(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage here at re:MARS. I'm John Furrier, host of theCUBE. It's the event where it's part of the "re:" series: re:MARS, re:Inforce, re:Invent. MARS stands for machine learning, automation, robotics, and space. And a lot of conversation is all about AI machine learning. This one's about AI and business transformation. We've got Stepan Pushkarev CTO, CEO, Co-Founder of Provectus. Welcome to theCUBE. And Russ Lamb, eCommerce Retail Data Engineering Lead at PepsiCo, customer story. Gentlemen, thanks for coming on theCUBE. >> Great to be here, John. >> Yeah, thanks for having us. >> I love the practical customer stories because it brings everything to life. This show is about the future, but it's got all the things we want, we love: machine learning, robotics, automation. If you're in DevOps, or you're in data engineering, this is the world of automation. So what's the relationship? You guys, you're a customer. Talk about the relationship between you guys. >> Sure, sure. Provectus as a whole is a professional services firm, premier, a AWS partner, specializing in machine learning, data, DevOps. PepsiCo is our customer, our marquee customer, lovely customer. So happy to jointly present at this re:Invent, sorry, re:MARS. Anyway, Russ... >> I made that mistake earlier, by the way, 'cause re:Invent's always on the tip of my tongue and re:MARS is just, I'm not used to it yet, but I'm getting there. Talk about what are you guys working together on? >> Well, I mean, we work with Provectus in a lot of ways. They really helped us get started within our e-commerce division with AWS, provided a lot of expertise in that regard and, you know, just hands-on experience. >> We were talking before we came on camera, you guys just had another talk and how it's all future and kind of get back to reality, Earth. >> Russ: Get back to Earth. >> If we're on earth still. We're not on Mars yet, or the moon. You know, AI's kind of got a future, but it does give a tell sign to what's coming, industrial change, full transformation, 'cause cloud does the back office. You got data centers. Now you've got cloud going to the edge with industrial spaces, the ultimate poster child of edge and automation safety. But at the end of the day, we're still in the real world. Now people got to run businesses. And I think, you know, having you here is interesting. So I have to ask you, you know, as you look at the technology, you got to see AI everywhere. And the theme here, to me, that I see is the inflection point driving all this future robotics change, that everyone's been waiting for by the way, but it's like been in movies and in novels, is the machine learning and AI as the tipping point. This is key. And now you're here integrating AI into your company. Tell us your story. >> Well, I think that every enterprise is going to need more machine learning, more, you know, AI or data science. And that's the journey that we're on right now. And we've come a long way in the past six years, particularly with our e-commerce division, it's a really data rich environment. So, you know, going from brick and mortar, you know, delivering to restaurants, vending machines and stuff, it's a whole different world when you're, people are ordering on Amazon every couple minutes, or seconds even, our products. But they, being able to track all that... >> Can you scope the problem statement and the opportunity? Because if I just kind of just, again, I'm not, you're in, it's your company, you're in the weeds, you're at the data, you're everything, But it just seems me, the world's now more integration, more different data sources. You've got suppliers, they have their different IT back ends. Some are in the cloud, some aren't in the cloud. This is, like, a hard problem when you want to bring data together. I mean, API certainly help, but can you scope the problem, and, like, what we're talking about here? >> Well, we've got so many different sources of data now, right? So we used to be relying on a couple of aggregators who would pull all this data for us and hand us an aggregated view of things. But now we're able to partner with different retailers and get detail, granular information about transactions, orders. And it's just changed the game, changed the landscape from just, like, getting a rough view, to seeing the nuts and bolts and, like, all the moving parts. >> Yeah, and you see in data engineering much more tied into like cloud scale. Then you got the data scientists, more the democratization application and enablement. So I got to ask, how did you guys connect? What was the problem statement? How did you guys, did you have smoke and fire? You came in solved the problem? Was it a growth thing? How did this, how did you guys connect as a customer with Provectus? >> Yeah, I can elaborate on that. So we were in the very beginning of that journey when there was, like, just a few people in this new startup, let's call it startup within PepsiCo. >> John: Yeah. >> Calling like a, it's not only e-commerce, it was a