(soft music) >> Hi, everyone. Welcome back to the Cube's covers of Splunk's dot com virtual event, their annual summit. I'm John Ferry, host of the cube. We've been covering dot conf since twenty twelve. Usually a physical event in person. This year it's virtual. I'm here with Claire Hockin, the CMO of Splunk. She's been here three and a half years. Your first year as CMO, and you got to go virtual from physical. Welcome to the cube. Good to see you. >> Thank you very much, John. Great. >> I got to ask you, I mean, this has been the most impressive virtual venue, you've taken over the hotel here in Silicon valley. You're entire teams here. It feels like there's a dynamic of like the teamwork. You can kind of feel the vibe. It's almost like a little VIP Splunk event, but you're broadcasting it to the world. Tell us what's happening. >> Yeah, it's been, I think for everyone a year where we really hope to be back to having a hybrid event, so having a big virtual component, but running dot conf as we had before from Las Vegas, which wasn't possible. So what we thought in the last six weeks is that we would actually bring the Splunk studio to a physical location. So we've been live all of this week from California, where we're sitting today and really thought through bringing the best of that programming to our, you know, our amazing audience of twenty six thousand people. So we were sitting here in a studio, we have a whole live stage and we've activated the best of dot conf to bring as many Splunkers as we can. And as many external guests to make it feel as real and as vibrant as possible. So. >> I have to say I'm very impressed. Since twenty twelve we've been watching the culture evolve. Splunk has always been that next big thing. And then the next big thing again, it seems to be the theme as data becomes so bigger and more important even than ever. There's a new Splunk emerging, another kind of next big thing. And this kind of says the patterns like do something big, that's new, operationalize it and do something new again. This is a theme, big part of this culture here. Can you share more about how you see this evolving? >> Sure. And I think that's what makes Splunk such a great place to be. And I think it attracts people who like to continually challenge reinvent. And I think we've spent a lot of time this year building out our portfolio, going through this cloud transformation. It just gives you a whole new landscape of how you unlock that power of data and how customers use it. So we've had a lot of fun, always building on top of that building, you know, our partnerships, what customers do and really having fun with it. I think one of the best things about Splunk is we do have this incredibly fun and playful brand and as data just becomes something that is more and more powerful, it's really relatable. And we have fun with activating that and storytelling. So, yeah. >> And you have a new manager, Teresa Carlson came in from Amazon web services. You have a lot more messaging kind of building on previous messaging. How are you handling and looking at the aperture of, that's growing from a messaging standpoint, you have a partner verse, which has rebranded of your solution of your ecosystem, kind of a lot of action going on in your world. What's the update? >> Yeah. It keeps us busy. And I think at one end, you know, the number of people that are using Splunk inside any customer base is just growing. So you have different kinds of users. And this year we're really working hard on how to partner and position Splunk with developers, but at the top end of that, the value of data and the idea of having a data foundation is something that's incredibly compelling for CTOs. So working really hard about looking at Splunk and data from that perspective, as well as the individual uses across areas like security and observability. So. >> You know, one of the things I wanted to ask you is, I was thinking about this when I was driving in this morning, Splunk has a lot of customers and you keep your customers and you've have a lot of customers that organically came into the Splunk through the product leadership and just great product. And then as security became more important, Splunk kind of takes that territory now. Now mainstream enterprise with the platform are leaning into Splunk solutions, and now you've got an ecosystem. So it's just becoming bigger and bigger just seems that the scale of the Splunk is growing radically bigger than it was, Is that happening? And what's your take on that? >> I think that's definitely a thing, John. So I think that the power of the ecosystem is amazing. We have customers, partners, as you've seen and everything just joins up. So we're seeing more and more dot joining through data. And we're just seeing this incredible velocity in terms of what's possible and how we can co-build with our partners and do more and more with our customers. So Splunk moves incredibly quickly. And I think if anything, we're just, gaining velocity, which is fun and also really challenging. >> Cloud-scale. And certainly during the pandemic, you guys had a tailwind on the business side, talk about the journey that you've had with Splunk as in your career and also for the customers. How are they reacting and what can they expect as Splunk continues to evolve? >> I think we're working really hard to make sure that Splunk is easier to use. Everything gets every more integrated. And I think our goal and our vision is you just capture your data and you can apply it to any use case using Splunk. And to make it sort of easier see that data in action. And one of the things I love from today was the dashboard studio. They're just these beautiful visualizations that really are inspiring around how data is working in your organization. And for me, I've been a Splunker for three and a half years. And I just think there is just so much to do, and there's so much of our story ahead of us and so much potential. So just really enjoying working with customers on the next data frontier, really. >> You have the Jedi Knight from Star Wars speaking, you had the F1 car racing. Lando was here, kind of the young Jedi, the old Jedi. The generations are coming together. You're seeing that old IT world, which relied on Splunk. And now you have this new developer real-time shifting left with security DevOps now going mainstream, you kind of have the confluences of these cultures coming together. It's not really clashing. It's kind of jelling. How are you handling that? How do you see that? What's Splunk kind of doing? Because I can see the themes, am I right? >> No, no. One of the stories from this morning that really struck me is we have Cal Poly and we worked with Cal Poly on their security and they actually have their students using Splunk and they run their whole security environment. And at the very top end, you have Walmart, the Fortune one, just using Splunk at a massive, incredible scale. And I think that's the power of data. I mean, data is something that everyone should and can be able to use. And that's what we're really seeing is unlocking the ability to bring, you know, bring all of your data in service of what you're trying to do, which is fun. And it just keeps growing. >> We had Zach Brown, the CEO of F1 McLaren Racing Team, here on the queue earlier. And it was interesting cause I was like driving the advantage with data, you know, kind of cliche, but they're using data very specifically, highly competitive. It almost kind of feels like a cloud kind of scale model because we've got thousands of people working on the team. They're on the track, they're competing, they're using data, they got to be agile and they got to be fast real time. Kind of sounds like the current enterprise's these days. >> Absolutely. And I think what's interesting about McLaren that the thing I love is either they have hundreds of terabytes of data moving at just at incredible speed through Splunk Enterprise, but it all goes back to their mission control in the UK. And there are 32 people that look at all that data. And I think it's got a half second delay and they make all the decisions for the car on the track. And that I think is a great lesson to any enterprises you have to, you know, you have to bring all that data together and you have to look at it and take decisions centrally for the benefit of your whole team. And I think McLaren is a really good example of when you do that it pays dividends and the team has had a really, really great season. >> Well, I want to say congratulations for pulling off a great virtual event. I know you had your physical event was on track and literally canceled the last minute because of the pandemic with the Delta virus. But it was amazing, made for digital TV kind of event. >> Absolutely, >> This is the future of media. >> Absolutely. And it is a lot of fun. And I think I'm really proud. We have done all of this with our in-house team, the brand, the experiences that you see, which is really fantastic. And it's given us a lot of ideas for sort of, you know, digital media and how we story tell, and really connect to our twenty thousand customers or two hundred and thirty thousand community members and keep everyone connected through digital. So this has been a lot of fun and a really nice moment for us this week. >> You know it's interesting, I was saying to the team here on one of our breaks, is that when you have this kind of agility with media to tell your own story directly, you're almost telling more stories there before. And there's a lot to tell you have a lot of successful customers, the new partners. What's the coolest story that you've seen. What would you share that you think is your favorite? If you could pick one or a few of them, what are your top stories that you see happening? >> So I've talked about Cal Poly, which I love because it's students and you know, the scale of Walmart, but there are so many stories. And I think the ones that I love most are the data heroes. We talk about the data here is a lot of Splunk and the people that are able to harness that data and to take action on that data and make something amazing happen. And we just see that time and time again, across all kinds of organizations where data heroes are surfacing, those insights. Those red flags, if you like and helping organizations stay on step ahead. And Conf is really a celebration of that. I think that's why we do this every year. And we really celebrate those data heroes. So across the program, probably too many to mention, but in every industry and at every scale, people are, you know, making things happen with data and that's an incredibly exciting place to be. >> Well you have a lot of great customers to, to use as references. But I got to ask you that as you go forward this year in marketing, what are your plans to take on this new dynamic? You've got hybrid events, you've got the community is always popular and thriving with Splunk at large-scale enterprises, global system integrators, doing business deals with you guys, as you guys are continuing to grow and grow and grow, what's the strategy? How do you keep the Splunk coolness going? Cause that's, you know, you guys are growing so fast. That's your job, is to keep things on track. What's your strategy? >> I think I look at that and just, we put the customer at the heart of that. And we think, you know, who are the personas, who are the people that use Splunk? What's their experience? What are they trying to do? What are those challenges? And we design those moments to help them move forward faster. And so that I think is just a really good north star. It is really unifying and our partners and customers, and every Splunker gets really behind that. So stay focused on that. >> Thanks for coming on the Cube, really appreciate it. Congratulations for great event. And thanks for having the Cube. We love coming in and sharing our media partnership with you. Thank you for coming. >> Thank you so much. And next year is your tenth year John. So we look forward to celebrating that as well. Thank you very much. >> Thank you. Thanks for coming on. Okay it's the Cube coverage here live in the Splunk studios. We are a virtual event, but it's turning out to be a hybrid event. It's like a VIP event, a lot of great stories. Check them out online. They'll be recycling through so much digital content. This is truly a great digital event. Jeffery, hot of the Cube. Thanks for watching. (soft music)

>>The cube presents, Coon and cloud native con Europe, 2022, brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Valencia Spain in Coon cloud native con 2022 Europe. I'm Keith Townsend, along with my cohot on Rico senior, Etti senior it analyst at gig home. We are talking to amazing people, creators people contributing to all these open source projects. Speaking of open source on Rico. Talk to me about the flavor of this show versus a traditional like vendor show of all these open source projects and open source based companies. >>Well, first of all, I think that the real difference is that this is a real conference. Hmm. So real people talking about, you know, projects about, so the, the open source stuff, the experiences are, you know, on stage and there are not really too many product pitches. It's, it's about, it's about the people. It's about the projects. It's about the, the challenges they had, how they, you know, overcome some of them. And, uh, that's the main difference. I mean, it's very educative informative and the kind of people is different. I mean, developers, you know, SREs, you know, you find ends on people. I mean, people that really do stuff that that's a real difference. I mean, uh, quite challenginghow discussing with them, but really, I mean, because they're really opinionated, but >>So we're gonna get talked to, to a company that has boosts on the ground doing open source since the, almost the start mm-hmm <affirmative> Kirsten newcomer, director of hybrid platform security at red hat and, uh, Connor Gorman, senior principal software engineer at red hat. So Kirsten, we're gonna start with you security and Kubernetes, you know, is Kubernetes. It's a, it's a race car. If I wanted security, I'd drive a minivan. <laugh> >>That's, that's a great frame. I think, I think though, if we stick with your, your car analogy, right, we have seen cars in cars and safety in cars evolve over the years to the point where you have airbags, even in, you know, souped up cars that somebody's driving on the street, a race car, race cars have safety built into, right. They do their best to protect those drivers. So I think while Kubernetes, you know, started as something that was largely, you know, used by Google in their environment, you know, had some perimeter based security as Kubernetes has become adopted throughout enterprises, as people. And especially, you know, we've seen the adoption accelerate during the pandemic, the move to both public cloud, but also private cloud is really accelerated. Security becomes even more important. You can't use Kubernetes in banking without security. You can't use it, uh, in automotive without security telco. >>And Kubernetes is, you know, Telco's adoption, Telco's deploying 5g on Kubernetes on open shift. Um, and, and this is just so the security capabilities have evolved over time to meet the customers and the adopters really red hat because of our enterprise customer base, we've been investing in security capabilities and we make those contributions upstream. We've been doing that really from the beginning of our adoption of Kubernetes, Kubernetes 1.0, and we continue to expand the security capabilities that we provide. And which is one of the reasons, you know, the acquisition of stack rocks was, was so important to us. >>And, and actually we are talking about security at different levels. I mean, so yeah, and different locations. So you are securing an edge location differently than a data center or, or, or maybe, you know, the cloud. So there are application level security. So there are so many angles to take this. >>Yeah. And, and you're right. I mean, I, there are the layers of the stack, which starts, you know, can start at the hardware level, right. And then the operating system, the Kubernetes orchestration all the services, you need to have a complete Kubernetes solution and application platform and then the services themselves. And you're absolutely right. That an edge deployment is different than a deployment, uh, on, you know, uh, AWS or in a private da data center. Um, and, and yet, because there is this, if you, if you're leveraging the heart of Kubernetes, the declarative nature of Kubernetes, you can do Kubernetes security in a way that can be consistent across these environments with the need to do some additions at the edge, right? You may, physical security is more important at the edge hardware based encryption, for example, whereas in a, in a cloud provider, your encryption might be at the cloud provider storage layer rather than hardware. >>So how do you orchestrate, because we are talking about orchestration all day and how do you orchestrate all these security? >>Yep. So one of the things, one of the evolutions that we've seen in our customer base in the last few years is we used to have, um, a small number of large clusters that our customers deployed and they used in a multi-tenant fashion, right? Multiple teams from within the organization. We're now starting to see a larger number of smaller clusters. And those clusters are in different locations. They might be, uh, customers are both deploying in public cloud, as well as private, you know, on premises, um, edge deployments, as you mentioned. And so we've invested in, uh, multi cluster management and, or, you know, sort of that orchestration for orchestrators, right? The, and because again of the declarative nature of Kubernetes, so we offer, uh, advanced cluster management, red hat, advanced cluster management, which we open sourced as the multi cluster engine CE. Um, so that component is now also freely available, open source. We do that with everything. So if you need a way to ensure that you have managed the configuration appropriately across all of these clusters in a declarative fashion, right. It's still YAML, it's written in YAML use ACM use CE in combination with a get ops approach, right. To manage that, uh, to ensure that you've got that environment consistent. And, and then, but then you have to monitor, right. You have to, I'm wearing >>All of these stack rocks >>Fits in. I mean, yeah, sure. >>Yeah. And so, um, you know, we took a Kubernetes native approach to securing all of this. Right. And there's kind of, uh, we have to say, there's like three major life cycles. You have the build life cycle, right. You're building these imutable images to go deployed to production. Right. That should never change that are, you know, locked at a point in time. And so you can do vulnerability scanning, you can do compliance checks at that point right. In the build phase. But then you put those in a registry, then those go and be deployed on top of Kubernetes. And you have the configuration of your application, you know, including any vulnerabilities that may exist in those images, you have the R back permissions, right. How much access does it have to the cluster? Is it exposed on the internet? Right. What can you do there? >>And then finally you have, the runtime perspective of is my pod is my container actually doing what I think it's supposed to do. Is it accessing all the right things? Is it running all the right processes? And then even taking that runtime information and influencing the configuration through things like network policies, where we have a feature called process baselining that you can say exactly what processes are supposed to run in this pod. Um, and then influencing configuration in that way to kind of be like, yeah, this is what it's doing. And let's go stamp this, you know, declaratively so that when you deploy it the next time you already have security built in at the Kubernetes level. >>So as we've talked about a couple of different topics, the abstraction layers, I have security around DevOps. So, you know, I have multi tendency, I have to deal with, think about how am I going to secure the, the, the Kubernetes infrastructure itself. Then I have what seems like you've been talking about here, Connor, which is dev SecOps mm-hmm <affirmative> and the practice of securing the application through policy. Right. Are customers really getting what's under the hood of dev SecOps? >>Do you wanna start or yeah. >>I mean, I think yes and no. I think, um, you know, we've, some organizations are definitely getting it right. And they have teams that are helping build things like network policies, which provide network segmentation. I think this is huge for compliance and multi-tenancy right. Just like containers, you know, one of the main benefits of containers, it provides this isolation between your applications, right? And then everyone's familiar with the network firewall, which is providing network segmentation, but now in between your applications inside Kubernetes, you can create, uh, network segmentation. Right. And so we have some folks that are super, super far along that path and, and creating those. And we have some folks who have no network policies except the ones that get installed with our products. Right. And then we say, okay, how can we help you guys start leveraging these things and, and creating maybe just basic name, space isolation, or things like that. And then trying to push that back into more the declarative approach. >>So some of what I think we hear from, from what Connor just te teed up is that real DevSecOps requires breaking down silos between developers, operations and security, including network security teams. And so the Kubernetes paradigm requires, uh, involvement actually, in some ways, it, it forces involvement of developers in things like network policy for the SDN layer, right? You need to, you know, the application developer knows which, what kinds of communication he or she, his app or her app needs to function. So they need to define, they need to figure out those network policies. Now, some network security teams, they're not familiar with YAML, they're not necessary familiar with software development, software defined networking. So there's this whole kind of, how do we do the network security in collaboration with the engineering team? And when people, one of the things I worry about, so DevSecOps it's technology, but it's people in process too. >>Right. And one of the things I think people are very comfortable adopting vulnerability scanning early on, but they haven't yet started to think about the network security angle. This is one area that not only do we have the ability in ACS stack rocks today to recommend a network policy based on a running deployment, and then make it easy to deploy that. But we're also working to shift that left so that you can actually analyze app deployment data prior to it being deployed, generate a network policy, tested out in staging and, and kind of go from the beginning. But again, people do vulnerability analysis shift left, but they kind of tend to stop there and you need to add app config analysis, network communication analysis, and then we need appropriate security gates at deployment time. We need the right automation that helps inform the developers. Not all developers have security expertise, not all security people understand a C I C D pipeline. Right. So, so how, you know, we need the right set of information to the right people in the place they're used to working in order to really do that infinity loop. >>Do you see this as a natural progression for developers? Do they really hit a wall before, you know, uh, finding out that they need to progress in, in this, uh, methodology? Or I know >>What else? Yeah. So I think, I think initially there's like a period of transition, right? Where there's sometimes there's opinion, oh, I, I ship my application. That's what I get paid for. That's what I do. Right. <laugh> um, and, and, but since, uh, Kubernetes has basically increased the velocity of developers on top, you know, of the platform in order to just deploy their own code. And, you know, we have every, some people have commits going to production, you know, every commitment on the repo goes to production. Right. Um, and so security is even more at the forefront there. So I think initially you hit a little bit of a wall security scans in CI. You could get some failures and some pushback, but as long as these are very informative and actionable, right. Then developers always wanna do the right thing. Right. I mean, we all want to ship secure code. >>Um, and so if you can inform you, Hey, this is why we do this. Or, or here's the information about this? I think it's really important because I'm like, right, okay. Now when I'm sending my next commits, I'm like, okay, these are some constraints that I'm thinking about, and it's sort of like a mindset shift, but I think through the tooling that we like know and love, and we use on top of Kubernetes, that's the best way to kind of convey that information of, you know, honestly significantly smaller security teams than the number of developers that are really pushing all of this code. >>So let's scale out what, talk to me about the larger landscape projects like prime cube, Litner, OPPI different areas of investment in, in, in security. Talk to me about where customers are making investments. >>You wanna start with coup linter. >>Sure. So coup linter was a open source project, uh, when we were still, uh, a private company and it was really around taking some of our functionality on our product and just making it available to everyone, to basically check configuration, um, both bridging DevOps and SecOps, right? There's some things around, uh, privileged containers, right? You usually don't wanna deploy those into your environment unless you really need to, but there's other things around, okay, do I have anti affinity rules, right. Am I running, you know, you can run 10 replicas of a pod on the same node, and now your failure domain is a single node. Now you want them on different nodes, right. And so you can do a bunch of checks just around the configuration DevOps best practices. And so we've actually seen quite a bit of adoption. I think we have like almost 2000 stars on, uh, and super happy to see people just really adopt that and integrate it into their pipelines. It's a single binary. So it's been super easy for people to take it into their C I C D and just, and start running three things through it and get, uh, you know, valuable insights into, to what configurations they should change. Right. >>And then if you're, if you were asking about things like, uh, OPPA, open policy agent and OPPA gatekeeper, so one of the things happening in the community about OPPA has been around for a while. Uh, they added, you know, the OPPA gatekeeper as an admission controller for Cobe. There's also veno another open source project that is doing, uh, admission as the Kubernetes community has, uh, kind of is decided to deprecate pod security policies, um, which had a level of complexity, but is one of the key security capabilities and gates built into Kubernetes itself. Um, OpenShift is gonna continue to have security context constraints, very similar, but it prevents by default on an OpenShift cluster. Uh, not a regular user cannot deploy a privileged pod or a pod that has access to the host network. Um, and there's se Linux configuration on by default also protects against container escapes to the file system or mitigates them. >>So pod security policies were one way to ensure that kind of constraint on what the developer did. Developers might not have had awareness of what was important in terms of the level of security. And so again, the cube and tools like that can help to inform the developer in the tools they use, and then a solution like OPPA, gatekeeper, or SCCs. That's something that runs on the cluster. So if something got through the pipeline or somebody's not using one of these tools, those gates can be leveraged to ensure that the security posture of the deployment is what the organization wants and OPPA gatekeeper. You can do very complex policies with that. And >>Lastly, talk to me about Falco and Claire, about what Falco >>Falco and yep, absolutely. So, um, Falco, great runtime analysis have been and something that stack rocks leveraged early on. So >>Yeah, so yeah, we leveraged, um, some libraries from Falco. Uh, we use either an EB P F pro or a kernel module to detect runtime events. Right. And we, we primarily focus on network and process activity as, um, as angles there. And then for Claire, um, it's, it's now within red hat again, <laugh>, uh, through the acquisition of cores, but, uh, we've forked in added a bunch of things around language vulnerabilities and, and different aspects that we wanted. And, uh, and you know, we're really interested in, I think, you know, the code bases have diversion a little bit Claire's on V4. We, we were based off V2, but I think we've both added a ton of really great features. And so I'm really looking forward to actually combining all of those features and kind of building, um, you know, we have two best of best of breed scanners right now. And I'm like, okay, what can we do when we put them together? And so that's something that, uh, I'm really excited about. >>So you, you somehow are aiming at, you know, your roadmap here now putting everything together. And again, orchestrated well integrated yeah. To, to get, you know, also a simplified experience, because that could be the >>Point. Yeah. And, and as you mentioned, you know, it's sort of that, that orchestration of orchestrators, like leveraging the Kubernetes operator principle to, to deliver an app, an opinionated Kubernetes platform has, has been one of the key things we've done. And we're doing that as well for security out of the box security policies, principles based on best practices with stack rocks that can be leveraged in the community or with red hat, advanced cluster security, combining our two scanners into one clear based scanner, contributing back, contributing back to Falco all of these things. >>Well, that speaks to the complexity of open source projects. There's a lot of overlap in reconciling. That is a very difficult thing. Kirsten Connor, thank you for joining the cube Connor. You're now a cube alone. Welcome to main elite group. Great. From Valencia Spain, I'm Keith Townsend, along with en Rico senior, and you're watching the cue, the leader in high tech coverage.

