>> Live from Las Vegas, it's theCUBE. Covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome back. We're in Las Vegas at VMworld 2018. You're watching theCUBE and one of the key themes of the show we've been talking about is multi-cloud. At Wikibon, in our research, when we talk about multi-cloud, really at the center of it, you're talking about data. We're going to have a center we're going to talk about here. I'm Stu Miniman, my cohost for the segment and the next couple ones is Justin Warren. Happy to welcome back to the program two gentlemen from Druva, Jaspreet Singh, to my right. He is the founder and CEO, and Dave Packer is the President of Product and Alliance Marketing. Gentlemen, thanks so much for joining us. >> Thanks a lot and good to see you again. >> Yeah, absolutely. First of all, I have to say, you guys had a fun thing going on this week. The mountain. The world's strongest man. (all laughing) >> He's my cousin, by the way. (all laughing) >> That's amazing. He got the height, you got something else. I'm not filing a complaint but I do have video proof of him putting his hands around my neck. Luckily, he didn't squeeze too much. Some fun stuff. We like to have some fun in the community here. Jaspreet, let's start. Your presence at the show, the importance of data, the importance of virtualization as it comes to Druva. >> Absolutely. I think, that, I'm going to give a cliche, data is the new oil. It's center of everything, the whole digital transformation. We think about that the data being transforming the world and we think about how very old school legacy industrialists still transforming themselves. Data is the core of it. Talk about energy sector, talk about commodity trading, talk about consumer electronics, or any of those core transformations. At the center of this whole transformationist data, and Druva's all about passion, about how do we manage it holistically in this new world for multi-cloud. >> I've seen way too many debates. We try to come up with simple analogies and I think what we're, in general, all agree is we're understating how important data is. But maybe, you can touch on, when big data came out, it was the three Vs of what we were doing. When you talk about multi-cloud, there's a lot of aspects, what's changed with data? Druva's been around for a number of years now and helped customers get their arms around leveraging and managing and dealing with data. What, recently, is so important about data, and some of the options there? >> That's a great question. I'll talk about two key trends which are shaping the way how people think about data. One is the change in the in the data itself. If you think about last two years, the 80% of data being created is not machine-generated. It's about human text messages, the new kind of data is born. Second, there's a whole generation of data analysis. Color cognitive systems, search analysis, data enablement systems being born between, and they're all growing at a massively fast rate. A combination of these two, enterprises are trying to understand how they manage more disperse and diverse information in a very meaningful manner without having to learn any new platform from the start. This is where Druva comes in. Druva's promise is data management as a service, which means you have data everywhere and we will give you a simple SLA-based, always on, on-demand service to manage it wherever it resides. Be it in private cloud like vSAN or vSphere or (mumbles) or it be public cloud like VMC or AWS. We have built organic systems, acquired companies to sort of give the whole breadth of management solutions built from a same console or platform offered to enterprises. >> Dave, want to pull you into the discussion here. >> Yeah. >> Years ago, it used to be every company, let's vertically integrate everything. Oracle will become your one-stop shop. IBM, example there. Now, we know it takes a village to interact with your data ecosystem and community. Big discussion points here. Maybe talk about how important that is to Druva. Who you work with, and the like. >> The way we look at it is foundational to what we do is really about how you get at the data. Regardless of its source, be it endpoints, be it servers, physical or virtual, be it cloud workloads, which is kind of the new operating environment for a lot of businesses. When you think about building out the ecosystem, you have to figure out, you've got this rich set of data you've brought together. You've got the meta data, you've got all the information about where it is, who has it, who's doing what with it. Then the ecosystem that builds out there is then much more use case basis, like security oriented. If you can think of one element around how do I know when there's something happening in my environment that's actually going to impact my business ahead of time, or at a point? You can use some solutions to solve that today, but when you have that bigger picture, you have a better understanding of what's going on and what's not going on. I think where we've gotten to as an organization is being able to provide this framework, to have this kind of unified view and access to all these different data sources. To manage them for the foundational elements of resiliency and DR, etc. But also, we find that in our large enterprise customers have big use cases around data governance, compliance, GDPR. All the