huge belief from the top management that it's going to bring tremendous value to the enterprise. So there was no single use case, "Hey, do this and you're going to get that." So it's a huge belief that e-commerce is the future. Some industry trends like from brand-centric to consumer-centric. So brand, product-centric. Amazon has the mission to build the most customer-centric customer company. And I believe that success, it gets a lot of enterprises are being influenced by that success. So I remember that time, PepsiCo had a huge belief. We started building just from scratch, figuring out what does the business need? What are the business use cases? We have not started with the IT. We have not started with this very complicated migrations, modernizations. >> John: So clean sheet of paper. >> Yeah. >> From scratch. >> From scratch. >> And so you got the green light. >> Yeah. >> And the leadership threw the holy water on that and said, "Hey, we'll do this."? >> That's exactly what happened. It was from the top down. The CEO kind of set aside the e-commerce vision as kind of being able to, in a rapidly evolving business place like e-commerce, it's a growing field. Not everybody's figured it out yet, but to be able to change quickly, right? The business needs to change quickly. The technology needs to change quickly. And that's what we're doing here. >> So this is interesting. A lot of companies don't have that, actually, luxury. I mean, it's still more fun because the tools are available now that all the hyper scales built on their own. I mean, back in the day, 10 years ago, they had to build it all, Facebook. You didn't know, I had people on here from Pinterest and other companies. They had to build all of that from scratch. Now cloud's here. So how did you guys do this? What was the playbook? Take us through the AI because it sounds like the AI is core, you know, belief principle of the whole entire system. What did you guys do? Take me through the journey there. >> Yeah. Beyond management decisions, strategic decisions that has been made as a separate startup, whatever- >> John: That's great. >> So some practical, tactical. So it may sound like a cliche, but it's a huge thing because I work with many enterprises and this, like, "center of excellence" that does a nice technology stuff and then looks for the budget on the different business units. It just doesn't go anywhere. It could take you forever to modernize. >> We call that the Game of Thrones environment. >> Yes. >> Yeah. Nothing ever gets done 'till it blows up at the end. >> Here, these guys, and I have to admit, I don't want to steal their thunder. I just want to emphasize it as an external person. These guys just made it so differently. >> John: Yeah. >> They even physically sat in a different office in a WeWork co-working and built that business from scratch. >> That's what Andy Jackson talked about two years ago. And if you look at some of the big successes on AWS, Capital One, all the big, Goldman Sachs. The leadership, real commitment, not like BS, like total commitment says, "Go." But enough rope to give you some room, right? >> Yeah. I think that's the thing is, there was always an IT presence, right, overseeing what we were doing within e-commerce, but we had a lot of freedoms to make design choices, technology choices, and really accelerate the business, focus on those use cases where we could make a big impact with a technology choice. >> Take me through the stages of the AI transformation. What are some of the use cases and specific tactics you guys executed on? >> Well, I think that the supply chain, which I think is a hot topic right now, but that was one use case where we're using, like, data real time, real time data to inform our sales projections and delivery logistics. But also our marketing return on investment, I feel like that was a really interesting, complex problem to solve using machine learning, Because there's so much data that we needed to process in terms of countries, territories, products, like where do you spend your limited marketing budget when you have so many choices, and, using machine learning, boil that all down to, you know, this is the optimal choice, right now. >> What were some of the challenges and how did you overcome them in the early days to get things set up, 'cause it takes a lot of energy to get it going, to get the models. What were some of the challenges and how did you overcome them? >> Well, I think some of it was expertise, right? Like having a partner like Provectus and Stepan really helped because they could guide us, Stepan could guide us, give his expertise and what he knows in terms of what he's seen to our budding and growing business. >> And what were the things that you guys saw that you contributed on? And was there anything new that you had to do together? >> Yeah, so yeah. First of all, just a very practical tip. Yes, start with the use cases. Clearly talk to the business and say, "Hey, these are the list of the use cases" and prioritize them. So not with IT, not with technology, not