 On The Ground, presented by theCUBE. Here's your host, John Furrier. Hello everyone, welcome to a special On The Ground executive interview with Rob Beard, the CEO of Hortonworks. I'm John Furrier with theCUBE. Rob, welcome to this On The Ground. Thank you. So I got to ask you, you're five years old this year, you're company Hortonworks in June at Hadoop Summit coming up. What a magical run you guys went public. Give us a quick update on Hortonworks and what's going on the five year birthday, any special plans? Well, we're gonna actually host the 10 year birthday party of Hadoop, which is, you know, started at Yahoo in the open source community. So everyone's invited, hopefully you'll be able to make it as well. We've accomplished a lot in the last five years. We've grown to over 1,000 employees, over 900 customers this year. It is our first full year of being public company. And the street has us at $265 million in buildings. So tremendous progress has happened and we've seen the entire data architecture begin to replatform around Hadoop now. CEOs across the globe are facing profound challenges, data, cloud, mobile, obviously it's digital transformation. What are you seeing out there as you talk to your customers? Well, they view that the digital transformation is a massive opportunity for value creation for that enterprise. And they realize that they can really shift their business models from being very reactive post-transaction to actually being able to consolidate all of the new paradigm data with the existing transaction data and actually get to a very proactive model pre-transaction. And so they can, they understand their customers' patterns. They understand the kinds of things that their customers wanna buy before they ever engage in the procurement process and they can make better and more compelling offers at better price points and be able to serve their customers better and that's really the transformation that's happening and they realize the value that that creates between them and their customer. You know, one of the exciting things about theCUBE is we go to all these different industry events and you were speaking last week at an event where data is at the center of the value proposition around digital transformation. And that's really been the key trend that we've been seeing consistently that buzzword digital transformation. What does that mean to you? Because this is coming up over and over again around this digital platform, whether it's digital media or digital engagement, it's all around data. What's your thoughts and what is, from your perspective, digital transformation? Well, it's about being able to drive value from your data and to be able to take that value back to your customers and to your supply chain and to be able to create a completely new engagement with how you're managing your interaction with your customers and your supply chain from the data that they're generating and the data that you have about them. When you talk to CEOs and people in the business out in the field, how much of this digital transformation do you see as real in terms of progress, real progress in terms of total transitions or is it just being talked about now? What's your progress bar meter? How would you peg this trend? Yeah, I would say we're at four and I believe we'll be at six by the end of 2016. And it's one of the biggest movements I've seen since the 90s in ERP because it's so transformational into the business model by being able to transform the data that we have about our collective entity and our collective customer and collective supply chain and be able to apply predictive and real-time interactions against that data as events and occurrences are happening and to be able to quickly offer products and services and the velocity that that creates to monetization and the value creation back is at a pace that's never been able to happen. And they've really understood the importance of doing that or being disintermediated in their existing spaces. You mentioned ERP, it kind of shows our age but I'll ask the question back in the 90s, ERP, CRM. These were processes that were well known that people automated with technology that was at that time unknown. You had a rise of client server technology, local area networking, TCPIP was emerging. So you had some unknown technology stuff happening but known processes that were being automated in, hence you saw that boom. Now you mentioned today, it's interesting because Peter Burris at Wikibon's thesis says, today the processes are unknown and the technology's known. So there's now a new dynamic, it's almost flipped upside down where this digital transformation is exact opposite. IoT is a great use case where these all these unknown things are coming into the enterprise that are value opportunities. Get the technologies known. So now the challenge is how to use technology to deploy it and be agile to capture and automate these future and or real time unknown processes. Your thoughts on that premise? But the answers are buried in the data is the great news. And so the technology, as you said, is there and you have these new unknown processes through internet of things, the new paradigm data sets with sensors and clickstream and mobile data. And the good news is, they generate the data and we can apply technology to the data through AI and machine learning to really make sure that we understand out how to transform the value out of that, out of those data sets. So how does IT deal this? Cause you know, going back 30 years, IT was a clear line of sight, again, automating those known processes. Now you have unknown opportunities. Be able to be in a position for that. Call that cloud, call it DevOps, call it data driven, whatever the metaphor is. People are being agile, be ready for it. How is that different now? And what is the future of data in that paradigm? And how does a customer come to grips and rationalize this notion of I need a clear line of sight of the value, not knowing what the processes is with data. What should they be doing? Well, we don't know the processes