buzzwords we've heard of in the last year. Fundamentally, how do you help companies better align to those particular use cases? We can do some of that, but we don't do all of it and I think that's fundamentally where you get into the ecosystem of building out. >> Druva's cloud-native, as well. >> Yes. >> What is it about the cloud, multi-cloud and cloud, cloud all the things is, clearly, one of the reasons why we're here at this show. What is it about Druva's cloud native abilities that provides the value to customers, as distinct from some other solutions? >> When you really think about what's the difference, what is cloud-native, it's basically treating the cloud like an operating system. You're building using the native databases, storage architectures, all the different elements of what the service provides. For a customer's point of view, what it really means is agility of access to services. On-demand, if I've got to scale up, I just acquired a company and I want to add in a petabyte of data, how do I do that if I've got a traditional on-premises environment. It's very hard to do. It's very hard to protect. How do I figure out the DR around it, everything else? With native cloud, it's just basically you turn it on and you go. I think people today, it's a nuance that not everybody's really intimate with, understanding the native environment versus maybe running an instance in the cloud and trying to scale out multiple instances of a service. In our world, you get the agility, you bring in cost efficiency. Because we're using micro-services, we're using the data storage layer smartly, intelligently, instead of just dumping the data into it. That gives the customer the ability to have a better understanding and really move over to a consumption model of purchasing services. Month to month, I know what I'm doing. I have predictability into it. I understand where my pricing is and what I'm actually paying for rather than paying for it all up front and then growing into it over time. >> I'll give you one example. Same difference between VMware traditional and the VMware cloud. The whole notion of consuming something as you go, on-demand, burst capacity. SLA-driven to a customer at a price point anywhere it'll go in the world. An example of that with Druva is we promised a customer, we're testing with a customer would you care if we dropped the price of backup by 30% if you do off-peak hours backup? That's the power of size, the power of as a service. They don't have to orchestrate a service-building software, cloud hardware, and put it all together. A simple service delivered to a customer at the price point they need. >> Yeah that's data as a utility, really. >> Right. >> Exactly. >> As Jaspreet said, data is the new oil. One advantage of the cloud is that you now will be able to turn that oil into other products. Plastics, Vaseline, all of these different by-products. Because now you can use the various services of the cloud, whether they be AWS or multi-cloud, and connect them into a service level architecture. To be able to do data analytics, to get better intelligence out of your data, and use it for more than just the traditional data protection services. Really use it as a data management platform. >> One of the conversations I've had with a lot of users at this show, in previous years at VMworld is multi-cloud, hybrid cloud, it's where they're going, but there's a spectrum of where they are. A lot of them are still a little bit trepidatious as to how they get there, how do they start making some steps. You guys are architected like this is where you need to go. How do you help them along those journeys? What are some pros and cons? How do you help a typical VMware customer move along those journey? >> That's a great question. If I'm a data admin, if I'm an admin worried about either the business continuity or the legal aspect of data, to adhere to cloud and learn a new platform, a new paradigm. The Druva really comes handy there. Now you can get the cloudiness, all the goodness of cloud without having to learn a new platform. Druva attaches a service with your vSphere. Some momentum to spin up a bunch of VMs. They're already protected, without provisioning more hardware and software. That's number one. Number two is we have a customer journey map that says Mr. Customer, the number one thing you should worry about, which you already worry about is business continuity. We've got it covered. A simple SLA applied to your VMware, whatever it is, on-premise or on the cloud. As you get through your business continuity needs covered, you have data governance need, compliance need. Where Druva can now get the value extracted from the data you're protecting to give you compliance and governance needs out of the data you already have with us. Then comes the intelligence piece, the automation, the higher value security operations, which are now working with the customer, building with them, to solve those high-value use cases, and completely abstracting out. That they don't have to learn a new platform. They don't have to know Kinesis or Redshift. We know it all to give them a single use case they pay the money for. >> It does make it easy for customers to then just say, look, if I were to, for example, buy another company as you mentioned before, then, well, it's already covered. I can use the same system I've already got, that will work