with the migration thing. Don't touch anything on legacy systems. Second, get data in. So you may have your legacy systems or some other third party systems that you work with. There's no AI without data. Get all the pipelines, get data. Quickly boat strap the data lake house. Put all the pipelines, all the governance in place. And yeah, literally took us three months to get up and running. And we started delivering first analytical reports. It's just to have something back to business and keep going. >> By the way, that's huge, speed. I mean, this is speed. You go back and had that baggage of IT and the old antiquated systems, you'd be dragging probably months. Right? >> It's years, years. Imagine you should migrate SAP to the cloud first. No, you don't do don't need to do that. >> Pipeline. >> Just get data. I need data. >> Stream that data. All right, where are we now? When did you guys start? I want to get just going to timeline my head 'cause I heard three months. Where are we now? You guys threw it. Now you have impact. You have, you have results. >> Yeah. I mean that for our marketing ROI engine, we've built it and it's developed within e-commerce, but we've started to spread it throughout the organization now. So it's not just about the digital and the e-commerce space. We're deploying it to, you know, regionally to other, to Europe, to Latin America, other divisions within PepsiCo. And it's just grown exponentially. >> So you have scale to it right now? >> Yeah. Well- >> How far are you in now? What, how many years, months, days? >> E-commerce, the division was created six years ago, which is, so we've had some time to develop this, our machine learning capabilities and this use case particular, but it's increasingly relevant and expansion is happening as we speak. >> What are you most proud of? You look back at the impact. What are you most proud of? >> I think the relationship we built with the people, you know, who use our technology, right. Just seeing the impact is what makes me proud. >> Can you give an example without revealing any confidential information? >> Yeah. Yeah. I mean, there was an example from my talk about, I was approached recently by our sales team. They were having difficulty with supply chain, monitoring our fill rate of our top brands with these retailers. And they come up to me, they have this problem. They're like, "How do we solve it?" So we work together to find a data source, just start getting that data in the hands of people who can use it within days. You know, not talking like a long time. Bring that data into our data warehouse, and then surface the data in a tool they can use, you know, within a matter of a week or two. >> I mean, the transformation is just incredible. In fact, we were talking on theCUBE earlier today around, you know, data warehouses in the cloud, data meshes of different pros and cons. And the theme that came out of that conversation was data's a product now. >> Yes. >> Yes. >> And what you're kind of describing is, just gimme the product or find it. >> Russ: Right. >> And bring it in with everything else. And there's some, you know, cleaning and stuff people do if they have issues with that. But, if not, it's just bring it in, right? It's a product. >> Well, especially with the data exchanges now. AWS has a data exchange and this, I think, is the future of data and what's possible with data because you don't have to start from, okay, I've got this Excel file somebody's been working with on their desktop. This is a, someone's taken that file, put it into a warehouse or a data model, and then they can share it with you. >> John: So are you happy with these guys? >> Absolutely, yeah. >> You're actually telling the story. What was the biggest impact that they did? Was it partnering? Was it writing code, bringing development in, counseling, all the above, managed services? What? >> I think the biggest impact was the idea, you know, like being able to bring ideas to the table and not just, you know, ask us what we want, right? Like I think Provectus is a true partner and was able to share that sort of expertise with us. >> You know, Andy Jackson, whenever I interview on theCUBE, he's now in charge of all Amazon. But when he was at (inaudible). He always had to use their learnings, get the learnings out. What was the learnings you look back now and say, Hey, those were tough times. We overcome them. We stopped, we started, we iterated, we kept moving forward. What was the big learning as you look back, some of the key success points, maybe some failures that you overcome. What was the big learnings that you could share with folks out there now that are in the same situation where they're saying, "Hey, I'd rather start from scratch and do a reset." >> Yeah. So with that in particular, yes, we started this like sort of startup within the enterprise, but now we've got to integrate, right? It's been six years and e-commerce is now sharing our data with the rest of the organization. How do we do that, right? There's an enterprise solution, and