>>Hi, everyone. Welcome back to the cubes coverage of splunk.com, virtual 2021. I'm John Ford, your host of the cube. We're here with Teresa Carlson, special guests cube alumni. Who's now the president and chief growth officer of Splunk. Teresa, welcome back to the queue. >>So glad you're here with us >>As the president of Splunk. Great to see you. Great to see you. So we've had many conversations in the queue. When you were the chief of public sector of Amazon web services, you grew that business significantly over the years. We've documented on the cube and we've talked about I've written about it. Um, now Splunk, it feels a lot like AWS was back in LA a couple of years ago, where you have this amazing product everyone's using. They don't lose customers. They're getting customers they're in the middle of the security thing, which you know a lot about, and they have this large enterprise base growing. It's just a minute. Grazer leaning in Splunk is, seems to be going to the next level. >>Totally. Well, you nailed it. I would say we're definitely in a scale mode at this point at Splunk. And also to your point, our customers are so loyal to us and we're seeing actually customers with more than a million dollars doubling their spend almost with us. Uh, it's pretty cool. And now we have this cloud portfolio, which is one of my jobs, as you know, I love, I've got my cloud shirt on. I've been believer in cloud. I'm a real believer. You know, I saw the transformational effects of cloud in real time, over 11 years and bringing that here even more to utilize that in our security and observability spaces is quite phenomenal. And then you see again in a much more, uh, set of segmented workloads, how customers take advantage of this. And of course today, like no other John security is just top of mind. It's always been you and I talked earlier about how security kind of evolved over the years and public sector led some of that over time. And then commercial industry say, you know, wow, that today it's, I mean, it's more than top of mind for not just every enterprise organization and government entity, but it's also every board out there. It's something that we think about internal threat, external threat. How do we manage it? How do we get the data around it to understand it? And then how do we take action on it? >>I seen you up on stage as a senior leader here at Splunk, um, at the virtual venue at a great keynote was a lot of news. And we'll get into that in a second, but I want to ask you, knowing you personally and covering you over the years of Amazon web services, you've been a fierce competitor. Okay. But you also have been a great people, person, people loved working for you, Splunk, is it the same? We've been covering them just as long as we cover an ADFS. The culture seemed to fit because Splunk is kind of competitive, but they're kind of quiet, competitive culture. Yeah. Interesting. Tell us about, tell us about your experience. >>Well, and I think we can, yeah, we can do it in our own Spanky way. I'm learning new it's six minutes today that I've been as blind quiches and believable that I've been here this long already, but, uh, Splunk has a very quirky culture, which I led. They have a lot of fan. They have a big following and I'm so sorry that everyone couldn't attend in person, but the virtual social media feeds are off the charts. I mean, I'm just, I'm having so much fencing high already. They come together. It's a real community, but, uh, yeah, on the competition front, here's what reminds me so much about my old world is that I always love that when somebody wakes up and realizes that it's a huge industry and they want to participate. And that's kind of what happened when I was at AWS and now it's blank. >>I'm like, Hey, all these companies are waking up and saying, data's this real thing. It's like a $90 billion plus industry and growing, and then data with security. Hello, are you kidding me? So I feel like really that's kind of what's happened. And Splunk has such a unique set of tools and solutions that just work, they work. And that's what customers, I have heard that statement from customers and partners so much that it just works. And the other thing that's pretty unique about us, I would say John is our ability to navigate between an on-prem world and a cloud world in a unique set of areas like IOT, edge computing. So wherever customer's data is multiple clouds, we're able to take advantage of that for the customer. So they make the choice of where that data comes from and they use the splint tooling then to be able to get those insights and information >>Well, great to have you on the Cuban grid, that's swung to have you, and they're going to be lucky to have you going to do a lot stuff, knowing you and knowing the Splunk community and the team here. A great team. Now talking about the announcements, look at what's going on. Obviously security is still in everything. Yep. A couple of things, rebranding of the partner versus sends a huge message of the ecosystem. You know, that movie you've seen that movie before, um, digital journey for customer success. Again, they have tons of customers that have been with them from beginning and new customers, but they've got to go government action going on here. Whereas you know, a lot about the government logging in monetization program. >>Yeah. Well, as you know, the government, uh, you got 11, but they do continually come up with N fended mandates. And my government customers always have said, oh my gosh, I've got another unfunded mandate. So we're really helping them at that because yes, while it's infested in this budget this year, as it states, they know how important it is. And I do think this initiative is something that is going to have a waterfall effect into the commercial industries. Also just like a lot of these things do and around security, uh, but it's important that we help our government customer made as best as they can. So we've come up with, I think, a very unique offering that they can take advantage of for Splunk and we're going to be out there helping them every way. And, and hopefully John L also helped them learn more about cross governmental, what they're doing and how they can understand from their logs and metrics even more about how to protect. Yes. >>One of the things that we've talked about before in the past, but how cloud-scale, and as creates ecosystems, Amazon VMware, you seeing all these ecosystems that have been thriving for, for decades, Splunk has an ecosystem developing very, very fast. Their partners are, are loyal and they're making money with them. And they're being delivered solutions as data becomes the new enablement. How do you see the role of the partners that growing? How do you see them evolving over time? >>Well, let me just tell you, I'm, I'm a real believer in the partner community. I mean, firsthand over the years, my time at Microsoft at AWS, I saw it as an unbelievable force multiplier to your business. And I mean that, and they do things that you don't even think of. I, you know, I'm always amazed at partners. I'm like, oh, you're using the tool for that. Wow. So while we are broadly good, we're, we're very good at what we do, but we cannot understand every horizontal or vertical industry out there. And the reason it's important to have partners, they can take you to places that you never dreamed. And for us, if you look at the categories, we need our CSP or cloud service providers to be able to really help us make sure that we take advantage of the cloud platforms that are out there and our primary, we AWS, and then Google cloud. >>Uh, and then after that we work, we work with both those a migration. You saw Steve Schmidt today. Good friend of mine love Steve. And the work we're doing. And you saw, we were one of the first migration partners with AWS. You'll see us continue that program. We'll work together to continue to look for security services jointly that we can offer. And we're a customer of theirs. They're a customer of ours. It makes a good partnership. And then additionally, you have, uh, you have your MSPs, right? Your managed service providers. And today we talked about blue buoyant who had multiples, and these are partners out there that have a unique offering for me, generally managed security or observability in the marketplace. They take the Splunk toolkit, they add to it and they have it off, offered out to their customers. Um, and then you have your largest size like Accenture. I'm so excited about that. First of all, led Julie Sweet. She's an amazing CEO and leader. Uh, and w in what they're doing with this, they've been a long-standing partner of ours, but now they've actually made us part of their, one of their 11 business groups. So it's Accenture plus Splunk, and now they'll take us into all of their industries together. So it's huge. And, you know, >>Does that mean cause, cause this is a business deal. This isn't just like a, you know, some sort of deal where you guys saying we're going together. This is a specific division. >>That's right. That's right. So they have a leaven partners that they work with. AWS is one of them. SAP's one of them. Uh, IBM's one of them, Salesforce, I believe is one of them. And they have, they have experts at Accenture that can go into customers to implement tools and services for customers at the enterprise level. And so they have selected. Splunk is one of those business partners that you heard Paul today talk about. We already have 400 customers together and growing, we will expand that, but it's a joint effort of both go-to-market selling and technical resources that will deliver. But for Splunk, again, it's back to that horizontal and vertical slicing where they can take us into security practice that they have chosen. Splunk is one of their security offerings and it's important that we really support them. But also in the splint, a partner verse, we're going to do some new things. >>John, if I just first take and talk about it, we've had a great partner program, but now we're going to Korea's credits, uh, technology, architecture, tooling support, uh, getting in, you know, to certify themselves, to be pro serve ready for those migrations and modernizations. But also really what we heard from a lot of them is they need more training and education remaster to understand our new cloud offerings. And that makes sense. So it's more digital and more cloud oriented with these partners. And then guess what they would love for us to talk about how great they are and we should. So when we get them out there that helps our customers really understand the offerings they have in the marketplace >>At Brooke honeymoon was saying she didn't do a lot more listening and they're working on this next level partner verse. I found that really interesting, all sorts of Katie beyond key. I talked with she's the SVP of customer success, something you're I know you're obsessed about. You always work backwards from the customers as the AWS way. How do you view customer stuff? Because you have a lot of different customers, you have diverse customers. What's important. What are you going to keep Katie's on top of this, but what's your view. >>We ha we do have a lot of different customers. However, we have a concentration of the largest, most important and influential customers in the world. So our customer base is very large enterprise oriented, multiple departments within that enterprise take advantage of Splunk. We work with 90 to the 100 fortune 100 companies, and we've worked with them for a long time. And like I said, we're continuing to see them use more of splice, not less as blank. And the way that that happens is, and I hear from him, I sit and talk to him and they're like, now we're using Splunk in these multiple departments and we need to bring it all together at the enterprise level for the C-suite to look at it. Now, I know it sounds a little strange John, but that's changed a bit over the years. And that is because, you know, if you look at big spenders at an enterprise, he spends a lot of money because they need to at dev, you know, uh, security, right. Security infrastructure, and they need to monitor all that. They need to understand it, but guess what they want, understand it now at the corporate level. And they need it at the CIO, they need at the Cisco level for threat analysis. And then now boards want more and more that information they want to roll up of what's happening. So we're seeing a trend where the C-suite, the senior executives really are much more interested in Splunk. It used to be very departmental. >>I'll throw another wrench in the equation. There is one developers want shifting left. They want real time data security policy in the development, CDC at pipelining. So another problem. Yeah. >>Yeah. And developers lever tools. And again, they're, they're another unique group I should totally talk about. That takes your tools to another level and really fears that ways within their customer set to take advantage of the tooling. >>He's a great to see you. Congratulations on a new opportunity here. And the leadership at Splunk, um, really perfectly poised to take the growth of the cloud. That's. So I have to ask you, what's your mission? What's your mission for the next year as you come on? You're six months in what's the, >>Well, for us, here's blankets, continuing to scale, really listening to our customers and partners. It sounds, I don't want it to sound like a cliche. We really are spending time listening and working back, Sean and I are working. He's their president of technology products and technology. He and I are working very closely to look at features and functionality that we need to be talking about. Uh, it is about taking advantage of the partner community in a way to support them, to help again, get us into new areas of the business. And then lastly, continue to make sure that we have the training and education for customers directly because our tools and technologies are evolving. And if I've learned anything over the last 11 years is cloud is a step change for a lot of customers and they're still hybrid. So it's important that we meet them where they are, but help them get over that bridge so that they have that full digital journey. So that's what you're going to see me focused on. I'm super excited. >>I was talking with Claire, the CMO just before you leave, I want to get your reaction. This event went virtual the last minute. It became a studio here in Silicon valley. You're a media company now Splunk. Yeah. >>It's like it. I mean, it is amazing what we accomplished today. Uh, I, you know, I don't want to pre give numbers, but we had way, way over 20,000 today, online and, uh, growing. So the numbers we're still looking at, but it was unbelievable. And we had, I think we had had like 22,000 registered and we even got more. So people joined in, they stay, they watched the keynote, there were out narrow specialty sessions. And I all agree, like it was pretty cool. It was a step change because we were thinking about doing it in person. We took a pulse and we said, you know, we think we can actually do a better job this year because of COVID steel. If we do it all virtually and it turned out and we have you, so look at this, you're like, we have you here. And I love your cool backdrop here, John. Yeah. >>Well, you guys do a great job. You guys are a media company. Now you're telling your own stories direct. There's a lot of stories to tell. Thank you for coming on the cube. Great to see you >>Again. John's great to see you because the >>Cubes coverage here at.com 2021 virtual I'm John for your host of the cube. Thanks for watching.

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

>>Individuals create developers, translate ideas to code, to create great applications and great applications. Touch everyone. A Docker. We know that collaboration is key to your innovation sharing ideas, working together. Launching the most secure applications. Docker is with you wherever your team innovates, whether it be robots or autonomous cars, we're doing research to save lives during a pandemic, revolutionizing, how to buy and sell goods online, or even going into the unknown frontiers of space. Docker is launching innovation everywhere. Join us on the journey to build, share, run the future. >>Hello and welcome to Docker con 2021. We're incredibly excited to have more than 80,000 of you join us today from all over the world. As it was last year, this year at DockerCon is 100% virtual and 100% free. So as to enable as many community members as possible to join us now, 100%. Virtual is also an acknowledgement of the continuing global pandemic in particular, the ongoing tragedies in India and Brazil, the Docker community is a global one. And on behalf of all Dr. Khan attendees, we are donating $10,000 to UNICEF support efforts to fight the virus in those countries. Now, even in those regions of the world where the pandemic is being brought under control, virtual first is the new normal. It's been a challenging transition. This includes our team here at Docker. And we know from talking with many of you that you and your developer teams are challenged by this as well. So to help application development teams better collaborate and ship faster, we've been working on some powerful new features and we thought it would be fun to start off with a demo of those. How about it? Want to have a look? All right. Then no further delay. I'd like to introduce Youi Cal and Ben, gosh, over to you and Ben >>Morning, Ben, thanks for jumping on real quick. >>Have you seen the email from Scott? The one about updates and the docs landing page Smith, the doc combat and more prominence. >>Yeah. I've got something working on my local machine. I haven't committed anything yet. I was thinking we could try, um, that new Docker dev environments feature. >>Yeah, that's cool. So if you hit the share button, what I should do is it will take all of your code and the dependencies and the image you're basing it on and wrap that up as one image for me. And I can then just monitor all my machines that have been one click, like, and then have it side by side, along with the changes I've been looking at as well, because I was also having a bit of a look and then I can really see how it differs to what I'm doing. Maybe I can combine it to do the best of both worlds. >>Sounds good. Uh, let me get that over to you, >>Wilson. Yeah. If you pay with the image name, I'll get that started up. >>All right. Sen send it over >>Cheesy. Okay, great. Let's have a quick look at what you he was doing then. So I've been messing around similar to do with the batter. I've got movie at the top here and I think it looks pretty cool. Let's just grab that image from you. Pick out that started on a dev environment. What this is doing. It's just going to grab the image down, which you can take all of the code, the dependencies only get brunches working on and I'll get that opened up in my idea. Ready to use. It's a here close. We can see our environment as my Molly image, just coming down there and I've got my new idea. >>We'll load this up and it'll just connect to my dev environment. There we go. It's connected to the container. So we're working all in the container here and now give it a moment. What we'll do is we'll see what changes you've been making as well on the code. So it's like she's been working on a landing page as well, and it looks like she's been changing the banner as well. So let's get this running. Let's see what she's actually doing and how it looks. We'll set up our checklist and then we'll see how that works. >>Great. So that's now rolling. So let's just have a look at what you use doing what changes she had made. Compare those to mine just jumped back into my dev container UI, see that I've got both of those running side by side with my changes and news changes. Okay. So she's put Molly up there rather than mobi or somebody had the same idea. So I think in a way I can make us both happy. So if we just jumped back into what we'll do, just add Molly and Moby and here I'll save that. And what we can see is, cause I'm just working within the container rather than having to do sort of rebuild of everything or serve, or just reload my content. No, that's straight the page. So what I can then do is I can come up with my browser here. Once that's all refreshed, refresh the page once hopefully, maybe twice, we should then be able to see your refresh it or should be able to see that we get Malia mobi come up. So there we go, got Molly mobi. So what we'll do now is we'll describe that state. It sends us our image and then we'll just create one of those to share with URI or share. And we'll get a link for that. I guess we'll send that back over to you. >>So I've had a look at what you were doing and I'm actually going to change. I think that might work for both of us. I wondered if you could take a look at it. If I send it over. >>Sounds good. Let me grab the link. >>Yeah, it's a dev environment link again. So if you just open that back in the doc dashboard, it should be able to open up the code that I've changed and then just run it in the same way you normally do. And that shouldn't interrupt what you're already working on because there'll be able to run side by side with your other brunch. You already got, >>Got it. Got it. Loading here. Well, that's great. It's Molly and movie together. I love it. I think we should ship it. >>Awesome. I guess it's chip it and get on with the rest of.com. Wasn't that cool. Thank you Joey. Thanks Ben. Everyone we'll have more of this later in the keynote. So stay tuned. Let's say earlier, we've all been challenged by this past year, whether the COVID pandemic, the complete evaporation of customer demand in many industries, unemployment or business bankruptcies, we all been touched in some way. And yet, even to miss these tragedies last year, we saw multiple sources of hope and inspiration. For example, in response to COVID we saw global communities, including the tech community rapidly innovate solutions for analyzing the spread of the virus, sequencing its genes and visualizing infection rates. In fact, if all in teams collaborating on solutions for COVID have created more than 1,400 publicly shareable images on Docker hub. As another example, we all witnessed the historic landing and exploration of Mars by the perseverance Rover and its ingenuity drone. >>Now what's common in these examples, these innovative and ambitious accomplishments were made possible not by any single individual, but by teams of individuals collaborating together. The power of teams is why we've made development teams central to Docker's mission to build tools and content development teams love to help them get their ideas from code to cloud as quickly as possible. One of the frictions we've seen that can slow down to them in teams is that the path from code to cloud can be a confusing one, riddle with multiple point products, tools, and images that need to be integrated and maintained an automated pipeline in order for teams to be productive. That's why a year and a half ago we refocused Docker on helping development teams make sense of all this specifically, our goal is to provide development teams with the trusted content, the sharing capabilities and the pipeline integrations with best of breed third-party tools to help teams ship faster in short, to provide a collaborative application development platform. >>Everything a team needs to build. Sharon run create applications. Now, as I noted earlier, it's been a challenging year for everyone on our planet and has been similar for us here at Docker. Our team had to adapt to working from home local lockdowns caused by the pandemic and other challenges. And despite all this together with our community and ecosystem partners, we accomplished many exciting milestones. For example, in open source together with the community and our partners, we open sourced or made major contributions to many projects, including OCI distribution and the composed plugins building on these open source projects. We had powerful new capabilities to the Docker product, both free and subscription. For example, support for WSL two and apple, Silicon and Docker, desktop and vulnerability scanning audit logs and image management and Docker hub. >>And finally delivering an easy to use well-integrated development experience with best of breed tools and content is only possible through close collaboration with our ecosystem partners. For example, this last year we had over 100 commercialized fees, join our Docker verified publisher program and over 200 open source projects, join our Docker sponsored open source program. As a result of these efforts, we've seen some exciting growth in the Docker community in the 12 months since last year's Docker con for example, the number of registered developers grew 80% to over 8 million. These developers created many new images increasing the total by 56% to almost 11 million. And the images in all these repositories were pulled by more than 13 million monthly active IP addresses totaling 13 billion pulls a month. Now while the growth is exciting by Docker, we're even more excited about the stories we hear from you and your development teams about how you're using Docker and its impact on your businesses. For example, cancer researchers and their bioinformatics development team at the Washington university school of medicine needed a way to quickly analyze their clinical trial results and then share the models, the data and the analysis with other researchers they use Docker because it gives them the ease of use choice of pipeline tools and speed of sharing so critical to their research. And most importantly to the lives of their patients stay tuned for another powerful customer story later in the keynote from Matt fall, VP of engineering at Oracle insights. >>So with this last year behind us, what's next for Docker, but challenge you this last year of force changes in how development teams work, but we felt for years to come. And what we've learned in our discussions with you will have long lasting impact on our product roadmap. One of the biggest takeaways from those discussions that you and your development team want to be quicker to adapt, to changes in your environment so you can ship faster. So what is DACA doing to help with this first trusted content to own the teams that can focus their energies on what is unique to their businesses and spend as little time as possible on undifferentiated work are able to adapt more quickly and ship faster in order to do so. They need to be able to trust other components that make up their app together with our partners. >>Docker is doubling down and providing development teams with trusted content and the tools they need to use it in their applications. Second, remote collaboration on a development team, asking a coworker to take a look at your code used to be as easy as swiveling their chair around, but given what's happened in the last year, that's no longer the case. So as you even been hinted in the demo at the beginning, you'll see us deliver more capabilities for remote collaboration within a development team. And we're enabling development team to quickly adapt to any team configuration all on prem hybrid, all work from home, helping them remain productive and focused on shipping third ecosystem integrations, those development teams that can quickly take advantage of innovations throughout the ecosystem. Instead of getting locked into a single monolithic pipeline, there'll be the ones able to deliver amps, which impact their businesses faster. >>So together with our ecosystem partners, we are investing in more integrations with best of breed tools, right? Integrated automated app pipelines. Furthermore, we'll be writing more public API APIs and SDKs to enable ecosystem partners and development teams to roll their own integrations. We'll be sharing more details about remote collaboration and ecosystem integrations. Later in the keynote, I'd like to take a moment to share with Docker and our partners are doing for trusted content, providing development teams, access to content. They can trust, allows them to focus their coding efforts on what's unique and differentiated to that end Docker and our partners are bringing more and more trusted content to Docker hub Docker official images are 160 images of popular upstream open source projects that serve as foundational building blocks for any application. These include operating systems, programming, languages, databases, and more. Furthermore, these are updated patch scan and certified frequently. So I said, no image is older than 30 days. >>Docker verified publisher images are published by more than 100 commercialized feeds. The image Rebos are explicitly designated verify. So the developers searching for components for their app know that the ISV is actively maintaining the image. Docker sponsored open source projects announced late last year features images for more than 200 open source communities. Docker sponsors these communities through providing free storage and networking resources and offering their community members unrestricted access repos for businesses allow businesses to update and share their apps privately within their organizations using role-based access control and user authentication. No, and finally, public repos for communities enable community projects to be freely shared with anonymous and authenticated users alike. >>And for all these different types of content, we provide services for both development teams and ISP, for example, vulnerability scanning and digital signing for enhanced security search and filtering for discoverability packaging and updating services and analytics about how these products are being used. All this trusted content, we make available to develop teams for them directly to discover poll and integrate into their applications. Our goal is to meet development teams where they live. So for those organizations that prefer to manage their internal distribution of trusted content, we've collaborated with leading container registry partners. We announced our partnership with J frog late last year. And today we're very pleased to announce our partnerships with Amazon and Miranda's for providing an integrated seamless experience for joint for our joint customers. Lastly, the container images themselves and this end to end flow are built on open industry standards, which provided all the teams with flexibility and choice trusted content enables development teams to rapidly build. >>As I let them focus on their unique differentiated features and use trusted building blocks for the rest. We'll be talking more about trusted content as well as remote collaboration and ecosystem integrations later in the keynote. Now ecosystem partners are not only integral to the Docker experience for development teams. They're also integral to a great DockerCon experience, but please join me in thanking our Dr. Kent on sponsors and checking out their talks throughout the day. I also want to thank some others first up Docker team. Like all of you this last year has been extremely challenging for us, but the Docker team rose to the challenge and worked together to continue shipping great product, the Docker community of captains, community leaders, and contributors with your welcoming newcomers, enthusiasm for Docker and open exchanges of best practices and ideas talker, wouldn't be Docker without you. And finally, our development team customers. >>You trust us to help you build apps. Your businesses rely on. We don't take that trust for granted. Thank you. In closing, we often hear about the tenant's developer capable of great individual feeds that can transform project. But I wonder if we, as an industry have perhaps gotten this wrong by putting so much emphasis on weight, on the individual as discussed at the beginning, great accomplishments like innovative responses to COVID-19 like landing on Mars are more often the results of individuals collaborating together as a team, which is why our mission here at Docker is delivered tools and content developers love to help their team succeed and become 10 X teams. Thanks again for joining us, we look forward to having a great DockerCon with you today, as well as a great year ahead of us. Thanks and be well. >>Hi, I'm Dana Lawson, VP of engineering here at get hub. And my job is to enable this rich interconnected community of builders and makers to build even more and hopefully have a great time doing it in order to enable the best platform for developers, which I know is something we are all passionate about. We need to partner across the ecosystem to ensure that developers can have a great experience across get hub and all the tools that they want to use. No matter what they are. My team works to build the tools and relationships to make that possible. I am so excited to join Scott on this virtual stage to talk about increasing developer velocity. So let's dive in now, I know this may be hard for some of you to believe, but as a former CIS admin, some 21 years ago, working on sense spark workstations, we've come such a long way for random scripts and desperate systems that we've stitched together to this whole inclusive developer workflow experience being a CIS admin. >>Then you were just one piece of the siloed experience, but I didn't want to just push code to production. So I created scripts that did it for me. I taught myself how to code. I was the model lazy CIS admin that got dangerous and having pushed a little too far. I realized that working in production and building features is really a team sport that we had the opportunity, all of us to be customer obsessed today. As developers, we can go beyond the traditional dev ops mindset. We can really focus on adding value to the customer experience by ensuring that we have work that contributes to increasing uptime via and SLS all while being agile and productive. We get there. When we move from a pass the Baton system to now having an interconnected developer workflow that increases velocity in every part of the cycle, we get to work better and smarter. >>And honestly, in a way that is so much more enjoyable because we automate away all the mundane and manual and boring tasks. So we get to focus on what really matters shipping, the things that humans get to use and love. Docker has been a big part of enabling this transformation. 