>> Live from New York, it's theCUBE, covering theCUBE, New York City, 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> And welcome to theCUBE here in New York City. We're live from CUBE NYC, this is our big data now: AI, now all things cloud 9 years covering the beginning of Hadoop. Now into cloud and data as the center of the value I'm John Furrier with David Vellante. Our special guest is Rob Bearden, CEO of Hortonworks CUBE alumni, been on many times Great supporter of theCUBE, legend in OpenSource Great to see you. >> It's great to be here, thanks. Yes, absolutely. >> So one of the things I wanted to talk to you about is that OpenSource certainly has been a big part of the Ethos, just seeing it in all sectors, again, growing even in Blockchain, Open Ethos is growing. The role of data now certainly in the center. You guys have been on this vision of open data, if you will and making data, and move and flight, maybe rest all these things are going on. Certainly the Hadoop world has changed, not just Hadoop and data lakes anymore, it's data. All things data, it's happening. This is core to your business, you guys have been banging this drum for a long time. Stock's at an all-time high. Congratulations on the business performance. So it's working, things are working for you guys. >> I think the model in this strategy are really coming together nicely. And to your point, it's about all the data. It's about the entire life-cycle of the data and bringing all data under management through its entire life-cycle. And being able to give the enterprise that accessibility to that data across each tier on-prem, private cloud, and across all the multi-clouds. And that's really changed, really in many regards, the overall core architecture of Hadoop and how it needs to manage data. And how it needs to interact with other data sources. And our model and strategy is been about not going above the Hadoop stack, but actually going out to the edge, and bringing data under management from the point of origination through its entire movement life-cycle until it comes at rest, and then have the ability, to deploy and access that data across each tier and across a multi-cloud environment. And it's a hybrid architecture world now. >> You guys have been on this trend for a while now, it's kind of getting lift obviously you're seeing the impact that cloud, impact AI cause the faster computer you have, the faster you can process data, the faster the data can be used, machine learning it's a nice flywheel. So again, that flywheel is being recognized. So I have to ask you, what is in your opinion, been the impact of cloud computing, specifically the Amazons, and the Azures, and now Google where certainly AI is in the center of their proposition, now hybrid cloud is validated with Amazon announcing RDS on the premises on VMWARE. That's the first Amazon ever, ever on premises activity. So this is clearly a validation of hybrid cloud. How has the cloud impacted the data space, and if you will, it used to be data warehousing, cloud has changed that. What's your opinion? >> Well what's it's done is given a, an architectural extension to the enterprise of what their data architecture needs to be, and the real key is, it's now, it's not about hybrid or cloud or on-prem, it's about having a data strategy overall. And how do I bring all my different assets, and bring a connected community together, in real-time? because what enterprise is trying to do is, connect and have higher velocity and faster visibility between the enterprise, the product, their customer, and their supply chain. And to do that, they need to be able to aggregate data into the best economic platform from the point of origination, maybe starting from the component on their product, a single component, and be able to bring all that data together through its life-cycle, aggregate it, and then deploy it on the most economically feasible tier. Whether that's on-prem, or a private cloud, or across multiple public clouds. And our platform with HDF, HDP, and data plane and complete that hybrid data architecture. And by doing that, the real value is then the cloud, AI and machine learning capabilities have the ability now to access all data across the enterprise, whether it be their tier in the cloud, or whether that be on-prem. And our strategy is around bringing that and being that fabric, to bring all the interconnectivity irrespective of whether it sits on the edge and the cloud is somewhere in between. Because the more accessibility AI has to data, the faster velocity of driving value back in to that AI cycle. >> Yeah, people don't want to move data if they don't have to And so, and we've been on this for a while, that this idea that you want to bring the cloud model to your data, and not the data to the cloud always. And so, how do you do that? How do you make it this kind of same, same environment? What role does HortonWorks play in it? >> Well the first thing we want to do is, bring the data under management from and through its life-cycle where HDF goes to the edge, brings the data through its movement cycle, aggregates the streams. HDP is the data at rest platform that can sit on-prem and a public cloud or a private cloud. And then data plains that fabric, that ensures that we have connectivity to all types of data across all tiers. And then serves as the common security and governance framework, irrespective of which tier that is. And that's very very important. And then that then gives the AI platforms the ability to bring AI onto a broader