are affecting virtually every aspect of business but especially every aspect of technology. Especially security. So what does it mean to envision a world in which significant new classes of services are being provided through cloud mechanisms and modes, but you retain and in fact, even enhance the quality of security that your enterprise can utilize. To have that conversation, we're joined today by a great guest, Amit Sinha is president and CTO at Zscaler. Amit, welcome back to theCUBE. >> Thank you Peter, it's a pleasure to be here. >> So before we get into it, what's new at Zscaler? >> Well, at Zscaler our mission is to make the internet and cloud a secure place for businesses and as I engage with our global 2000 customers and prospects, they are going through some of the digital transformation challenges that you just alluded to. Specifically for security, what is happening is that they had a lot of applications that were sitting in a data center or in their headquarters and that center of gravity is now moving to the cloud. They probably adopt their Office 365, and Box, and Salesforce, and these applications have moved out. Now in addition, the users are everywhere. They're accessing those services, not just from offices but also from their mobile devices and home. So if your users have left the building, and your applications are no longer sitting in your data center, that begs that question: Where should the security stack be? You know, it cannot be your legacy security appliances that sat in your DMZ and your IT closets. So that's the challenge that we see out there, and Zscaler is helping these large global organizations transform their security and network for a more mobile and a cloud-first world. >> Distributed world? So let me make sure I got this right. So basically, cause I think I totally agree with you >> Right. >> Just to test it, that many regarded the cloud as a centralization strategy. >> Correct. >> What we really see happening, is we're seeing enterprises more distribute their data, more distribute their processing, but they have not updated how they think about security so the presumption is, "yeah we're going to put more processing data out closer to the action but we're going to backhaul a whole bunch back to our security model," and what I hear you saying is no, you need to push those security services out to where the data is, out to where the process, out to where the user is. Have I got that right? >> You have nailed it, right. Think of it this way, if I'm a large global 2000 organization, I might have thousands of branches. All of those branches, traditionally, have used a hub-and-spoke network model. I might have a branch here in Palo Alto but my headquarters is in New York. So now I have an MPLS circuit connecting this branch to New York. If my Exchange server and applications and SAP systems are all there, then that hub-and-spoke model made sense. I am in this office >> Right. >> I connect to those applications and all my security stack is also there. But fast forward to today, all of those applications are moving and they're not just in one cloud. You know, you might have adopted Salesforce.com for CRM, you might have adopted Workday, you might have adopted Office 365. So these are SaaS services. Now if I'm sitting here in Palo Alto, and if I have to access my email, it makes absolutely no sense for me to VPN back to New York only to exit to the internet right there. What users want is a fast, nimble user experience without security coming in the way. What organizations want is no compromise in their security stack. So what you really need is a security stack that follows the user wherever they are. >> And the data. >> And the data, so my data...you know Microsoft has a front-door service here in Redwood City and if if you are a user here and trying to access that, I should be able to go straight with my entire security stack right next to it. That's what Gartner is calling SASE these days. >> Well, let's get into that in a second. It almost sounds as though what you're suggesting is that the enterprise needs to look at security as a SaaS service itself. >> 100 percent. If your users are everywhere and if your applications are in the cloud, your security better be delivered as a consistent "as-a-service," right next to where the users are and hopefully co-located in the same data center as where the applications are present so the only way to have a pervasive security model is to have it delivered in the cloud, which is what Zscaler has been doing from day one. >> Now, a little spoiler alert for everybody, Zscaler's been talking about this for 10-plus years. >> Right. >> So where are we today in the market place starting to recognize and acknowledge this transformation in the basic security architecture and platform that we're going through? >> I'm very excited to see that the market is really adopting what Zscaler has been talking about for over a decade. In fact, recently, Gartner released a paper titled "SASE," it stands for Secure Access Service Edge and there are, I believe, four principal tenets of SASE. The first one, of course, is that compute and security services have to be right at the edge. And we talked about that. It makes sense. >> For where the service is being delivered. >> You can't backhaul traffic to your data center or you can't backhaul traffic to Google's central data center somewhere. You need to have compute capabilities with things like SSL Interception and all the security services running right at the edge, connecting users to applications in the shortest path, right? So that's sort of principle number one of SASE. The second principle that Gartner talks about, which again you know, has been fundamental to Zscaler's DNA, is to keep your devices and your branch offices light. Don't shove too much complexity from a security perspective on the user devices and your branches. Keep it simple. >> Or the people running those user devices >> Absolutely >> in the branches >> Yeah, so you know, keep your branch offices like a light router, that forwards traffic to the cloud, where the heavy-lifting is done. >> Right. >> The third principle they talk about is to deliver modern security, you need to have a proxy-based architecture and essentially what a proxy architecture allows you to do is to look at content, right? Gone are the days where you could just say, stop a website