we've got this scrappy or, I mean, not scrappy anymore, but we've got our own, you know, way of doing. >> Kind of boot strap. I mean, you were kind of given charter. It's a start up within a big company, I mean- >> But our data platform now is robust, and it's one of the best I've seen. But how do we now get those systems to talk? And I think Provectus has came to us with, "Here, there's this idea called data mesh, where you can, you know, have these two independent platforms, but share the data in a centralized way. >> So you guys are obviously have a data mesh in place, big part of the architecture? >> So it is in progress, but we know the next step. So we know the next step. We know the next two steps, what we're going to do, what we need to do to make it really, to have that common method, data layer. between different data products within organization, different locations, different business units. So they can start talking to each other through the data and have specific escalates on the data. And yeah. >> It's smart because I think one of the things that people, I think, I'd love to get your reaction to this is that we've been telling the story for many, many years, you have horizontally scalable cloud and vertically specialized domain solutions, you need machine learning that's smart, but you need a lot of data to help it. And that's not, a new architecture, that's a data plane, it's control plane, but now everyone goes, "Okay, let's do silos." And they forget the scale side. And then they go, "Wait a minute." You know, "I'm not going to share it." And so you have this new debate of, and I want to own my own data. So the data layer becomes an interesting conversation. >> Yeah, yes. Meta data. >> Yeah. So what, how do you guys see that? Because this becomes a super important kind of decision point architecturally. >> I mean, my take is that there has to be some, there will always be domains, right? Everyone, like there's only so much that you can find commonality across, like in industry, for example. But there will always be a data owner. And, you know, kind of like what happened with rush to APIs, how that enabled microservices within applications and being sharing in a standardized way, I think something like that has to happen in the data space. So it's not a monolithic data warehouse, it's- >> You know, the other thing I want to ask you guys both, if you don't mind commenting while I got you here, 'cause you're both experts. >> We just did a showcase on data programmability. Kind of a radical idea, but like data as code, we called it. >> Oh yeah. >> And so if data's a product and you're acting on, you've got an architecture and system set up, you got to might code it's programmable. You need you're coding with data. Data becomes like a part of the development process. What do you guys think of when you hear data as code and data being programmable? >> Yeah, it's a interesting, so yeah, first of all, I think Russ can elaborate on that, Data engineering is also software engineering. Machine learning engineering is a software. At the end of the day, it's all product. So we can use different terms and buzz words for that but this is what we have at the end of the day. So having the data, well I will use another buzz word, but in terms of the headless architecture- >> Yes. >> When you have a nice SDK, nice API, but you can manipulate with the data as your programming object to build reach applications for your users, and give it, and share not as just a table in Redshift or a bunch of CSV files in S3 bucket, but share it as a programmable thing that you can work with. >> Data as code. >> Yeah. This is- >> Infrastructure code was a revolution for DevOps, but it's not AI Ops so it's something different. It's really it's data engineering. It's programming. >> Yeah. This is the way to deliver data to your consumers. So there are different ways you can show it on a dashboard. You can show it, you can expose it as an API, or you can give it as an object, programmable interface. >> So now you're set up with a data architecture that's extensible 'cause that's the goal. You don't want to foreclose. You must think about that must keep you up at night. What's going to foreclose that benefit? 'Cause there's more coming. Right? >> Absolutely. There's always more coming. And I think that's why it's important to have that robust data platform to work from. And yeah, as Stepan mentioned, I'm a big believer in data engineering as software engineering. It's not some like it's not completely separate. You have to follow the best practices software engineers practice. And, you know, really think about maintainability and scalability. >> You know, we were riffing about how cloud had the SRE managing all those servers. One person, data engineering has a many, a one to many relationships too. You got a lot going on. It's not managing a database. It's millions of data points and data opportunity. So gentlemen, thanks for coming on theCUBE. I really appreciate it. And thanks for telling the story of Pepsi. >> Of course, >> And