10, 20 years ago, we had Tomcat containers, which are not Docker containers. And for y'all hearing this the first time go Google it. But that was the way we built our applications. We had to segment them on the server and give them resources. Today. We have Docker containers, these little mini Oasys and Docker images. You can do it multiple times in an orchestrated manner with the power of actions enabled and Docker. It's just so incredible what you can do. And by the way, I'm showing you actions in Docker, which I hope you use because both are great and free for open source. >>But the key takeaway is really the workflow and the automation, which you certainly can do with other tools. Okay, I'm going to show you just how easy this is, because believe me, if this is something I can learn and do anybody out there can, and in this demo, I'll show you about the basic components needed to create and use a package, Docker container actions. And like I said, you won't believe how awesome the combination of Docker and actions is because you can enable your workflow to do no matter what you're trying to do in this super baby example. We're so small. You could take like 10 seconds. Like I am here creating an action due to a simple task, like pushing a message to your logs. And the cool thing is you can use it on any the bit on this one. Like I said, we're going to use push. >>You can do, uh, even to order a pizza every time you roll into production, if you wanted, but at get hub, that'd be a lot of pizzas. And the funny thing is somebody out there is actually tried this and written that action. If you haven't used Docker and actions together, check out the docs on either get hub or Docker to get you started. And a huge shout out to all those doc writers out there. I built this demo today using those instructions. And if I can do it, I know you can too, but enough yapping let's get started to save some time. And since a lot of us are Docker and get hub nerds, I've already created a repo with a Docker file. So we're going to skip that step. Next. I'm going to create an action's Yammel file. And if you don't Yammer, you know, actions, the metadata defines my important log stuff to capture and the input and my time out per parameter to pass and puts to the Docker container, get up a build image from your Docker file and run the commands in a new container. >>Using the Sigma image. The cool thing is, is you can use any Docker image in any language for your actions. It doesn't matter if it's go or whatever in today's I'm going to use a shell script and an input variable to print my important log stuff to file. And like I said, you know me, I love me some. So let's see this action in a workflow. When an action is in a private repo, like the one I demonstrating today, the action can only be used in workflows in the same repository, but public actions can be used by workflows in any repository. So unfortunately you won't get access to the super awesome action, but don't worry in the Guild marketplace, there are over 8,000 actions available, especially the most important one, that pizza action. So go try it out. Now you can do this in a couple of ways, whether you're doing it in your preferred ID or for today's demo, I'm just going to use the gooey. I'm going to navigate to my actions tab as I've done here. And I'm going to in my workflow, select new work, hello, probably load some workflows to Claire to get you started, but I'm using the one I've copied. Like I said, the lazy developer I am in. I'm going to replace it with my action. >>That's it. So now we're going to go and we're going to start our commitment new file. Now, if we go over to our actions tab, we can see the workflow in progress in my repository. I just click the actions tab. And because they wrote the actions on push, we can watch the visualization under jobs and click the job to see the important stuff we're logging in the input stamp in the printed log. And we'll just wait for this to run. Hello, Mona and boom. Just like that. It runs automatically within our action. We told it to go run as soon as the files updated because we're doing it on push merge. That's right. Folks in just a few minutes, I built an action that writes an entry to a log file every time I push. So I don't have to do it manually. In essence, with automation, you can be kind to your future self and save time and effort to focus on what really matters. >>Imagine what I could do with even a little more time, probably order all y'all pieces. That is the power of the interconnected workflow. And it's amazing. And I hope you all go try it out, but why do we care about all of that? Just like in the demo, I took a manual task with both tape, which both takes time and it's easy to forget and automated it. So I don't have to think about it. And it's executed every time consistently. That means less time for me to worry about my human errors and mistakes, and more time to focus on actually building the cool stuff that people want. Obviously, automation, developer productivity, but what is even more important to me is the developer happiness tools like BS, code actions, Docker, Heroku, and many others reduce manual work, which allows us to focus on building things that are awesome. >>And to get into that wonderful state that we call flow. According to research by UC Irvine in Humboldt university in Germany, it takes an average of 23 minutes to enter optimal creative state. What we call the flow or to reenter it after distraction like your dog on your office store. So staying in flow is so critical to developer productivity and as a developer, it just feels good to be cranking away at something with deep focus. I certainly know that I love that feeling intuitive collaboration and automation features we built in to get hub help developer, Sam flow, allowing you and your team to do so much more, to bring the benefits of automation into perspective in our annual October's report by Dr. Nicole, Forsgren. One of my buddies here at get hub, took a look at the developer productivity in the stork year. You know what we found? >>We found that public GitHub repositories that use the Automational pull requests, merge those pull requests. 1.2 times faster. And the number of pooled merged pull requests increased by 1.3 times, that is 34% more poor requests merged. And other words, automation can con can dramatically increase, but the speed and quantity of work completed in any role, just like an open source development, you'll work more efficiently with greater impact when you invest the bulk of your time in the work that adds the most value and eliminate or outsource the rest because you don't need to do it, make the machines by elaborate by leveraging automation in their workflows teams, minimize manual work and reclaim that time for innovation and maintain that state of flow with development and collaboration. More importantly, their work is more enjoyable because they're not wasting the time doing the things that the machines or robots can do for them. >>And I remember what I said at the beginning. Many of us want to be efficient, heck even lazy. So why would I spend my time doing something I can automate? Now you can read more about this research behind the art behind this at October set, get hub.com, which also includes a lot of other cool info about the open source ecosystem and how it's evolving. Speaking of the open source ecosystem we at get hub are so honored to be the home of more than 65 million developers who build software together for everywhere across the globe. Today, we're seeing software development taking shape as the world's largest team sport, where development teams collaborate, build and ship products. It's no longer a solo effort like it was for me. You don't have to take my word for it. Check out this globe. This globe shows real data. Every speck of light you see here represents a contribution to an open source project, somewhere on earth. >>These arts reach across continents, cultures, and other divides. It's distributed collaboration at its finest. 20 years ago, we had no concept of dev ops, SecOps and lots, or the new ops that are going to be happening. But today's development and ops teams are connected like ever before. This is only going to continue to evolve at a rapid pace, especially as we continue to empower the next hundred million developers, automation helps us focus on what's important and to greatly accelerate innovation. Just this past year, we saw some of the most groundbreaking technological advancements and achievements I'll say ever, including critical COVID-19 vaccine trials, as well as the first power flight on Mars. This past month, these breakthroughs were only possible because of the interconnected collaborative open source communities on get hub and the amazing tools and workflows that empower us all to create and innovate. Let's continue building, integrating, and automating. So we collectively can give developers the experience. They deserve all of the automation and beautiful eye UIs that we can muster so they can continue to build the things that truly do change the world. Thank you again for having me today, Dr. Khan, it has been a pleasure to be here with all you nerds. >>Hello. I'm Justin. Komack lovely to see you here. Talking to developers, their world is getting much more complex. Developers are being asked to do everything security ops on goal data analysis, all being put on the rockers. Software's eating the world. Of course, and this all make sense in that view, but they need help. One team. I told you it's shifted all our.net apps to run on Linux from windows, but their developers found the complexity of Docker files based on the Linux shell scripts really difficult has helped make these things easier for your teams. Your ones collaborate more in a virtual world, but you've asked us to make this simpler and more lightweight. You, the developers have asked for a paved road experience. You want things to just work with a simple options to be there, but it's not just the paved road. You also want to be able to go off-road and do interesting and different things. >>Use different components, experiments, innovate as well. We'll always offer you both those choices at different times. Different developers want different things. It may shift for ones the other paved road or off road. Sometimes you want reliability, dependability in the zone for day to day work, but sometimes you have to do something new, incorporate new things in your pipeline, build applications for new places. Then you knew those off-road abilities too. So you can really get under the hood and go and build something weird and wonderful and amazing. That gives you new options. Talk as an independent choice. We don't own the roads. We're not pushing you into any technology choices because we own them. We're really supporting and driving open standards, such as ISEI working opensource with the CNCF. We want to help you get your applications from your laptops, the clouds, and beyond, even into space. >>Let's talk about the key focus areas, that frame, what DACA is doing going forward. These are simplicity, sharing, flexibility, trusted content and care supply chain compared to building where the underlying kernel primitives like namespaces and Seagraves the original Docker CLI was just amazing Docker engine. It's a magical experience for everyone. It really brought those innovations and put them in a world where anyone would use that, but that's not enough. We need to continue to innovate. And it was trying to get more done faster all the time. And there's a lot more we can do. We're here to take complexity away from deeply complicated underlying things and give developers tools that are just amazing and magical. One of the area we haven't done enough and make things magical enough that we're really planning around now is that, you know, Docker images, uh, they're the key parts of your application, but you know, how do I do something with an image? How do I, where do I attach volumes with this image? What's the API. Whereas the SDK for this image, how do I find an example or docs in an API driven world? Every bit of software should have an API and an API description. And our vision is that every container should have this API description and the ability for you to understand how to use it. And it's all a seamless thing from, you know, from your code to the cloud local and remote, you can, you can use containers in this amazing and exciting way. >>One thing I really noticed in the last year is that companies that started off remote fast have constant collaboration. They have zoom calls, apron all day terminals, shattering that always working together. Other teams are really trying to learn how to do this style because they didn't start like that. We used to walk around to other people's desks or share services on the local office network. And it's very difficult to do that anymore. You want sharing to be really simple, lightweight, and informal. Let me try your container or just maybe let's collaborate on this together. Um, you know, fast collaboration on the analysts, fast iteration, fast working together, and he wants to share more. You want to share how to develop environments, not just an image. And we all work by seeing something someone else in our team is doing saying, how can I do that too? I can, I want to make that sharing really, really easy. Ben's going to talk about this more in the interest of one minute. >>We know how you're excited by apple. Silicon and gravis are not excited because there's a new architecture, but excited because it's faster, cooler, cheaper, better, and offers new possibilities. The M one support was the most asked for thing on our public roadmap, EFA, and we listened and share that we see really exciting possibilities, usership arm applications, all the way from desktop to production. We know that you all use different clouds and different bases have deployed to, um, you know, we work with AWS and Azure and Google and more, um, and we want to help you ship on prime as well. And we know that you use huge number of languages and the containers help build applications that use different languages for different parts of the application or for different applications, right? You can choose the best tool. You have JavaScript hat or everywhere go. And re-ask Python for data and ML, perhaps getting excited about WebAssembly after hearing about a cube con, you know, there's all sorts of things. >>So we need to make that as easier. We've been running the whole month of Python on the blog, and we're doing a month of JavaScript because we had one specific support about how do I best put this language into production of that language into production. That detail is important for you. GPS have been difficult to use. We've added GPS suppose in desktop for windows, but we know there's a lot more to do to make the, how multi architecture, multi hardware, multi accelerator world work better and also securely. Um, so there's a lot more work to do to support you in all these things you want to do. >>How do we start building a tenor has applications, but it turns out we're using existing images as components. I couldn't assist survey earlier this year, almost half of container image usage was public images rather than private images. And this is growing rapidly. Almost all software has open source components and maybe 85% of the average application is open source code. And what you're doing is taking whole container images as modules in your application. And this was always the model with Docker compose. And it's a model that you're already et cetera, writing you trust Docker, official images. We know that they might go to 25% of poles on Docker hub and Docker hub provides you the widest choice and the best support that trusted content. We're talking to people about how to make this more helpful. We know, for example, that winter 69 four is just showing us as support, but the image doesn't yet tell you that we're working with canonical to improve messaging from specific images about left lifecycle and support. >>We know that you need more images, regularly updated free of vulnerabilities, easy to use and discover, and Donnie and Marie neuro, going to talk about that more this last year, the solar winds attack has been in the, in the news. A lot, the software you're using and trusting could be compromised and might be all over your organization. We need to reduce the risk of using vital open-source components. We're seeing more software supply chain attacks being targeted as the supply chain, because it's often an easier place to attack and production software. We need to be able to use this external code safely. We need to, everyone needs to start from trusted sources like photography images. They need to scan for known vulnerabilities using Docker scan that we built in partnership with sneak and lost DockerCon last year, we need just keep updating base images and dependencies, and we'll, we're going to help you have the control and understanding about your images that you need to do this. >>And there's more, we're also working on the nursery V2 project in the CNCF to revamp container signings, or you can tell way or software comes from we're working on tooling to make updates easier, and to help you understand and manage all the principals carrier you're using security is a growing concern for all of us. It's really important. And we're going to help you work with security. We can't achieve all our dreams, whether that's space travel or amazing developer products ever see without deep partnerships with our community to cloud is RA and the cloud providers aware most of you ship your occasion production and simple routes that take your work and deploy it easily. Reliably and securely are really important. Just get into production simply and easily and securely. And we've done a bunch of work on that. And, um, but we know there's more to do. >>The CNCF on the open source cloud native community are an amazing ecosystem of creators and lovely people creating an amazing strong community and supporting a huge amount of innovation has its roots in the container ecosystem and his dreams beyond that much of the innovation is focused around operate experience so far, but developer experience is really a growing concern in that community as well. And we're really excited to work on that. We also uses appraiser tool. Then we know you do, and we know that you want it to be easier to use in your environment. We just shifted Docker hub to work on, um, Kubernetes fully. And, um, we're also using many of the other projects are Argo from atheists. We're spending a lot of time working with Microsoft, Amazon right now on getting natural UV to ready to ship in the next few. That's a really detailed piece of collaboration we've been working on for a long term. Long time is really important for our community as the scarcity of the container containers and, um, getting content for you, working together makes us stronger. Our community is made up of all of you have. Um, it's always amazing to be reminded of that as a huge open source community that we already proud to work with. It's an amazing amount of innovation that you're all creating and where perhaps it, what with you and share with you as well. Thank you very much. And thank you for being here. >>Really excited to talk to you today and share more about what Docker is doing to help make you faster, make your team faster and turn your application delivery into something that makes you a 10 X team. What we're hearing from you, the developers using Docker everyday fits across three common themes that we hear consistently over and over. We hear that your time is super important. It's critical, and you want to move faster. You want your tools to get out of your way, and instead to enable you to accelerate and focus on the things you want to be doing. And part of that is that finding great content, great application components that you can incorporate into your apps to move faster is really hard. It's hard to discover. It's hard to find high quality content that you can trust that, you know, passes your test and your configuration needs. >>And it's hard to create good content as well. And you're looking for more safety, more guardrails to help guide you along that way so that you can focus on creating value for your company. Secondly, you're telling us that it's a really far to collaborate effectively with your team and you want to do more, to work more effectively together to help your tools become more and more seamless to help you stay in sync, both with yourself across all of your development environments, as well as with your teammates so that you can more effectively collaborate together. Review each other's work, maintain things and keep them in sync. And finally, you want your applications to run consistently in every single environment, whether that's your local development environment, a cloud-based development environment, your CGI pipeline, or the cloud for production, and you want that micro service to provide that consistent experience everywhere you go so that you have similar tools, similar environments, and you don't need to worry about things getting in your way, but instead things make it easy for you to focus on what you wanna do and what Docker is doing to help solve all of these problems for you and your colleagues is creating a collaborative app dev platform. >>And this collaborative application development platform consists of multiple different pieces. I'm not going to walk through all of them today, but the overall view is that we're providing all the tooling you need from the development environment, to the container images, to the collaboration services, to the pipelines and integrations that enable you to focus on making your applications amazing and changing the world. If we start zooming on a one of those aspects, collaboration we hear from developers regularly is that they're challenged in synchronizing their own setups across environments. They want to be able to duplicate the setup of their teammates. Look, then they can easily get up and running with the same applications, the same tooling, the same version of the same libraries, the same frameworks. And they want to know if their applications are good before they're ready to share them in an official space. >>They want to collaborate on things before they're done, rather than feeling like they have to officially published something before they can effectively share it with others to work on it, to solve this. We're thrilled today to announce Docker, dev environments, Docker, dev environments, transform how your team collaborates. They make creating, sharing standardized development environments. As simple as a Docker poll, they make it easy to review your colleagues work without affecting your own work. And they increase the reproducibility of your own work and decreased production issues in doing so because you've got consistent environments all the way through. Now, I'm going to pass it off to our principal product manager, Ben Gotch to walk you through more detail on Docker dev environments. >>Hi, I'm Ben. I work as a principal program manager at DACA. One of the areas that doc has been looking at to see what's hard today for developers is sharing changes that you make from the inner loop where the inner loop is a better development, where you write code, test it, build it, run it, and ultimately get feedback on those changes before you merge them and try and actually ship them out to production. Most amount of us build this flow and get there still leaves a lot of challenges. People need to jump between branches to look at each other's work. Independence. Dependencies can be different when you're doing that and doing this in this new hybrid wall of work. Isn't any easier either the ability to just save someone, Hey, come and check this out. It's become much harder. People can't come and sit down at your desk or take your laptop away for 10 minutes to just grab and look at what you're doing. >>A lot of the reason that development is hard when you're remote, is that looking at changes and what's going on requires more than just code requires all the dependencies and everything you've got set up and that complete context of your development environment, to understand what you're doing and solving this in a remote first world is hard. We wanted to look at how we could make this better. Let's do that in a way that let you keep working the way you do today. Didn't want you to have to use a browser. We didn't want you to have to use a new idea. And we wanted to do this in a way that was application centric. We wanted to let you work with all the rest of the application already using C for all the services and all those dependencies you need as part of that. And with that, we're excited to talk more about docket developer environments, dev environments are new part of the Docker experience that makes it easier you to get started with your whole inner leap, working inside a container, then able to share and collaborate more than just the code. >>We want it to enable you to share your whole modern development environment, your whole setup from DACA, with your team on any operating system, we'll be launching a limited beta of dev environments in the coming month. And a GA dev environments will be ID agnostic and supporting composts. This means you'll be able to use an extend your existing composed files to create your own development environment in whatever idea, working in dev environments designed to be local. First, they work with Docker desktop and say your existing ID, and let you share that whole inner loop, that whole development context, all of your teammates in just one collect. This means if you want to get feedback on the working progress change or the PR it's as simple as opening another idea instance, and looking at what your team is working on because we're using compose. You can just extend your existing oppose file when you're already working with, to actually create this whole application and have it all working in the context of the rest of the services. >>So