(lively brass music) >> Narrator: Live from New Orleans, Louisiana. It's the Cube covering .NEXTconference 2018. Brought to you by Nutanix. >> So, you're watching the Cube and there's 55 hundred in attendance, here at the Nutanix .NEXTconference. Getting ready for a big party this evening at Mardi Gras world, get a flavor for the local cuisine and one of the things we always love at the show is, really, being able to dig in with the practitioners. Happy to welcome to the program, first, my guest Mike Spencer, vice president of hosting and managed services at ICF Olson, thanks so much for joining us. >> Well thank you very much for having me. It's been a great event, so far. Very inspiring keynote speech this morning. >> Awesome, so Mike. First of all, it's your first time here at .NEXT, tell us what brought you here and a little bit of background of yourself and your organization. >> Yep, so one of the reasons why we came here is my team is up for an award. We've been a user of the Nutanix platform for about three and a half years and it's done a lot to help us in our position in the marketplace, and so part of this is giving a little bit back, and some of it's, you know, coming to hear about what's next, so. >> So actually, could you tell us, what does this award mean to you, your team, and everything like, some people, like there's vendor awards, there's show awards, and like what's that like? >> Well, you know, I think my team is really excited to have some sort of external validation that, you know, the last three and a half or four years that we've been working towards this, you know, journey towards dev ops and infrastructures code, that somebody externally is starting to recognize that what we've done is great, and appreciating that work, so. >> Alright, so Kieth and I, I think are, you know, excited to dig in, we hear things like dev ops and infrastructures code. Something we've been documenting and talked to a lot of customers about, kind of digital transformation. Can you tell a little bit of the story? Bring us back. What was the challenge? What'd your organization look like and walk us through what you did. >> Yeah, so I think initially, very traditional IT team. Really managing things on a per-server basis, on a per-client basis and really needing that guy there to click next or to pay attention to a server. Really kind of that old adage of treating all of our servers like a pet versus more like cattle, which is where we are today and the efficiency around it. So we had some issues around stability, performance, availability, those types of things that really drove us to take a different look at the way we were doing things and so that's kind of what kicked us off on the journey to start looking at, how do we totally rethink this whole space and bring innovation in, in a space that historically doesn't have a ton of innovation. >> So let's talk about the innovation because, you know, the whole thing, whole thing services, you buy commodity hardware as cheap as possible, let it run as long as possible. When I think of Nutanix, I don't think commodity. Help bring the story together for us. >> Yep, so, you know, as architecturally, as we looked at everything we were doing, one of the unique things that we did is we decided to look at our infrastructure as more of a service-based architecture, which is very much more of a software development look at the world, versus an infrastructure look and some of the key tenants in that space are around driving for simplicity in your environment, and the Nutanix platform helped us eliminate a lot of the specialties that we needed in our area, right? So we are very much a commodity type person when it comes to servers, right? The name on the front of the server wasn't really important but what was really important for us and what Nutanix brought to the table was, they merged together all of the pieces in the server part of the stack down to the network stack. We no longer had to deal with things like storage. I didn't need to have SMEs on staff that were specialists in that space. It helped to simplify our networks. It helped us manage things through a single pane of glass, right? And we did it all in a very cost effective way. For us, it really helped us take that 25% of our labor in that space and refocus about 25% of it into really driving forward with the infrastructures code and dev ops methodologies. >> Mike, what does this mean for your business? Funny, I look at your website. It's a customer experience agency built to help you through this digital transformation. It's like, wow, it's what we're talking about at this kind of show. What does that mean to your company and, you know, your end users? >> So ICF Olson is the marketing services wing of ICF, our parent company, which is a large consulting wing but from a customer experience agency standpoint, we span everywhere from PR, brand, all the way down the stack, including managed services and hosting. A lot of our clients say, hey, you know what, you guys are really good at designing this. Why don't you guys go and run it for us? And so that's really where my art comes into place, is not just the hosting of something but also the running of something and working with the clients. It allowed us to become more of an end to end agency, right? It allowed our clients to focus on things more important like, you know, how they were going to change their brand, how they were going to look at the market, how they were going to advertise. And so from a business perspective, itself, one of the things that it did is it helped enable, you know, frankly we want a lot more business, right? Because we were willing to take these things on. We were able to repeat those types of things with a high level of success, so. >> How do you measure success? >> Success is... In our space, in particular? Honestly it is our clients not having to interact with us. (laughing) Right? We're not the sexy part of the digital ecosystem. (laughing) >> Modernization of data center is a critical piece of it. Clients are looking to you to basically make that invisible. The data center should be just something that they consume. As Nutanix has moved, you've been a customer for three years and Nutanix has moved from a hardware, software appliance, where they're selling you the entire platform to software only solution, how has, what has that meant to your business? >> Well, I mean, it's allowed us to take our focus off being experts in the hardware space. Again, something that didn't necessarily bring value, even in our private cloud. We do manage both public and private cloud but our private cloud space, it allowed us to not have to focus on the energy there and really allowed our infrastructure team to become more of a software development team. So that's been a big, big win for us. >> Talk to us a little bit about the organizational dynamics, rolling out dev ops. What did that mean for your team? You say things are invisible now. Was there a adjustment in head count, or roles, or retraining that you can share? >> Yes to all of that. In its simplest form, yeah. So a lot of people look at the implementation of dev ops being something that's kind of done to an infrastructure team. Right, it's designed to make an infrastructure team look more like a software development team or work more fluidly with a software development team and I think those things are all true but it also helped us transform our overall SDLC for software development. There's a lot of things. As we continue to build skill and trade out skill, right, continue to move up the stack, we basically became middleware developers, to where, now, our software developers for our core products and things that we sell for our clients, and support for our clients, those developers are now working on purely code and the aesthetics of things, the UX side of it. Where we are much more managing the middleware component, which interacts nicely with the hyper-converged platform. Right, Nutanix. There was a shift in labor, without a doubt. As you mature through the process you do a lot of investment in people. Right, making sure that they're kind of keeping up with the times, understanding the new methodologies. Huge shifts from the methodologies that a traditional IT team would use to what a software development team uses, right? It wasn't only moving an infrastructure team into that methodology, it was also getting the business and the software development team we work with used to us working more like them versus more like the old IT team. And so honestly, we probably caught the software development team more off guard than we did ourselves, so. (laughing) >> There's another side of that coin. As you develop that skill, as you develop that capability, retention becomes a problem. There's a natural headcount where, you know what, you don't need as many people to come in at midnight to do firmware revisions, do the low level work, but as they skill up, you look around, you know, you look at what happens in the rest of the dev ops movements, where you have entire teams leaving the fortune 500s to go to another fortune 500 to implement their dev ops. How do you encourage your team to stay? >> So to me, it's all about culture, right? Our team can work remote. They all choose to be in the office, right? They enjoy each other. It's also