really nicely. For the same reason, something went around MNA that people often don't talk about is the divestiture part. If I wanted to split off a business unit and I have to somehow unwrap it from my existing backup system, being able to just say, well, I can just turn off that little portion or. Can you explain how would that actually be handled in Druva? Can you just split out a service and say well, that piece is now being handled by a different company? >> That's a great question. Actually, one of our customers, Allergan actually, one of their (mumbles) use cases around MNA. They've acquired, in the last two or three years, about 10 or 12 companies. Bringing them in, but they've also had to deal with divestitures as well. One of the advantages of the service we provide is they can just quickly deploy the agents and start pulling in the data. They don't have to integrate the data centers and figure that part out. >> Yeah. >> Then divestitures, just basically doing the opposite. Dropping off the agents and then purging that data from the system as needed. One of the beauties of the cloud is that, having kind of that master catalog across all those different spots of your data allows you to go in and say I want to remove this particular data set, which is also really beneficial for things like GDPR, where you might want to find a piece of data and purge it out of the system. We'll remove it from the source as well as all the backup set shots, which is something that's kind of unique, but something that we can provide because of the way we actually handle it. >> Before we let you go, we're talking about MNA. CloudRanger was a recent acquisition. How's that fit into the overall story? >> We had a vision to build what we call a (mumbles) to manage native clouds in Amazon, Redshift, RDS, EC2. The data management aspects of them. We saw a great team, a great founder, a great vision, and they already had some great traction on the common DNA-driver has. Building out a service business model to address these pain points around data management. Off-native or clouds. We acquired them and we kept the team and the BU intact. Druva's also building its platform to a cloud platform where we have a single search across all data sources. We have single reporting, alerting, even consumptions. Customers can consume what they like and get billed for the total usage which is having the provision software and hardware. CloudRanger cleanly fits into that. Now, they can protect with Druva endpoints, data center, cloud-native or (mumbles), VMware cloud and all the SaaS services. You got the entire umbrella covered very well. >> Jaspreet and Dave, really appreciate the updates. Congrats on the acquisition and thanks, we had some fun here at the show, too. For Justin Warren, I'm Stu Miniman, back with lots more coverage here at VMworld 2018. Thank you for watching theCUBE. (electronic tones)

>>from the cube studios >>in Palo alto in >>boston connecting with thought leaders all around the world. This >>is a cute >>conversation. Hello welcome to the cubes coverage of HPD discovered 2021 virtual. I'm john for your host of the cube we're here with CUBA alumni. One of the original cube guests 2020 11 back in the day kurt president and chief architect of Hewlett Packard labs. He's also a Hewlett Packard enterprise fellow and vice president. Great to see you and you're in Vegas. I'm in Palo Alto. We've got a little virtual hybrid going on here. Thanks for spending time. >>Thanks john it's great to be back with you >>so much going on. I love to see you guys having this event kind of everyone in one spot. Good mojo. Great CHP, you know, back in the saddle again. I want to get your, take, your in the, in the, in the action right now on the lab side, which is great disruptive innovation is the theme. It's always been this year, more than ever coming out of the pandemic, people are looking for the future, looking to see the signs, they want to connect the dots. There's been some radical rethinking going on that you've been driving and in the labs, you hope you look back at last, take us through what's going on, what you're thinking, what's the, what's the big trends? >>Yeah, John So it's been interesting, you know, over the last 18 months, all of us had gone through about a decade's worth of advancement in decentralization, education, healthcare, our own work, what we're doing right now suddenly spread apart. Uh, and it got us thinking, you know, we think about that distributed mesh and as we, as we try and begin to return to normal and certainly think about all that we've lost, we want to move forward, we don't want to regress. And we started imagining, what does that world look like? And we think about the world of 20 2500 and 35 zeta bytes, 100 and 50 billion connected things out there. And it's the shape of the world has changed. That's where the data is going to be. And so we started thinking about what's it like to thrive in that kind of world. We had a global Defense research institute came to us, Nasa's that exact question. What's the edge? What do we need to prepare for for this age of insight? And it was kind of like when you had those exam questions and I was one of those kids who give you the final exam and if it's a really good question, suddenly everything clicked. I understood all the material because there was that really forcing question when they