called "evil.com" and allow a website "good.com," right? It's not like that anymore. You have to look at content, you know. You might get malware from a Google Drive link. You can't block Google now, right? So looking at SSL-encrypted content is needed and firewalls just can't do it. You have to have a proxy architecture that can decrypt SSL connections, look at content, provide malware services, provide policy-based access control services, et cetera and that's kind of the third principle. And finally what Gartner talks about is SASE has to be cloud-native, it has to be, sort of, born and bred in the cloud, a true multitenant, cloud-first architecture. You can't take, sort of, legacy security appliances and shove it in third-party infrastructure like AWS and GCP and deliver a cloud service and the example I use often is, just because you had a great blu-ray player or a DVD player in your home theater, you can't take 100,000 of these and shove it into AWS and become a Netflix. You really need to build that service from the ground up. You know, in a multitenant fashion and that's what we have done for security as a service through the cloud. >> So we are now, the market seems to be kind of converging on some of the principles that Zscaler's been talking about for quite some time. >> Right. >> When we think about 2020, how do you anticipate enterprises are going to respond as a consequence of this convergence in acknowledging that the value proposition and the need are starting to come together? >> Absolutely, I think we see the momentum picking up in the market, we have lots of conversations with CIO's who are going through this digital transformation journey, you know transformation is hard. There's immune response in big organizations >> Sure. >> To change. Not much has changed from a security and network architecture perspective in the last two decades. But we're seeing more and more of that. In fact, over 400 of global 2000 organizations are 100 percent deployed on Zscaler. And so that momentum is picking up and we see a lot of traction with other prospects who are beginning to see the light, as we say it. >> Well as you start to imagine the relationship between security and data, between security and data, one of the things that I find interesting is many respects to cloud, especially as it becomes more distributed, is becoming better acknowledged almost as a network of services. >> Right. >> As opposed to AWS as a data center here and that makes it a cloud data center. >> Right. >> It really is this network of services, which can happen from a lot of different places, big cloud service providers, your own enterprise, partners providing services to you. How does the relationship between Zscaler and kind of an openness >> Hm-mm. >> Going to come together? Hm-mm. >> So that you can provide services from a foreign enterprise to the enterprise's partners, customers, and others that the enterprise needs to work with. >> That's a great question, Peter and I think one of the most important things I tell our customers and prospects is that if you look at a cloud-delivered security architecture, it better embrace some of the SASE principles. One of the first things we did when we built the Zscaler platform was to distribute it across 150 data centers. And why did we do that? We did that because when a user is going to destinations, they need to be able to access any destination. The destination could be on Azure, could be on AWS, could be Salesforce, so by definition, it has to be carrier-neutral, it has to be cloud-neutral. I can't build a service that is designed for all internet traffic in a GCP or AWS, right. So how did we do that? We went and looked at one of the world's best co-location facilities that provide maximum connectivity options in any given region. So in North America, we might be in an Equinix facility and we might use tier one ISPs like GTT and Zayo that provide excellent connectivity to our customers and the destinations they want to visit. When you go to China, there's no GCP there, right so we work with China Unicom and China Telecom. When we are in India, we might work with an Airtel or a Sify, when we are in Australia, we might be working with Telstra. So we work with, you know, world class tier one ISPs in best data centers that provide maximum connectivity options. We invested heavily in internet exchange connectivity. Why? Because once you come to Zscaler, you've solved the physics problem by building the data center close to you, the next thing is, you want quickly go to your application. You don't want security to be in the way >> Right. >> Of application access. So with internet exchange connectivity, we are peered in a settlement-free way or BGP with Microsoft, with Akamai, with Apple, with Yahoo, right. So we can quickly get you to the content while delivering the full security stack, right? So we had to really take no shortcuts, back to your point of the world is very diverse and you cannot operate in a walled garden of one provider anymore and if you really build a cloud platform that is embracing some of the SASE principles we talked about, you have to do it the hard way. By building this one data center at a time. >> Well, you don't want your servicers to fall down because you didn't put the partnerships in place >and hardend them Correct. >> As much as you've hardened some of the other traffic. So as we think about kind of, where this goes, what do you envision Zscaler's, kind of big customer story is going to be in 2020 and beyond? Obviously, the service is going to be everywhere, change the way you think about security, but how, for example, is the relationship between the definition of the edge and the definition of the secure service going to co-evolve? Are people going to think about the edge differently as they start to think more in terms of a secure edge or where the data resides and the secure data, what do you think? >> Let's start off with five years and go back, right? >> We're going forward. >> Work our way back. Well, five years from now, hopefully everyone is on a 5G phone, you know, with blazing-fast internet connections, on devices that you love, your applications are everywhere, so now think of it from an IT perspective. You know, my span of control is becoming thinner and thinner, right? my users are on devices that I barely control. My network is the internet that I