it's actually the whole environment you're working with module one service that doesn't really understand what it's doing alone. And with that, let's jump into a quick demo. So you can see here, two dev environments up and running. First one here is the same container dev environment. So if I want to go into that, let's see what's going on in the various code button here. If that one open, I can get straight into my application to start making changes inside that dev container. And I've got all my dependencies in here, so I can just run that straight in that second application I have here is one that's opened up in compose, and I can see that I've also got my backend, my front end and my database. So I've got all my services running here. So if I want, I can open one or more of these in a dev environment, meaning that that container has the context that dev environment has the context of the whole application. >>So I can get back into and connect to all the other services that I need to test this application properly, all of them, one unit. And then when I've made my changes and I'm ready to share, I can hit my share button type in the refund them on to share that too. And then give that image to someone to get going, pick that up and just start working with that code and all my dependencies, simple as putting an image, looking ahead, we're going to be expanding development environments, more of your dependencies for the whole developer worst space. We want to look at backing up and letting you share your volumes to make data science and database setups more repeatable and going. I'm still all of this under a single workspace for your team containing images, your dev environments, your volumes, and more we've really want to allow you to create a fully portable Linux development environment. >>So everyone you're working with on any operating system, as I said, our MVP we're coming next month. And that was for vs code using their dev container primitive and more support for other ideas. We'll follow to find out more about what's happening and what's coming up next in the future of this. And to actually get a bit of a deeper dive in the experience. Can we check out the talk I'm doing with Georgie and girl later on today? Thank you, Ben, amazing story about how Docker is helping to make developer teams more collaborative. Now I'd like to talk more about applications while the dev environment is like the workbench around what you're building. The application itself has all the different components, libraries, and frameworks, and other code that make up the application itself. And we hear developers saying all the time things like, how do they know if their images are good? >>How do they know if they're secure? How do they know if they're minimal? How do they make great images and great Docker files and how do they keep their images secure? And up-to-date on every one of those ties into how do I create more trust? How do I know that I'm building high quality applications to enable you to do this even more effectively than today? We are pleased to announce the DACA verified polisher program. This broadens trusted content by extending beyond Docker official images, to give you more and more trusted building blocks that you can incorporate into your applications. It gives you confidence that you're getting what you expect because Docker verifies every single one of these publishers to make sure they are who they say they are. This improves our secure supply chain story. And finally it simplifies your discovery of the best building blocks by making it easy for you to find things that you know, you can trust so that you can incorporate them into your applications and move on and on the right. You can see some examples of the publishers that are involved in Docker, official images and our Docker verified publisher program. Now I'm pleased to introduce you to marina. Kubicki our senior product manager who will walk you through more about what we're doing to create a better experience for you around trust. >>Thank you, Dani, >>Mario Andretti, who is a famous Italian sports car driver. One said that if everything feels under control, you're just not driving. You're not driving fast enough. Maya Andretti is not a software developer and a software developers. We know that no matter how fast we need to go in order to drive the innovation that we're working on, we can never allow our applications to spin out of control and a Docker. As we continue talking to our, to the developers, what we're realizing is that in order to reach that speed, the developers are the, the, the development community is looking for the building blocks and the tools that will, they will enable them to drive at the speed that they need to go and have the trust in those building blocks. And in those tools that they will be able to maintain control over their applications. So as we think about some of the things that we can do to, to address those concerns, uh, we're realizing that we can pursue them in a number of different venues, including creating reliable content, including creating partnerships that expands the options for the reliable content. >>Um, in order to, in a we're looking at creating integrations, no link security tools, talk about the reliable content. The first thing that comes to mind are the Docker official images, which is a program that we launched several years ago. And this is a set of curated, actively maintained, open source images that, uh, include, uh, operating systems and databases and programming languages. And it would become immensely popular for, for, for creating the base layers of, of the images of, of the different images, images, and applications. And would we realizing that, uh, many developers are, instead of creating something from scratch, basically start with one of the official images for their basis, and then build on top of that. And this program has become so popular that it now makes up a quarter of all of the, uh, Docker poles, which essentially ends up being several billion pulse every single month. >>As we look beyond what we can do for the open source. Uh, we're very ability on the open source, uh, spectrum. We are very excited to announce that we're launching the Docker verified publishers program, which is continuing providing the trust around the content, but now working with, uh, some of the industry leaders, uh, in multiple, in multiple verticals across the entire technology technical spec, it costs entire, uh, high tech in order to provide you with more options of the images that you can use for building your applications. And it still comes back to trust that when you are searching for content in Docker hub, and you see the verified publisher badge, you know, that this is, this is the content that, that is part of the, that comes from one of our partners. And you're not running the risk of pulling the malicious image from an employee master source. >>As we look beyond what we can do for, for providing the reliable content, we're also looking at some of the tools and the infrastructure that we can do, uh, to create a security around the content that you're creating. So last year at the last ad, the last year's DockerCon, we announced partnership with sneak. And later on last year, we launched our DACA, desktop and Docker hub vulnerability scans that allow you the options of writing scans in them along multiple points in your dev cycle. And in addition to providing you with information on the vulnerability on, on the vulnerabilities, in, in your code, uh, it also provides you with a guidance on how to re remediate those vulnerabilities. But as we look beyond the vulnerability scans, we're also looking at some of the other things that we can do, you know, to, to, to, uh, further ensure that the integrity and the security around your images, your images, and with that, uh, later on this year, we're looking to, uh, launch the scope, personal access tokens, and instead of talking about them, I will simply show you what they look like. >>So if you can see here, this is my page in Docker hub, where I've created a four, uh, tokens, uh, read-write delete, read, write, read only in public read in public creeper read only. So, uh, earlier today I went in and I, I logged in, uh, with my read only token. And when you see, when I'm going to pull an image, it's going to allow me to pull an image, not a problem success. And then when I do the next step, I'm going to ask to push an image into the same repo. Uh, would you see is that it's going to give me an error message saying that they access is denied, uh, because there is an additional authentication required. So these are the things that we're looking to add to our roadmap. As we continue thinking about the things that we can do to provide, um, to provide additional building blocks, content, building blocks, uh, and, and, and tools to build the trust so that our DACA developer and skinned code faster than Mario Andretti could ever imagine. Uh, thank you to >>Thank you, marina. It's amazing what you can do to improve the trusted content so that you can accelerate your development more and move more quickly, move more collaboratively and build upon the great work of others. Finally, we hear over and over as that developers are working on their applications that they're looking for, environments that are consistent, that are the same as production, and that they want their applications to really run anywhere, any environment, any architecture, any cloud one great example is the recent announcement of apple Silicon. We heard from developers on uproar that they needed Docker to be available for that architecture before they could add those to it and be successful. And we listened. And based on that, we are pleased to share with you Docker, desktop on apple Silicon. This enables you to run your apps consistently anywhere, whether that's developing on your team's latest dev hardware, deploying an ARM-based cloud environments and having a consistent architecture across your development and production or using multi-year architecture support, which enables your whole team to collaborate on its application, using private repositories on Docker hub, and thrilled to introduce you to Hughie cower, senior director for product management, who will walk you through more of what we're doing to create a great developer experience. >>Senior director of product management at Docker. And I'd like to jump straight into a demo. This is the Mac mini with the apple Silicon processor. And I want to show you how you can now do an end-to-end arm workflow from my M one Mac mini to raspberry PI. As you can see, we have vs code and Docker desktop installed on a, my, the Mac mini. I have a small example here, and I have a raspberry PI three with an led strip, and I want to turn those LEDs into a moving rainbow. This Dockerfile here, builds the application. We build the image with the Docker, build X command to make the image compatible for all raspberry pies with the arm. 64. Part of this build is built with the native power of the M one chip. I also add the push option to easily share the image with my team so they can give it a try to now Dr. >>Creates the local image with the application and uploads it to Docker hub after we've built and pushed the image. We can go to Docker hub and see the new image on Docker hub. You can also explore a variety of images that are compatible with arm processors. Now let's go to the raspberry PI. I have Docker already installed and it's running Ubuntu 64 bit with the Docker run command. I can run the application and let's see what will happen from there. You can see Docker is downloading the image automatically from Docker hub and when it's running, if it's works right, there are some nice colors. And with that, if we have an end-to-end workflow for arm, where continuing to invest into providing you a great developer experience, that's easy to install. Easy to get started with. As you saw in the demo, if you're interested in the new Mac, mini are interested in developing for our platforms in general, we've got you covered with the same experience you've come to expect from Docker with over 95,000 arm images on hub, including many Docker official images. >>We think you'll find what you're looking for. Thank you again to the community that helped us to test the tech previews. We're so delighted to hear when folks say that the new Docker desktop for apple Silicon, it just works for them, but that's not all we've been working on. As Dani mentioned, consistency of developer experience across environments is so important. We're introducing composed V2 that makes compose a first-class citizen in the Docker CLI you no longer need to install a separate composed biter in order to use composed, deploying to production is simpler than ever with the new compose integration that enables you to deploy directly to Amazon ECS or Azure ACI with the same methods you use to run your application locally. If you're interested in running slightly different services, when you're debugging versus testing or, um, just general development, you can manage that all in one place with the new composed service to hear more about what's new and Docker desktop, please join me in the three 15 breakout session this afternoon. >>And now I'd love to tell you a bit more about bill decks and convince you to try it. If you haven't already it's our next gen build command, and it's no longer experimental as shown in the demo with built X, you'll be able to do multi architecture builds, share those builds with your team and the community on Docker hub. With build X, you can speed up your build processes with remote caches or build all the targets in your composed file in parallel with build X bake. And there's so much more if you're using Docker, desktop or Docker, CE you can use build X checkout tonus is talk this afternoon at three 45 to learn more about build X. And with that, I hope everyone has a great Dr. Khan and back over to you, Donnie. >>Thank you UA. It's amazing to hear about what we're doing to create a better developer experience and make sure that Docker works everywhere you need to work. Finally, I'd like to wrap up by showing you everything that we've announced today and everything that we've done recently to make your lives better and give you more and more for the single price of your Docker subscription. We've announced the Docker verified publisher program we've announced scoped personal access tokens to make it easier for you to have a secure CCI pipeline. We've announced Docker dev environments to improve your collaboration with your team. Uh, we shared with you Docker, desktop and apple Silicon, to make sure that, you know, Docker runs everywhere. You need it to run. And we've announced Docker compose version two, finally making it a first-class citizen amongst all the other great Docker tools. And we've done so much more recently as well from audit logs to advanced image management, to compose service profiles, to improve where you can run Docker more easily. >>Finally, as we look forward, where we're headed in the upcoming year is continuing to invest in these themes of helping you build, share, and run modern apps more effectively. We're going to be doing more to help you create a secure supply chain with which only grows more and more important as time goes on. We're going to be optimizing your update experience to make sure that you can easily understand the current state of your application, all its components and keep them all current without worrying about breaking everything as you're doing. So we're going to make it easier for you to synchronize your work. Using cloud sync features. We're going to improve collaboration through dev environments and beyond, and we're going to do make it easy for you to run your microservice in your environments without worrying about things like architecture or differences between those environments. Thank you so much. I'm thrilled about what we're able to do to help make your lives better. And now you're going to be hearing from one of our customers about what they're doing to launch their business with Docker >>I'm Matt Falk, I'm the head of engineering and orbital insight. And today I want to talk to you a little bit about data from space. So who am I like many of you, I'm a software developer and a software developer about seven companies so far, and now I'm a head of engineering. So I spend most of my time doing meetings, but occasionally I'll still spend time doing design discussions, doing code reviews. And in my free time, I still like to dabble on things like project oiler. So who's Oberlin site. What do we do? Portal insight is a large data supplier and analytics provider where we take data geospatial data anywhere on the planet, any overhead sensor, and translate that into insights for the end customer. So specifically we have a suite of high performance, artificial intelligence and machine learning analytics that run on this geospatial data. >>And we build them to specifically determine natural and human service level activity anywhere on the planet. What that really means is we take any type of data associated with a latitude and longitude and we identify patterns so that we can, so we can detect anomalies. And that's everything that we do is all about identifying those patterns to detect anomalies. So more specifically, what type of problems do we solve? So supply chain intelligence, this is one of the use cases that we we'd like to talk about a lot. It's one of our main primary verticals that we go after right now. And as Scott mentioned earlier, this had a huge impact last year when COVID hit. So specifically supply chain intelligence is all about identifying movement patterns to and from operating facilities to identify changes in those supply chains. How do we do this? So for us, we can do things where we track the movement of trucks. >>So identifying trucks, moving from one location to another in aggregate, same thing we can do with foot traffic. We can do the same thing for looking at aggregate groups of people moving from one location to another and analyzing their patterns of life. We can look at two different locations to determine how people are moving from one location to another, or going back and forth. All of this is extremely valuable for detecting how a supply chain operates and then identifying the changes to that supply chain. As I said last year with COVID, everything changed in particular supply chains changed incredibly, and it was hugely important for customers to know where their goods or their products are coming from and where they were going, where there were disruptions in their supply chain and how that's affecting their overall supply and demand. So to use our platform, our suite of tools, you can start to gain a much better picture of where your suppliers or your distributors are going from coming from or going to. >>So what's our team look like? So my team is currently about 50 engineers. Um, we're spread into four different teams and the teams are structured like this. So the first team that we have is infrastructure engineering and this team largely deals with deploying our Dockers using Kubernetes. So this team is all about taking Dockers, built by other teams, sometimes building the Dockers themselves and putting them into our production system, our platform engineering team, they produce these microservices. So they produce microservice, Docker images. They develop and test with them locally. Their entire environments are dockerized. They produce these doctors, hand them over to him for infrastructure engineering to be deployed. Similarly, our product engineering team does the same thing. They develop and test with Dr. Locally. They also produce a suite of Docker images that the infrastructure team can then deploy. And lastly, we have our R and D team, and this team specifically produces machine learning algorithms using Nvidia Docker collectively, we've actually built 381 Docker repositories and 14 million. >>We've had 14 million Docker pools over the lifetime of the company, just a few stats about us. Um, but what I'm really getting to here is you can see actually doctors becoming almost a form of communication between these teams. So one of the paradigms in software engineering that you're probably familiar with encapsulation, it's really helpful for a lot of software engineering problems to break the problem down, isolate the different pieces of it and start building interfaces between the code. This allows you to scale different pieces of the platform or different pieces of your code in different ways that allows you to scale up certain pieces and keep others at a smaller level so that you can meet customer demands. And for us, one of the things that we can largely do now is use Dockers as that interface. So instead of having an entire platform where all teams are talking to each other, and everything's kind of, mishmashed in a monolithic application, we can now say this team is only able to talk to this team by passing over a particular Docker image that defines the interface of what needs to be built before it passes to the team and really allows us to scalp our development and be much more efficient. >>Also, I'd like to say we are hiring. Um, so we have a number of open roles. We have about 30 open roles in our engineering team that we're looking to fill by the end of this year. So if any of this sounds really interesting to you, please reach out after the presentation. >>So what does our platform do? Really? Our platform allows you to answer any geospatial question, and we do this at three different inputs. So first off, where do you want to look? So we did this as what we call an AOI or an area of interest larger. You can think of this as a polygon drawn on the map. So we have a curated data set of almost 4 million AOIs, which you can go and you can search and use for your analysis, but you're also free to build your own. Second question is what you want to look for. We do this with the more interesting part of our platform of our machine learning and AI capabilities. So we have a suite of algorithms that automatically allow you to identify trucks, buildings, hundreds of different types of aircraft, different types of land use, how many people are moving from one location to another different locations that people in a particular area are moving to or coming from all of these different analyses or all these different analytics are available at the click of a button, and then determine what you want to look for. >>Lastly, you determine when you want to find what you're looking for. So that's just, uh, you know, do you want to look for the next three hours? Do you want to look for the last week? Do you want to look every month for the past two, whatever the time cadence is, you decide that you hit go and out pops a time series, and that time series tells you specifically where you want it to look what you want it to look for and how many, or what percentage of the thing you're looking for appears in that area. Again, we do all of this to work towards patterns. So we use all this data to produce a time series from there. We can look at it, determine the patterns, and then specifically identify the anomalies. As I mentioned with supply chain, this is extremely valuable to identify where things change. So we can answer these questions, looking at a particular operating facility, looking at particular, what is happening with the level of activity is at that operating facility where people are coming from, where they're going to, after visiting that particular facility and identify when and where that changes here, you can just see it's a picture of our platform. It's actually showing all the devices in Manhattan, um, over a period of time. And it's more of a heat map view. So you can actually see the hotspots in the area. >>So really the, and this is the heart of the talk, but what happened in 2020? So for men, you know, like many of you, 2020 was a difficult year COVID hit. And that changed a lot of what we're doing, not from an engineering perspective, but also from an entire company perspective for us, the motivation really became to make sure that we were lowering our costs and increasing innovation simultaneously. Now those two things often compete with each other. A lot of times you want to increase innovation, that's going to increase your costs, but the challenge last year was how to do both simultaneously. So here's a few stats for you from our team. In Q1 of last year, we were spending almost $600,000 per month on compute costs prior to COVID happening. That wasn't hugely a concern for us. It was a lot of money, but it wasn't as critical as it was last year when we really needed to be much more efficient. >>Second one is flexibility for us. We were deployed on a single cloud environment while we were cloud thought ready, and that was great. We want it to be more flexible. We want it to be on more cloud environments so that we could reach more customers. And also eventually get onto class side networks, extending the base of our customers as well from a custom analytics perspective. This is where we get into our traction. So last year, over the entire year, we computed 54,000 custom analytics for different users. We wanted to make sure that this number was steadily increasing despite us trying to lower our costs. So we didn't want the lowering cost to come as the sacrifice of our user base. Lastly, of particular percentage here that I'll say definitely needs to be improved is 75% of our projects never fail. So this is where we start to get into a bit of stability of our platform. >>Now I'm not saying that 25% of our projects fail the way we measure this is if you have a particular project or computation that runs every day and any one of those runs sale account, that is a failure because from an end-user perspective, that's an issue. So this is something that we know we needed to improve on and we needed to grow and make our platform more stable. I'm going to something that we really focused on last year. So where are we now? So now coming out of the COVID valley, we are starting to soar again. Um, we had, uh, back in April of last year, we had the entire engineering team. We actually paused all development for about four weeks. You had everyone focused on reducing our compute costs in the cloud. We got it down to 200 K over the period of a few months. >>And for the next 12 months, we hit that number every month. This is huge for us. This is extremely important. Like I said, in the COVID time period where costs and operating efficiency was everything. So for us to do that, that was a huge accomplishment last year and something we'll keep going forward. One thing I would actually like to really highlight here, two is what allowed us to do that. So first off, being in the cloud, being able to migrate things like that, that was one thing. And we were able to use there's different cloud services in a more particular, in a more efficient way. We had a very detailed tracking of how we were spending things. We increased our data retention policies. We optimized our processing. However, one additional piece was switching to new technologies on, in particular, we migrated to get lab CICB. >>Um, and this is something that the costs we use Docker was extremely, extremely easy. We didn't have to go build new new code containers or repositories or change our code in order to do this. We were simply able to migrate the containers over and start using a new CIC so much. In fact, that we were able to do that migration with three engineers in just two weeks from a cloud environment and flexibility standpoint, we're now operating in two different clouds. We were able to last night, I've over the last nine months to operate in the second cloud environment. And again, this is something that Docker helped with incredibly. Um, we didn't have to go and build all new interfaces to all new, different services or all different tools in the next cloud provider. All we had to do was build a base cloud infrastructure that ups agnostic the way, all the different details of the cloud provider. >>And then our doctors just worked. We can move them to another environment up and running, and our platform was ready to go from a traction perspective. We're about a third of the way through the year. At this point, we've already exceeded the amount of customer analytics we produce last year. And this is thanks to a ton more albums, that whole suite of new analytics that we've been able to build over the past 12 months and we'll continue to build going forward. So this is really, really great outcome for us because we were able to show that our costs are staying down, but our analytics and our customer traction, honestly, from a stability perspective, we improved from 75% to 86%, not quite yet 99 or three nines or four nines, but we are getting there. Um, and this is actually thanks to really containerizing and modularizing different pieces of our platform so that we could scale up in different areas. This allowed us to increase that stability. This piece of the code works over here, toxin an interface to the rest of the system. We can scale this piece up separately from the rest of the system, and that allows us much more easily identify issues in the system, fix those and then correct the system overall. So basically this is a summary of where we were last year, where we are now and how much more successful we are now because of the issues that we went through last year and largely brought on by COVID. >>But that this is just a screenshot of the, our, our solution actually working on supply chain. So this is in particular, it is showing traceability of a distribution warehouse in salt lake city. It's right in the center of the screen here. You can see the nice kind of orange red center. That's a distribution warehouse and all the lines outside of that, all the dots outside of that are showing where people are, where trucks are moving from that location. So this is really helpful for supply chain companies because they can start to identify where their suppliers are, are coming from or where their distributors are going to. So with that, I want to say, thanks again for following along and enjoy the rest of DockerCon.