array of data, that they can then have a higher and better impact on it than just having an isolated AI impact on just a single tier I data in the cloud. >> Well that messages seems to be resonating, we talked earlier about the stock price, but also I think Neil Bushery and Frank Sluben popularized the metric of number of seven-figure deals. You guys are closing some big deals, and remember in the early days Robert Vor Breath, people are like how these guys going to sell anything, it's all open-source and you're doing a lot of a million plus dollar deals. So it's resonating not only with the streep but also with enterprises, your thoughts. >> Last quarter we, I think the key is that the industry really understands, the investors understand, the enterprises really now understand the importance of hybrid and hybrid cloud. And it's not going to be all about managing data lakes on-prem. All the data's not going to go and have this giant line of demarkation and now all reside in the cloud. It has to coexist across each tier and our role is to be that aggregation point. >> And you've seen the big cloud players now, all it's the big three, all have on-prem strategies. Azure with Azure Stack, Google we saw Kubernetes on-prem, and even AWS now, the last load up putting RDS on-prem announced that VMWorld. So they've all sort of recognized that not everything's going to go into the cloud. So that's got to be, you know good confirmation for you guys >> It's great validation. What is also says though is, we must have cloud first architecture and a cloud first approach with all of our tech. And the key to that is, from our standpoint, within our strategy is to containerize everything. And we had an announcement earlier this week that was really a three-way announcement between us, Red Hat, and IBM; and the essence of that announcement is we've adopted the Kubernetes distro from Red Hat. To where we're are containerizing all of our platforms with Red Hat's Kubernetes distribution. And what that does, is gives us the ability to optimize our platforms for OpenShift, the Red Hat pass, and optimize then the deployment of that and the IBM private cloud, right. And naturally data plane will also then give us the ability, to extend those workloads; those very granular workloads up in to the public clouds, and we can even leverage their native objects stores. >> So that's an interesting love triangle right? You and Red Hat are kind of birds of a feather with open-source. IBM has always been a big proponent of open-source, you know funded Linux in the early days. And then brings this, a massive channel and brand, you know to that world. >> Yes. And you know this is really going to accelerate our movement into a cloud first architecture, with pure containerization. And the reason that's so important is, it gives us that modularity to move those applications and those workloads, across whichever tiers most appropriate architecturally for it to run and be deployed. >> You know we said this on theCUBE many many years ago, and continues to be this theme, enterprise is one really wanting hardened solutions, but they don't mind experimenting. And Stu Miniman and I, were always talking about and comparing OpenStack ecosystem to what's happened in the Hadoop ecosystem. There's some pockets of relevance and it's a lot of work to build your own, and OpenStack has a great solution for certain use cases, now mostly on the infrastructure side But when cloud came in and changed the game, because you saw things like Kubernetes. I mean we're here at the Hadoop show that started with Hadoop, now it's AI, the word Kubernetes is being talked about. You mentioned hybrid cloud, these aren't words that were spoken at an event like this. So the IT problem in multi-cloud has always been a storage issue. So you do some storage work, you got to store the data somewhere, but now you're talking about Kubernetes. You're talking about orchestration around workloads, the role of data in workloads. This is what enterprise IT actually cares about right now. This is not like, a small little thing, it's a big deal because data is not only in the workloads, they're using instrumentation with containers, with service meshes around the coin. You're starting to see policy, this is hardcore B2B enterprise features. >> This is where with what we're seeing is a massive transformational shift of how the IT architecture's going to look for the next 20 years. Right. The IT world it is been horribly constrained from this very highly configured, very procedural-based applications and now they want to create high velocity engagement between the enterprise, their product, their customer and supply chain. They were so constrained with these very procedural-based applications and containerization gives the ability now to create that velocity and to move those workloads, and those interactions between that four pillars. >> Now let's talk about the edge. Cause the pendulum is clearly swinging sort of back to some decentralization going on, and the edge to us is a data play. We talk about it all the time. What are your thoughts on the edge, where does HortonWorks fit? What's your vision of the data modeling and how that evolves? >> That goes back to, the insight to that would be our strategy and what we did and had the great fortune, quite frankly, of having the ability to merge on Yara and HortonWorks back in 2015. And we wanted, and the whole goal of that besides working with a great team, Joe Witt had built, is being able to get to the edge. And what we wanted