really don't control. My applications have moved to the cloud or either hosted in third-party infrastructure or run as a SaaS application, which I really don't control. Now, in this world, how do I provide security? How do I provide user experience? Imagine if you are the CIO and your job is to make all of this work, where will you start, right? So those are some of the big problems that we are helping our customers with. So this-- >> Let me as you a question 'cause here's where I was going with the question. I would start with, if I can't control all these things, I'm going to apply my notion of security >> Hm-mm. >> And say I am going to control that which is within >> Right. >> my security boundaries, not at a perimeter level, not at a device level, but at a service level. >> Absolutely and that's really the crux of the Zscaler platform service. We build this Zero Trust architecture. Our goal is to allow users to quickly come to Zscaler and Zscaler becomes the policy engine that is securely connecting them to all the cloud services that they want to go to. Now in addition, we also allow the same users to connect to internal applications that might have required a traditional VPN. Now think of it this way, Peter. When you connect to Google today, do you VPN to Google's network? To access Gmail? No. Why should you have to VPN to access an internal application? I mean, you get a link on your mobile phone, you click on it and it didn't work because it required a separate form of network access. So with Zscaler Internet Access and Zscaler Private Access, we are delivering a beautiful service that works across 150 data centers. Users connect to the service and the service becomes a policy engine that is securely connecting you to the destinations that you want. Now, in addition, you asked about what's going to happen in a couple of years. The same service can be extended for partners. I'm a business, I have hundreds of partners who want to connect to me. Why should I allow legacy VPN access or private circuits that expose me? I don't even know who's on the other end of the line, right? They come onto my network and you hear about the Target breaches because some HVAC contract that had unrestricted access, you hear about the Airbus breach because another contract that had access. So how do we build a true Zero Trust cloud platform that is securely allowing users, whether it's your employees, to connect to named applications that they should, or your partners that need access to certain applications, without putting them on the network. We're decoupling application access from network access. And there's one final important linchpin in this whole thing. Remember we talked about how powerless organizations >> Right. >> feel in this distributed model? Now imagine, your job is to also ensure that people are having a good user experience. How will you do that, right? What Zscaler is trying to do now is, we've been very successful in providing the secure and policy-based connectivity and our customers are asking us, hey, you're sitting in between all of this, you have visibility into what's happening on the user's device. Clearly you're sitting in the middle in the cloud and you see what's happening on the left-hand side, what's happening on the right-hand side. You know, you have the cloud effect, you can see there's a problem going on with Microsoft's network in the China region, right? Correlate all of that information and give me proactive intelligence around user experience and that's what we launched recently at Zenith Live. We call it Zscaler Digital Experience, >> Hmm. >> So overall the goal of the platform is to securely connect users and entities to named applications with Zero Trust principles. We never want security and user experience to be orthogonal requirements that has traditionally been the case. And we want to provide great user experience and visibility to our customers who've started adopting this platform. >> That's a great story. It's a great story. So, once again, I want to thank you very much for coming in and that's Amit Sinha, who is the president and CTO at Zscaler, focusing a lot on the R&D types of things that Zscaler's doing. Thanks again for being on theCUBE. >> It's my pleasure, Peter. Always enjoy talking to you. >> And thanks for joining us for another CUBE conversation. I'm Peter Burris, see you next time. (funk music) (funk music)

(upbeat music) [Reporter] - Live from San Diego, California it's theCUBE covering Goodcloud and Cloud- Native cloud. Brought to you by Red Hat the Cloud-Native computing foundation. and its ecosystem partners. >> Welcome back, we're here at Kubecon Cloud-Native con 2019 in San Diego, I'm Stu Miniman. We've got over 12,000 in attendance here and we have a three guest lineup of Kubecon veterans here. To my right is Loris Degioanni who's the CTO and founder of Sysdig. To his right, representing the Tiger is Amit Gupta who's vice president of business development and Product Management at Tigera, and also Knox Anderson who's Director of Product Management. We know from the Octopus, Amit, that also means that he's with Sysdig. So gentlemen, thank you all for joining. [Loris]- Octopus and Tiger >> Octopus and Tiger, bringing it all together on the tube. We have a menagerie as it were. So Loris, let's start as they said, you know all veterans, you've been here, you've almost been to every single one, something about a you know, a child being born made you miss one. [Loris] - The very first one. >> So, why don't you bring us in kind of what's so important about this ecosystem, why it's growing so fast and Sysdig's relationship with the community? >> Yeah, I mean, you can just look around, right? Kubecon is growing year after year, it's becoming bigger and bigger and this just a reflection of the community getting bigger and bigger every year, right? It's really looks like we are, you know, here with this community creating the next step, you know? For computing, for cloud computing, and really, you know, Kubernetes is becoming the operating system powering, you know, the cloud and the old CNC ecosystem around it is really becoming, essentially the ecosystem around it. And the beauty of it is it's completely open this time, right? For the first time in history. >> All right, so since you are the founder, I need to ask, give me the why? So we've been saying you know, we've been