great conversation. Congratulations on this great customer. And thanks for >> coming on theCUBE. >> Thanks, thank you. Thanks, Russ, would you like to wrap it up with the pantry shops story? >> Oh, yeah! I think it will just be a super relevant evidence of the agility and speed and some real world applicable >> Let's go. Close us out. >> So when, when the pandemic happened and there were lockdowns everywhere, people started buying things online. And we noticed this and got a challenge from our direct to consumer team saying, "Look, we need a storefront to be able to sell to our consumers, and we've got 30 days to do it." We need to be able to work fast. And so we built not just a website, but like everything that behind it, the logistics of supply chain aspects, the data platform. And we didn't just build one. We built two. We got pantry shop.com and snacks.com, within 30 days. >> Good domains! >> The domain broker was happy on that one. Well continue the story. >> Yeah, yeah. So I feel like that the agility that's required for that kind of thing and the like the planning to be able to scale from just, you know, an idea to something that people can use every day. And, and that's, I think.- >> And you know, that's a great point too, that shows if you're in the cloud, you're doing the work you're prepared for anything. The pandemic was the true test for who was ready because it was unforeseen force majeure. It was just like here it comes and the people who were in the cloud had that set up, could move quickly. The ones that couldn't. >> Exactly. >> We know what happened. >> And I would like to echo this. So they have built not just a website, they have built the whole business line within, and launched that successfully to production. That includes sales, marketing, supply chain, e-commerce, aside within 30 days. And that's just a role model that could be used by other enterprises. >> Yeah. And it was not possible without, first of all, right culture. And second, without cloud Amazon elasticity and all the tools that we have in place. >> Well, the right architecture allows for scale. That's the whole, I mean, you did everything right at the architecture that's scale. I mean, you're scaling. >> And we empower our engineers to make those choices, right. We're not, like, super bureaucratic where every decision has to be approved by the manager or the managers manager. The engineers have the power to just make good decisions, and that's how we move fast. >> That's exactly the future right there. And this is what it's all about. Reliability, scale agility, the ability to react and have applications roll out on top of it without long timeframes. Congratulations. Thanks for being on theCUBE. Appreciate it. All right. >> Thank you. >> Okay, you're watching theCUBE here at re:MARS 2020, I'm John Furrier. Stay tuned. We've got more coverage coming after this short break. (upbeat music)

(uptempo techno music) >> Announcer: Live from New Orleans, Louisiana. It's theCUBE covering .NEXT Conference 2018. Brought to you by Nutanix. >> One of the only constants in the technology world is that everything is always changing. Talking a lot about digital transformation. If I roll back to 2012, converged infrastructure, changes in data centers and infrastructure were all of the buzz, and it was before we were talking about things like hyper-converged infrastructure. We ran across a company called Nutanix. First interview we did in 2012 with Dheeraj Pandey, the CEO of the now public company. Surprised us a little bit in that not how they put things together but the why and what they had behind it. That almost 40 minute interview with John Furrier and I did really talked about the biggest challenge of our time is distributed architectures. Not about boxes, not about even just reconfiguring some of the silos but really some of the softer challenges that we've been attacking for decades really in our industry. Fast forward here we are in 2018 and want to welcome you to theCUBE's coverage of Nutanix .NEXT Conference here in New Orleans. I'm Stu Miniman, joined by my co-host for these two days of broadcast, Keith Townsend. Keith, thanks for joining me. >> Thanks for having me Stu. >> So we spend time, it's like what are we doing today? I think right down the block from here is the World War II monument, and how many years after World War II before it was called World War II? >> Keith: Yeah, good point. >> When we look back at what was happening converged infrastructure was a wave. At Wikibon, we were tracking cool things like flash really invading what's going on. Hyperscale architecture, for me personally I'd gone from looking at these enterprise architectures really hardware focused, failure domains, make sure that nothing ever breaks to the softer model of applications where you expect everything is going to break. And that's okay, chaos monkey rules supreme. At the end of the day, your application lives on. Much more granular, we weren't talking microsegmentation architectures and the like. Want to bring you in here, we've