necessarily per se, but we do know what the data is telling us because we can bring all that data under management. We can apply the right kind of algorithm, the right kind of tools on it to give us the outcomes that we want and have the ability to monetize and unlock that value very quickly. Hortonworks architecture is kind of designed now at the last Hadoop Summit in Dublin. We heard about the platform. Your architecture is going beyond Hadoop. It says Hadoop Summit, is all Hadoop was the key to big data. Going beyond Hadoop means other things. What does that mean for the customer? Because now they're seeing these challenges. How would you, and how does Hortonworks describe that and what value do you bring to those customers? Yeah, big data was about data at rest and being able to drive the transformation that has been able to consolidate all the transactional platforms into central data architecture. Be able to bring all the new paradigm data sets of the mobile, the clickstream, the IOT data and bring that together and be able to really transition from being reactive post transaction to be able to be predictive and interactive pre-transaction. And that's a very, very powerful value proposition and you create a lot of value doing that. But what's really learned through that process is in the digital transformation journey that actually the further upstream that we can get to engaging with the data, even if we can get to it at the point of origination, at the further stage, at the point of sensor, at the actual time of clickstream and we can engage with that data as those events and occurrences are happening and we can process against those events as they're happening, it creates higher levels of value. And so from the Hortonworks platform, we have the ability to manage data at rest with Hadoop as well as data in motion with the Hortonworks data flow platform. And our view is that we must be able to engage with all data all the time. And so we bring the platforms to bring data under management from the point of origination all the way through as it's in motion and to the point it comes at rest and be able to aggregate those interactions through the entire process. It's interesting you mentioned real time and one of the ideas of Hadoop was it was always going to be a data warehouse killer. Because it makes a lot of sense. You can store the data, it's unstructured data and you can blend in, structure it on top of that and build on top of that. Has that happened and does real times kind of change that equation because there's still a role for a data warehouse if someone has an investment? Are they being modernized? What is the clear that up for me because I just can't kind of rationalize that yet. Data warehouses are old, the older ones, but they're not going away anytime soon from what we're hearing. Your thoughts as Hadoop as the data warehouse killer. Yeah, well our strategy from day one has never been to go in and disintermediate any of the existing platforms or any of the existing applications or services. In fact, to the contrary, what we wanted to do and have done from day one was just be able to leverage Hadoop as an extension of those data platforms. The DW architecture has limitations to it in terms of how much data pragmatically and economically is really viable to go into the data warehouse. And so our model says let's bring more data under management as an extension to the existing data warehouses and give the existing data warehouses the ability to have a more holistic view of data. Now I think the next generation of evolution is happening right now and the enterprise is saying that's great. We're able to get more value longer from our existing data warehouse and tools investment by bringing more data under management, leveraging a combined architecture of Hadoop and data warehouse. But now they're trying to redefine really what does the data warehouse of the future look like. And it's really about how we make decisions, right? And at what point do we make decisions? Because in the world of DW today, it assumes that data is aggregated post transaction, right? In the new world of data architectures across the IT landscape, it says we wanna engage with data from the point it's originated and we wanna be able to process and make decisions as events and as occurrences and as opportunities arise before that transaction potentially ever happens. And so the data warehouse of the future is much different in terms of how and when a decision's made and when that data's processed. And in many cases it's pre-transaction versus post-transaction. Well also I would just add and I wanna get your thoughts on this real time because now in the moment at the transaction, you have now cloud resources, potentially other resources that could become available why even go to the data warehouse. So how has real time changed the game? This data in motion kind of implies real time, whether it's IoT or some sort of bank transaction or something else. How has real time changed the game? Well it's at what point we can engage with the customer but what it really has established is the data has to be able to be processed whether it be on-prem in the cloud or in a hybrid architecture. And we can't be constrained by where the data's processed. We need to be able to take the processing to the data versus having to wait for the data to come to the processing. And I think that's the very powerful part of cloud, the on-prem and software-defined networking. And when you bring all of those platforms together you get the ability to have a very powerful and elastic processing capability at any point in the life cycle of the data. And we've never been able to put all those pieces together on an economically viable model. So gotta ask you, you guys are five years old in June, Hadoop's only 10 years old, still young, still kind of in the early days but yet you guys are a public company. How are you guys looking at the growth strategy for you guys? Because the trend is for people to go private. You guys went public, you're out in the open. Certainly, you