starting this program almost 10 years ago and the big challenge of our time is you know building software for distributed systems. Cloud's doing that, Edge is taking that even further. Bring us back to that moment of the birth of Sysdig and how that plays into all the open source and that growth you're talking about. >> Yeah, I mean, Sysdig was born, so first of all, a little bit of background of me. I've been working in open source and networking for my whole career. My previous company was the business behind washer, then it took on a live service, so, a huge open source community and working with enterprises all around the world, essentially to bring visibility over their neighbors. And then I started realizing the stack was changing radically, right? With the event of cloud computing. With the event of containers and Docker. With the event of Kubernetes. It, legacy ways of approaching the problem were just not working. Were not working the technical level because, you need to create something completely new for the new stack but they were also not working at the approach level. Every thing was proprietary. Every thing was in silos, right? So the approach now is much more, like inclusive and community first, and that's why I decided to start Sysdig. >> All right. so Amit, we know things are changing all the time. One thing that does not ever change is security is paramount. I really say, I go back 10 or 15 years you know, they've got a lot of lip service around security. Today, it's a board level discussion. Money, development, especially here in the Cloud-Native space it's really important so, talk about Tigera relationship with Sysdig and very much focused on the Kubernetes ecosystems. >> Absolutely. So I couldn't agree with you more, Stu. I mean, security is super critical and more so now as folks are deploying more and more mission critical applications on the Kubernetes based platform. So, Sysdig is a great partner for us. Tigera provides networking and network security aspects of that Kubernetes deployment. And if you think about it how modern applications are built today, you've taken a big large model and decomposed into hundreds of micro services so there's procedural cause that were happening inside the code and now API calls on the network so you've got a much bigger network with that service a highly distributed environment. So the traditional architectures where you manage the security typically with the firewall or a gateway, it's not sufficient. It's important, it's needed and that's really where, as people design their architecture, they have to think about how do you design security across that entire infrastructure in a distributed fashion or done in the early stages of your projects. >> Knox, help us understand the relationship here, how it fits into Sysdig's product with Tigera. >> Yeah, so we're great partners with Tigera. Tigera lives at the network security level. Sysdig's secure in that the product we built extends the instrumentation that Loris started off with our open source tool, to provide security across the entire container lifecycle. So at build time, making sure your images are properly configured, free of vulnerabilities at run time, looking at all the activity that's happening and then the big challenge in the Kubernetes space is around incident response and audit. So if something happens in that pod, Kubernetes is going to kill it before anyone can investigate and Sysdig helps you with those work flows. >> Maybe it would help, we all throw around those terms, Cloud-Native a lot and it's a term I've heard for a number of years. But the definition like cloud itself is one that you know matures over time and when we get there so, maybe if we focus in a little bit on Cloud-Native security. You know, what is it we're hearing from customers, what does it mean to really build Cloud-Native Security. What makes that different from the security we've been building in our data centers, in clouds for years? >> Well I thought Cloud-Native was just a buzzword. Does it actually mean something? (laughs) >> Well hopefully it's more than just a buzzword and that's what I'm hoping you could explain. >> Yeah, so again, the way I see it is the real change that you are witnessing is how software is being written. And we're touching a little bit on it at this point. Software intended to be architected as big monoliths now is being splayed into smaller components. And this is just a reflection of software development teams in a general way being much more efficient when you can essentially, break the problem into sub-problems and break the responsibilities into sub-responsibilities. This is perhaps something that is extremely beneficial especially in terms of productivity. But also, sort of revolutionizes the way you write software, you run software, you maintain software, CICD, you know continues development, continues integration, pipelines, the reliance on GIT and suppository to store everything. And this also means that, securing, monitoring, troubleshooting infrastructures becomes much different. And one of things we are seeing is legacy two's don't work anymore and the new approaches like Calico Networking or like Falco and runtime security or like Sysdig secure, for the lifecycle and security of containers are something bubbling up as alternatives to the old way of doing things. >> I would add to that I agree with you. I would add that if you're defining a Cloud-Native security the Cloud-Native means it's a distributed architecture. So your security architecture has got to be distributed as well, absolutely got a plan for that. And then to your point, you have to automate the security as part of the various aspects of your lifecycle. Security can not be an afterthought you have to design for that right from the beginning and then one last thing I would add is just like your applications are being deployed in an automated fashion your security has to be done in that fashion so, policy is good, infrastructure is good and the security is just baked in as part of that process. It's critical you design that way to get the best outcomes. >> Yeah, and I'd say the asset landscape has completely changed. Before you needed to surface finding against a host or an IP. Now you need to surface vulnerabilities and findings against clusters, name spaces, deployments, pods, services and that huge explosion of assets is