asked us that for me, it it solidified what I've been thinking about all the work we've done at labs over the last the last 10 years. And it's really about what does it take to survive and thrive. And for me it's three things. One is, success is going to go to whoever can reason over more information, who can gain the deepest insights from that information in time that matters and then can turn that insight into action at scale. So reason, insight and action. And it certainly was clear to me everything we've been trying to push for in labs, all those boundaries. We've been pushing all those conventions we've been defying are really trying to do that for, for our customers and our partners to bring in more information for them to understand, to be able to allow them to gain insight across departments across disciplines and then turn that insight into action at scale where scale is no longer one cloud or one company or one country, let alone one data center >>lot there. I love the dot I love that metadata and meta reasoning incites always been part of that. Um and you mentioned decentralization. Again, another big trend. I gotta ask you where is the big opportunity because a lot of people who are attending discover people watching are trying to ask what should they be thinking about. So what is that next big opportunity? How would you frame that and what should attendees look for coming out at HP discover. >>So one thing we're seeing is that this is actually a ubiquitous trend, whether we're talking about transportation or energy or communications, they all are trying to understand and how will they admit more of that data to make those real time decisions? Our expectation in the middle of this decade when we have the 125 petabytes, You know, 30% of that data will need real time action out of the edge where the speed of light is now material. And also we expect that at that point in time three out of four of those 185 petabytes, they'll never make it back to the data center. So understanding how we will allow that computation, that understanding to reach out to where the data is and then bringing in that's important. And then if we look at at those, all of those different areas, whether it's energy and transportation, communications, all that real time data, they all want to understand. And so I I think that as many people come to us virtually now, hopefully in person in the future when we have those conversations that labs, it's almost immediate takes a while for them and then they realize away that's me, this is my industry too, because they see that potential and suddenly where they see data, they see opportunity and they just want to know, okay, what does it take for me to turn that raw material into insight and then turn that insight into >>action, you know, storage compute never goes away, it gets more and more, you need more of it. This whole data and edge conversations really interesting. You know, we're living in that data centric, you know, everyone's gonna be a date a couple, okay. That we know that that's obvious. But I gotta ask you as you start to see machine learning, um cloud scale cloud operations, a new Edge and the new architecture is emerging and clients start to look at things like AI and they want to have more explain ability behind I hear that all the time. Can you explain it to me? Is there any kind of, what is it doing? Good as our biases, a good bad or you know, is really valuable expect experimental experiential. These are words are I'm hearing more and more of >>not so much a speeds >>and feeds game, but these are these are these are these are outcomes. So you got the core data, you've got a new architecture and you're hearing things like explainable ai experiential customer support, a new things happening, explain what this all means, >>You know, and it's it's interesting. We have just completed uh creating an Ai ethical framework for all of Hewlett Packard enterprise and whether we're talking about something that's internal improving a process, uh something that we sell our product or we're talking about a partnership where someone wants to build on top of our services and infrastructure, Build an AI system. We really wanted to encompass all of those. And so it was it was challenging actually took us about 18 months from that very first meeting for us to craft what are some principles for us to use to guide our our team members to give them that understanding. And what was interesting is we examined our principles of robustness of uh making sure they're human centric that they're reliable, that they are privacy preserving, that they are robust. We looked at that and then you look at where people want to apply these Ai today's AI and you start to realize there's a gap, there's actually areas where we have a great challenge, a human challenge and as interesting as possibly efficacious as today's A. I. S. R. We actually can't employ them with the confidence in the ethical position that we need to really pull that technology in. And what was interesting is that then became something that we were driving at labs. It began gave us a viewpoint into where there are gaps where, as you say, explica bility, you know, as fantastic as it is to talk into your mobile phone and have it translated into another one of hundreds of languages. I mean that is right out of Star trek and it's something we can all do. And