>>Welcome to the convergence of file and object, a special program made possible by pure storage and co-created with the cube we're running. What I would call a little mini series and we're exploring the conversions of file and object storage. What are the key trends? Why would you want to converge file and object? What are the use cases and architectural considerations and importantly, what are the business drivers of U F F O so-called unified fast file and object in this program, you'll hear from Matt Burr, who was the GM of pure flash blade business. And then we'll bring in the perspectives of a solutions architect, Garrett who's from CDW, and then the analyst angle with Scott St. Claire of the enterprise strategy group ESG. And then we'll wrap with a really interesting technical conversation with Chris and bond CB bond, who is a lead data architect at Microfocus. And he's got a really cool use case to share with us. So sit back and enjoy the pros.

>> Announcer: From around the globe, it's "theCUBE" with digital coverage of AWS "re:Invent" 2020. Sponsored by Intel, AWS and our community partners. >> Hi, everyone, welcome back to theCUBE's virtual coverage of AWS "re:Invent" virtual 2020. It's not an in-person event this year. It's remote, it's virtual, "theCUBE" is virtual and our guests and our interviewers will be remote as well. And so we're here covering the event for the next three weeks, throughout the next three cause we're weaving in commentary from "theCUBE", check out the cube.net and all of our coverage. And here at AWS we have special feature programming, we got a great segment here talking about big data in the cloud, governance, data lakes, all that good stuff. Rik Tamm-Daniels, vice-president strategic ecosystems and technology for Informatica, and Rick Turnock, head of enterprise data services, Invesco, customer of Informatica. Welcome to the cube. >> Hey John, thanks for having us. >> So Rik, with a K from Informatica, I want to ask you first, we've been covering the company journey for many, many years. Always been impressed with the focus on data and specifically cloud and all the things that you guys have been announcing over the years, have been spot on the mark. You know, AI with CLAIRE, you know, making things, cloud native, all that's kind of playing out now with the pandemic, "re:Invent", that's the story here. Building blocks with high level services, cloud native, but data is the critical piece again. More machine learning, more AI, more data management. What's your take on this year's "re:Invent". >> Absolutely John and again, we're always excited to be here at "re:Invent", we've been here since the very first one. You know, it's a deep talk to a couple of key trends there, especially the era of the global pandemic here. There's so many challenges that so many enterprises are experiencing. I think the big surprise has been, that has actually translated into a tremendous amount of demand for digital transformation, and cloud modernization in particular. So we've seen a huge uptake in our cloud relationships with AWS when it comes to transformational architecture solutions around data and analytics, and using data as a fundamental asset for digital transformation. And so some of those solution areas are things like data warehouse, modernization of the cloud, or end-to-end data governance. That's a huge topic as well for many enterprises today. >> Before coming into "re:Invent", I had a chance to sit down an exclusive interview with Andy Jassy. I just spoke with Matt Garman who's now heading up sales and marketing, who ran EC too. Rick, you're a customer of Informatica. Their big talking point to me and validation to the trends is, there's no excuse to go slow anymore because there's a reason to go fast cause there's consequences and the pandemic has highlighted that you got to move faster otherwise, you know, you're going to be on the wrong side of history and necessity is the mother of all invention. Okay, great. I buy that by the way. So I have no complaints on talking point there from Amazon Web Services. The problem is, you got to manage the data. (John chuckles) To go fast. The gas in the tank is data, and if it's screwed up, it's not going to go well, all right? So it's like putting gas in a car. So, this is where I see the data lake coming into the cloud and all the benefits and look at the successes of companies. The cloud is a real enabler. What's your take on this? The importance of data governance, because cloud scale is here, people want to go faster, data is like the key thing. >> Yeah. The data governance was a critical component when we started our enterprise data platform and looking at, you know, how can we build a modern-day architecture with scale, bringing our enterprise data, but doing it in a governed fashion. So, when we did it, we kind of looked at, you know, what are critical partners? How can we apply data governance and the full catalog capabilities of knowing what data's coming in, identifying it, and then really controlling the quality of it to meet the needs of the organization. It was a critical component for us because typically it's been difficult to get access to that right data. And as we look in the future and even current needs, we really need to understand our data and bring the right data in and make it easily accessible and governance, and quality of that is a critical component of it. >> I want to just follow up with that if you don't mind cause you know, I've done so many of these interviews, I've been on the block now 30 years in the industry, I've seen the waves come and go, and you see a lot of these mandates, you know, "Data governance, we're adding data governance." From the Ivory tower, or you hear, "Everything got to be a service." But when you peel back and look under the hood to make that happen, it's complicated. You've got to have put things in place and it's got to be set up properly, you got to do your work. How important it is to have... And well what's under the covers to this? Cause governance, yeah, it's a talking point, I get that. But to make it actually happen well, it's hard. >> We started really with the operating models from the start. So I kind of took over data governance seven years ago and had a governing global architecture that's been around for 40 years, and it was hard. So this was really our shot and time to get it right. So we did an operating model, a governance model, and it really ingrained it through the whole build and execution process. And so it was part with technology and it was foundational to the process to really deliver it. So it wasn't governance from a governance saying, it was really part of our operating model and process to build this out and really succeed at it. >> Rik, on the Informatica side, I got to get your take on the new solution you guys announced, "The Governed Data Lake", I think it was solution. Does this tie into that? Take a minute to explain the announcement, and how does this tie in? >> Yeah, absolutely John. So I think you take a step back, look at... We talked about some of the drivers of why companies are investing in cloud data lakes. And I think what comes down to is, when you think about that core foundation of data analytics, you know, they're really looking at, you know, how do we go ahead and create a tremendous leap forward in terms of their ability to leverage data as an asset. And again, as we talked about, one of the biggest challenges is trust around the data. And what the solution does though, is it really looks to say, "Okay, first and foremost, "let's create that foundation of trust "not just for the cloud data lake, "but for the entire enterprise. "To ensure that when we start to build this "new architecture, one, we understand the data assets "that are coming in at the very get-go." Right? It's much harder to add data governance after the fact, but you put it in upfront, you understand your existing data landscape. And once that data is there, you make sure you understand the quality of the information, you cleanse the data, you also make sure you put it under the right data management policies. So many policies that enterprises are subject to now like CCPA and GDPR. They have to be concerned about consumer privacy and being able as part of your governance foundational layer, to make sure that you're in compliance as data moves through your new architectures. It's fundamental having that end trust and confidence to be able to use that data downstream. So our solution looks to do that end-to-end across a cloud environment, and again, not just the cloud environment, but the full enterprise as well. One thing I do want to touch on if you don't mind is on the AI side of things and the tooling side of things. Because I think data governance has been around a while, as you said, it's not that it's a new concept. But how do you do it efficiently in today's world? >> John: Yeah. >> And this is where Informatica is focused on a concept of data 4.0. Which is the use of metadata and AI and machine learning and intelligence, to make this process much, much more efficient. >> Yeah that's a good point, Rik, from these two Rickes, I got to go, one's with a K, one with a C, and CK. So Rick, CK and from Invesco customer, I want to just check that with you because I was your customer of Informatica, by they brought up a good point about governance. And I saw this movie before, we've all seen this before, people just slap on solutions or tooling to a pre-existing architecture. You see that with security, you know, now it's, you can't have a conversation without saying, "Oh security's got to be baked in from the beginning." Okay cool, I get that. There's no debate there. Governance, same kind of thing, you know, you're hearing this over and over again, if you don't bake governance into the beginning of everything, you're going to be screwed. Okay? So how important is that foundation of trust for this peace. (Rick mumbling) >> It's critical and to do it at beginning, right? So you're profiling the data, you're defining entitlements and who has access to it, you're defining data quality rules that you can validate that, you define the policies, is there a PII data, all of that, as you do that from the start, then you have a well-governed and documented data catalog and taxonomy that has the policies and the controls in place to allow that to use. If you do it after the fact, then you're always going to be catching up. So a part of our process and policies and where the really Informatica tools delivered for us is to make it part of that process. And to use that as we continue to build out our data platform with the quality controls and all the governance processes built in. >> I got to ask on your journey, that's seven years ago, you took over the practice. You were probably right in the middle of the sea change when everyone kind of woke up and said, "Hey, you know, Amazon, you go back seven years, "look at Amazon where they were to where they are today." Okay? Significantly strong then, total bellwether now in terms of value opportunity. So, how did you look at the cloud migration? How do you think about the cloud architecture? Because I'm sure, and I'd love to get your story here about how you thought about cloud, in the midst of architecting the data foundational platform there. >> Yeah, we're a global company that had architecture, we grew it by acquisition. So a lot of our data architecture was on-prem, difficult really to pull that enterprise data together to meet the business needs. So when we started this, we really wanted to leverage cloud technology. We wanted a modern stack. We wanted scale, flexibility, agility, security, all the things that the cloud brought us too. So we did a search, and looking at that, and looked at competitors, but really landed on to Amazon just bought by core capabilities and scale they have innovation and just the services to bring a lot what we're looking at and really deliver on what we wanted from a platform. >> Why Informatica and AWS, why the combination? Can you share some of the reasons why you went with Informatica with AWS? >> Yeah, again, when we started this off, we looked at the competitors, right? And we were using IVQ. So we had an Informatica product on-prem, but we looked at a lot of the different governance competitors, and really the integrated platform that Informatica brings to us, what was the key deliverer, right? So we can really have the technical metadata with EDC and enterprise data client, catalog, scan our sources, our file, understanding the data and lineage of what it is. And we can tie that into axon and the governance tools to really define business costs returns. We were very critical of defining all our key data elements business glossary, and then we can see where that is by linking that to the technical metadata. So we know where our PII data, where are all our data and how it flows, both tactically and from a business process. And then the IDQ. So when we've defined and understand the data, we want to bring in the delight and how we want to conform it, to make it easily accessible, we can define data quality rules within the governance tool, and then execute that with IDQ, and really have a well-defined data quality process that really takes it from governance in theory to governance in really execution. >> That's awesome. Hey, you are using the data, you're using the cloud, you're getting everything you need out of it. That's the whole idea, isn't it? >> Yeah. >> That's good stuff, Rik at Informatica, tell us about what's going on, you mentioned data 4.0, I think people should pay attention to some of the interviews I've done with your team. They're online also, it's part of that next-gen, next level thinking. Here at "re:Invent", what should customers pay attention to, that you guys are doing? Great customer example here of cloud scale. What's the story for "re:Invent" this year for Informatica. >> But what John, it comes down to when customers think about their cloud journey, right? And the difference, especially with their data centric workloads and priorities and initiatives, all the different hurdles that they need to overcome. I think Informatica we're uniquely positioned to help customers address all those different challenges and you heard Rick speak about a whole bunch of those along the way. And I think particularly at "re:Invent", first of all, I just welcome folks to... They want to learn more about our data governance solution. Please come by our virtual booth. We also have a great interactive experience that encouraged folks to check out. One of the key components of our solution is our enterprise data catalog. And attendees at "re:Invent" can actually get hands on with our data catalog through the demo jam, the AWS demo jam as part of "re:invent". So I'd encourage folks to check that out as well, just to see what we're talking about yet actually. >> Awesome. Final question for you guys, as "re:Invent" is going on, a lot app stores are popping up, you seeing obviously the same trends, machine learning and you know, outpost is booming, so a cloud operations is clearly here, Rick from Invesco, what do you think the most important story is for your peers as they're here? It's a learning conference and you guys have seven years in the cloud working together with Informatica, in your opinion, what should people be paying attention to as they looked at this pandemic and what they got to get through? And then coming out of it with the growth strategy, it's all got to be more about the data, there's more data coming in, more sources, IoT data, certainly the work at home is causing these workloads, workplace, workflows, everything's changed, the future of work. What's your advice to peers out there on what to pay attention to and what to think about? >> We really started with a top-down strategy, right? To really the vision and the future. What do we want to get out of our data? Data is just data, right? But it's the information, it's the analytics, it's really delivering value for our clients, shareholders, and employees to really do their job, simplify our architecture. So really defining that vision of what you want and approach, and then executing on it, right? So how do you build it in a way to make it flexible and scalable, and how do you build an operating and governance model really to deliver on it because, you know, garbage in is garbage out, and you really got to have those processes, I think to really get the full value of what you're building. >> Get the data out there at the right place, at the right time and the right quality data. That's a lot more involved now and you need to be agile. And I think agile data is a way to go. Rick Turnock... >> And then with channels and capabilities that makes it easier, right? It makes it doable. And I think that's what cloud and the Informatica tools, right? Where in the past, you know, it was people hard coding and doing it right? The capability of that cloud and these tools give us makes it really achievable. >> You know, we have an old saying here in our CUBE team, you know, "If there's a problem, "you got to see if it's important, "and then look at the consequences "of not solving that problem, quantify the value of "solving or not solving that problem, "and then look and deploy solutions to do it." I think now with the data, you can actually do that and say, "This ain't quite the consequences of not doing this "or doing this, have a quantifiable value." I just loved that because it brings the whole ROI back to the table. And, you know, it's a dark art, it used to be, you mentioned the old days, you know, you got to do all this custom work, it was like a dark art. Oh yeah, the ROI calculation, payback. I mean, it was a moving train. That's the way it used to be. Not anymore. >> You got to do it to survive, really, if you're not doing it, you know, I don't know. >> Necessity is the mother of all inventions I think, now more than ever, data's going to be the key. Rik final word from Informatica. What should people pay attention to? >> Yeah, I mean, I think as you mentioned there, data is obviously a critical asset, right? And, and to your point with cloud, you can not only realize ROI quickly, but, you can actually iterate so much more quickly, where you can get that ROI immediately or you can validate that ROI, you can adjust your approach. But again, from an Informatica standpoint, we are seeing such a huge uptake in demand for customers who want to go to the cloud, who are modernizing. Every day we're investing heavily and how do we make sure that customers can get there quickly? They can maximize the ROI from their data assets, and we're doing it with all things, data management, from traditional data integration, all the way to the data governance, all the capabilities we talked about today. >> Yeah. Congratulations. That's the benefit of investing in a platform and having a set of out of the box tooling with SaaS, platform as a service, really it can enable success. And I think the pandemic is pretty obvious who's taking advantage of it, so congratulations and continued success. Thanks for coming on. Appreciate it. Rick Turnock, head of data service, enterprise data services at Invesco, customer of Informatica sharing his insight. Great insight there. Necessity is the mother of all inventions, baking it in from the beginning data governance foundational, it's not a bolt on, that's the message. I'm John Furrier with theCUBE. Thanks for watching. (soft music)

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hello, everyone. Welcome to this CUBE conversation. I'm John Furrier, host of theCUBE. We're here in our Palo Alto studios in California for a CUBE conversation with Suresh Menon, who's the senior vice president and general manager of Informatica of the master data group. Suresh, great to see you. We couldn't see you in person. Three-time CUBE alumni at Informatica World, industry executive. We're remote. Great to see you. >> Good to see you, John. Great to be back. Wish this was in person, but I think this is fantastic. >> Well, one of the things that's clear in my interviews over the past four months, we've been doing our best to hit the road and we've got a quarantine crew here. We're doing our part telling the stories that matter. Data now more than ever, COVID-19 has shown that the companies that are prepared, that have done the work, for the digital transformation, you know, putting the cliche aside, is real and the benefits are definitely there. And you're seeing things like reaction time, war rooms are being put together, because business still needs to go on. This is the reality. And so companies are seeing some exposure and some opportunities, and so a lot of things are going on. So I want to get your reaction to that, because there are changes on how customers are evolving with data. You guys have been at the forefront of that, pioneering this horizontal data fabric, data 4.0, amidst talks about. What are you seeing from customers? How are they approaching this? Because at the end of the day, they got to come out of the pandemic with a growth strategy and they got to solve the problems they've got to do today and be in position. What are you seeing for changes? >> So one of the most important things that we started seeing, there are about three big trends that we began to see starting in about late March, and share some of the data points that we saw across the world, starting with Italy, which was in the news earlier this year with the pandemic. We saw that in one week, the stats were that online or digital sales increased by 81% in a single week. And it's obvious when you lock down a large population, commerce moves to, away from the brick and mortar kind of model to being completely online and digital. The other part of it that we started seeing is we had already started seeing a lot of our customers starting to struggle with supply chain issues. As borders started closing, opening, and then closing again, how do you maintain a resilient supply chain? And a resilient supply chain also means being able to be really agile in terms of trying to identify alternate supply sources, be able to quickly onboard new suppliers, maybe in different parts of the world that are not so affected. And then finally, the last piece that we saw were every single CFO, chief financial officer, people who ran finance organizations at all of these companies, for them, it is almost as if you're driving down the highway and you suddenly run into, enter this fog bank. The first reaction is to hit the brakes, of course, because you don't know what's (microphone cuts out) so every CFO around the world started saying, I need to be able to understand what my cash flow situation is. Where is it coming in from? Where is it going out of? How do I reconcile across the geographies, lines of business? Because everybody realized that without an adequate cash reserve, who knows how long this thing is going to carry on? We need to be able to survive. And then the fourth element that has always been important for our customers is all about customer engagement, getting the best possible customer experience. That's just being turned up to 11, the volume, because as organizations are saying, there's disruption happening now. There are new ways in which consumers are going out there and buying products and services, and these things might stick. There's also an opportunity for some of these organizations to go out and enter into markets, gain market share, that they were not able to do in the past. And then how do you come out of this, whenever it is, how do we come out of it? It's always by making sure you're retaining your customers and getting more of them. So the underpinnings across all of this, whether it's supplier data, whether it's getting the most accurate product information delivered to your online channels, whether it is being able to understand your supply chain holistically with our data platform under it, and then finally customer experience depends on understanding everything end to end, including everything you need to know about your customer. So data continues to become top of mind for all of these organizations. >> You know, Suresh, we've had conversations over the past three years, and I can remember them vividly all about, and we've been really geeking out, but also getting very industry focused around, oh, the enablement of data and doing all these things, horizontal scalability, application enablement, AI CLAIRE, all these things are very relevant. But now with COVID-19, that that future's been pulled to the present. It's accelerated so fast that everything's impacted the business model. You mentioned supply chain and cash flow. The business is right there visible, and all these things are exposed and heightens the volume, as you said, and so everyone's seeing it happen. They can see the consequences, right? So this is like the most reality view of all time in any kind of is digital transformation, will it happen? So I want to get your thoughts on this, because I've been riffing on this idea of the future of work, the word work, workplaces, workforce, workloads, and workflows, right? So they all have work in them, right? We talk about workflows and workloads. That's a cloud term and a tech term. Workplace is the physical place, now home. Workforce are people, their emotional stability, their engagement. These things are all now exposed and all this new data's coming in. Now the executives have to make these decisions. This has really been a forcing function. So first, I'm sure you agree with all that, but what's your reaction to that? Because this brings up challenges that customers are facing. What's your thoughts on this massive reality? >> Yeah, I mean, this is where I think the other domain that is very important, which is most important for organizations if you have to be successful is really that employee or workforce understanding. We talk about customer 360s. We have to talk about employee 360s, right? And tie that to locations. And there are very few enlightened organizations, I would say, maybe three, four, five years ago, who had said, we really do need to understand everything about employees, where they work from, what are the different locations they go to, whether