making it much harder for teams to triage events, vulnerabilities and it's really changing the process in how the sock works. >> And I think that the landscape of the essence is changing also is reflected on the fact that the persona landscape is changing. So, the separation between attempts and operation people is becoming thinner and thinner and more and more security becomes a responsibility of the operation team, which is the team in charge of essentially owning the infrastructure and taking care of it, not only for the operational point of view but also from the security. >> Yeah, I think I've heard the point that you've made a many times. Security can't be a bolt on or an afterthought. It's really something fundamental, we talk about DevOps is, it needs to be just baked into the process, >> Yeah. >> It's, as I've heard chanted at some conferences, you know, security is everyone's responsibility, >> Correct. >> make sure you step up. We're talking a lot about open source here. There's a couple of projects you mentioned, Falco and Calico, you're partners with Red hat. I remember going to the Red Hat show years ago and they'd run these studies and be like, people are worried that open source and security couldn't go side by side, but no, no you could actually, you know open source is secure but taking the next step and talking about building security products with open source give us, where that stands today and how customers are you know embracing that? And how can it actually keep up with the ever expanding threat surfaces and attacks that are coming out? >> Yeah. First of all as we know open source is actually more secure and we're getting proof of that you know, pretty much on a daily basis including you know, the fact that tools like Kubernetes are regularly scrutinized by the security ecosystem and the vulnerabilities are found early on and disclosed. In particular, Sysdig is the original creator of Falco which is an open source, CNCF phased anomaly detection system that is based on collecting high granular data from a running Kubernetes environment. For example, through the capture of the system calls and understanding the activity of the containers and being able to alert about the anomalous behavior. For example, somebody being able to break into your container, extricating data or modifying binaries, or you know perpetrating an attack or stuff like that. We decided to go with an approach that is open source first because, first of all, of course, we believe into participating with the community and giving something as an inclusive player to the community. But also we believe that you really achieve better security by being integrated in the stack, right? It's very hard , for example, to have, I don't know, security in AWS that is deeply integrated with the cloud stack upon us, alright? Because this it's propietary. Why would Kubernetes solutions like Falco or even like Calico, we can really work with the rest of the community to have them really tightly coupled and so much more effective than we could do in the past. >> You know, I mean I would make one additional point to your question. It's not only that users are adopting open source security. It's actually very critical that security solutions are available as an open source, because, I mean, look around us here this is a community of open source people, they're building and distributing infrastructure platform from that is all open source so we're doing this service if we don't offer a good set of security tools to them, not an open source. So that's really our fundamental model that's why Calico provides two key problems networking and network security for our users, you deploy your clusters, your infrastructures, and you have all the bells and whistles you need to be able to run a highly secure, highly performing cluster in your environment and I believe that's very critical for this community. >> Yeah, and I'd say that and now with open source, prevention has moved into the platform. So, with network policy and things like Calico or in our 3.0 launch we incorporated the ability to automate tests and apply pod security policies. And those types of prevention mechanisms weren't available on your platforms before. >> Okay, I often find if you've got any customer examples, talk about, you know, how they're running this production kind of the key, when they use your solutions you know, the benefits that they're having? >> Yeah, I'll take a few examples. I mean, today it is probably fair to say Calico from the partial phone home data we get a 100,000 plus customers across the globe, some of the, I can't take the actual names of the customers but, so the largest banks are using Calico for their enterprise networking scenarios and essentially, the policies, the segmentation inside the clusters should be able to manage the security for those workloads inside their environments. So that's how I would say. >> Yeah, and Sysdig, we, have an open core base with Falco, and then we offer a commercial product called Sysdig secure, in particular, last week we release version 3.0 of our commercial product which is another interesting dynamic because if we can offer the open core essentially to the community but then offer additional features with our commercial product. And Falco is installed in many, many thousands extension of platforms. and Sysdig secure you know secures, and offers visibility to the biggest enterprises in the world. We have deployments that are at a huge scale with the biggest banks, insurance companies, media companies, and we tend to fall to cover the full life cycle of applications because as the application and as the software moves in the CICD pipeline so security needs to essentially accompany the application through the different stages. >> All right, well thank you all three of you for providing the update. Really appreciate you joining us in the program and have a great rest of the week >> Thank you very much. >> Thank you. >> Thank you. >> We'll be back with more coverage here from Kubecon, Cloud-Nativecon. I'm Stu Miniman and thanks for watching theCUBE. (upbeat music)