know, your competitor Cloudera's private but you know, people can get that, they're kind of behind the curtain. Some say public with the $3 billion valuation but for the most part, you're public. So the question is, how are you guys gonna sustain the growth? What is the growth strategy? What's your innovation strategy? Well, if you look at the companies that are going private those are the companies that are the older platforms, the older technologies in a very mature market that have not been able to innovate those core platforms and they sort of reach their maturity cycle. And I think going private gives them the ability to do that innovation, maybe change their licensing model to subscription and make some of the transformations they need to make and I have no doubt they'll be very successful doing that. Our situation's much different. As the modern IT landscape is re-architecting itself almost across every layer from if you look at what's happening in the networking layer going to SDN certainly in our space with data and it's moving away from just transactional siloed environments to central data architectures and next generation data platforms and being able to go all the way out to the edge and bring data under management through the entire movement cycle. We're in a market that we're able to innovate rapidly. Not only in terms of the architecture of the data platform of being able to bring batch real-time applications together simultaneously on a central dataset and consolidate all of the data but also then be able to move out and do the data in motion and be able to control an entire life cycle. There's a tremendous amount of innovation that's gonna happen there and these are significant growth markets both the data in motion and data at rest market. The data at rest market's a $50 billion marketplace. The data in motion market is a $1 trillion town. So when you look at the massive opportunity to create value in these high growth markets and the ability to innovate and create the next generation data platforms there's a lot of room for growth and a lot of room for scale and that's exactly why you should be public when you're going through these large growth markets in a space that's replatforming because the CIO wants to understand and have transparent visibility into their platform partners. They want to know how you're doing. Are you executing the plan? Or are you hiding behind a facade of one perception or another? Or pivoting or some sort of re-architecture. That's what you're applying. So I think it's very appropriate in a high growth, high innovation market where the IT platforms are going through a re-architecture that you actually are public going through that growth phase. Now it forces discipline around how you operationalize the business and how you run the business. So I think that's very healthy for both the tech and the company. Michael Dell told me he wanted to go private mainly because he had to do some work essentially behind the curtain. Didn't want the 90-day shot clock the demands of Wall Street. Other companies do it because they can't stand alone. They don't have a platform and they're constantly pivoting internally to try to grow up and find that groove swing, if you will. You're saying that you guys have your groove swing and as Dave Vellante always says, always get behind a growing total adjustable market or TAM, you're saying that. Okay, you buy that. So the TAM's growing. What are you guys doing on the platform side that's enabling your customers to re-platform and take advantage of their current data situation as well as the upcoming IoT boom that's being forecast? Well, the first thing is the genesis of which we started the company around, which is we transformed Hadoop from being a batch architecture, single data set, single application to be able to actually manage a central data architecture where all data comes under management and be able to drive and evolve from batch to batch interactive and real-time simultaneously over that central data set and then making sure that it's truly an enterprise viable, enterprise ready platform to manage mission critical water loads at scale. And those are the areas where we're continuing to innovate around security, around data governance, around life cycle management, operations and the management consoles. But then we wanna expand the markets that we operate in and be world-class and best tech on planet earth for that data at rest and our core Hadoop business. But as we then see the opportunities to go out to the edge and from the point of origination, truly manage and bring that data under management through its entire life cycle, through the movement process and create value. And so we wanna continue to extend the reach of when we have data under management and the value we bring to the data through its entire life cycle. And then what's next is, as you have that data in its life cycle, you then move into the modern data applications. And if you look at what we've done with cybersecurity and some of the offerings that we've engaged in the cybersecurity space, that was our first entry and that's proven to be a significant game changer for us and our customers both. Cyber security is certainly a big data problem. Also a cloud opportunity with the horsepower you can get with computing. Give us the update. What are you seeing there from a traction standpoint? What's some of the level of engagements you're having with enterprises outside of the NSA and then the big government stuff, which I'm sure their customers have disclosed that. But for the most part, a normal enterprise are constantly planning as if they're already attacked and they're having different schemes that they're deploying. How are they using your platform for that right now? Well, the nature of attacks has changed and it's evolved from just trying to find the hole in the firewall or where we get into the gateway to how we find a way through a back door and just hang out in your network and watch for patterns and watch for the ability to aggregate relationships and then pose as a known