investing in people and investing in their growth. So it's not always about, necessarily, the size of a paycheck. It's also about work-life balance, the willingness of the organization to invest in their people, and giving them time to innovate. I mean, when you talk to the majority of infrastructure guys or even technology guys out there, what drives them every day is not necessarily their paycheck. That's a side effect of the good work they do. It's really the challenge, the pure problem solving of IT. We give them that opportunity to be able to innovate. >> Tell us a little bit about your Nutanix solution that you have, what you started with, how much you grew, what's not on your Nutanix today? >> So private cloud, we are 100% Nutanix today. We started with a four node environment that was, really, purpose built around our analytics platforms. We were looking for some way to isolate IOPS from our production environment. More of a standard, three tier architecture (clearing throat) and we did some research out there, this is at the same time that we're rethinking the architecture of everything, really kind of looking at the way we do business, and we came across several vendors, one of them being Nutanix. It was a very young company, fairly unproven, in at least our market, but their message was exactly the same message that we had developed and so we decided to take a chance on them. We put them in. You know, we did some load testing between that platform and our traditional platform and were very pleasantly surprised to find what we found. Almost a three X increase in disc IOPS and so we went live with this analytics platform, and really did a lot of testing there, right? And then we kind of started the natural process after we got comfortable with it for about six months of hey, why don't we start working through the life cycle process and bring through, bringing in Nutanix to offset? Instead of buying, you know, a storage shelf, right? I can go get a Nutanix cluster that has the same amount of storage but also brings compute with it. (clearing throat) So once we started doing that, we started putting production workloads onto the Nutanix platform and seeing great results. We expedited our journey. Within about a year and a half, we had replaced all of our traditional stand and compute plaforms. So the infrastructure guys, once they saw it in action, once the business saw the results, even the financial side of it, (laughing) you know, we were almost asked to expedite the process of moving towards Nutanix. Which, for us, it was great because it was less to manage. >> So as you guys moved to the Nutanix infrastructure, talk about the more advanced services that they've offered over the past few years. Specifically, the hypervisor, haven't you guys embraced AHV? >> So we have in dev. We are not running it as our primary hypervisor right now. In our architecture, we run VMware today. I'm not probably supposed to say that here but we run VMware. We have been looking at Acropolis. Really, the way we look at the hypervisor is as a component in our service space architecture. We are in a position where we can replace that because it's not an important part to us. We just haven't had the cycles in our roadmap to be able to put towards the replacement of VMware, yet. But it is certainly something on our roadmap and something we're marching towards because the APIs have continued to evolve on the Nutanix platform, we work quite closely with Nutanix on that. They seem to accommodate a lot of our asks but, yeah, it really has been more of a time thing, you know? There's so many things to code in this space, right now. >> You've got the award but what were you looking to really accomplish this week? Are there sessions you're looking for, are there products you're looking to dig into for you and your team? >> A lot of it was about vision, right? How well does the Nutanix vision align with our vision? And, like I said, from the keynote speeches this morning and some of the new services we see coming out, I think they're doing a great job. Their head is where our head is. They're headed the same direction we are. You know, in a lot of places where we're doing custom development, we can actually go in and say, hey, why don't we acquire this? You know, one of the exciting announcements this morning was around Beam. The ability to do compliance across our cloud platforms. We run today about 50% public cloud, 50% private cloud just depending on what the solution is we're providing, so it gives us that one pane of glass. >> What public clouds are you using and how does that, kind of the hybrid, hybrid world that Nutanix laid out this morning fit into your vision? >> Well, so. The right answer for me should be it shouldn't matter what cloud I'm running. But we are running Azure as well as AWS, just depending on the solution. So we have partnerships on both sides. But we don't necessarily look at them as being a long running relationship because, you know, this is a very, this space is changing at a very rapid pace. You know, who knows who the next person is that's going to stand up that we need to support. So we're very platform-agnostic when you look at it. When we deploy something, it really doesn't matter if it's on private cloud, public cloud, doesn't really matter. To us, it's just all building blocks that we plug in together and let code do its job. So, in that model, you guys do 50% public, 50% private. Nutanix has an opinionated view of cloud. How does that impact your business and services? >> Nutanix's approach? >> Yeah they're vision versus the...? >> Yeah, well I think their vision's great, right? Because it is a fairly agnostic vision. With them being, obviously, wanting the private cloud side of that but understanding that there is no 100%, you know, private cloud and 100% public cloud in today's world. It is all hybrid cloud environment and that certain workloads are better on prim, and certain workloads are better in the public cloud. I think that was in total alignment with everything we do. Our primary job is web hosting. So we deal with geographic workloads all the time. >> Well, Mike Spencer. I wish best of luck to the ICF Olson team. >> Yeah, thank you very much for having me. >> On the award this afternoon. You're a big winner in our books either way. Kieth Townsend, I'm Stuart Miniman. Thanks so much for watching the Cube. We'll be back with lots more. (electronic music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

>>Welcome back to super cloud 22, our inaugural event. It's a pilot event here in the cube studios we're live and streaming virtually until we do it in person. Maybe next year. I'm John fury, host of the cube with Dave Lon two great guests, distinguished engineers managers, CTOs investors. Mariana Tessel is a CTO of Intuit ins Ray founder of vertex ventures. Both have a lot of DNA. Founder allow cloud here with mark Andre and Ben Horowitz, a variety of other great ventures you've done. And now you're an investor. Yep. Maria, you've been a seasoned CTO, VP of engineering, VMware Docker Intuit. Now thanks for joining us. >>Absolutely. >>So super cloud is a, is a thing. And apparently it's got a lot of momentum and you guys got stats over there at, at Intuit in, so you're investing and we were challenged on super cloud. Our initial thesis was you build on the clouds, get all that leverage like snowflake, you get a good differentiation and then you compete and then move to other clouds. Now it's becoming a thing where I can do this. Every enterprise could possibly do it. So I want to get your guys thoughts on what you think of super cloud concept and where are the holes in it, what needs to be defined. And so we'll start with you. You've done a lot of cloud things in your day. What >>Do you think? Yeah, it's the whole cloud journey started with a desire to consolidate and desire to actually provide uniformity and, and standards driven ways of doing things. And I think Amazon was a leader there. They helped kind of teach everybody else. You know, when I was in loud cloud, we were trying to do it with proprietary stacks just wouldn't work. But once everyone standardized upon Unix and you know, the chip sets no longer became as relevant. They did a lot of good things there, but what's happened since then is now you've got competing standards at the API layer at the interface layer no longer at the chip set layer, no longer at the operating system layer. Right? So the evolution of the, the, the battles are still there. When you talk about multicloud and super cloud, though, like one of the big things you have to keep in mind is latency is not free. Latency is very expensive and it's getting even more expensive now with, with multi-cloud. So you have to really understand where the separations of boundaries are between your data, your compute, and, and the network is just there as a facilitator to help binding compute and data. Right? And I think there's a lot of bets being made across different vendors like CloudFlare Akamai, as well as Amazon Google Microsoft in terms of how they think we should take computing either to the edge, from the core or back and forth. >>These, this is structural change. I mean, this is structural, >>It's desired by incumbents, but it's not something that I'm seeing from the consumption. I'd love to