>> Live from Las Vegas, it's theCUBE covering Informatica World 2019 brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World. I am your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Amit Walia, President - Product and Marketing here at Informatica. Thank you for coming back on theCUBE. So we're here at Informatica World, there's a lot of buzz, a lot of energy, obviously CLAIRE is a big story, your company got great press yesterday from The Wall Street Journal teaming up with Google to tame the data. One of the themes we keep hearing is that data needs AI, but AI needs data. Elaborate on that a little bit. >> That's a great point, in fact I would extend that and say I believe, and I will talk about that today in the closing keynote, is the language that AI needs or speaks is data. Because to be honest, without data, there's no great AI. And I think something that we've known all this while, but now that AI is really becoming pervasive and has skill, you really, really need to give it relevant, good, contextual data for a Siri or a Cortana or Alexa to make some contextual decisions, right? And we see that happening a lot in the world of enterprise now. Finally enterprises are arriving at the point where they want to use AI for P-to-P use cases, not just consumer use cases that you and me are used to. And then, to your other question, AI is a part of everything that we do in data. Because, to be honest, it really helps improve productivity, automate mundane tasks. And I think we were talking before this, there is a massive skills gap. And I think you look around, the economy's kind of fully saturated with jobs, and there's still so much more work to be done with more data, different data, so AI's helping making some of those mundane activities become a lot more easier or autonomous, if I may. >> What's the use cases for CLAIRE in AI around as it grows? Because, you know, the data world, you guys have been doing it for 25 years at Informatica, private for 4 so, innovating on the products side, but it used to be, here's the data department, they handle it. The data warehousing in the fenced out area in the company, now it's strategically part of everything, right? So you guys have the MDM, you've got the Catalog, you've got all kinds of solutions. How is that role changing within your customer base? And what are some of those use cases? Because now they have to think end-to-end, you've got Cloud and On-premise, these are challenges and opportunities. But the role of data and the data teams is expanding rapidly. >> In a significant way. A significant way. I think I kind of was joking with our practitioners yesterday that they were all becoming, they were going from heroes to superheroes, if you are enjoying the Avengers movies, and that analogy. But genuinely, because if you think about it, right, I think what we are seeing in this world, we call it the data three data where the data is becoming a platform of a sort. It is getting decoupled from the data bases, from the applications, from the infrastructure, because to truly be able to leverage AI, and build applications on top, you cannot let it be siloed and be hostage to its individual infrastructure components. So we're seeing that fundamental change happening where data as a platform is coming along, and in that context the catalog becomes a very, very pivotal start, because you want to get a full view of everything. And look, you're not going to be able to move all your data in one place, it's impossible. But understanding that through metadata is where enterprises are going, and then from there, John, as Rebecca's talked about, you can have a customer experience journey with MDM. You can have a analytics journey in the Cloud with an AWS or (inaudible) or a JCP. Or you can have a complete governance and security and privacy journey understanding anomalous activity. >> So before I go any further I just want to ask you about this one point because you guys made a big bet with the Catalog >> Okay, and it's looking good. A lot of good bets. You know, AI, Catalog, Cloud, early on the Cloud, but one of the things I hear a lot is that data's at the blood stream, you want the blood flowing around the system, the body. People looking at data like an operating system kind of architecture where you got to have the data free flowing. So the Catalog seems to be a big bet there. How is that helping the AI peeps because if you can have the data flowing -- >> Yep. No I think -- >> You're going to have feeding the machine learning >> Absolutely. >> The machine learning feeds the application of AI, you got to have the data, the data's not flowing, you can't just inject it at certain times. >> The way we think about it is, you're exactly right. I would just, in fact it's so ah, interesting, the analogy I use is that data is everywhere. It's like the blood flowing through your body, right? You're not going to get all the data in one place to do any kind of analytics, right? You're going to let it be there. So we say metadata is the new OS. Bring the metadata, which is data about the data in one place. And from there let AI run on it. And what we think about AI is that, think about this. LinkedIn is a beautiful place where they leveraged the machine learning algorithm to create and social graph about you and me. So if I'm connected with John, I know now that I can be connected with you. The same thing can happen to the data layer. So when I'm doing analytics, and I'm basically searching for some report, I don't know, through that same machine learning algorithm at the catalog level, now we can tell you, you know what? This is another table. This is another report. This is another user. And so on. And we can give you back ratings within that environment for you to do what I call analytics on your fingertips at enterprise scale. So that's an extremely powerful use case of taking analytics which is the most commonly done activity in an enterprise and make it accurate at an enterprise scale. >> Well the LinkedIn example, you know, of course I have a different opinion on that. They're a siloed platform. They don't have any API's, it's only within LinkedIn. But it begs the question, since you're both that kind of consumer, look at a company like Slack, going public, very successful, their numbers are off the charts in terms of adoption, usage, a simple utility in IRC message chat room that has a great UI on it. But their success came when they integrated. >> Sure. >> Integration was a big part of their success. They wanted to have API's and let customers use the software, SAS software, with a lot of data. So they were really open. >> Yes. >> How were you guys