had the pleasure of being at every single Nutanix show. This is your first one for you so give us your first impressions of Nutanix .NEXT and what you're seeing. >> I go to a awful lot of shows and I've heard that Nutanix .NEXT was special and all to itself. I had breakfast with just customers, regular attendees, and there is slightly a different energy here. I was surprised at how open customers are about talking about their journey. Just talking about how they're using Nutanix. Where they have it deployed. Their origin stories much different atmosphere than many of the conferences that I've attended. >> And actually so when you talk software companies. There's certain shows where there's the passion and love. Keith, you and I cut our teeth on the virtualization community. >> Right. >> And I use to have the I love VMware bumper stickers and things like that. We've got a team at ServiceNow Knowledge. Dave Vellante said is one of the most passionate groups there. And it's interesting, some of the board members of Nutanix actually co-populate with what's going at ServiceNow. Another show we have going on this week is Red Hat. Obviously the open source community. Very passionate communities. The goal that Nutanix has is rather audacious. When they set out it's not like they said, "Hey, we want to be the leading "hyper-converged infrastructure player." They started in 2009. That word didn't even exist in our lexicon. They have a rather audacious goal. They want to be the next VMware in the model of Microsoft platform. What do they own, where does it fit? What does their ecosystem look like? And we've been watching this maturity, and we're going to have a lot of guests, customers, partners and executives but yeah, comments there. >> The goal is three billion dollars in software billings by 2011. I mean sorry by 2021. That is a big, big number. I think VMware revenues are somewhere around eight billion to put this into perspective, big ambitions. I think on stage, Sunil said that Nutanix is the world's best or leading cloud OS. That was a bold, bold statement. While one part of the Nutanix is a lot bravado backed with some pretty decent technology. The customers that we've talked to have said, they have not ran into a more humble company, and wanting to build brick by brick a relationship to help solve. I'm surprised that customers used this word, partner. They believe Nutanix is truly a partner in their journey towards cloud in delivering IT services. So while again, very bold from the financial statement, very bold from a technology statement. The customer passion here about Nutanix being a true partner in their journey. That's quite real. >> Yeah and it's interesting when you look at the pace of change. The half life of how long people love a brand has been shrinking very fast. >> Keith: Right. >> You think of the old days, it was brands like IBM and Microsoft had decades that they were in love. Apple still beloved by many but they get poked and poked and prodded. We talked about VMware, talked about Nutanix. The landscape today is one of the things. Let's talk about cloud for a second. You and I were making some comments in the Twitter stream during the keynote. When I think hybrid cloud, and I think who's got leadership there. Well first of all, you can't talk about cloud without talking about AWS. >> Keith: Right. >> First solution that anyone's going to support. The Nutanix solutions. It's either API compatibility or integration with what Amazon is doing. Secondly, you talk about hybrid. That's Microsoft's strategy from day one. Azure Stack, same OS, same operating model that's there. So for Nutanix to say they have the best. It's like Microsoft been doing this for a few years. They have a few more customers than Nutanix. >> Right. Not saying Nutanix is not doing great. They're adding a thousand customers a quarter, which is great for an infrastructure company. For a software company, it's good. >> Keith: Yes. >> It's not blowing it out of the water. If you're a Salesforce and you said, you're only adding a thousand new companies a quarter. It's like well Wall Street is not thrilled. So different space, how they're positioning themselves. We mentioned revenue. They're well over a billion dollars. Looking back, some of the shows we've done. I think it's like a $1.4 billion run rate. Market cap, a phenomenal nine billion dollars. When we talk about just value creation, the customers that they're doing. A lot of things really in the Nutanix tail wind pushing them along. As you said, coming to these shows it's always when you talk to the customers. When you talk to the customers in the hallway, are there certain things. It's like oh well we're glad the micro-segmentation stuff is something that we really wanted, but not the big gripes. They're not yet complaining about the pricing models. >> There is not a Nutanix text yet. Not a age retext. And it will be interesting, they made a lot of announcements today. Around Kalm, around flow, around database management. A lot of features. Extremely ambitious