it's home and whether it's the multiple office locations that the organization might have. And it started quite realistically in the healthcare organization. There's a large healthcare provider here in California who many, many years ago decided that they want to create an employee 360, and considering it's doctors, it's nurses, it's hospital technicians and so on, who move from one hospital to another different outpatient clinics. And we are in a disaster-prone state, and what they said is I need to build this data foundation about my employees to understand where someone is at any given point in time and be able to place them so that if there is, let's say, an earthquake in one part of the state, I want to know who's affected, and more importantly, who's not affected who can go out and help. And we started seeing that mindset now go across every single organization, organizations that said, hey, I was not able to keep track, when the lockdowns were started, I was not able to keep track of which one of my employees were in the air at that time, crossing borders, stuck in different parts of the world. So as much as we talk about product, customer financial data, supplier data, employee data, and an employee 360, and now with a lot of state and local governments creating citizens 360s has also now become top of mind because being able to pull all of this data together, and it's not just your traditional structured data. We're also talking about all the data that you're getting, the interaction data from folks carrying their phones, mobile devices, the swipes that people are doing in and out of locations, being able to capture all of that, tie it all together. Again, we talk about an explosion in volume, which I think is to your point, bringing in more automation with CLAIRE, with artificial intelligence, machine learning techniques, is really the only way to get ahead of this, because it's not humanly possible to say, as your data scales, we need to get the same linearly, the same number of people. That's not going to happen. So technology, AI, has to solve it. >> Well, I want to get to AI in a second. It's on my list to ask you about CLAIRE, get the update there. But you mentioned 360 view of business and the employee angle's definitely relevant. Talk more about this 360 business approach, how are customers approaching it across the enterprise. Certainly now more than ever, it's critical. >> Right, so the 360s have always been around, John, and I think we've had these conversations about 360s now, for the last few years now, and a lot of organizations have gone out and said, create a 360 around a particular, whichever one specific business-critical domain that they want to create a 360 out of. So typically for most organizations, you're buying parts, raw materials from a supplier. So create a supplier 360. You really need to understand is there risk there in the supply chain? Am I allowed to do business with a lot of these suppliers? It's data that helps them create that supplier 360. The product is always important, whether you're manufacturing your own, or if you're a retailer, you're buying these from your suppliers and then selling them via your different channels. And then finally, the third one was always customers, without which none of those organizations would be in business. So customer 360 was always top of mind. But, and there are ancillary domains, whether it's that's the employee 360 we just talked about, finance 360, which are of interest maybe to specific lines of business. These are all being done in silos. If you think about creating a full 360 profile of your suppliers, of your products, of your customers, the industry has been doing it now for a few years, but where this pandemic has really taught a lot of organizations is now it's important to use that platform to start connect (microphone cuts out) a line all the way from your customers via their experience all the way back to your suppliers and all the different functions and domains and 360s that it needs to touch. And the most, I guess real-world example a lot of us had to deal with was the shortages in the grocery stores, right? And that ties all the way back to the supply chain. And you're not providing your best possible customer experience if the goods and products and services that customers want to buy from you are not available. That's when organizations started realizing, we need to start connecting the customer profiles, their preferences, to the products, our inventory, all the way back down to suppliers, and are, for example, can we turn up the production in a particular factory, but maybe that location is under one of the most stringent lockdown conditions and we're not able to bring in or increase capacity there. So how do you get a full 360 across your entire business starting with customer all the way back to supplier. That is what we are saying, the end-to-end 360 view of a business, or as we, there's too many words, we just call it business 360. >> Yeah, it's interesting, and I'm interviewing a lot of your customers lately and talking some of the situations around COVID. There's the pre-COVID, before COVID, during COVID, now looking after COVID. Some have been very happy and well-prepared because they have been using, say, Informatica, and had done the work and are taking advantage of those benefits. I've talked to other practitioners who are struggling with trying to figure out how to architect, because what your customers who've been successful have been telling me is that, look at, we're in good shape right now because we did the work prior to COVID, and now they are being forced to have a 360 view not because it's a holistic corporate mission. It's they have to, right? People are at home, so it's not like, hey, let's get a 360 view of the business and do an assessment and do better and enable things. No, no, no. There's business pressure. So they're enabled. Now new types of data's coming in. So again, back to the catalog and back to some of the things that you guys have been working on. How do you talk to your customers now that they're in COVID for the ones that have been set up before COVID and the ones now that are coming to the table saying, okay, I need to now get quickly deployed with Informatica while I'm in, during the state of COVID so I can have a growth strategy coming out of it, so I don't make these mistakes again. What's your thoughts? >> Absolutely, and I think that the, whether an organization has already, a customer has already laid the groundwork, has the foundation before COVID, and the ones who are now moving full steam ahead because they're missing capabilities in those functions. The conversation is in reality more or less the same, because even for those who have the foundation, what they're starting to see is new forms of data coming in, new forms of, new requirements being placed on the, by the business on that infrastructure, the data infrastructure, and being able to, most importantly, react very, very quickly. And even for those who are starting off right now from scratch, it's the same thing. It's need to get up and running, need to get the answers to these questions, need to get the, we need to get the problems to these solutions as soon as possible. And that the theme, or I guess the talking points for both of those customers is really two things. One is you need agility. You need to be able to bring these solutions up to life and delivering as soon as possible, which means that the capabilities, the solutions you need, whether it's bringing the catalog, understanding where your data is very, very quickly, your business critical information. How do you bring that in, all of that data, and integrate that data into a 360 solution, be able to make sure it's of the highest quality, enrich it, master it, create those 360 profiles by joining it to all of this interaction, transaction data. All that has to be done with the power of technologies like CLAIRE, with artificial intelligence, so that you are up and running in a matter of days or weeks, as opposed to months and years, because you don't have that time. And then the other one which is quite important is cloud, because all of this capability needs infrastructure, hardware to run on. And we've started seeing a lot of, let's say cloud-hesitant verticals, entire verticals now in the last two to three months suddenly going from yeah, cloud is maybe somewhere down the road, as far as our future's concerned. But to now saying, we understand that we have to go to a cloud when our technicians are not able to get access to our data centers to add new machinery in there to take care of the new demands, that migration to cloud. So it's that agility and cloud which really is the common theme when we talk to customers, both- >> Yeah, and now more than ever, they need it, 'cause it's an important time, and it's going to be an inflection point, for sure. There'll be winners and losers, and people want to be on the right side of history here. Suresh, I got to ask you about AI. Obviously CLAIRE's been a big part of it. Now more than ever, if you have bad data, AI can be bad too. So understanding the relationship between data and AI is super important. This is going to be critical to help people move faster and deal with more data as soon as they're dealing with now. What's your thoughts on the role AI will play? >> Oh, AI has a huge role to play. It's already begun to play a huge role in our solutions, whether we start from catalog to integration to 360 solutions. The first thing that AI can really do very, very well is, we've gone from folks who said, let's take supply chain. There were maybe three sources of supplier data that used to come into creating a supplier 360. Today, there are hundreds of sources. If you go all the way to the customer 360, and we are talking about 1,300, 1,400 different sources of data with 90% of them sitting up in the cloud. How is it humanly possible to bring all of that data together? First of all, understand where customer information is sitting across all of those different places, whether it's your clickstream data, call log data, whether it's the actual interaction data that customers are having with in-store, online, collecting all of that information, and from your traditional systems like CRM, ERP, and billing, and all of that, bringing all that together for understanding where it is, catalog gives you that Google for the enterprise view, right? It tells you where all this data is. But then once you've got that there, it also tells you what its relative quality is, what needs to be done to it, how usable is it. To your point of if it's bad data, at least what AI can do first of all is tell you that these are unreliable attributes, these are ones that can be enriched. And then, and this is where AI now moves to the next level, which is to start inferring what kind of rules that are in our, let's say, repository across integration, quality, and mastering, and bring, and matching, bring all that together and say, here, you as the developer who's been tasked with making this happen in a matter of days, we are going to infer for you what you need to do with this data, and then we will be able to go in and bring all these sources in, connect it, load it up into a 360 solution, and create those 360 profiles that everybody downstream, whether it's your engagement systems and other. So it's really about that discovery, that automation, as well as the ability to refine and suggest new rules in order to make your data better and better as you go along. I think that's really the power of CLAIRE and AI. >> I love the Google for the enterprise or data, because the metaphor really is about finding what you're looking for. It's the discovery piece, as you said, to make it easy, and Google did make it easy to find things, which is what their search engine did. But if you look at what Google did after that, they had to have large scales. SREs is what they call them, site reliability engineers, one engineer for thousands and thousands of servers, which they, revolutionizing IT and cloud. You guys are kind of thinking the same way, data scale, right? So it's Google in terms of discovery, right? Find what you're looking for, catalog, get it in, and get it out quest, make it available for applications. But you're kind of teasing out this other point where the AI comes in. That's scale. >> Yes. >> That's super important nuance. >> Absolutely. >> But it's key to the future. >> Absolutely, because when we are starting to talk about now not just tens of millions of records when it comes to customer data or product experience dat and so on. We are already talking about organizations like Dell, for example, with our customer 360, with billions of records going in, which would be equivalent to the scale of, if you look at Google search engine business back maybe 10, 12 years ago. So yes, we are talking about within the context of a single organization or a single company, we're already talking about volumes that were unthinkable even five years ago. So being able to manage that scale, be able to have architectures, technologies that are able to autoscale, and the advantage of course is now we've got an architectural platform that has microservices. As loads start increasing, be able to spawned new instances of those microservices seamlessly. Again, this is another part where AI comes in, AI being able to say, in the old days it was somebody had to see that the CPUs are overloaded to about 100% before someone realized that we have to go out and do something about it. In this new world with AI managing the ops layer, being able to look at is this customer bringing in another, in the cloud rack, cloud world, in a SaaS world, bringing in a billion records that they want to push through in the next 10 minutes, be able to anticipate that, spawn the new infrastructure and the microservices, and be able to take care of that load and then dial those back down when the work is done. This again, from an ops perspective as well, from, so we are able to scale instead of sort of having, let's say, 1000 SREs, I think, to your example, John, have only 10 SREs to make sure that every, look at the dashboard and make sure everything is going well. >> Well, I've been covering you guys for a long time. You guys know that. And I'm a big fan. I always had been a fan of the vision that's playing out. Large scale data, large scale discovery, fast and easy, integrating that into applications for business value. It's not just the data warehouse and just park something over here. This is a mindset. It's a foundational enablement model. You guys have done an amazing job. And now more than ever, it's I think more understood because of the pandemic. >> Absolutely, and people are making that direct connection between the business outcome and the value of having this data foundation that does all the things we described. >> Suresh, great to see you, and bummer we couldn't be in person, but hey, the pandemic hit. Informatica World when virtual. A lot of different events. I know you guys have a lot of things going on virtually, and you're engaging well. Everyone's working at home. Not a problem. Most of the techies can work at home. It's not a big deal. But you've got remote customers. You guys are engaging with them. And congratulations and great to see you. >> Same here. Thank you so much. >> All right, Suresh Menon. He is senior vice president, general manager of master data at Informatica. Data's more important than ever. We're seeing it, this is a foundational thing. If it's not enabling value, then it's not going to be a good solution. This is the new criteria. This is where action matters. People who need data and need to integrate into new workloads, new applications across workforces and new workplaces. This is the reality of the future. I'm John Furrier with theCUBE. Thanks for watching. (bright music)

(ambient music) >> Narrator: From the cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello welcome to this cube conversation. I'm John Furrier, host of theCUBE here in our Palo Alto studios. During this quarantine, crew doing all the interviews, getting all the top story especially during this COVID pandemic. Great conversation here Jitesh Ghai, Senior Vice President and General Manager of Data Management with Informatica, CUBE alumni multi time. We can't be in person this year, because of the pandemic but a lot of great content. We've been doing a lot of interviews with you guys. Jitesh great to see you. Thanks for coming on. >> Hey, great to see you again. We weren't able to make it happen in person this year, >> but if not in person, >> virtually will have to work. >>In our past conversations on theCUBE and through all the Informatica employees it's always been kind of an inside baseball, kind of inside the ropes conversation in the industry >> about data. >> Now more than ever, with the pandemic, you starting to see people seeing it. Oh, I get it now. I get why data is important. I can see why Cloud First, Mobile First, Data First strategies and now Virtual First, is now this transformational scene. Everyone's feeling it, you can't help not ignore it. It's happening. It's also highlighting what's working, what's not. I have to ask you in the current environment Jitesh what are you seeing as some of those opportunities that your customers are dealing with approach to data? 'Cause clearly, you're working with that data layer, there's a lot of innovation opportunities, you've got CLAIRE on the AI side, all great. But now with the pandemic, it's really forcing that conversation. I got to rethink about what's going to happen after and have a really good strategy. >> Yeah, you're exactly right. There's a broad based realization that, I'll take a step back. First, we all know that as global 2000 organizations or in general, we all need to be data driven, we need to make fact based decisions. And there is a lot of that good work that's happened over the last few years as organizations have realized just how important data is to innovate and to deliver new products and services, new business models. What's really happened is that, during this COVID pandemic, there is a greater appreciation for trust in data. Historically, organizations became data driven, we're on the journey of being increasingly data driven. However, there was some element of Oh, gut or experience and that combined with data will get us to the outcomes we're looking for, will enable us to make the decisions. In this pandemic world of great uncertainty, supply chains falling apart on occasion, groceries not getting delivered on time et cetra, et cetra. The appreciation and critical importance on the quality on the trust of data is greater than ever to drive the insights for organizations. Leaders are less hesitant or sorry, leaders are more hesitant to just go with your gut type of approaches. There is a tremendous reliance on data. And we're seeing it in particular, more than ever, as you can imagine in the healthcare provider sector, in the public sector with federal state and local, as all of these organizations are having to make very difficult decisions, and are increasingly relying on high quality, trustworthy governed data to help them make what can be life or death decision. So a big shift and appreciation for the importance and trustworthiness in their data, their data state and their insights. >> So as the GM of data management and Senior Vice President at Informatica, you get a good view of things. I got to ask you love this data 4.0 concept. Talk about what that means to you because you got customers have been doing data management with you guys for a while, but now it's data 4.0 that has a feeling of agility to it. It's got kind of a DevOps vibe. It feels like a lot of automation being discussed and you mentioned trust. What is data 4.0 mean? >> So data 4.0 for us is where AI and ML is powering data management. And so what do I mean by that? There is a greater insight and appreciation for high quality trustworthy data to enable organizations to make fact based decisions to be more data driven. But how do you do that when data is exponentially growing in volume, where data types are increasing, where data is moving increasingly between Clouds, between On-premises and Clouds between various ecosystems, new data sources are emerging, the internet of things is yet another exploding source of data. This is a lot of different types of data, a lot of volume of data, a lot of different locations, and gravity of data where data resides. So the question becomes how do you practically manage this data without intelligence and automation. And that's what the era of data 4.0 is. Where AI and ML is powering data management, making it more intelligent, automating more and more of what was historically manual to enable organizations to scale, to enable them to scale to the breadth of data that they need to get a greater understanding of their data landscape within the enterprise, to get a greater understanding of the quality of the data within their landscape, how it's moving, and the associated privacy implications of how that data is being used, how effectively it's protected, so on and so forth. All underpinned by our CLAIRE engine, which is AI and ML applied to metadata, to deliver the intelligence and enable the automation of the data management operations. >> Awesome. Thanks for taking the time to define that, love that. The question I want to ask you, I'll put you on the spot here because I think this is an important conversation we've been having and also writing a lot about it on siliconangle.com and that is customers say to us, "Hey, John, I'm investing in Cloud Native technologies, using Cloud data warehouse as a data lakes. I need to make this work because this is a scale opportunity. I need to come out of this pandemic with really agile, scalable solutions that I can move fast on my applications." How do you comment on that? What's your thoughts on this because, you guys are in the middle of all this with the data management. >> I couldn't agree more. Increasingly, data workloads are moving to the Cloud. It's projected that by 2022, 75% of all databases will be in the Cloud, and COVID-19 is really accelerating it. It's opening the eyes of leadership of decision makers to be truly Cloud First and Cloud Native, now more than ever. And so organizations, traditional banking organizations, highly regulated industries that have been hesitant to move to the cloud, are now aggressively embarking on that journey. And industries that were early adopters of the Cloud are now accelerating that journey. I mentioned earlier that, we had a very seamless transition as we moved to a work from home environment, and that's because our IT is Cloud First Cloud Native. And why is that? It's because it's through being Cloud First and Cloud Native that you get the resiliency, the agility, the flexibility benefits in these uncertain times. And we're seeing that with the data and analytics stack as well. Customers are accelerating the move to Cloud data warehouses to Cloud data lakes, and become Cloud Native for their data management stack in addition to the data analytics platforms. >> Great stuff which I agree with hundred percent. Cloud Native is where it goes but you aren't they're (laughs) yet. Still on Hybrid and Multi-cloud is a big discussion. I want to get your thoughts >> Completely. >> On how that's going to play up because if you put Hybrid cloud and Multi-cloud I see Public cloud it's amazing, we know that. But Hybrid and Multi-cloud as the next generation of kind of interoperability framework of Cloud services, you're going to have to overlay and manage data governance and privacy. It's going to get more complicated, right? So how are you seeing your customers approach that piece, on the Public side, and then with Hybrid, because that's become a big discussion point. >> So Hybrid is an absolutely critical enabling capability as organizations modernize their on premise estate into the Cloud. You need to be able to move and connect to your On-premise applications, databases, and migrate the data that's important into the Cloud. So Hybrid is an essential capability. When I say Informatica is Cloud First Cloud Native, being Cloud First Cloud Native as a data management as a service provider if you will, requires essentially capabilities of being able to connect to On-premise data sources and therefore, be Hybrid. So Hybrid architecture is an essential part of that. Equally, it's important to enable organizations to understand what needs to go to the Cloud. As you're modernizing your infrastructure, your applications, your data and analytics stack. You don't need to bring everything to the Cloud with you. So there's an opportunity for organizations to introduce efficiencies. And that's done by enabling organizations to really scan the data landscape On-premise, scan the data that already exists in the various Public clouds that they partner with, and understand what's important, what's not, what can be decommissioned and left behind to realize savings and what is important for the business and needs to be moved into a Cloud Native analytic stack. And that's really where our CLAIRE metadata intelligence capabilities come to bear. And that's really what serves as the foundation of data governance, data cataloging and data privacy, to enable organizations to get the right data into the Cloud. To do so, while ensuring privacy. And to ensure that they govern that data in their new now Cloud Native analytics stack, whether it's AWS, Azure, GCP, snowflake data, bricks, all partners, all deep partnerships that we have. >> Jitesh, I want to get your thoughts on something. I was having a Zoom call a couple weeks ago, with a bunch of CXO friends, people, practitioners, probably some of them are probably your customers. It was kind of a social get together. But we were talking about, how the world we're living in pandemic, from COVID data, fake news, and one of the comments was, finally the whole world now realized what my life like. And in referring to how we're seeing fake news and misinformation kind of screw up an election and you got COVID's got 10 zillion different data points and people are making it to tell stories. And what does it really mean? There's