entity that you can then cascade in. And in the world of cybersecurity, you have to be able to understand those anomalies and be able to detect those anomalies that sit there and watch for their patterns to change. And as you go through a whole life cycle of data management between a cloud on prem and a hybrid architecture, it opens up many, many opportunities for the bad guys to get in and have very new schemes. And our cybersecurity models give the ability to really track how those anomalies are attaching, where the patterns are emerging and to be able to detect that in real time. And we're seeing the major enterprises shift to these new models and it's become a very big part of our growth. So I got to change gears and ask about open source. You've been in open source really from the beginning, I would call first generation commercial, but it was not a tier one citizen at that time. It was an alternative to other proprietary platforms, whether you look at the network stack or certainly from software. Now today, it's tier one. Still we hear business people kind of like open source. Why should a business executive care about open source now? And what would you say to that person who's watching about the benefits of open source and some of the new models that could help them? Well, open source in general is gonna give a number of things. One, it's gonna probably provide the best tech, the most innovation in a space. And it's whether that be at the network layer, whether that be at the middleware layer, the tools layer or certainly the data layer. And you're gonna see more innovation typically happen on those platforms much faster and you've got transparent visibility into it. And it brings an ecosystem with it. And I think that's really one of the fundamental issues that someone should be concerned with is what does the ecosystem around my tech look like? And open source really draws forward a very big ecosystem in terms of innovators of the tech but also enablers of the tech and adopters of the tech in terms of incremental applications, incremental tool sets. And what it does and the benefit to the end customer is the best tech, the most innovation and typically operating models that don't generate lock-in for them. And it gives them optionality to use the tech and the most appropriate architecture and the best economic model without being locked in into a proprietary path that they end up with no optionality. So talk about the do-it-yourself mentality, an IT that's always been frowned upon because it's been expensive time consuming. Yet now with organic open source and now with cloud, you saw that first generation do-it-yourself, you're standing up stuff on Amazon what not is being very viable. It funded shadow IT in a variety of other great things around virtualization, visualization and so on. Today, we're seeing that same pattern swing back to do-it-yourself is good for organic innovation but causes some complexities. So I want to get your thoughts on this because this seems to be a common thread on our CUBE interviews and at Hadoop Summit and at Big Data, S&V is part of Big Data Week when we were in town. We heard from customers and we heard the following. It's still complex and the total cost of ownership still too high. That seems to be the common theme for slowing down the rapid acceleration of Hadoop and its ecosystem in general. One, do you agree with that? And two, if so, or what would be the answer to make that go faster? Well, I think you're seeing it excel, right? I think you're seeing the complexities dwindle away through both the innovation in the tech and the maturing of the tech as well as just new tool sets and applications that are leveraging it, that take away any complexity that was there. But what I think has been acknowledged is the value that it creates and that it's worth the do it yourself and bringing together the spare tech because of the innovation that it brings the new architectures and the value that it creates as these platforms move into the different use cases that they're enabling. So I've got to ask you this question. I know you're not going to like it and all the people will always say, which why is everyone always asked that same question? You guys have a radically different approach than Cloudera. It's the number one question I get. Ask them about Cloudera. Cloudera, ask them about Hortonworks. You guys have been battling. They were first, you guys came right fastball over a second, but the other thing we've been following you guys since day one. Explain the difference between Cloudera because now a couple things have changed over the past few years. One is Hadoop wasn't the be all end all for big data. There's been a lot of other things, certainly Spark and some other stuff happening, but yet now enterprises are adopting and coexisting with other stuff. So we've seen Cloudera make some pivots and they certainly got some good technology but they've had some good right answers and some wrong answers. How are you guys managing it? Because you're now public so we can see all the numbers as we know what the business is doing. But relative to the industry, how are you guys compared to Cloudera? What's the differences? And what are you guys doing differently that makes Hortonworks a better vendor than Cloudera? I can't speak to all the Cloudera models and strategies. What I'll tell you is the foundation of our model and strategy is based on, you know, when we founded the company, we wanted, as you mentioned, three or four years pre or post, excuse me, Cloudera's founding, we felt like we needed to evolve Hadoop in terms of the architecture and we didn't want to adopt the batch-oriented architecture. And instead we took the core Hadoop platform and threw yarn and enabled it to bring a central data architecture together as well as be able to be generating batch interactive and real-time applications, leveraging yarn as the data operating system for Hadoop. And then in the real strategy behind that was to open up the datasets, open up the different types of use cases, be able to do it on a