technically, and those technologies have to be paid for somehow. So long term, I really want to see if that love extends into when Nutanix needs to get to that three billion dollar in revenue. >> Yeah so maybe quick take on the announcement so far and the keynotes. I thought it was a good balance. A little bit of pageantry upfront, Mardi Gras. >> Keith: Marching band. >> The marching band and everything coming through. They had partners, Hackathon winners, customers up on the floats coming in. No beat probably four. Wanted to make sure that they weren't pegging somebody in the head with that stuff. But they had a good mix, I felt. They had a few customers onstage tell their stories. They got through the announcements. Some real meaningful announcements. Their first SaaS product with Beam. One of the four acquisitions that they've had over the last couple of years. That was from Minjar, was the acquisition. Netsol is another acquisition that they had recently and then Kalm was the basis. >> Keith: Right. >> A long with PernixData a couple years ago. Saccharin Vagoni, PernixData is somebody working on the IoT in Edge stuff. Keynote, announcements, what's your take? >> You know what, there's a lot there. They are innovating extremely fast. I think I interviewed Gar-iage, maybe a couple of years ago at Dell EMC World and I asked, is Nutanix a platform yet? And he say, "You know what. "We might be a little bit early to call Nutanix a platform. "I think today we've solved the completion of the foundation "of being called a platform." As we look out onto the show floor, we're starting to see a growing number of partners who are looking to integrate. We'll have Beam on later on in the program but specific announcements. The things that I'm somewhat excited about Netsil. They're taking a very different approach to network segmentation. And their micro segmentation and VM warriors. There are some advantages, disadvantages. Really looking forward to having that conversation. One click database management with Oracle and Microsoft. There's some guard rails around that we're thinking wow, how does Nutanix walk the line of making database administration deployment simple, but not anger Oracle to the point if there is court action. That's going to be an interesting set of conversation. >> I mean Keith, you know better than me. I hear database migrations and I just think of all the customer horror stories. David Foro from our Wikibon team has talked about, it's never easy. You'll get 80% or 90% of the way there and then things break, and you have to put it back together. AWS has been doing a lot of database migrations, and they've got 80, 90,000 of these that they've done. So how do they do this? It's great to say push button simplicity, but the proof is in the pudding. What are customers seeing? >> Yeah, when you're talking about big database mission critical. And that's another thing we heard on the stage this morning. A lot about mission critical. They're trying to shed this persona of being a VDI platform and that the platform is ready for mission critical applications. We've talked to customers that are indeed using it for mission critical stuff. But again, migration. They've had the relationship with IBM and Power for a couple of years now. And they still ran into a lot of customers that are saying they have no plans of moving AIX to Nutanix, however there's a plate. >> Well since you mention it actually, that was one of the announcements today. Nutanix is now supporting the AAX. >> Keith: Right. >> So before it was Power, now you need to get over to Linux, and that's something we've heard, gosh Keith. How long have we've been hearing the migration from Unix to Linux with the work load. 10 years ago, I remembered going events, and we were talking about that. And it's challenging, you need to-- >> Yeah, I remember getting excited about being-- >> The platform, the tool. >> Having IBM support Linux on mainframes, and thinking man I can finally get this stuff off of AIX. And then to Linux, and that was literally almost 20 years ago, so there you go. >> Yes, so many different announcements but started some the basic piece of it. 'Cause if you talk, there are customers that they have that are drawing over new things. We've got one of the customers that was on the keynote stage, Northern Trust. And he's throwing out things like PaaS and CaaS, which I'm hoping is containers as a service that he's talking about. Some of us propeller heads love talking about this. Lamb-dogot mentioned in the keynote talking about server list but the average Nutanix customers. This is the sand replacement. Many of the customers come and they say, going from my three tiered architecture, server, storage whether that be a traditional storage array or even an all flash array. I'm going to save 20%-40% just by collapsing it down to this architecture. Multi-Hypervisor, VMware of course very heavy, interesting dynamic always between VMware and Nutanix. Aged V growth, a little bit less of the aged V, the Acropolis Hypervisor and surrounding Acropolis