a lot of trust involved. People are confused, and all that's going on. Again, in that backdrop, he said that that's my world. >> Right. This is back down to some of the things you're talking about, trust. We've talked about metadata services in the past. This authenticity, the duck democratization has been around for a while in the enterprise, so that dealing with bad data or fake data or too much data, you can make data (laughs) into whatever you want. You got to make sense of it. What's your thoughts on the reaction to his comment? I mean, what does it make you feel? >> Completely agree, completely agree. And that goes back to the earlier comment I made about making fact based decisions that you can have confidence in because the insight is based on trusted data. And so you mentioned data democratization. Our point of view is to democratize data, you have to do it on a foundational governance, right? There's a reason why traffic lights exist, it's to facilitate or at least attempt to facilitate the optimal free flow of traffic without getting into accidents, without causing congestion, so on and so forth. Equally, you need to have a foundation of governance. And I realized that there's an optical tension of democratized data, which is, free data for everybody consume it whenever and however you want, and then governance, which seems to imply, locking things down controlling them. And really, when I say you need a foundation of data governance, you need to enable for organizations to implement guardrails so that data can be effectively democratized. So that data consumers can easily find data. They can understand how trustworthy it is, what the quality of it is, and they can access it in easy way and consume it, while adhering to the appropriate privacy policies that are fit for the use of that particular set of data that a data and data consumer wants to access. And so, how do you practically do that? That's where data 4.0 AI power data management comes into play. In that, you need to build a foundation of what we call intelligent data governance. A foundation of scanning metadata, combining it with business metadata, linking it into an enterprise knowledge graph that gives you an understanding of an organization and enterprises data language. It auto tags auto curates, it gives you insight into the quality of the data, and now enables organizations to publish these curated data sets into a capability, what we call a data marketplace, so that much like Amazon.com, you can shop for the data, you can browse home and garden, electronics various categories. You can identify the data sets that are interesting to you, when you select them, you can look at the quality dimensions that have already been analyzed and associated with the data set. And you can also review the privacy policies that govern the use of that data set. And if you're interested in it, find the data sets, add them to your shopping cart, like you would do with Amazon.com, and check out. And when you do that triggers off an approval workflow to enable organizations to that last mile of governing access. And once approved, we can automatically provision the datasets to wherever you want to analyze them, whether it's in Tableau Power BI, an S3 market, what have you. And that is what I mean by a foundation of intelligent data governance. That is enabling data democratization. >> A common metadata layer gives you capabilities to use AI, I get that, There's a concept that you guys are talking a lot about, this augmentation to the data. This augmented data management activities that go on. What does that mean? Can you describe and explain that further and unpack that? This augmented data management activity? >> Yeah, and what do we mean by augmented data management, it's a really a first step into full blown automation of data management. In the old world, a developer would connect to a source, parse the source schema, connect to another source, parse its source schema, connect to the target, understand the target schema, and then pick the appropriate fields from the various sources, structure it through a mapping and then run a job that transforms the data and delivers it to a target database, in its structure, in its schema, in its format. Now that we have enterprise scale metadata intelligence, we know what source of data looks like, we know what targets exist as you simply pick sources and targets, we're able to automatically generate the mappings and automate this development part of the process so that organizations can more rapidly build out data pipelines to support their AI to operationalize AIML, to enable data science, and to enable analytics. >> Jitesh great insight. I really appreciate you explaining all this concept and unpacking that with me. Final point, I'd love you to have you just take a minute to put the plug in there for Informatica, what you're working on? What are your customers doing? What are some of the best practices coming out of the current situation? Take a minute to talk about that. >> Yeah, thank you, I'm happy to. It really comes down to focusing on enabling organizations to have a complete understanding of their data landscape. And that is, where we're enabling organizations to build an enterprise knowledge graph of technical metadata, business metadata, operational usage metadata, social metadata to understand and link and develop the necessary context to understand what data exists, where how it's used, what its purpose is and whether or not you should be using. And that's where we're building the Google for the enterprise to help organizations develop that. Equally, leveraging that insight, we're building out the necessary that insight and intelligence through CLAIRE, we're building out the automation in the data quality capabilities, in the data integration capabilities, in the metadata management capabilities, in the master data management capabilities, as well as the data privacy capability. So things that our tooling historically used to do manually, we're just automating it so that organizations can more productively access data, understand it and scale their understanding and insight and analytics initiatives with greater trust greater insight. It's all built on a foundation of our intelligent data platform. >> Love it, scaling data. It's that's really the future fast, available, highly available, integrated to the applications for AI. That's the future. >> Exactly right. Data 4.0, (laughs) AI power data management. >> I love talking about data in the future, because I think that's really valuable. And I think developers, and I've always been saying for over a decade now data is a critical piece for the applications, and AI really unlocks that of having it available, and surface is critical. You guys doing a great job. Thanks for the insight, appreciate you Jitesh. Thank you for coming on. >> Thanks for having me. Pleasure to be here. >> You couldn't do it in person with Informatica world but we're getting the conversations here on the remote CUBE, CUBE virtual. I'm John Furrier, you're watching CUBE conversation with Jitesh Ghai Senior Vice President General Manager, Data Manager at Informatica. Thanks for watching. (upbeat music)

(ambient music) >> Narrator: From the cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello welcome to this cube conversation. I'm John Furrier, host of theCUBE here in our Palo Alto studios. During this quarantine, crew doing all the interviews, getting all the top story especially during this COVID pandemic. Great conversation here Jitesh Ghai, Senior Vice President and General Manager of Data Management with Informatica, CUBE alumni multi time. We can't be in person this year, because of the pandemic but a lot of great content. We've been doing a lot of interviews with you guys. Jitesh great to see you. Thanks for coming on. >> Hey, great to see you again. We weren't able to make it happen in person this year, but if not in person, virtually will have to work. >> One of the things, I'm a half glass half full kind of guy but you can't look at this without saying man, it's bad. But it really highlights how things are going on. So first, how are you doing? How's everyone Informatica doing over there? You guys are doing okay? >> We are well, we are well, families well, the Informatica family is well. So overall, can't complain can't complain, I think it was remarkable how quickly we were able to transition to a work from home environment for our global 5000 plus organization. And really, the fact that we're Cloud First Cloud Native, both in our product offerings, as well as an IT organization really helped make that transition seamless. >> In our past conversations on theCUBE and through all the Informatica employees it's always been kind of an inside baseball, kind of inside the ropes conversation in the industry about data. Now more than ever, with the pandemic, you starting to see people seeing it. Oh, I get it now. I get why data is important. I can see why Cloud First, Mobile First, Data First strategies and now Virtual First, is now this transformational scene. Everyone's feeling it, you can't help not ignore it. It's happening. It's also highlighting what's working, what's not. I have to ask you in the current environment Jitesh what are you seeing as some of those opportunities that your customers are dealing with approach to data? 'Cause clearly, you're working with that data layer, there's a lot of innovation opportunities, you've got CLAIRE on the AI side, all great. But now with the pandemic, it's really forcing that conversation. I got to rethink about what's going to happen after and have a really good strategy. >> Yeah, you're exactly right. There's a broad based realization that, I'll take a step back. First, we all know that as global 2000 organizations or in general, we all need to be data driven, we need to make fact based decisions. And there is a lot of that good work that's happened over the last few years as organizations have realized just how important data is to innovate and to deliver new products and services, new business models. What's really happened is that, during this COVID pandemic, there is a greater appreciation for trust in data. Historically, organizations became data driven, we're on the journey of being increasingly data driven. However, there was some element of Oh, gut or experience and that combined with data will get us to the outcomes we're looking for, will enable us to make the decisions. In this pandemic world of great uncertainty, supply chains falling apart on occasion, groceries not getting delivered on time et cetra, et cetra. The appreciation and critical importance on the quality on the trust of data is greater than ever to drive the insights for organizations. Leaders are less hesitant or sorry, leaders are more hesitant to just go with your gut type of approaches. There is a tremendous reliance on data. And we're seeing it in particular, more than ever, as you can imagine in the healthcare provider sector, in the public sector with federal state and local, as all of these organizations are having to make very difficult decisions, and are increasingly relying on high quality, trustworthy governed data to help them make what can be life or death decision. So a big shift and appreciation for the importance and trustworthiness in their data, their data state and their insights. >> So as the GM of data management and Senior Vice President at Informatica, you get a good view of things. I got to ask you love this data 4.0 concept. Talk about what that means to you because you got customers have been doing data management with you guys for a while, but now it's data 4.0 that has a feeling of agility to it. It's got kind of a DevOps vibe. It feels like a lot of automation being discussed and you mentioned trust. What is data 4.0 mean? >> So data 4.0 for us is where AI and ML is powering data management. And so what do I mean by that? There is a greater insight and appreciation for high quality trustworthy data to enable organizations to make fact based decisions to be more data driven. But how do you do that when data is exponentially growing in volume, where data types are increasing, where data is moving increasingly between Clouds, between On-premises and Clouds between various ecosystems, new data sources are emerging, the internet of things is yet another exploding source of data. This is a lot of different types of data, a lot of volume of data, a lot of different locations, and gravity of data where data resides. So the question becomes how do you practically manage this data without intelligence and automation. And that's what the era of data 4.0 is. Where AI and ML is powering data management, making it more intelligent, automating more and more of what was historically manual to enable organizations to scale, to enable them to scale to the breadth of data that they need to get a greater understanding of their data landscape within the enterprise, to get a greater understanding of the quality of the data within their landscape, how it's moving, and the associated privacy implications of how that data is being used, how effectively it's protected, so on and so forth. All underpinned by our CLAIRE engine, which is AI and ML applied to metadata, to deliver the intelligence and enable the automation of the data management operations. >> Awesome. Thanks for taking the time to define that, love that. The question I want to ask you, I'll put you on the spot here because I think this is an important conversation we've been having and also writing a lot about it on siliconangle.com and that is customers say to us, "Hey, John, I'm investing in Cloud Native technologies, using Cloud data warehouse as a data lakes. I need to make this work because this is a scale opportunity. I need to come out of this pandemic with really agile, scalable solutions that I can move fast on my applications." How do you comment on that? What's your thoughts on this because, you guys are in the middle of all this with the data management. >> I couldn't agree more. Increasingly, data workloads are moving to the Cloud. It's projected that by 2022, 75% of all databases will be in the Cloud, and COVID-19 is really accelerating it. It's opening the eyes of leadership of decision makers to be truly Cloud First and Cloud Native, now more than ever. And so organizations, traditional banking organizations, highly regulated industries that have been hesitant to move to the cloud, are now aggressively embarking on that journey. And industries that were early adopters of the Cloud are now accelerating that journey. I mentioned earlier that, we had a very seamless transition as we moved to a work from home environment, and that's because our IT is Cloud First Cloud Native. And why is that? It's because it's through being Cloud First and Cloud Native that you get the resiliency, the agility, the flexibility benefits in these uncertain times. And we're seeing that with the data and analytics stack as well. Customers are accelerating the move to Cloud data warehouses to Cloud data lakes, and become Cloud Native for their data management stack in addition to the data analytics platforms. >> Great stuff which I agree with hundred percent. Cloud Native is where it goes but you aren't they're (laughs) yet. Still on Hybrid and Multi-cloud is a big discussion. I want to get your thoughts >> Completely. >> On how that's going to play up because if you put Hybrid cloud and Multi-cloud I see Public cloud it's amazing, we know that. But Hybrid and Multi-cloud as the next generation of kind of interoperability framework of Cloud services, you're going to have to overlay and manage data governance and privacy. It's going to get more complicated, right? So how are you seeing your customers approach that piece, on the Public side, and then with Hybrid, because that's become a big discussion point. >> So Hybrid is an absolutely critical enabling capability as organizations modernize their on premise estate into the Cloud. You need to be able to move and connect to your On-premise applications, databases, and migrate the data that's important into the Cloud. So Hybrid is an essential capability. When I say Informatica is Cloud First Cloud Native, being Cloud First Cloud Native as a data management as a service provider if you will, requires essentially capabilities of being able to connect to On-premise data sources and therefore, be Hybrid. So Hybrid architecture is an essential part of that. Equally, it's important to enable organizations to understand what needs to go to the Cloud. As you're modernizing your infrastructure, your applications, your data and analytics stack. You don't need to bring everything to the Cloud with you. So there's an opportunity for organizations to introduce efficiencies. And that's done by enabling organizations to really scan the data landscape On-premise, scan the data that already exists in the various Public clouds that they partner with, and understand what's important, what's not, what can be decommissioned and left behind to realize savings and what is important for the business and needs to be moved into a Cloud Native analytic stack. And that's really where our CLAIRE metadata intelligence capabilities come to bear. And that's really what serves as the foundation of data governance, data cataloging and data privacy, to enable organizations to get the right data into the Cloud. To do so, while ensuring privacy. And to ensure that they govern that data in their new now Cloud Native analytics stack, whether it's AWS, Azure, GCP, snowflake data, bricks, all partners, all deep partnerships that we have. >> Jitesh, I want to get your thoughts on something. I was having a Zoom call a couple weeks ago, with a bunch of CXO friends, people, practitioners, probably some of them are probably your customers. It was kind of a social get together. But we were talking about, how the world we're living in pandemic, from COVID data, fake news, and one of the comments was, finally the whole world now realized what my life like. And in referring to how we're seeing fake news and misinformation kind of screw up an election and you got COVID's got 10 zillion different data points and people are making it to tell stories. And what does it really mean? There's a lot of trust involved. People are confused, and all that's going on. Again, in that backdrop, he said that that's my world. >> Right. This is back down to some of the things you're talking about, trust. We've talked about metadata services in the past. This authenticity, the duck democratization has been around for a while in the enterprise, so that dealing with bad data or fake data or too much data, you can make data (laughs) into whatever you want. You got to make sense of it. What's your thoughts on the reaction to his comment? I mean, what does it make you feel? >> Completely agree, completely agree. And that goes back to the earlier comment I made about making fact based decisions that you can have confidence in because the insight is based on trusted data. And so you mentioned data democratization. Our point of view is to democratize data, you have to do it on a foundational governance, right? There's a reason why traffic lights exist, it's to facilitate or at least attempt to facilitate the optimal free flow of traffic without getting into accidents, without causing congestion, so on and so forth. Equally, you need to have a foundation of governance. And I realized that there's an optical tension of democratized data, which is, free data for everybody consume it whenever and however you want, and then governance, which seems to imply, locking things down controlling them. And really, when I say you need a foundation of data governance, you need to enable for organizations to implement guardrails so that data can be effectively democratized. So that data consumers can easily find data. They can understand how trustworthy it is, what the quality of it is, and they can access it in easy way and consume it, while adhering to the appropriate privacy policies that are fit for the use of that particular set of data that a data and data consumer wants to access. And so, how do you practically do that? That's where data 4.0 AI power data management comes into play. In that, you need to build a foundation of what we call intelligent data governance. A foundation of scanning metadata, combining it with business metadata, linking it into an enterprise knowledge graph that gives you an understanding of an organization and enterprises data language. It auto tags auto curates, it gives you insight into the quality of the data, and now enables organizations to publish these curated data sets into a capability, what we call a data marketplace, so that much like Amazon.com, you can shop for the data, you can browse home and garden, electronics various categories. You can identify the data sets that are interesting to you, when you select them, you can look at the quality dimensions that have already been analyzed and associated with the data set. And you can also review the privacy policies that govern the use of that data set. And if you're interested in it, find the data sets, add them to your shopping cart, like you would do with Amazon.com, and check out. And when you do that triggers off an approval workflow to enable organizations to that last mile of governing access. And once approved, we can automatically provision the datasets to wherever you want to analyze them, whether it's in Tableau Power BI, an S3 market, what have you. And that is what I mean by a foundation of intelligent data governance. That is enabling data democratization. >> A common metadata layer gives you capabilities to use AI, I get that, There's a concept that you guys are talking a lot about, this augmentation to the data. This augmented data management activities that go on. What does that mean? Can you describe and explain that further and unpack that? This augmented data management activity? >> Yeah, and what do we mean by augmented data management, it's a really a first step into full blown automation of data management. In the old world, a developer would connect to a source, parse the source schema, connect to another source, parse its source schema, connect to the target, understand the target schema, and then pick the appropriate fields from the various sources, structure it through a mapping and then run a job that transforms the data and delivers it to a target database, in its structure, in its schema, in its format. Now that we have enterprise scale metadata intelligence, we know what source of data looks like, we know what targets exist as you simply pick sources and targets, we're able to automatically generate the mappings and automate this development part of the process so that organizations can more rapidly build out data pipelines to support their AI to operationalize AIML, to enable data science, and to enable analytics. >> Jitesh great insight. I really appreciate you explaining all this concept and unpacking that with me. Final point, I'd love you to have you just take a minute to put the plug in there for Informatica, what you're working on? What are your customers doing? What are some of the best practices coming out of the current situation? Take a minute to talk about that. >> Yeah, thank you, I'm happy to. It really comes down to focusing on enabling organizations to have a complete understanding of their data landscape. And that is, where we're enabling organizations to build an enterprise knowledge graph of technical metadata, business metadata, operational usage metadata, social metadata to understand and link and develop the necessary context to understand what data exists, where how it's used, what its purpose is and whether or not you should be using. And that's where we're building the Google for the enterprise to help organizations develop that. Equally, leveraging that insight, we're building out the necessary that insight and intelligence through CLAIRE, we're building out the automation in the data quality capabilities, in the data integration capabilities, in the metadata management capabilities, in the master data management capabilities, as well as the data privacy capability. So things that our tooling historically used to do manually, we're just automating it so that organizations can more productively access data, understand it and scale their understanding and insight and analytics initiatives with greater trust greater insight. It's all built on a foundation of our intelligent data platform. >> Love it, scaling data. It's that's really the future fast, available, highly available, integrated to the applications for AI. That's the future. >> Exactly right. Data 4.0, (laughs) AI power data management. >> I love talking about data in the future, because I think that's really valuable. And I think developers, and I've always been saying for over a decade now data is a critical piece for the applications, and AI really unlocks that of having it available, and surface is critical. You guys doing a great job. Thanks for the insight, appreciate you Jitesh. Thank you for coming on. >> Thanks for having me. Pleasure to be here. >> You couldn't do it in person with Informatica world but we're getting the conversations here on the remote CUBE, CUBE virtual. I'm John Furrier, you're watching CUBE conversation with Jitesh Ghai Senior Vice President General Manager, Data Manager at Informatica. Thanks for watching. (upbeat music)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to again sunset legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, This is John Furrier here inside theCUBE studios we serve 85 of the Fortune 100, over half the global 2000. for an update with Informatica's digital conference. The reason why customers come back and speak on our behalf Take a look at it, check it out online. and literally thousands of customers speak on our behalf, Join the community. Be part of those thousands of customers that they have, it's humbling, is because we have the best and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. intelligent data platform in the market. I'm John Furrier, thanks for watching. And we also understand our customers aren't buying software. (soft music) They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight.