frankly, it's, you know, we're expecting it now as efficacious as that is as we echo some other problems, it's not enough. We actually need to be explainable. We need to be able to audit these decisions. And so that's really what's informed now are trustworthy ai research and development program at Hewlett Packard Labs. Let's look at where we want to play. I I we look at what keeps us from doing it and then let's close the technology gap and it means some new things. It means new approaches. Sometimes we're going back back back to some of the very early ai um that things that we sort of left behind when suddenly the computational capability allowed us to enter into a machine learning and deep neural nets. Great applications, but it's not universally applicable. So that's where we are now. We're beginning to construct that second generation of AI systems where that explica bility where that trustworthiness and were more important that you said, understanding that data flow and the responsibility we have to those who created that data, especially when it's representing human information, that long term responsibility. What are the structures we need to support that ethically? >>That's great insight, Kirk, that's awesome stuff. And it reminds me of the old is new again, right? The cycles of innovation, you mentioned a I in the eighties, reminds me of dusting off and I was smiling because the notion of reasoning and natural language that's been around for a while, these other for a lot of Ai frame which have been around for a while But applied differently becomes interesting. The notion of Meta reasoning, I remember talking about that in 1998 around ontology and syntax and data analysis. I mean, again, well formed, you know, older ways to look at data. And so I gotta ask you, you know, you mentioned reasoning over information, getting the insights and having actions at scale. That doesn't sound like an R and D or labs issue. Right? I mean that that should be like in the market today. So I know you, there's stuff out there, what's different around the Hewlett Packard labs challenge because you guys, you guys are working on stuff that's kind of next gen, so why, what's next gen about reasoning moreover, information and getting insights? Because you know, there's a zillion startups out there that claim to be insights as a service, um, taking action outcomes >>and I think there were going to say a couple things. One is the technologies and the capabilities that God is this far. Uh, they're actually in an interesting position if we think of that twilight of moore's law is getting a little darker every day. Um, there's been such a tail wind behind us tremendous and we would have been foolish not to take advantage of it while it lasted, but as it now flattens out, we have to be realistic and say, you know what that ability to expect anticipate and then planned for a doubling and performance in the next 18 to 24 months because there's twice as many transistors in that square of silicon. We can't count on that anymore. We have to look now broader and it's not just one of these technology inflection points. There's so many we already mentioned ai it's voraciously vowing all this data at the same time. Now that data is all at the edge is no longer in the data center. I mean we may find ourselves laughing chuckling at the term itself data center. Remember when we sent it all the data? Because that's where the computers were. Well, that's 2020 thinking right, that's not even 2025. Thinking also security, that cyber threat of Nation State and criminal enterprises, all these things coming together and it's that confluence of discontinuities, that's what makes a loud problem. And the second piece is we don't just need to do it the way that we've been doing it because that's not necessarily sustainable. And if something is not sustainable is inherently inequitable because we can't afford to let everyone enjoy those benefits. So I think that's all those things, the technology confluence of technology, uh, disruptions and this desire to move to really sustainable, really inherently inequitable systems. That's what makes it a labs problem. >>I really think that's right on the money. And one of things I want to get your thoughts on, cause I know you have a unique historic view of the trajectory arc. Cloud computing that everyone's attention lift and shift cloud scale. Great cloud native. Now with hybrid and multi cloud clearly happening, all the cloud players were saying, oh, it's never gonna happen. All the data set is going to go away. Not really. The, the data center is just an edge big age. So you brought up the data center concept and you mentioned decentralization there, it's a distributed computing architecture, There is no line anymore between what's cloud and what's not the cloud is just the cloud and the data center is now a big fat edge and edges are smaller and bigger. Their nodes distribute computing now is the context. So this is not a new thing for Hewlett Packard enterprise. I mean you guys been doing distributed computing paradigms, supplying software and hardware and solutions Since I can remember since it was founded, what's new now, what do you say that folks are saying, what is HP doing for this new architecture? Because now an operating system is the word, the word that they want. They want to have an operating model, deV ops to have sex