central data architecture. But then as other processing engines emerged, whether it be a spark as you brought up or some of the other ones that we see coming down the pipe, we can then integrate those engines through yarn onto the central data platform, right? And then we open up the number of opportunities and that's the core basis and I think that's different than some of the other competitors' technology architecture. Looking back now five years, are there moves that you were going to make that others have made that you looked back at? So I'm glad we didn't do that given today's landscape. Yeah, well, what I'm glad we did do is open up to the most use cases and water clothes and datasets as possible through yarn. And that's proven to be a very, very fundamentally differentiation of our modeling strategy for anybody in the Hadoop space, certainly. And I'm also very happy that we saw the opportunity about a year ago that it needed to be more than just about data at rest on Hadoop. And that actually to truly be the next generation data architecture that you've gotta be able to provide the platforms for data at rest and data in motion and our acquisition of Vanyara to be able to get the NIFI technology so that we're truly capturing the data from the point of origination all the way through the movement cycle until it comes at rest has given us now the ability to do a complete life cycle management for an entire data supply chain. And those decisions have proven to be very, very differentiation between us and any of our other competitors and it's opened up some very, very big markets. More importantly, it's accelerated the time to value that our customers get and the use cases that they're enabling through us. How would you talk about the scenario that people are saying about Hadoop not being the end all be all industry at the same time, cause big data as Arun Murky said on the Cuban Dublin, you know, it's bigger than Hadoop now but Hadoop has become synonymous with big data generally. Where's the leadership coming from your mind? Because we're certainly not seeing in the data warehouse side. Cause those guys still have the old technology trying to coexist and replatform for the future. So the question is, is Hortonworks viewing Hadoop as still leading it generically as a big data industry or has it become a sidebar of the big data industry? Of Hadoop, Hadoop is the platform and we believe ground zero for big data but we believe it's bigger than that. It's about all data and being able to manage the entire life cycle of all data and that starts from the point of origination until it comes at rest and be able to continue to drive that entire life cycle. Hadoop certainly is the underpinning of the platform for big data but it's really gotta be about all data. Data at rest, data in motion and what you'll see as the next leg in this is the modern data applications that then emerge from that. How has the ecosystem in the Hadoop industry, I would agree with by the way that the Hadoop players are leading big data in general in terms of innovation. The ecosystem's been a big part of it. You guys have invested in it and it's certainly a lot of developers and it's open source. How has the ecosystem changed given the current situation from where it was and where do you see the ecosystem going? With the replatforming, not everyone can have a platform. There's a ton of guys out there that have tools that are looking for a home that's trying to figure out the chess board on what's going on with the ecosystem. What's your thoughts of the current situation and how it will evolve in your view? Well, I think one of the strongest statements from day one is whether it's EDW or BI or relational, none of the traditional platform players say the way you solve your big data problem is with my platform. They to a company have a Hadoop platform strategy of some form to bring all of that huge volume of big data under management. And it fits our model very well in that we're not trying to disintermediate but extend those platforms by leveraging HDP as an extension of their platform. And what that's done is it's created pool markets. It's brought Hadoop into the enterprise with a very specific value proposition in use case, bringing more data under management for that tool, that application or that platform. And then the enterprises realize there's other opportunities beyond that and new use cases and new data sets we can also gain more leverage from. And that's what's really accelerated. So you see growth in the ecosystem. We're actually seeing exponential acceleration of the growth around the ecosystem. Not only in terms of the existing platform and tools and applications for Hadoop or adopting Hadoop but now new startup companies building completely from scratch applications just for the big data sets. Let's talk about startups. So we were talking before we sat down about the challenges being an entrepreneur. You mentioned the exponential acceleration of entrepreneurs coming into the ecosystem. That's a safe harbor right now. It seems to be across the board and a lot of the big platforms have robust and growing ecosystems. What's the current landscape of startups? I know you're an active investor yourself and you're involved in a lot of different startup conversations and advisor. What's your view of the current landscape right now? Series A, B, C, growth, stalling. What needs to be in place for these companies to be successful? What are some of the things you're seeing? You have to be surgically focused right now or on a very particular problem set maybe even by industry and understand how to solve the problem and have an absolute correlation to a value proposition and a very well-defined and clear model of how you're gonna go solve that problem, monetize it and scale. Or you have to have an incredibly well-financed and deep war chest to go after a platform play that's going after a very large TAM that is enabling or replatforming at one of the levels. And the new IT landscape. So laser focus in a stack or vertical and or a huge cash-funded benchmark