hear, hear from our end's per perspective, from a consumption point of view, like how much edge computing really matters. Right. >>Mario. >>So I think there's like, there's kind of a, a story of like two, like it's kind of, you can cut it for both edges. No, no pun intended on one end. It is really simplifying to actually go into like a single cloud and standardize on it and just have everything there. But I think what over time companies find is that they end up in multiple clouds, whether like, you know, through acquisitions or through like needing to use a service in another cloud. So you do find yourself in a situation where you have multi multi-cloud and you have to kind of work through it and understand how to make it all like work and latency is an issue, but also for many, many workloads, you can work around it and you can make it work where you have workloads that actually span multiple vendors and clouds. You know, again, having said that, I would say the world is such, that is still a simplifying assumption. When if you go to a single cloud, it's much easier to just go and, and bet on that >>Easier in terms of everything's integrated, IAS works with SAS, they solve a lot of problems. >>Correct. And you can do like for your developers, you can actually provide an environment that's super homogenous, simple. You can use services easily up and down the stack. And, you know, we, we actually made that deliberate decision. When we started migrating to the cloud at the beginning, it was like, oh, let's do like hybrid we'll, you know, make it, so it work anywhere. It was so complicated. It was not worth it. >>When was the, when did you give up, what was the moment? Was there a flash point where you said, oh, this is terrible. This is >>Dead. Yeah. When, when we started to try to make it interoperable and you just see what it requires to do that and the complexity of the architecture that it just became not worth it for the gains you have. >>So speaking obviously as a SAS provider, right. So it just doesn't, it didn't make business case sense for you guys to do that. So it was super cloud. Then an infrastructure thing we just heard from Ben wa deja VI that they're not, they're going beyond instantiating their, their data cloud. They're actually running, you know, their own little snow grid. They called it. And, and then when I asked him, well, what about latency? He said, well, we copied data over, you know, so, okay. That's you have to do, but that's a singular experience with the same governance or the same security. Just wasn't worth it for you guys is what I'm hearing. >>Correct. But again, like for some workload or for some services that we want to use, we are gonna go there and we are gonna then figure out what is the work around the latency issue, whether it's like copy or, you know, redundancy. >>Well, the question I have Dave on snowflake is maybe the question for you and in the panel is snowflake a tan expansion opportunity, or is there a technical reason to go to other clouds? >>I think they wanted to leverage the hyperscale infrastructure globally. And they said that they're out there, it's a free gift. We're gonna go take it. I, I think it started with we're on AWS. Do you think? And then we're on Azure and then we're on Google. And then they said, why don't we just connect all these and make it a singular experience? And yeah, I guess it's a TA expansion as a differentiator and it's, it adds value. Right. If I can share data across that global network, >>We have customers on Azure now, >>Right? Yeah. Yeah. Of course. >>You guys don't need to go CP. What do you think about that? >>Well, I think Snowflake's in a good position cuz they work mostly with analytical workloads and you have capacity. That's always gonna increase like no one subtracts, their analytical workload like ever, right. So there was just compounded growth is like 50% or 80% for, you know, many enterprises despite their best intentions, not to collect more data, they just can't stop doing it. So it's different than if you're like an Oracle or a transactional database where you don't have those, you know, like kind of infinite growth paths. So Snowflake's gonna continue to expand footprint their customers. They don't mind as long as you, they can figure out the, the lowest cost on denominator for, for that. >>Yeah. So it makes sense to be in all the clouds >>For them, for, for them, for sure. Yeah. >>But, but, but Oracle just announced with Microsoft what I would call super cloud, a, a cross cloud database service running on OCI and Azure with very low latency and a database that looks like a, the singular experience. Yeah. With, with a PAs layers >>That lost me after OCI that's >>Okay. You know, but that's the, that's the, the BS answer for all U VCs. The do nobody develops on Oracle? Well, it's a 240 billion market cap company. Show me who you all want be. >>We're gonna talk about SRDF and em C next, you >>All want Oracle. So there we go. You throw that into, you all want Oracle to buy your companies, your funding, you know, cause, cause we all wanna be like Oracle with that kinda cash flow. But, but anyway, >>Here's, here's one thing that I'm noticing that is gonna be really practical. I think for companies that do run SA is because like, you know, you have all these solutions, whether it's like analytics or like monitoring or logging or whatever. And each one of them is very data hungry and all of them have like SAS solutions that end up copy the data, moving data to their cloud, and then they might charge you by the size of your data. It does become kind of overwhelming for companies to use that many tools and basically maybe have that data kind of charge for it, multiple places because you use it for different purposes or just in general, if you have a lot of data, you know, that that is becoming an issue. So that's something that I've noticed in our, in our own kind of, you know, a world, but it's just something that I think companies need to think about how they solve because eventually a lot of companies will say, I cannot have all these solutions, so there's no way I'm gonna be willing to have so many copies of the data and actually pay for that. >>So many times, just something to think about. >>But one of the criticisms of the super cloud concept is that it's just SAS. If I'm running workload on prem and I, and I've got, you know, a connection to the cloud, which you probably do, that's, that's SAS, what's, what's the big deal and that's not anything new or different. So I'd love to get your thoughts on that. But Goldman Sachs, for instance, just announced the service last reinvent with AWS, connecting their tools, their data, and their software from on-prem to AWS, they're offering it as a service. I'm like, Hmm. Kind of looking like Supercloud, but maybe it's just SAS. >>It could be. And like, what I'm talking about is not so much like, you know, like what you wanna connect your data. But the idea is like a lot of the providers of different services, like in the past and, and like higher layer, they're actually COPI the data. They need the data in their cloud or their solution. And it just becomes complicated and expensive is, is kind of like my point. So yes, connecting it like for you to have the data in one place and then be able to connect to it. I think that is a valid, if, if that's kinda what you think about as a super cloud, that is a valid need, I think that companies will >>Have where developers actually want access to tools that might exist. >>Also the key is developers, right? Yeah. Developers decide all decisions, not database on administrators, not, you know, a hundred percent security engineers, not admins. So what's really interesting is where are the developers going next? If you look at the current winners in the current ecosystem, companies like MongoDB, I mean, they capture the minds of yeah. The JavaScript, you know, no JS developers absolutely very early on. And I started catch base and I could tell you like the difference was that capture motion was so important. So developers are basically used to this game-like experience now where they want to see tools that are free, whether it's open source or not, they actually don't care. They just want, and they want it SAS. They want it SAS delivered on demand. Right. And pay as you go. And so there's a lot of these different frameworks coming out next generation, no code, low code, whether it's Java, JavaScript, rust, you know, whatever, you know, go Lang. And there's a lot of people fighting religious wars about how to develop the next kind of modern pattern design pattern. Okay. And that's where a lot of excitement is how we look at like investment opportunities. Like where are those big bets who are, you know, frustrated developers, who are they frustrated, what's wrong with their current environment? You know, do they really enjoy using Kubernetes or trying to use Kubernetes? Yeah. Right. Like developers have a very different view than operator, >>But you mentioned couch base. I mean, I look at couch base what they're doing with Capellas as a form of Supercloud. I mean, I think that's an excellent, they're bringing that out to the edge. We're gonna hear later on from someone from couch base. That's gonna talk about that now. It's kind of a lightweight, you know, sort of, it's gonna be a, a