from a business standpoint taking that concept of SAS openness connecting with other apps because I might have, bring my own app to the table as data, and integrate that piece into Informatica. How does that work? >> Very similarly. So the way we've done it is that our whole platform is fully API based. So we have opened up the API's, any application can hook on to that. So we believe that we are the Switzerland of data. So you may have any underlying infrastructure stack. On-prem, in the Cloud, multi-Cloud, whatever it is. Different applications, different Cloud applications, right? So our goal is that at the layer which is the metadata layer on which CLAIRE runs, we've opened up the API's, we've hooked to everything, and so we can consume the metadata, and there we truly provide a true data platform to our organization. So if you are running a Server Snap, a Salesforce.com, Adobe, Google, AWS, you can still bring all that stuff together and make contextual business decisions. >> One of the things you had talked about on the main stage is how the Millennials that you're hiring have higher expectations in their personal lives from the technology that they're using, and that's really pushing you to deliver different kinds of products and services that have the same level of innovation and high touch. Can you talk a little bit about that and how, and how this new generation of the workforce, and there's obviously Gen Y coming right behind it, is really pushing innovation in your company. >> Well you know, I have a fourteen-year-old, so I get a taste of that every day at home. (laughing) So you know, what they want to experience, so I, you know, I use this word, experiences are changing. And by the way they are pushing the boundary for us too. We grew up in the infrastructure software world which you know, twenty-five years ago was all, you can go down to the command line interface. Not any more. You really really have to make it simple. I think users today don't want to waste their time what I call doing mundane activities. They want to get to value fast. That's pushing the boundary for us. In fact that's where we're leveraging AI in our products to make sure we can remove the mundane clutter activities for them, for them to do value added activities. For example, I want to discover data to do some analysis. I don't want to go around discovering. Discover it for me. So that's where CLAIRE comes in and the catalog, right? Discover it for me. You know what? I don't want to figure out whether this data is accurate or not accurate. Tell me. So we are taking that philosophy, really really pushing the boundary for us, but in a good way. Because definitely those users want what I call very simplified and value added experiences. >> And that's really what SAS and consumer applications have shown us, and that's proven to be hard in the enterprise. So I got to ask you as you take this data concept to the infrastructure, a lot of enterprises are re-architecting, you hear words like multi-Cloud, hybrid Cloud, public Cloud, and you start to see a holistic new kind of persona, a Cloud architect. >> Yes. >> They're re-architecting their infrastructure to be SAS-like, to take advantage of data. >> Correct. >> That's kind of known out there, it's been reported on, we've been reporting on it. So the question is, that isn't alignment, that's not just the data people, it's data meets infrastructure. >> Absolutely. >> What's your advice to the companies out there that are doing this, because you guys have Cloud, Google, Amazon, Azure, Cloud, On-premise. You can work anywhere. What's you're advice? >> Yeah, no, I think it's a very good, it's a very topical question. Because I do think that the infra, the old days of separating different layers of the stack are are gone. Especially the old infrastructure all the way to platform as a server stack has to be very well though out together. To your point, customers running a hybrid multi-cloud world, right? So think about it, if you're in the world of improving customer experiences, I may have my marketing cloud running somewhere, I may have my sales cloud running somewhere, and a service cloud running somewhere. But to give a great experience I have to bring it all together. So you have to think about the infrastructure and the data together for enterprises to give a better experience to their customers. And I see innovative customers of companies truly think through that one and succeed. And the ones that are still lagging behind are still looking at that in silos. And then be able to have the data layer for hyper scale. Well these are all hyper scale platforms. You cannot run a little experiment over here. So we've invested in that whole concept of hyperscale, multi-cloud, hybrid cloud, and make sure it touches everything through API's. >> So we've been covering you guys for four years here at Informatica World. It's great to see the journey, nothing's really changed on the messaging and the strategy, you say you're going to do something and you keep doing it, and some little course corrections here, and acquisitions here and there to kind of accelerate it. But when we talk to your customers we hear a couple of different things. We hear platform, Informatica, when describing Informatica. You guys win the whole data thing, you're there, it's the business you're in. In the data business. But I'm hearing new words, platform. Scale. These are kind of new signals we're hearing from your customer base and some of the people here at the show. Talk about that impact, how you guys are investing in the platform, what it means for customers, and what does scale mean for your business and customers? >> No, we've heard that from our customers too. Customers said look, they all recognize that they have to invest in data as a platform. But you know, it's not like an original platform so they want it because we serve the broader state of management needs, so they want us to be like a platform. So we've invested that, couple of years ago we went completely ground-up, re-built everything, micro-services based. All API driven. Containerized. Modular. So the idea is that nobody is buying a monolithic platform. Nobody buying a platform, it just builds by itself. And they can compartmentize it, I want this now, I want that later, so like a Lego block it builds. And, you know what, through an API it also hooks into any of