or other tier one VCs to have a differentiator. They have to have some sort of- To enable the next generation platform and something that's very transformational as a platform that really evolves the IT stack. What strategies would you advise entrepreneurs in terms of either white spaces to attack and or their orientation to this new data layer because if this plays out as we were talking about you're gonna have a horizontal data layer where you need that interoperability. You need to have data in motion but data aware, smart data integrated and disparate systems. Breaking down the siloed concept. How should an entrepreneur or developer look at that? Is there a certain model you've seen work successfully? Is there a certain open source group they can jump into? What thoughts would you share? It seems to be the toughest nut to crack for entrepreneurs. Right now you're seeing a massive shift in the IT data architecture is one example. You're seeing another massive shift in the network architecture for example to SDN. You're seeing I think a big shift in the kinds of applications getting away from application functionality to data enabled applications. And I think it's important for the entrepreneur to understand where in the landscape do they really want to position? Where do they bring intellectual capital that can be monetized? Some of the areas that I think you'll see emerge very quickly in the next four, six, eight quarters are the new optimization engines and so things around AI and machine learning. And now that we have all of the data under management through its entire life cycle how do I now optimize both where that data is processed in the cloud or on prem or as it's in motion? And there's a massive opportunity through software defined networking to actually come in and now optimize at the purest price point and or efficiency where that data is managed, where that data is stored and let it continue to reap the benefits. Just as Amazon's done in retail, if you like this you should look at that. Just as Yahoo did, I'll point out with Hadoop, it's advertising models and strategies of being able to put specific content in front of you. Those kind of opportunities are now available for the processing and storage of data through the entire life cycle across any architectural strategy. Are you seeing data from a developer standpoint being instrumental in their use cases? Meaning as I'm developing on top of data platforms like court and worse or others, where does disparate data? What's their interaction? What's their relationship to the data? How are they using it? What do they need to know? Where's the line in terms of their involvement in the data? Well, what we're seeing is a very big movement with the developed community that they now wanna be able to just let the data tell them where the application service needs to be. Because in the new world of data, they understand what the entity relationships are with their customers and the patterns that their customer's happening. And they now can highly optimize when their customers are about to crossover into from one event to the other. And what that typically means and therefore what the inverted action should be to create the best experience with their customer, to create a higher level of service, to be able to create a better packaged price point at a better margin. They also have the ability to understand in real time based on what the data trend is flowing, how well their product's performing. Any obstacles or issues that are happening with their product. So they don't wanna have to have application logic that then they run a report on three days, three weeks after some events happened. They now are taking the data and as that data and events are happening in the data and it's telling them what to do and they're able to prescriptively act on whatever event or circumstances. They want the data now. They want real time data embedded in the apps as a frontline developer. And they wanna optimize what that data is doing as it's unfolding through its natural life cycle. Let's talk about your customer base and what their expectations are. What questions should a customer or potential customer ask to their big data vendor as they look at the future? What are the key questions they should ask? They should really be comparing what is your architectural strategy first and foremost for managing data, right? And what kinds of data can I manage? What are the limitations in your architecture? What workloads and data sets can't I manage, right? What are the latency issues that your architecture would create for me, right? What's your business model that's associated with us engaging together, right? How much of the life cycle can you enable of my data, right? How secure are you making my data, right? What kind of long tail of visibility in chain of custody can I have around the governance? What kind of governance standards are you applying to the data? How much of my governance standards can you help me automate, right? How easy is it to operate and how intuitive is it? How big is your ecosystem, right? What's your roadmap and your strategy? What's next in your application stack? So enterprises are looking at simplicity and they'll get that total cost of ownership. How is big data innovation going to solve that problem? Because with IoT, again, a lot of new stuff's happening really, really fast. How do they get their arms around this simplicity question and this total cost of ownership? How should they be thinking about it? Well, what the Hadoop platforms have to do and the data and motion platforms have to do is to be able to bring the data under management and bring all of the enterprise services that they have in their existing data platforms in the areas of security and the areas of management and the areas of data governance so that they can truly run mission critical workloads at scale with all the same levels of predictability that they have in isolation and their existing proprietary platforms and be able to do it in a way that's very intuitive for their existing platforms