synchronization, but it's the beginning >>A cool new venture deal that I'm not in, but was like duck DB. I'm like, what's duck DB like, well, it's an Emory database that has like this like remote store thing. I'm like, okay, that sounds interesting. Like let's call Mike Olson cuz that sounds like sleepy cat redone red distributed world. But like it's, it's like there's a lot of people refactoring design patterns that we're all grew up with since the popup days of, you know, typical round. Right? >>Yeah. That's the refactory I think that's the big pattern. So I have to ask you guys, what are you guys investing in? We've got a couple minutes left to chat about that. What are you investing at into it from a, from a, a CTO engineering perspective and what are you investing in that feels super cloud like to you? >>Well, the, the thing that like I'm focused on is to make sure that we have absolutely best in the world development environment for our engineers, where it's modern, it's easy to use and it incorporates as many things as we can into that environment. So the engineers don't have to think about it. Like one big example would be security and how we incorporated that into development environment. So again, the engineers don't have to bother with trying to think through how they secure their workloads and every step of the way their other things that we incorporated, whether it's like rollbacks or monitoring or, you know, like baly enough other things. But I think that's really an investment that has panned off for us. We actually started investing in development environment several years ago. We started measure our development velocity and we, it actually went up by six X justly investing. So >>User experience, developer experience and productivity pretty much right. >>Yeah. AB absolutely. Yeah. That's like a big investment area for us that, you know, cloud cloud >>Sounds like super cloudlike factor and I'm assuming it's you're on AWS. >>We are mostly on AWS. Yes. >>And so what are you investing in that from a VC money doling out standpoint? That feels super cloudlike >>So very similar to what we just touched on a lot of developer tool experiences. We have a company that we've invested in called ops level that the service catalogs it's, it's helping, you know, understand your, where your services live and how they could be accessed and, and you know, enterprise kind of that come with that. And then we have a company called Lugo that helps you do serverless debugging container debugging, cuz it turns out debugging distributed, you know, applications is a real problem right now just you can only do so much by log tracing, right? We have a company haven't announced yet that's in the web assembly space. So we're looking at modernizing the next generation past stack and throwing everything out the window, including Java and all of the, you know, current prebuilt components because turns out 90% of enterprise workloads are actually not used. They're they're just policy code. You compiled with they're sitting there as vulnerabilities that no one's actually accessing, but you still have to compile with all of it. So we have a lot of bloatware happening in the enterprise. So we're thinking about how do you skinny that up with the next generation paths that's enterprise capable with security context and frameworks >>Super pass. >>Well, yeah, super pass. That's a kind of good way to, well, is >>It, is it a consistent developer experience across clouds? >>It is. And, and, and, and web assembly is a very raw standard if you can call it that. I mean it's, but it's supported by every modern browser, every major platform, vendor cloud, and Adobe and others, and are using it for their uses. And it's not just about your edge browser compute. It's really, you can take the same framework and compile it down to server side as well as client site, just like JavaScript was a client side tool before it became node. Right. Right. So we're looking at that as a very interesting opportunity. It's very nascent. Yeah. >>Great patterns. Yeah. Well, thanks so much for spending the time outta your busy day. Ariana. Thanks for your commentary. Appreciate your coming on the cubes first in IGUR super cloud event, pilot. Thanks for, for sharing. Thanks for having, thanks for having us. Okay. More coverage here. Super cloud 2022. I'm Jeff David Alane stay with us. We got our cloud ARA panel coming up next.

>> Live from New York, it's theCUBE, covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Okay, welcome back, everyone. We're here live in New York City for CUBE NYC. This is our ninth year covering the big data ecosystem, now it's AI, machine-learning, used to be Hadoop, now it's growing, ninth year covering theCUBE here in New York City. I'm John Furrier, with Dave Vellante. Our next guest, Josh Rogers, CEO of Syncsort. I'm going back, long history in theCUBE. You guys have been on every year. Really appreciate chatting with you. Been fun to watch the evolution of Syncsort and also get the insight. Thanks for coming on, appreciate it. >> Thanks for having me. It's great to see you. >> So you guys have constantly been on this wave, and it's been fun to watch. You guys had a lot of IP in your company, and then just watching you guys kind of surf the big data wave, but also make some good decisions, made some good calls. You're always out front. You guys are on the right parts of the wave. I mean now it's cloud, you guys are doing some things. Give us a quick update. You guys got a brand refresh, so you got the new logo goin' on there. Give us a quick update on Syncsort. You got some news, you got the brand refresh. Give us a quick update. >> Sure. I'll start with the brand refresh. We refreshed the brand, and you see that in the web properties and in the messaging that we use in all of our communications. And, we did that because the value proposition of the portfolio had expanded so much, and we had gained so much more insight into some of the key use cases that we're helping customers solve that we really felt we had to do a better job of telling our story and, probably most importantly, engage with the more senior level within these organizations. What we've seen is that when you think about the largest enterprises in the world, we offer a series of solutions around two fundamental value propositions that tend to be top of mind for these executives. The first is how do I take the 20, 30, 40 years of investment in infrastructure and run that as efficiently as possible. You know, I can't make any compromises on the availability of that. I certainly have to improve my governance and secureability of that environment. But, fundamentally, I need to make sure I could run those mission-critical workloads, but I need to also save some money along the way, because what I really want to do is be a data-driven enterprise. What I really want to do is take advantage of the data that gets produced in these transactional applications that run on my AS400 or IBM I-infra environment, my mainframe environment, even in my traditional data warehouse, and make sure that I'm getting the most out of that data by analyzing it in a next-generation set of-- >> I mean one of the trends I want to get your thoughts on, Josh, cause you're kind of talking through the big, meagatrend which is infrastructure agnostic from an application standpoint. So the that's the trend with dev ops, and you guys have certainly had diverse solutions across your portfolio, but, at the end of the day, this is the abstraction layer customers want. They want to run workloads on environments that they know are in production, that work well with applications, so they almost want to view the infrastructure, or cloud, if you will, same thing, as just agnostic, but let the programmability take care of itself, under the hood, if you will. >> Right, and what we see is that people are absolutely kind of into extending and modernizing existing applications. This is in the large enterprise, and those applications and core components will still run on mainframe environments. And so, what we see in terms of use cases is how do we help customers understand how to monitor that, the performance of those applications. If I have a tier that's sitting on the cloud, but it's transacting with the mainframe behind the firewall, how do I get an end-to-end view of application performance? How do I take the data that ultimately gets logged in a DB2 database on the mainframe and make that available in a next-generation repository, like Hadoop, so that I can do advanced analytics? When you think about solving both the optimization and the integration challenge there, you need a lot of expertise in both sides, the old and the new, and I think that's what we uniquely offer. >> You guys done a good job with integration. I want to ask quick question on the integration piece. Is this becoming more and more table stakes, but also challenging at the same time? Integration and connecting systems together, if their stateless, is no problem, you use APIs, right, and do that, but as you start to get data that needs state information, you start to think to think about some of the challenges around different, disparate systems being distributed, but networked, in some cases, even decentralized, so distributed networking is being radically changed by the data