shops, all this is happening. What's the what's the state of the art from H. P. E. And how does the lab play into that vision? >>And it's so wonderful that you mentioned in our heritage because if you think about it was the first thing that Bill and they did, they made instruments of unparalleled value and quality for engineers and scientists. And the second thing they did was computerized that instrument control. And then they network them together and then they connect to the network measurement sensing systems to business computing. Right. And so that's really, that's exactly what we're talking about here. You know, and yesterday it was H. B. I. B. Cables. But today it is everything from an Aruba wireless gateway to a green Lake cloud that comes to you to now are cray exa scale supercomputing. And we wanted to look at that entire gamut and understand exactly what you said. How is today's modern developer who has been distinct in agile development in seven uh and devops and def sec ops. How can we make them as comfortable and confident deploying to any one of those systems or all of them in conjunction as confident as they've been deploying to a cloud. And I think that's really part of what we need to understand. And as you move out towards the edge things become interesting. A tiny amount of resources, the number of threats, physical and uh um cyber increased dramatically. It is no longer the healthy happy environment of that raised floor data center, It is actually out in the world but we have to because that's where the data is and so that's another piece of it that we're trying to bring with the labs are distributed systems lab trying to understand how do we make cloud native access every single bite everywhere from the tiniest little Edge embedded system, all the way up through that exa scale supercomputer, how do we admit all of that data to this entire generation and then the following subsequent generation, who will no longer understand what we were so worried about with things being in one place or another, they want to digest all the world's data regardless of where it is. >>You know, I was just having a conversation, you brought this up. Uh that's interesting around the history and the heritage, embedded systems is changing the whole hardware equations, changes the software driven model. Now, supply chain used to be constrained to software. Now you have a software supply chain, hardware, now you have software supply chain. So everything is happening in these kind of new use cases. And Edge is a great example where you want to have compute at the edge not having pulled back to some central location. So, again, advantage hp right, you've got more, you've got some solutions there. So all these like memory driven computing, something that you've worked on and been driving the machine product that we talked about when you guys launched a few years ago, um, looks like now a good R and D project, because all the discussions, I'm I'm hearing whether it's stuff in space or inside hybrid edges is I gotta have software running on an embedded system, I need security, I gotta have, you know, memory driven architecture is I gotta have data driven value in real time. This is new as a kind of a new shift, but you still need to run it. What's the update on the machine and the memory driven computing? And how does that connect the dots for this intelligent Edge? That's now super important in the hybrid equation. >>Yeah, it's fantastic you brought that up. You know, it's uh it's gratifying when you've been drawing pictures on your white board for 10 or 15 years and suddenly you see them printed uh and on the web and he's like, OK Yeah, you guys were there were there because we always knew it had to be bigger than us. And for a while you wonder, well is this the right direction? And then you get that gratification that you see it repeated. And I think one of the other elements that you said that was so important was talking about that supply chain uh and especially as we get towards these edge devices uh and the increasing cyber threat, you know, so much more about understanding the provenance of that supply chain and how we get beyond trust uh to prove. And in our case that proof is rooted in the silicon. Start with the silicon establish a silicon root of trust, something that can't be forged that that physically uncomfortable function in the silicon. And then build up that chain not of trust but a proof of measurable confidence. And then let's link that through the hardware through the data. And I think that's another element, understanding how that data is flowing in and we establish that that that provenance that's provable provenance and that also enables us to come back to that equitable question. How do we deal with all this data? Well, we want to make sure that everyone wants to buy in and that's why you need to be able to reward them. So being able to trace data into an AI model, trace it back out to its effect on society. All these are things that we're trying to understand the labs so that we can really establish this data economy and admit the day that we need to the problems that we have that really just are crying out for that solution bringing in that data, you just know where is the data, Where is the answer? Now I get to work with, I've worked for several years with the German center for your Degenerative Disease Research and