to be able to access it, very intuitive for their operations teams to be able to manage it and very clean and easy for their existing tools and platforms investments to leverage it. On the industry landscape right now, what are you seeing if a consolidation? Some are saying we're seeing some consolidation, a lot of companies going private, you're seeing people buckle down. It's almost, there's almost a line, if you weren't born before a certain date for the company, you might have the wrong architecture. Certainly enterprises are replatformed, I'm gonna agree with that, but as a supplier to customers, you're one of the young guys, you were born in the cloud, you're born and open source for it and works. Not everyone else is like that. I mean, certainly Oracle's one of the big guys that keep on doing well, IBM's been around, but they're all changing as well. And certainly a lot of these growth companies pre-IPO are kind of being sold off. What's your take on the current situation with the bubble, the softening, whatever people calling it, what's your thoughts? Yeah, I think you see some companies who got caught up in if we sort of unpack that to the ones who are going private now. Those are the companies that have operated in a very mature market space. They were able to not innovate as much as they would probably have liked to. They're probably walked into a proprietary technology in a non-subscription model of some sort, maybe a perpetual license model. And those are very different models than the enterprise wants to adopt today and their ability to innovate and grow because the market shrink forced them to go into very constrained environments. And ultimately they can be great companies, they have great value propositions, but they need to go through transformations that don't include a 90-day shot clock in the public market. In the markets where there's, maybe I was in the B round or the C round and I was focused on providing a niche offering into one of those mature spaces that's becoming disintermediated or evolved quickly because an open-source company has come into the space or that section of the IT stack has emerged or morphed into more of a cloud-centric or a SaaS-centric or an open-source-centric environment. They've gotten cut short, their market's gone away, their market's shrunk, they can't innovate their way out of it. And they then ultimately have to find a different approach. And they may or may not be able to get the financing to do that. We're in a much different position. Certainly the down rounds, you're seeing down rounds from the high valuations. That's the first sign of trouble. That's the first sign. I've gotten three calls this week from companies that are liquidating and have two weeks to find a new home. We'll look for some furniture for our new growing silk and angle office. I think you'll have some good values. You personally, looking back over five years now in this journey, what a credible run you guys have had and fun to watch you guys. What's the biggest thing that surprised you and what's the biggest thing that's happened? Talk about those two things. Because again, a lot's happened. The markets changed significantly. You guys went public, got a big office here. What surprised you and what was the biggest thing that you think was the catalyst of the current trajectory? How quickly the market grew. We saw from day one when we started the company that this was a billion dollar opportunity. And that was the bar for starting whatever we did. We were looking for new opportunities. We had to see a billion dollar opportunity. How quickly we have seen the growth and the formation of the market in general. And then how quickly some of the new opportunities have opened up in particular around streaming, Internet of Things, the new paradigm data sets. And how quickly the enterprises have seen the ability to create a next generation data architecture and the aggressiveness in which they're moving to do that with the do. And then how quickly in the last year it swung to also being able to want to bring data in motion under management as well. If you could talk to a customer right here, right now, and they asked you the following question. Rob, look around the corner five years out. Tell me something that someone else can't see, that you see, that I should be aware of in my business. And why should I go with Hortonworks? It's gonna be a table stake requirement to be able to understand from whether it be your customer or your supply chain, from the point they begin to engage in the first step towards engaging with your product or your service, what they're trying to accomplish. And to be able to interact with them from that first inception point. It's also gonna be table stakes to understand and be able to monitor your product in real time. And be able to understand how well it's performing down to the component level so that you can make real time corrections, improvements and be able to do that on the fly. The other thing that you're gonna see is that it's gonna be a table stake requirement to be able to aggregate the data that's happened in that life cycle. And so, and give your customer the ability to monetize the data about them. But you as the company, as the enterprise will be responsible for creating anonymity, confidentiality and security of the data. But you're gonna have to be able to provide that the data about your customers and give them the ability to, if they choose to monetize the data about them, the ability to do so. So I get that correct. You're basically saying 100% digital. Oh, it's by far within the next five years, absolutely. If you do not have a full digital model in most industries, you'll be disintermediated. Final question, what's the big bet that you're making right now at Hortonworks? That you say, okay, we're betting the company on blank. It's not about big data. It's about all data under management. Rob, thanks so much for spending the time here on the ground. Rob Beard, CEO of Hortonworks here for an executive on the ground. I'm John Furrier theCUBE. Thanks for watching.