decisions on the architecture, but also integration, call it API 2.0 or this new way to connect and integrate. >> Yeah, so what we've tried to focus on is kind of solving that piece between these older applications that run these legacy platforms and making them available to whatever the consumer is. Today, we see Kafka and in Amazon we see Kinesis as kind of key buses delivering data as a service, and so the role that we see ourselves playing and what we announced this week is an ability to track changed data, deliver it in realtime in these older systems, but deliver it to these new targets: Kafka, Kinesis, and whatever comes next. Because really that's the fundamental partner we're trying to be to our customers is we will help you solve the integration challenge between this infrastructure you've been building for 30 years and this next-generation technology that lets you get the next leg of value out of your data. >> So Jim, when you think about the evolution of this whole big data space, the early narrative in the trade press was, well, NoSQL is going to replace Oracle and DB2, and the data lake is going to replace the EDW, and unstructured data is all that matters, and so forth. And now, you look at what's really happened is the EDW is a fundamental component of making decisions and insights, and SQL is the killer app for Hadoop. And I take an example of say fraud detection, and when you think and this is where you guys sit in the middle from the standpoint of data quality, data integration, in order to do what we've done in the past 10 years take fraud detection down from well, I look at my statement a month or two later and then call the credit card company, it's now gone to a text that's instantaneous. Still some false positives, and I'm sure working on that even. So maybe you could describe that use case or any other, your favorite use case, and what your role is there in terms of taking those different data sources, integrating them, improving the data quality. >> So, I think when you think about a use case where I'm trying to improve the SLA or the responsiveness of how do manage against or detect fraud, rather than trying to detect it on a daily basis, I'm trying to detect it at transaction time. The reality is you want to leverage the existing infrastructure you have. So if you have a data warehouse that has detailed information about transaction history, maybe that's a good source. If you have an application that's running on the mainframe that's doing those transaction realtime, the ultimate answer is how do I knit together the existing infrastructure I have and embed the additional intelligence and capability I need from these new capabilities, like, for example, using Kafka, to deliver a complete solution. What we do is we help customers kind of tie that together, Specifically, we announced this integration I mentioned earlier where we can take a changed data element in a DB2 database and publish it into Kafka. That is a key requirement in delivering this real-time fraud detection if I in fact am running transactions on a mainframe, which most of the banks are. >> Without ripping and replacing >> Why would you want to rip out an application >> You don't. >> your core customer file when you can just extend it. >> And you mentioned the Cloudera 6 certification. You guys have been early on there. Maybe talk a little about that relationship, the engineering work that has to get done for you to be able to get into the press release day one. >> We just mentioned that my first time on theCUBE was in 2013, and that was on the back of our initial product release in the big data world. When we brought the initial DMX-h release to market, we knew that we needed to have deep partnerships with Cloudera and the key platform providers. I went and saw Mike Olson, I introduced myself, he was gracious enough to give me an hour, and explain what we thought we could do to help them develop more value proposition around their platform, and it's been a terrific relationship. Our architecture and our engineering and product management relationship is such that it allows us to very rapidly certify and work on their new releases, usually within a couple a days. Not only can customers take advantage of that, which is pretty unique in the industry, but we get some some visibility from Cloudera as evidenced by Tendu's quote in the press release that was released this week, which is terrific. >> Talk about your business a little bit. You guys are like a 50-year old startup. You've had this really interesting history. I remember you from when I first started in the industry following you guys. You've restructured the company, you've done some spin outs, you've done some M and A, but it seems to be working. Talk about growth and progress that you're making. >> We're the leader in the Big Iron to Big Data market. We define that as allowing customers to optimize their traditional legacy investments for cost and performance, and then we help them maximize the value of the data that get generated in those environments by integrating it with next-generation analytic environments. To do that, we need a broad set of capability. There's a lot of different ways to optimize existing infrastructure. One is capacity management, so we made an acquisition about a year ago in the capacity management space. We're allowing customers to figure out how do I make sure I've got not too much and not too little capacity. That's an example of optimization. Another area of capability is data quality. If I'm maximize the value of the data that gets produced in these older environments, it would be great that when it lands in these next-generation repositories it's as high quality as possible. We acquired Trillium about a year ago, or actually coming up >> How's that comin'? >> on two years ago and we think that's a great capability for our customers It's going terrific. We took their core data quality engine, and now it runs natively on a distributed Hadoop infrastructure. We have customers leveraging it to deliver unprecedented volume of matching, so not only breakthrough performance, but this whole notion of write once, run anywhere. I can run it on an SMP environment. I can run it on Hadoop. I can run it Hadoop in the cloud. We've seen terrific growth in that business based on our continued innovation, particularly pointing it at the big data space. >> One of the things that I'm impressed with you guys is you guys have transformed, so having a transformation message to your customers is you have a lot of credibility, but what's interesting is is that the world with containers and Kubernetes now and multi-cloud, you're seeing that you don't have to kill the legacy to bring in the new stuff. You can see you can connect systems, when you guys have done with legacy systems, look at connect the data. You don't have to kill that to bring in the new. >> Right >> You can do cloud-native, you can do some really cool things. >> Right. I think there's-- >> This rip and replace concept is kind of going away. You put containers around it too. That helps. >> Right. It's expensive and it's risky, so why do that. I think that's the realization. The reality is that when people build these mission-critical systems, they stay in place for not five years, but 25 years. The question is how do you allow the customers to leverage what they have and the investment they've made, but take advantage of the next wave, and that's what we're singularly focused on, and I think we're doing a great job of that, not just for customers, but also for these next-generation partners, which has been a lot of fun for us. >> And we also heard people doing analytics they want to have their own multi-tenent, isolated environments, which goes to don't screw this system up, if it's doing a great job on a mission-critical thing, don't bundle it, just connect it to the network, and you're good. >> And on the cloud side, we're continuing to look at our portfolio and say what capabilities will customers want to consume in a cloud-delivery model. We've been doing that in the data quality space for quite awhile. We just launched and announced over the last about three months ago capacity management as a service. You'll continue to see, both on the optimization side and on the integration side, us continuing to deliver new ways for customers to consume the capabilities they need. >> That's a key thing for you guys, integration. That's pretty much how you guys put the stake in the ground and engineer your activities around integration. >> Yeah, we start with the premise that your going to need to continue to run this older investments that you made, and you're going to need to integrate the new stuff with that. >> What's next? What's goin' on the rest of the year with you guys? >> We'll continue to invest heavily in the realtime and changed-data capture space. We think that's really interesting. We're seeing a tremendous amount of demand there. We've made a series of acquisitions in the security space. We believe that the ability to secure data in the core systems and its journey to the next-generation systems is absolutely critical, so we'll continue to invest there. And then, I'd say governance, that's an area that we think is incredibly important as people start to really take advantage of these data lakes they're building, they have to establish real governance capabilities around those. We believe we have an important role to play there. And there's other adjacencies, but those are probably the big areas we're investing in right now. >> Just continuing to move the ball down the field in the Syncsort cadence of acquisitions, organic development. Congratulations. Josh, thanks for comin' on. To John Rogers, CEO of Syncsort, here inside theCUBE. I'm John Furrier with Dave Vellante. Stay with us for more big data coverage, AI coverage, cloud coverage here. Part of CUBE NYC, we're in New York City live. We'll be right back after this short break. Stay with us. (techno music)