services. At least to me, it felt a little bit less than before just 'cause the portfolio is broadening. But you've got so many pieces, it's basically almost any server you want. Nutanix is either an OEM or they will support it. There's all the Hypervisors they can connect to the cloud. When I look at that hybrid cloud message. It does start in your data center but it does extend to all of those pieces. If there is a little criticism I have there is that, at least my quick take. 50%-75% of the Nutanix customers are mostly of the, I use SaaS but I don't use a ton of public cloud. And therefore, I want to control my environment as opposed to but there are other customers that are, I'm doing a ton on Amazon, and Nutanix is great there. So went on about a bunch of things there. But just the base platform, what do you hear from people that are using Nutanix specifically HCI in general, and how that fits in the overall cloud picture? >> So overall they're cautious like you said. A lot of what core customers that I have talked to are very lets call it cloud anti pattern. However they're consuming Kalm, they're consuming Prism, they are consuming Nutanix in a cloud-like manner on premises. They're looking to one customer said, "To their internal customer, they are the cloud." They make IT and consuming Nutanix infrastructure simple, so it is a perspective thing. As we start to expand out Kalm and expect design become much more critical to this long term vision. And customers are still in a wait and see pattern. They're saying, "Well let's look." One to two years where the technology gets to be a little bit more mature. A little bit more tested. Tested by who is a good question and that ability to extend their internal infrastructure and operations to the external public cloud becomes more of a reality. >> Okay, Keith want you to just, what are you looking forward to get out of these next two days. Quick take from me. The three pieces that Sunil and Dheeraj been talking for a couple years. Invisible infrastructure, solid basis. They're there, they've got great feature functionality. I think when we talked to customers, other than these two features that VMware has that aren't yet here. I can move 75%-85% AIX one piece to get another 5% of that if we need. Invisible data centers, making good progress. Can see what they're doing today. They have a lot of the pieces. Things like Prism and Kalm are, Prism has been out for years, but Kalms GA and making progress. And then invisible clouds. First pieces are in place. They've got some software pieces there. What are we, look at Nutanix 3-5 years from now, are they a SaaS player? Are they primarily an infrastructure software player? The question I want to point to them. I had an interview with Rowan from Cisco, the number two guy and he said, "Cisco, the networking company. "10 years from now, they're a software company." It's not boxes and ports and things like that. So how far did they go as opposed to you and I were at Dell last week. Dell wants to be the leading infrastructure company, and therefore servers, storage, network are key pieces there. Tie into software, tie into cloud but that's my quick take around as to what I'm looking for. The progress that they're making is we always sniff out what's real. What has some work. Marketing is okay as long as the proof is in the pudding. >> We heard a lot about the delivery. Enterprise, cloud, company is the tag line. That is part of the company's brand. I want to understand how they make the claim. Not just how, how and why they made the claim. They are the leading enterprise cloud company. What does enterprise cloud mean to them when they say that? And you can't have a conversation about enterprise cloud without talking about the developer. So Nutanix by saying that they are enterprise cloud company is they're going in the opposite direction especially of Dell EMC. Dell EMC provides infrastructure to cloud companies. They might point to pivotal in VMware as being the software components of a complete cloud strategy. But Dell EMC itself, infrastructure company. Nutanix is making the claim, they are an enterprise cloud company. How are they pursing the relationship and capability with developers, infrastructure team, operations to make sure that they can live up to that mantle. >> Yeah, Keith, great point to help us wrap up one of the segments we heard talking about Edge computing. Nutanix wants to make invisible Kubernetes Tensorflow functions as a service. Made my head spin a little bit because we know the maturity of those solutions in what you need to do to understand it. So being able to simplify that. Well that would truly be genius. >> That would, if they can Nutanixise that, that will be great. >> Alright, well Keith Townsend, the CTO advisor. Thank you for helping me break down, looking forward to two days of interviews. I'm Stu Miniman. We're going to have wall to wall coverage here from the New Orleans convention center. Nutanix .NEXT 2018. I'm Stu Miniman and thanks for watching theCUBE. (uptempo techno music)