I was teasing their director dr nakata. I said, you know, in a couple of years when you're getting that Nobel prize for medicine because you cracked Alzheimer's I want you to tell me how long was the answer hiding in plain sight because it was segregated across disciplines across geography and it was there. But we just didn't have that ability to view across the breath of the information and in a time that matters. And I think so much about what we're trying to do with the lab is that that's that reasoning moreover, more information, gaining insights in the time that matters and then it's all about action and that is driving that insight into the world regardless of whether it has to land in an exa scale supercomputer or tiny little edge device, we want today's application development teams to feel that degree of freedom to range over all of those that infrastructure and all of that data. >>You know, you bring up a great call out there. I want to just highlight that cause I thought that was awesome. The future breakthroughs are hiding in plain sight. It's the access to the people and the talent to solve the problems and the data that's stuck in the silos. You bring those together, you make that seamless and frictionless, then magic happens. That's that's really what we're talking about in this new world, isn't it? >>Absolutely, yeah. And it's one of those things that sometimes my kids as you know, why do you come in every day? And for me it is exactly that I think so many of the challenges we have are actually solvable if the right people knew the right information at the right time and that we all have that not again, not trust, but that proof that confidence, that measurable conference back to the instruments that that HP was always famous for. It was that precision and they all had that calibration tag. So you could measure your confidence in an HP instrument and the same. We want people to measure their confidence when data is flowing through Hewlett Packard Enterprise infrastructure. >>It's interesting to bring up the legacy because instrumentation network together, connecting to business systems. Hey, that sounds like the cloud observe ability, modern applications, instant action and actionable insights. I mean that's really the the same almost exact formula. >>Yeah, For me that's that, that the constant through line from the garage to right now is that ability to handle and connect people to the information that they need. >>Great, great to chat. You're always an inspiration and we could go for another hour talking about extra scale, green leg, all the other cool things going on at H P E. I got to ask you the final question, what are you most excited about for h B and his future and how and how can folks learn more to discover and what should they focus on? >>Uh so I think for me um what I love is that I imagine that world where the data you know today is out there at the edge and you know we have our Aruba team, we have our green Lake team, we have are consistent, you know, our core enterprise infrastructure business and now we also have all the way up through X scale compute when I think of that thriving business, that ability to bring in massive data analytics, machine learning and Ai and then stimulation and modeling. That's really what whether you're a scientist and engineer or an artist, you want to have that intersectionality. And I think we actually have this incredible, diverse set of resources to bring to bear to those problems that will span from edge to cloud, back to core and then to exit scale. So that's what really, that's why I find so exciting is all of the great uh innovators that we get to work with and the markets we get to participate in. And then for me it's also the fact it's all happening at Hewlett Packard Enterprise, which means we have a purpose. You know, if you ask, you know, when they did ask Dave Packer, Dave, why hp? And he said in 1960, we come together as a company because we can do something we could not do by ourselves and we make a contribution to society and I dare anyone to spend more than a couple of minutes with Antonio Neary and he won't remind you. And this is whether it is here to discover or in the halls at labs remind me our purpose, that Hewlett Packard Enterprise is to advance the way that people live and work. And for me that's that direct connection. So it's, it's the technology and then the purpose and that's really what I find so exciting about HPV. >>That's a great call out, Antonio deserves props. I love talking with him, he's the true Bill and Dave Bill. Hewlett Dave package spirit And I'll say that I've talked with him and one of the things that resident to me and resonates well is the citizenship and be interesting to see if Bill and Dave were alive today, that now it's a global citizenship. This is a huge part of the culture and I know it's still alive there at H P E. So, great call out there and props to Antonio and yourself and the team. Congratulations. Thanks for spending the time, appreciate it. >>Thank you john it's great to be with you again. >>Okay. Global labs. Global opportunities, radical. Rethinking this is what's happening within HP. Hewlett Packard Labs, Great, great contribution there from Kirk, have them on the cube and always fun to talk so much, so much to digest there. It's awesome. I'm john Kerry with the cube. Thanks for watching. >>Mm >>mhm Yeah.