>> Live from New York, it's theCUBE covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. (techy music) >> Hello, everyone, welcome to this CUBE special presentation here in New York City for CUBENYC. I'm John Furrier with Dave Vellante. This is our ninth year covering the big data industry, starting with Hadoop World and evolved over the years. This is our ninth year, Dave. We've been covering Hadoop World, Hadoop Summit, Strata Conference, Strata Hadoop. Now it's called Strata Data, I don't know what Strata O'Reilly's going to call it next. As you all know, theCUBE has been present for the creation at the Hadoop big data ecosystem. We're here for our ninth year, certainly a lot's changed. AI's the center of the conversation, and certainly we've seen some horses come in, some haven't come in, and trends have emerged, some gone away, your thoughts. Nine years covering big data. >> Well, John, I remember fondly, vividly, the call that I got. I was in Dallas at a storage networking world show and you called and said, "Hey, we're doing "Hadoop World, get over there," and of course, Hadoop, big data, was the new, hot thing. I told everybody, "I'm leaving." Most of the people said, "What's Hadoop?" Right, so we came, we started covering, it was people like Jeff Hammerbacher, Amr Awadallah, Doug Cutting, who invented Hadoop, Mike Olson, you know, head of Cloudera at the time, and people like Abi Mehda, who at the time was at B of A, and some of the things we learned then that were profound-- >> Yeah. >> As much as Hadoop is sort of on the back burner now and people really aren't talking about it, some of the things that are profound about Hadoop, really, were the idea, the notion of bringing five megabytes of code to a petabyte of data, for example, or the notion of no schema on write. You know, put it into the database and then figure it out. >> Unstructured data. >> Right. >> Object storage. >> And so, that created a state of innovation, of funding. We were talking last night about, you know, many, many years ago at this event this time of the year, concurrent with Strata you would have VCs all over the place. There really aren't a lot of VCs here this year, not a lot of VC parties-- >> Mm-hm. >> As there used to be, so that somewhat waned, but some of the things that we talked about back then, we said that big money and big data is going to be made by the practitioners, not by the vendors, and that's proved true. I mean... >> Yeah. >> The big three Hadoop distro vendors, Cloudera, Hortonworks, and MapR, you know, Cloudera's $2.5 billion valuation, you know, not bad, but it's not a $30, $40 billion value company. The other thing we said is there will be no Red Hat of big data. You said, "Well, the only Red Hat of big data might be "Red Hat," and so, (chuckles) that's basically proved true. >> Yeah. >> And so, I think if we look back we always talked about Hadoop and big data being a reduction, the ROI was a reduction on investment. >> Yeah. >> It was a way to have a cheaper data warehouse, and that's essentially-- Well, what did we get right and wrong? I mean, let's look at some of the trends. I mean, first of all, I think we got pretty much everything right, as you know. We tend to make the calls pretty accurately with theCUBE. Got a lot of data, we look, we have the analytics in our own system, plus we have the research team digging in, so you know, we pretty much get, do a good job. I think one thing that we predicted was that Hadoop certainly would change the game, and that did. We also predicted that there wouldn't be a Red Hat for Hadoop, that was a production. The other prediction was is that we said Hadoop won't kill data warehouses, it didn't, and then data lakes came along. You know my position on data lakes. >> Yeah. >> I've always hated the term. I always liked data ocean because I think it was much more fluidity of the data, so I think we got that one right and data lakes still doesn't look like it's going to be panning out well. I mean, most people that deploy data lakes, it's really either not a core thing or as part of something else and it's turning into a data swamp, so I think the data lake piece is not panning out the way it, people thought it would be. I think one thing we did get right, also, is that data would be the center of the value proposition, and it continues and remains to be, and I think we're seeing that now, and we said data's the development kit back in 2010 when we said data's going to be part of programming. >> Some of the other things, our early data, and we went out and we talked to a lot of practitioners who are the, it was hard to find in the early days. They were just a select few, I mean, other than inside of Google and Yahoo! But what they told us is that things like SQL and the enterprise data warehouse were key components on their big data strategy, so to your point, you know, it wasn't going to kill the EDW, but it was going to surround it. The other thing we called was cloud. Four years ago our data showed clearly that much of this work, the modeling, the big data wrangling, et cetera, was being done in the cloud, and Cloudera, Hortonworks, and MapR, none of them at the time really had a cloud strategy. Today that's all they're talking about is cloud and hybrid cloud. >> Well, it's interesting, I think it was like four years ago, I think, Dave, when we actually were riffing on the notion of, you know, Cloudera's name. It's called Cloudera, you know. If you spell it out, in Cloudera we're in a cloud era, and I think we were very aggressive at that point. I think Amr Awadallah even made a comment on Twitter. He was like, "I don't understand "where you guys are coming from." We were actually saying at the time that Cloudera should actually leverage more cloud at that time, and they didn't. They stayed on their IPO track and they had to because they had everything betted on Impala and this data model that they had and being the business model, and then they went public, but I think clearly cloud is now part of Cloudera's story, and I think that's a good call, and it's not too late for them. It never was too late, but you know, Cloudera has executed. I mean, if you look at what's happened with Cloudera, they were the only game in town. When we started theCUBE we were in their office, as most people know in this industry, that we were there with Cloudera when they had like 17 employees. I thought Cloudera was going to run the table, but then what happened was Hortonworks came out of the Yahoo! That, I think, changed the game and I think in that competitive battle between Hortonworks and Cloudera, in my opinion, changed the industry, because if Hortonworks did not come out of Yahoo! Cloudera would've had an uncontested run. I think the landscape of the ecosystem would look completely different had Hortonworks not competed, because you think about, Dave, they had that competitive battle for years. The Hortonworks-Cloudera battle, and I think it changed the industry. I think it couldn't been a different outcome. If Hortonworks wasn't there, I think Cloudera probably would've taken Hadoop and making it so much more, and I think they wouldn't gotten more done. >> Yeah, and I think the other point we have to make here is complexity really hurt the Hadoop ecosystem, and it was just bespoke, new projects coming out all the time, and you had Cloudera, Hortonworks, and maybe to a lesser extent MapR, doing a lot of the heavy lifting, particularly, you know, Hortonworks and Cloudera. They had to invest a lot of their R&D in making these systems work and integrating them, and you know, complexity just really broke the back of the Hadoop ecosystem, and so then Spark came in, everybody said, "Oh, Spark's going to basically replace Hadoop." You know, yes and no, the people who got Hadoop right, you know, embraced it and they still use it. Spark definitely simplified things, but now the conversation has turned to AI, John. So, I got to ask you, I'm going to use your line on you in kind of the ask-me-anything segment here. AI, is it same wine, new bottle, or is it really substantively different in your opinion? >> I think it's substantively different. I don't think it's the same wine in a new bottle. I'll tell you... Well, it's kind of, it's like the bad wine... (laughs) Is going to be kind of blended in with the good wine, which is now AI. If you look at this industry, the big data industry, if you look at what O'Reilly did with this conference. I think O'Reilly really has not done a good job with the conference of big data. I think they blew it, I think that they made it a, you know, monetization, closed system when the big data business could've been all about AI in a much deeper