to have the ability to do, was to operate on every sensor, on every device at the edge for the customer so that they could bring the data under management whenever that may be, through its entire life-cycle; so from point of origination through its movement until it comes at rest. So our belief is that if we can bring enough intelligence and faster insights as that data is being generated, and as events or conditions are happening, moving, or changing before it ever comes to rest we can process and take prescriptive action. Leveraging AI and machine learning as it's in its life-cycle we can dramatically decrease the amount of data we have to bring to rest. We can just bring the province the metadata to rest and have that insight. And we try to get to these high velocity, real-time insights starting with the data on the edge. And that's why we think it's so important to manage the entire life-cycle. And then, what's even more important is then put that data, on to what ever tier. That may be bring it back to rest in a day like on-prem, right, to aggregate with other like data structures. Or it may be, take it into cold storage on a native object store in a cloud, that has the lowest cost of storage structure for a particular time. >> Or take an action on the edge and leave it there. >> Yeah. You guys definitely think about the edge in a big way, that's pretty obvious. But what I want to get your thoughts on is an emerging area we're watching, and I'll call it for lack of a better description, programmable data. And you mentioned data architecture is being setup probably set a 10, 20 year run for enterprises they setup their data architecture with the cloud architects. Making data programmable is kind of a dev-ops concept right. And this is something that you guys have thought about with the data plane, what's your reaction to this notion of making data programmable? When you start talking about Kubernetes, you're going to have statefull applications, stateless applications, you have new dynamics I call it API 2.0 happening. Whole new infrastructure happening, data has to be programmable, going to need policy around it, the role of data's certainly changing rather than storing it somewhere. What's your view of programmable data, making it programmable? >> Well you've got to be able to, to truly have programmable data, you can't have slices of accessibility or window. You have to understand the lineage of that entire data, and the context of that data through its entire life-cycle. That's step and point number one. Point number two is, you have to be able to have that containerized so that you can take the module of data that you want to take prescriptive action against, or create action against a condition. And to be able to do that in granular bites or chunks, right. And then you've got to have accessibility to all the other contextual data, which means whether that's as its in motion as its at rest or, as its contextual cousin if you will, that sits up in an object store on another tier in a public cloud. Right. But what's important is that you have to be able to control and understand the entire lineage of that. And therefore, that's where our second step in this is data plane. And having the ability to have a full security model through that entire architectural chain, as well as the entire governance and lineage leveraging, leveraging atlas through data plane. And that then gives you the ability to take these very prescriptive actions that are driven through AI and machine learning insights. >> And that makes you very agile, love it. I mean the ethos of open-source and dev-ops is literally being applied to every thing. We see it with at the network layer, you see it at the data layer, you're starting to see this concept of dev and ops being applied in a big way. >> The next you know, previous years we've talked about what we're trying to accomplish. And we've started HortonWorks, it was about changing the data architecture for the next 20 years and how data was going to be managed. And that's had, to your earlier point we opened up the show, that's had twists and turns. Hadoop's evolved, the nature and velocity of data has evolved in the last five, six, seven, eight years you know. It's about going to the edge, it's about leveraging the cloud and we're very excited about where we're positioned as this massive transformation's happening. And what we're seeing is the iteration of change, is happening at an incredibly fast pace. Even much more so than it was two, three years ago. >> Yeah, the clock speeds definitely up, their data is working. People putting it to work. What works... >> They're able to get more value faster because of it. >> The AI is great. >> The data economy is here and now. And the enterprise understands it. So they want to now move aggressively to change and transform their business model to take advantage of what their data is giving them the ability to do. >> That's great. They always want the value, and they want it fast and anything gets in the way they'll remove the blockers as what we say. >> Alright, it's theCUBE here Rob Bearden, CEO of Hortonworks giving his vision but also an update on the company; data at the center of the value proposition. This is about AI, it's about big data, it's about the cloud. It's theCUBE bringing you, theCUBE data here in New York City. CUBENYC, that's the hashtag; check us out on Twitter. Stay with us for a live coverage all day today and tomorrow here in New York City. We'll be right back after this short break. (upbeat music)