the existing infrastructure they have, or anything new that they want to bring in. So that really pushed the boundary for us. We invested in that. By the way, that platform today, in the Cloud, which you call IICS, runs eight trillion transactions a month. Eight trillion transactions a month. And by the way, last Informatica World, it was running two-and-a-half trillion transactions. So in one year it's gone from two-and-a-half to eight. So we are seeing that really hyper scale. >> And you, and I'm going to ask you if you believe, just, and you can answer yes or no or maybe, or answer on your own, do you believe that data is critical for SAS success? >> Oh absolutely. No doubt about it. I have not met a single customer who ever said anything different. In fact, the thing that I see is like, it's becoming more and more and more a sea-level conversation. That hey, what are we going to do with our data? How do we bring that data together to make decisions? How do we leverage AI and data together? It's truly in our sea-level discussion, whereas it was never a sea-level discussion years ago, it was more about what application am I going to use? What infrastructure am I going to use? Now they're all about, how do I manage this data? >> I wanted to talk about ethics (laughs) and this is, because recently had published a paper about Tech for Good, and it's about this idea of using AI and machine learning to help society achieve better outcomes, and then also to help us measure it's impact on our welfare beyond GDP. Because think about the value that technology brings to our lives. What's your take on this? I mean how much value do you think AI brings to the enterprise in terms of this Tech for Good idea? >> No, so, by the way one of Informatica's values is "Do good". And we are firm believers in that look, there is an economic value to everything in life. But then we all have something to give back to the society. There is something to create value out there which is outside the realm of just pure economics which is the point you were asking. And we are firm believers in that. I do think that by the way, there is a very high bar for all of us in the industry to make sure that not only, it's not just about ethics of AI also at the same time, because we cannot abuse the data. We're collecting a lot of information. You and me as consumers are giving a lot of information and I talked about that yesterday as well, that we believe that the ethics of AI are going to play a fundamental and differentiating role going forward. I think the Millennials we're talking about, they are very aware of that one. They are very purposeful. So they'll look back and say, who actually has a values system to take this technology innovation and do something better with it, not just creating money out of it. And I think I totally agree, and by the way in the very early stages, industry has to still learn that, and internalize that, then do something about it. >> Well Amit, yeah I think you're right on, early days, and I can give you an anecdotal example is that this year, University of California, Berkeley, graduated its first inaugural class of data science analytics. First! First ever class for them. They're a pioneer, they're usually having protests and doing things with revolutionary things. That shows it's so early. So the question I got to ask you is, you've got your fourteen-year-old, you know I have kids, we follow each other on Facebook. I'm always asked the question and I want to get this exposed. People are really discovering new ways to learn. Not just in school, you got YouTube videos, you've got CUBE videos, you got all kinds of great things out there. But really people are trying to figure out where to double down on, what dials to turn, what classes to take, what disciplines are going to help me. It used to be oh, go into computer science, you'll get a great job. And certainly that's still true. But there's now new opportunities for people, data's now grown from you know, programming deeply to ethics. And you don't need to have a CS degree to get in and be successful to fill the job openings or contribute to society. So what are those areas that you see that people who are watching might say hey you know what? I'm good at that, I'm good at art, I'm good at society or philosophy or I'm really good at math or, what skills do you, should people think about if they want to be successful in data? >> You know, I think it's a very foundational question. I think you're right, I think programming has become a lot easier. So I think if I'd stepped back to the days we graduated, right? It's become a lot easier so I don't think that necessarily learning programming is a differentiating, I do think that back where you were going, people who'd generally think about what to do with that. I think there is analytical skills that we all need, but I think the soft skills I believe in the society, we are kind of leaving behind, right? A little bit of the psychology of how users want to use something. Design thinking. By the way I still think that design thinking is not yet completely out there. Um, the ability to marry what I call the left brain to the right brain, I mean, I think that's key. And I do think that we cannot run away completely to the right brain, as much as I am an analytical person myself. I think marrying the left and the right, I do believe, like I, as I said I have a fourteen-year-old. My advice to all those who say, he wants to do Computer Science, is to take enough psychology or design classes to kind of have that balance. So my encouragement would be have the balance. We cannot all just be hyper-analytical. We have to kind of have the balance to see ... >> I think just be smart, balance, I mean again, I have not found one, well I guess the answers are stats and math, have the check, that's easy to say, but ... >> The emotional skills. But you need more of those, I think a little bit more of those left-brain skills also to complement them. >> Well and also for the experience, study art, music, what delights people. What inspires the passion? >> I agree with that. >> Yeah. Absolutely. Amit, always a pleasure to see you. Thank you so much. >> Thank you very much. Always a pleasure to be here. >> Great conversation. Good insight. >> I'm Rebecca Knight for John Furrier, stay tuned at theCUBE's live coverage at Informatica World. (Upbeat music)