way. I think AI is subordinate to cloud, and you mentioned cloud earlier. If you look at all the action within the AI segment, Diane Greene talking about it at Google Next, Amazon, AI is a software layer substrate that will be underpinned by the cloud. Cloud will drive more action, you need more compute, that drives more data, more data drives the machine learning, machine learning drives the AI, so I think AI is always going to be dependent upon cloud ends or some sort of high compute resource base, and all the cloud analytics are feeding into these AI models, so I think cloud takes over AI, no doubt, and I think this whole ecosystem of big data gets subsumed under either an AWS, VMworld, Google, and Microsoft Cloud show, and then also I think specialization around data science is going to go off on its own. So, I think you're going to see the breakup of the big data industry as we know it today. Strata Hadoop, Strata Data Conference, that thing's going to crumble into multiple, fractured ecosystems. >> It's already starting to be forked. I think the other thing I want to say about Hadoop is that it actually brought such great awareness to the notion of data, putting data at the core of your company, data and data value, the ability to understand how data at least contributes to the monetization of your company. AI would not be possible without the data. Right, and we've talked about this before. You call it the innovation sandwich. The innovation sandwich, last decade, last three decades, has been Moore's law. The innovation sandwich going forward is data, machine intelligence applied to that data, and cloud for scale, and that's the sandwich of innovation over the next 10 to 20 years. >> Yeah, and I think data is everywhere, so this idea of being a categorical industry segment is a little bit off, I mean, although I know data warehouse is kind of its own category and you're seeing that, but I don't think it's like a Magic Quadrant anymore. Every quadrant has data. >> Mm-hm. >> So, I think data's fundamental, and I think that's why it's going to become a layer within a control plane of either cloud or some other system, I think. I think that's pretty clear, there's no, like, one. You can't buy big data, you can't buy AI. I think you can have AI, you know, things like TensorFlow, but it's going to be a completely... Every layer of the stack is going to be impacted by AI and data. >> And I think the big players are going to infuse their applications and their databases with machine intelligence. You're going to see this, you're certainly, you know, seeing it with IBM, the sort of Watson heavy lift. Clearly Google, Amazon, you know, Facebook, Alibaba, and Microsoft, they're infusing AI throughout their entire set of cloud services and applications and infrastructure, and I think that's good news for the practitioners. People aren't... Most companies aren't going to build their own AI, they're going to buy AI, and that's how they close the gap between the sort of data haves and the data have-nots, and again, I want to emphasize that the fundamental difference, to me anyway, is having data at the core. If you look at the top five companies in terms of market value, US companies, Facebook maybe not so much anymore because of the fake news, though Facebook will be back with it's two billion users, but Apple, Google, Facebook, Amazon, who am I... And Microsoft, those five have put data at the core and they're the most valuable companies in the stock market from a market cap standpoint, why? Because it's a recognition that that intangible value of the data is actually quite valuable, and even though banks and financial institutions are data companies, their data lives in silos. So, these five have put data at the center, surrounded it with human expertise, as opposed to having humans at the center and having data all over the place. So, how do they, how do these companies close the gap? How do the companies in the flyover states close the gap? The way they close the gap, in my view, is they buy technologies that have AI infused in it, and I think the last thing I'll say is I see cloud as the substrate, and AI, and blockchain and other services, as the automation layer on top of it. I think that's going to be the big tailwind for innovation over the next decade. >> Yeah, and obviously the theme of machine learning drives a lot of the conversations here, and that's essentially never going to go away. Machine learning is the core of AI, and I would argue that AI truly doesn't even exist yet. It's machine learning really driving the value, but to put a validation on the fact that cloud is going to be driving AI business is some of the terms in popular conversations we're hearing here in New York around this event and topic, CUBENYC and Strata Conference, is you're hearing Kubernetes and blockchain, and you know, these automation, AI operation kind of conversations. That's an IT conversation, (chuckles) so you know, that's interesting. You've got IT, really, with storage. You've got to store the data, so you can't not talk about workloads and how the data moves with workloads, so you're starting to see data and workloads kind of be tossed in the same conversation, that's a cloud conversation. That is all about multi-cloud. That's why you're seeing Kubernetes, a term I never thought I would be saying at a big data show, but Kubernetes is going to be key for moving workloads around, of which there's data involved. (chuckles) Instrumenting the workloads, data inside the workloads, data driving data. This is where AI and machine learning's going to play, so again, cloud subsumes AI, that's the story, and I think that's going to be the big trend. >> Well, and I think you're right, now. I mean, that's why you're hearing the messaging of hybrid cloud and from the big distro vendors, and the other thing is you're hearing from a lot of the no-SQL database guys, they're bringing ACID compliance, they're bringing enterprise-grade capability, so you're seeing the world is hybrid. You're seeing those two worlds come together, so... >> Their worlds, it's getting leveled in the playing field out there. It's all about enterprise, B2B, AI, cloud, and data. That's theCUBE bringing you the data here. New York City, CUBENYC, that's the hashtag. Stay with us for more coverage live in New York after this short break. (techy music)

>> Speaker: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its eco-system partners. (soothing electronic music) >> Good morning everybody, and welcome to Big Data SV. My name is Dave Vellante, and this is our 10th big data event, we started in New York City, we've done five now, and this'll be our fifth in Silicon Valley, we've done five in New York City. And we started SiliconANGLE and Wikibon started covering the Big Data space in 2010, we did our first Hadoop World, which was actually the second Hadoop World in New York City. In 2011, we put out the industry's first big data report, and it caught the industry by fire, it was the hot topic. The concept of Hadoop was profound in that the idea was to take five megabytes of code and bring it to a petabyte of data, metaphorically if you will. Because moving data around was so problematic, and that concept really took hold. We asked questions at the time. Who will be the Red Hat of big data? Is this going to be a winner-take-all market? Will this trend, this big data trend, solve the problems that decision support, and business intelligence couldn't solve? We're going to talk about that today, and throughout the week. We've just released Wikibon's big data market study, and big data market shares, and key findings, I'm here with Peter Burris, who heads up the Wikibon research organization, and George Gilbert, who leads our big data research, gentleman, welcome to theCUBE. >> Hi Dave. >> Good to see you guys. >> Good to be here. >> So, we have this open source marketplace, it's been plagued by complexity, competition, the cloud really changed things. Peter, you've been studying this for a while, you just dropped that awesome report on Wikibon.com, what did you find? What were the key trends that you saw in that report? Lay it out for us. >> Well the most important trend is that users are starting drive what happens in the big data universe. For many years, it was the individuals that were primarily responsible for creating a lot of these open source tools, and in the process of creating these open source tools, they solved each other's problems, as opposed to solving user problems. Users then found themselves, or in a process found themselves, building out clusters, deploying Hadoop, really focusing a lot on the infrastructure, which had its pluses and minuses. But what we see happening in the marketplace today really is an emphasis on bifurcation, in the big data space, where we're seeing a continuing focus on the infrastructure elements, and we'll spend a fair amount of time talking about what that means from a hardware database and related technology standpoint, and then, a much more focused, based on user and enterprise experience, of how to turn this into applications that actually have a consequential impact on the business on machine learning, AI, how the pipelines work, how the personnel work, integrating business change and the way business thinks about the role that data's going to play, and