>> 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)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to gain some legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, we serve 85 of the Fortune 100, over half the global 2000. The reason why customers come back and speak on our behalf and literally thousands of customers speak on our behalf, it's humbling, is because we have the best intelligent data platform in the market. And we also understand our customers aren't buying software. They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight. One final personal question I want to ask you. I've been following you guys for a long time, and we've had you on theCUBE many times. You've been a seasoned veteran in the industry. You've seen cycles of innovation. You've seen the ups and downs over the years. You've been on boards, you've been a leader, a senior leader. What do you talk about with your friends and peers when you look at this current inflection point? As there's the candid conversations are happening, it's really an opportunity, but also there are serious challenges. As a leader, how should leaders be thinking about getting through this? What's your personal view? You've seen many cycles. You've see many waves. This wave coming is going to be big. This change is certainly going to create an uptick, we believe, exponentially a step function transformation. What's your view? What are some of the conversations that you're having with your friends, peers around what to do? >> Yeah, so I think in any situation like the one that we're in, it's important first and foremost to take care of the employees, take care of the customers, take care of the short term needs. That's critical. And yet at the same time in parallel, to be thinking longer term because there is an opportunity when you go through a situation like this to regroup and to think about, what will be the key markets that come back the fastest? What will be your differentiation, your company's differentiation so that you come out of this when the market does start to rebound and really thriving. So it's always this constant balance of how you deal with the short-term and the realities that we're in because people are making some tough decisions. And yet at the same time, make sure that you're very clear on your long-term strategy so that you can come out of this swinging. >> Great advice. That's a masterclass right there. Thank you for sharing that. Of course, check out Informatica's CLAIREview event. Of course, the digital events are always online. Check them out. Tracey, thanks for your time and thanks for that insight and update, appreciate it. >> Yeah, great to be here, John. Look forward to seeing you in person soon. >> Okay, take care. This is John Furrier here inside theCUBE studios for an update with Informatica's digital conference. Take a look at it, check it out online. Join the community. Be part of those thousands of customers that they have, and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. I'm John Furrier, thanks for watching. (soft 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]

(upbeat music) >> From the Silicon Angle Media office in Boston, Massachusetts, it's theCube. Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman, and welcome to the first interview of theCube in our Boston area studio for 2020. And to help me kick it off, Jeremy Daly who is the host of Serverless Chats as well as runs the Serverless Day Boston. Jeremy, saw you at reInvent, way back in 2019, and we'd actually had some of the people in the community that were like hey, "I think you guys like actually live and work right near each other." >> Right. >> And you're only about 20 minutes away from our office here, so thanks so much for making the long journey here, and not having to get on a plane to join us here. >> Well, thank you for having me. >> All right, so as Calvin from Calvin and Hobbes says, "It's a new decade, but we don't have any base on the moon, "we don't have flying cars that general people can use, "but we do have serverless." >> And our robot vacuum cleaners. >> We do have robot vacuum cleaners. >> Which are run by serverless, as a matter of fact. >> A CUBE alum on the program would be happy that we do get to mention there. So yeah, you know serverless there are things like the iRobot, as well as Alexa, or some of the things that people, you know usually when I'm explaining to people what this is, and they don't understand it, it's like, Oh, you've used Alexa, well those are the functions underneath, and you think about how these things turn on, and off, a little bit like that. But maybe, we don't need to get into the long ontological discussion or everything, but you know you're a serverless hero, so you know give us a little bit, what your hearing from people, what are some of the exciting use cases out there, and you know where serverless is being used in that maturity today. >> Yeah, I mean well, so the funny thing about serverless and the term serverless itself, and I do not want to get into a long discussion about this, obviously. I actually wrote a post last year that was called stop calling everything serverless, because basically people are calling everything serverless. So it really, what it, what I look at it as, is something where, it just makes it really easy for developers to abstract away that back end infrastructure, and not having to worry about setting up Kubernetes, or going through the process of setting up virtual machines and installing software is just, a lot of that stuff is kind of handled for you. And I think that is enabled, a lot of companies, especially start-ups is a huge market for serverless, but also enterprises. Enabled them to give more power to their developers, and be able to look at new products that they want to build, new services they want to tackle or even old services that they need to, you know that may have some stability issues or things like long running ETL tasks, and other things like that, that they found a way to sort of find the preferal edges of these monolithic applications or these mainframes that they are using and find ways to run very small jobs, you know using functions as a server, something like that. And so, I see a lot of that, I think that is a big use case. You see a lot of large companies doing. Obviously, people are building full fledged applications. So, yes, the web facing user application, certainly a thing. People are building API's, you got API Gateway, they just released the new HEDP API which makes it even faster. To run those sort of things, this idea of cold starts, you know in AWS trying to get rid of all that stuff, with the new VPC networking, and some of the things they are doing there. So you have a lot of those type of applications that people are building as well. But it really runs the gambit, there are things all across the board that you can do, and pretty much anything you can do with the traditional computing environment, you can do with a serverless computing environment. And obviously that's focusing quite a bit on the functions as a service side of things, which is a very tiny part of serverless, if you want to look at it, you know sort of the broader picture, this service full or managed services, type approach. And so, that's another thing that you see, where you used to have companies setting up you know, mySQL databases and clusters trying to run these things, or even worse, Cassandra rings, right. Trying to do these things and manage this massive amount of infrastructure, just so that they could write a few records to a database and read them back for their application. And that would take months sometimes, for them to get it setup and even more time to try to keep running them. So this sort of revolution of managed services and all these things we get now, whether that the things like managed elastic search or elastic search cloud doing that stuff for you, or Big Table and Dynamo DB, and Manage Cassandra, whatever those things are. I'm just thinking a lot easier for developers to just say hey, I need a database, and okay, here it is, and I don't have to worry about the infrastructure at all. So, I think you see a lot of people, and a lot of companies that are utilizing all of these different services now, and essentially are no longer trying to re-invent the wheel. >> So, a couple of years ago, I was talking to Andy Jassy, at an interview with theCube, and he said, "If I was to build AWS today, "I would've built it on serverless." And from what I've seen over the last two or three years or so, Amazon is rebuilding a lot of there servers underneath. It's very interesting to watch that platform changing. I think it's had some ripple effect dynamics inside the company 'cause Amazon is very well known for their two pizza teams and for all of their products are there, but I think it was actually in a conversation with you, we're talking about in some ways this new way of building things is, you know a connecting fabric between the various groups inside of Amazon. So, I love your view point that we shouldn't just call everything serverless, but in many ways, this is a revolution and a new way of thinking about building things and therefore, you know there are some organizational and dynamical changes that happen, for an Amazon, but for other people that start using it. >> Yeah, well I mean I actually was having a conversation with a Jay Anear, whose one of the product owners for Lambda, and he was saying to me, well how do we sell serverless. How do we tell people you know this is what the next way to do things. I said, just, it's the way, right. And Amazon is realized this, and part of the great thing about dog fooding your own product is that you say, okay I don't like the taste of this bit, so we're going to change it to make it work. And that's what Amazon has continued to do, so they run into limitations with serverless, just like us early adopters, run into limitations, and they say, we'll how do we make it better, how do we fix it. And they have always been really great to listening to customers. I complain all the time, there's other people that complain all the time, that say, "Hey, I can't do this." And they say, "Well what if we did it this way, and out of that you get things like Lambda Destinations and all different types of ways, you get Event Bridge, you get different ways that you can solve those problems and that comes out of them using their own services. So I think that's a huge piece of it, but that helps enable other teams to get past those barriers as well. >> Jeremy, I'm going to be really disappointed if in 2020, I don't see a T-shirt from one of the Serverless Days, with the Mandalorian on it, saying, "Serverless, this is the way." Great, great, great marketing opportunity, and I do love that, because some of the other spaces, you know we're not talking about a point product, or a simple thing we do, it is more the way of doing things, it's just like I think about Cybersecurity. Yes, there are lots of products involved here but, you know this is more of you know it's a methodology, it needs to be fully thought of across the board. You know, as to how you do things, so, let's dig in a little bit. At reInvent, there was, when I went to the serverless gathering, it was serverless for everyone. >> Serverless for everyone, yes. >> And there was you know, hey, serverless isn't getting talked, you know serverless isn't as front and center as some people might think. They're some people on the outside look at this and they say, "Oh, serverless, you know those people "they have a religion, and they go so deep on this." But I thought Tim Wagner had a really good blog post, that came out right after reInvent, and what we saw is not only Amazon changing underneath the way things are done, but it feel that there's a bridging between what's happening in Kubernetes, you see where Fargate is, Firecracker, and serverless and you know. Help us squint through that, and understand a little bit, what your seeing, what your take was at reInvent, what you like, what you were hoping to see and how does that whole containerization, and Kubernetes wave intersect with what we're doing with serverless? >> Yeah, well I mean for some reason people like Kubernetes. And I honestly, I don't think there is anything wrong with it, I think it's a great container orchestration system, I think containers are still a very important part of the workloads that we are putting into a cloud, I don't know if I would call them cloud native, exactly, but I think what we're seeing or at least what I'm seeing that I think Amazon is seeing, is they're saying people are embracing Kubernetes, and they are embracing containers. And whether or not containers are ephemeral or long running, which I read a statistic at some point, that was 63% of containers, so even running on Kubernetes, or whatever, run for less than 10 minutes. So basically, most computing that's happening now, is fairly ephemeral. And as you go up, I think it's 15 minutes or something like that, I think it's 70% or 90% or whatever that number is, I totally got that wrong. But I think what Amazon is doing is they're trying to basically say, look we were trying to sell serverless to everyone. We're trying to sell this idea of look managed services, managed compute, the idea that we can run even containers as close to the metal as possible with something like Fargate which is what Firecracker is all about, being able to run virtual machines basically, almost you know right on the metal, right. I mean it's so close that there's no level of abstraction that get in the way and slow things down, and even though we're talking about milliseconds or microseconds, it's still something and there's efficiencies there. But I think what they looked at is, they said look at we are not Apple, we can't kill Flash, just because we say we're not going to support it anymore, and I think you mention this to me in the past where the majority of Kubernetes clusters that were running in the Public Cloud, we're running in Amazon anyways. And so, you had using virtual machines, which are great technology, but are 15 years old at this point. Even containerization, there's more problems to solve there, getting to the point where we say, look you want to take this container, this little bit of code, or this small service and you want to just run this somewhere. Why are we spinning up virtual containers. Why are we using 15 or 10 year old technology to do that. And Amazon is just getting smarter about it. So Amazon says hay, if we can run a Lambda function on Firecracker, and we can run a Fargate container on Firecracker, why can't we run, you know can we create some pods and run some pods for Kubernetes on it. They can do that. And so, I think for me, I was disappointed in the keynotes, because I don't think there was enough serverless talk. But I think what they're trying to do, is there trying to and this is if I put my analyst hat on for a minute. I think they're trying to say, the world is at Kubernetes right now. And we need to embrace that in a way, that says we can run your Kubernetes for you, a lot more efficiently and without you having to worry about it than if you use Google or if you use some other cloud provider, or if you run on-prem. Which I think is the biggest competitor to Amazon is still on-prem, especially in the enterprise world. So I see them as saying, look we're going to focus on Kubernetes, but as a way that we can run it our way. And I think that's why, Fargate and Kubernetes, or the Kubernetes for Fargate, or whatever that new product is. Too many product names at AWS. But I think that's what they are trying to do and I think that was the point of this, is to say, "Listen you can run your Kubernetes." And Claire Legore who showed that piece at the keynote, Vernor's keynote that was you know basically how quickly Fargate can scale up Kubernetes, you know individual containers, Kubernetes, as opposed to you know launching new VM's or EC2 instances. So I thought that was really interesting. But that was my overall take is just that they're embracing that, because they think that's where the market is right now, and they just haven't yet been able to sell this idea of serverless even though you are probably using it with a bunch of things anyways, at least what they would consider serverless. >> Yeah, to part a little bit from the serverless for a second. Talk about multi-cloud, it was one of the biggest discussions, we had in 2019. When I talk to customers that are using Kubernetes, one of the reasons that they tell me they're doing it, "Well, I love Amazon, I really like what I'm doing, "but if I needed to move something, it makes it easier." Yes, there are some underlying services I would have to re-write, and I'm looking at all those. I've talked to customers that started with Kubernetes, somewhere other than Amazon, and moved it to Amazon, and they said it did make my life easier to be able to do that fundamental, you know the container piece was easy move that piece of it, but you know the discussion of multi-cloud gets very convoluted, very easily. Most customers run it when I talk to them, it's I have an application that I run, in a cloud, sometimes, there's certain, you know large financials will choose two of everything, because that's the way they've always done things for regulation. And therefore they might be running the same application, mirrored in two different clouds. But it is not follow the sun, it is not I wake up and I look at the price of things, and deploy it to that. And that environment it is a little bit tougher, there's data gravity, there's all these other concerns. But multi-cloud is just lots of pieces today, more than a comprehensive strategy. The vision that I saw, is if multi-cloud is to be a successful strategy, it should be more valuable than the sum of its pieces. And I don't see many examples of that yet. What do you see when it comes to multi-cloud and how does that serverless discussion fit in there? >> I think your point about data gravity is the most important thing. I mean honestly compute is commoditized, so whether your running it in a container, and that container runs in Fargate or orchestrated by Kubernetes, or runs on its own somewhere, or something's happening there, or it's a fast product and it's running on top of K-native or it's running in a Lambda function or in an Azure function or something like that. Compute itself is fairly commoditized, and yes there's wiring that's required for each individual cloud, but even if you were going to move your Kubernetes cluster, like you said, there's re-writes, you have to change the way you do things underneath. So I look at multi-cloud and I think for a large enterprise that has a massive amount of compliance, regulations and things like that they have to deal with, yeah maybe that's a strategy they have to embrace, and hopefully they have the money and tech staff to do that. I think the vast majority of companies are going to find that multi-cloud is going to be a completely wasteful and useless exercise that is essentially going to waste time and money. It's so hard right now, keeping up with everything new that comes out of one cloud right, try keeping up with everything that comes out of three clouds, or more. And I think that's something that doesn't make a lot of sense, and I don't think you're going to see this price gauging like we would see with something. Probably the wrong term to use, but something that we would see, sort of lock-in that you would see with Oracle or with Microsoft SQL, some of those things where the licensing became an issue. I don't think you're going to see that with cloud. And so, what I'm interested in though in terms of the term multi-cloud, is the fact that for me, multi-cloud really where it would be beneficial, or is beneficial is we're talking about SaaS vendors. And I look at it and I say, look it you know Oracle has it's own cloud, and Google has it's own cloud, and all these other companies have their own cloud, but so does Salesforce, when you think about it. So does Twilio, even though Twilio runs inside AWS, really its I'm using that service and the AWS piece of it is abstracted, that to me is a third party service. Stripe is a third-party service. These are multi-cloud structure or SaaS products that I'm using, and I'm going to be integrating with all those different things via API's like we've done for quite some time now. So, to me, this idea of multi-cloud is simply going to be, you know it's about interacting with other products, using the right service for the right job. And if your duplicating your compute or you're trying to write database services or something like that that you can somehow share with multiple clouds, again, I don't see there being a huge value, except for a very specific group of customers. >> Yeah, you mentioned the term cloud-native earlier, and you need to understand are you truly being cloud-native or are you kind of cloud adjacent, are you leveraging a couple of things, but you're really, you haven't taken advantage of the services and the promise of what these cloud options can offer. All right, Jeremy, 2020 we've turned the calendar. What are you looking at, you know you're planning, you got serverless conference, Serverless Days-- >> Serverless Days Boston. >> Boston, coming up-- >> April 6th in Cambridge. >> So give us a little views to kind of your view point for the year, the event itself, you got your podcast, you got a lot going on. >> Yeah, so my podcast, Serverless Chats. You know I talk to people that are in the space, and we usually get really really technical. So if you're a serverless geek or you like that kind of stuff definitely listen to that. But yeah, but 2020 for me though, this is where I see what is happened to serverless, and this goes back to my "Stop calling everything serverless" post, was this idea that we keep making serverless harder. And so, as a someone whose a serverless purist, I think at this point. I recognize and it frustrates me that it is so difficult now to even though we're abstracting away running that infrastructure, we still have to be very aware of what pieces of the infrastructure we are using. Still have setup the SQS Queue, still have to setup Event Bridge. We still have to setup the Lambda function and API gateways and there's services that make it easier for us, right like we can use a serverless framework, or the SAM framework, or ARCH code or architect framework. There's a bunch of these different ones that we can use. But the problem is that it's still very very tough, to understand how to stitch all this stuff together. So for me, what I think we're going to see in 2020, and I know there is hints for this serverless framework just launched their components. There's other companies that are doing similar things in the space, and that's basically creating, I guess what I would call an abstraction as a service, where essentially it's another layer of abstraction, on top of the DSL's like Terraform or Cloud Formation, and essentially what it's doing is it's saying, "I want to launch an API that does X-Y-Z." And that's the outcome that I want. Understanding all the best practices, am I supposed to use Lambda Destinations, do I use DLQ's, what should I throttle it at? All these different settings and configurations and knobs, even though they say that there's not a lot of knobs, there's a lot of knobs that you can turn. Encapsulating that and being able to share that so that other people can use it. That in and of itself would be very powerful, but where it becomes even more important and I think definitely from an enterprise standpoint, is to say, listen we have a team that is working on these serverless components or abstractions or whatever they are, and I want Team X to be able to use, I want them to be able to launch an API. Well you've got security concerns, you've got all kinds of things around compliance, you have what are the vetting process for third-party libraries, all that kind of stuff. If you could say to Team X, hey listen we've got this component, or this piece of, this abstracted piece of code for you, that you can take and now you can just launch an API, serverless API, and you don't have to worry about any of the regulations, you don't have to go to the attorneys, you don't have to do any of that stuff. That is going to be an extremely powerful vehicle for companies to adopt things quickly. So, I think that you have teams now that are experimenting with all of these little knobs. That gets very confusing, it gets very frustrating, I read articles all the time, that come out and I read through it, and this is all out of date, because things have changed so quickly and so if you have a way that your teams, you know and somebody who stays on top of the learning this can keep these things up to date, follow the most, you know leading practices or the best practices, whatever you want to call them. I think that's going to be hugely important step from making it to the teams that can adopt serverless more quickly. And I don't think the major cloud vendors are doing anything in this space. And I think SAM is a good idea, but basically SAM is just a re-write of the serverless framework. Whereas, I think that there's a couple of companies who are looking at it now, how do we take this, you know whatever, this 1500 line Cloud Formation template, how do we boil that down into two or three lines of configuration, and then a little bit of business logic. Because that's where we really want to get to. It's just we're writing business logic, we're no where near there right now. There's still a lot of stuff that has to be done, around configuration and so even though it's nice to say, hey we can just write some business logic and all the infrastructure is handled for us. The infrastructure is handled for us, if we configure it correctly. >> Yeah, really remind me some of the general thread we've been talking about, Cloud for a number of years is, remember back in the early days, is cloud is supposed to be inexpensive and easy to use, and of course in today's world, it isn't either of those things. So serverless needs to follow those threads, you know love some of those view points Jeremy. I want to give you the final word, you've got your Serverless Day Boston, you got your podcast, best way to get in touch with you, and keep up with all you're doing in 2020. >> Yeah, so @Jeremy_daly on Twitter. I'm pretty active on Twitter, and I put all my stuff out there. Serverless Chats podcast, you can just find, serverlesschats.com or any of the Pod catchers that you use. I also publish a newsletter that basically talks about what I'm talking about now, every week called Off by None, which is, collects a bunch of serverless links and gives them some IoPine on some of them, so you can go to offbynone.io and find that. My website is jeremydaly.com and I blog and keep up to date on all the kind of stuff that I do with serverless there. >> Jeremy, great content, thanks so much for joining us on theCube. Really glad and always love to shine a spotlight here in the Boston area too. >> Appreciate it. >> I'm Stu Miniman. You can find me on the Twitter's, I'm just @Stu thecube.net is of course where all our videos will be, we'll be at some of the events for 2020. Look for me, look for our co-hosts, reach out to us if there's an event that we should be at, and as always, thank you for watching theCube. (upbeat music)