that bifurcation is going to carry forward over the next few years, as we gain more experience, and the entire industry is going to go through a process of restructuring itself to serve both sides of those needs. >> Great, so George, I want to ask you, so this is not a winner-take-all market, there is no Red Hat of big data, it certainly is not Cloudera, you know, Hortonworks kind of threw a wrench maybe into some of those plans, and tryin' to play the long game with the pure open source play. The return on investment of big data oftentimes turned out to be a reduction in the denominator, a reduction of investment, if you will. Lowering spending relative to traditional data warehouses. I ask you, you've been following this business for a long time, did the big data promise fail to live up to expectations? >> (laughs) There are multiple layers to that question, and to the answer. I would say that let's offload some data warehousing, processing, was the application that IT could attack to justify their experimentation with big data technologies, which remain notoriously complicated to provision and to manage on PRIM. But as Peter was saying, to get sort of more value out of this investment, we're sort of now bumping up against the complexity of all the data science pipelines, whereas before we were bumping up the complexity of administering these Hadoop clusters, so no we've got the data there, it's kind of hard to manage, but now we have to sort of learn how to apply that using much more sophisticated techniques. It's interesting that you say denominator shrinks, because the cost of operation as you move to the cloud, there are many more options, and they're managed much better, so that cost comes down as people have more cloud options. The last point I would make is I do think packaged applications, whether they're from the big guys, or a lot of vertically focused, or even semi-custom apps from folks like IBM, or Accenture, those are going to be what drives mainstream deployment, to reach hundreds of millions of users of this technology. >> So I would just observe that, in my view, this whole big data trend wasn't a failure, we observed early on that the folks that were going to make the most money in big data were the practitioners, not the vendors. So we made a correct call there. In many respects I look at this as, you know when you paint, you got to prep. I feel like that last eight years has been the preparatory phases, you know, scraping, and getting things ready, getting your house in order, and now Peter, we're setting up for the digital business era, and the digital business era is about data, it's about applying machine intelligence, it's certainly taking advantage of cloud economics. Do you buy that premise? That we're now in a position to actually, many companies anyway, or some companies, to affect digital transformation? >> Well, the whole concept of digital transformation starts with the idea of data, and our observation, here at theCUBE and Wikibon, ultimately, is that the difference between a business and a digital business is, a digital business uses data as an asset, and that has an enormous implications, on operations, how you engage customers, how you institutionalize work, what your relationships are with technology companies, et cetera. But that core concept of using your data differently, and creating value, is absolutely essential, to this notion of big data and all the various things that we're talking about, because big data is the process by which you create business value out of data, that's ultimately what we're trying to do with all this stuff. So, to George's point, if we think about where we've been, and where we're going, in many respects, fundamentally, we're just kind of following almost a normal adoption process. So if we go back 10 years, to Yahoo, Google, and some of the tech companies that initiated a lot of this motion, they had very specific types of problems that they wanted to solve, they had enormous volumes of data that they wanted to use to solve their problem, and they created technology to do so. Where we kind of get hung up is in the diffusion out of those relatively, certainly very challenging, and very rich set of problems, that Facebook, and Yahoo, and everybody else had, as they try to diffuse that technology into other industries, we got caught up in the bumps. We had more failures, and we didn't get the returns we wanted. So, now what's happening is a lot of that domain expertise is coming back in, we're startin' to say, "Now we know "how to solve the problem, we have an approach "to how we're going to solve the problem," and the technology's being snapped into place to solve problems, as opposed to technology being snapped into place, or solve business problems, as opposed to technology being snapped into place to solve the technology problems of big data. >> So we're here talking to Peter Burris and George Gilbert, two analysts at Wikibon, we're here at the Forager, in San Jose, it's at 420 1st Street, and theCUBE has a week long, 1/2 a week long anyway, set of activities going on, we've got an event going on this evening, I think it starts at six o'clock, so come by, we got a breakfast briefing tomorrow, where the Wikibon analysts are laying out their recent market studies, we just dropped two market studies on Wikibon, one is the overall market size, and the other goes into market shares. I want to touch on those briefly. We're lookin' at about a 35 billion dollar market, growing to 100 billion over the next 10 years. As we observed early on, open source software had an effect where, most businesses, most industries start off, software's a big component of it, because of open source, the software revenues were muted in this business, but they're really starting to pick up now, it was a heavily services-oriented business, and still is, about 40%, right? And then software comprises about 30%, and hardware about 29%. You guys see that changing over time, correct? >> Well yeah, and in many respects, again, this is following almost a natural evolution, that's made more interesting by the fact that these are very complex problems, and new types of business problems, but, certainly George has done a lot of research on this, ultimately, what every company that operates in this space should be thinking about is, how is the industry, in aggregate, going to get to 100, to 200 million users in the next decade. Where a user is not someone who's playing with the data, or looking at Tableau, but a user is fundamentally someone who's using an application, or making a decision that's informed by data, that's made possible by these tools. And that's not something that's going to happen at a very, very low, hardware, cluster, database, level. It's going to happen elsewhere, and one of the big trends we see is, that there's going to be a lot of new packaged applications entering into the marketplace, that consume these tools, and make them viable for business to actually use. >> Well George, in 2012, Mike Olson declared it the year of the big data applications, that never happened. The action in software has been around database and software infrastructure, but what do you see in terms of the evolution of that software business? >> Well, continuing on the theme of the bifurcation, it was interesting to hear Peter talk about how the infrastructure that the big tech companies, and internet companies developed as a byproduct of building their own services, that stuff didn't work for mainstream, it didn't even work for most of the sophisticated enterprises, on the infrastructure side, what we're doing now is, we're seeing a convergence, where we're putting those pieces together in a way where they fit easily together enough so admins, mere admin, mortal admins and developers can work with them-- >> With cloud being the ultimate convergence. >> Yes, yes. And I would also say then it's the applications will really take it mainstream. Because even when we fit the platform stuff together, it's not going to be enough to go mainstream. >> Okay, and we got to wrap, but I just wanted to touch on some of the market share stuff that you guys just produced, and we'll be presenting this data tomorrow morning, Thursday morning here at the Forager, it's 420 1st Street, in San Jose. Not surprisingly, IBM came out as the leader, because of the large services component, they got about 8% of that-- >> Well, they play in all parts. >> They play in all, but services they dominate. So IBM, Splunk, actually, who never used the term big data during their ascendancy, they didn't tie into that meme, but they are a big data company-- >> And an example of a packaged application company leading a-- >> Both-- >> Absolutely. >> Both, the platform and app. >> And apps, right. Dell, Oracle, and now if you look at this, that's the overall, if you look at the software top 10, Splunk comes out on top, then Oracle, then IBM, and we'll be getting into that tomorrow morning at the breakfast, Peter Burris, George Gilbert, thanks so much for setting this up, that's for watching, we've got wall-to-wall coverage here, this is day one, Big Data SV. From San Jose, you're watching theCUBE. We'll be right back. (soothing electronic music)

>> Announcer: Live from Midtown Manhattan, it's theCUBE covering Big Data New York City 2017. Brought to by SiliconANGLE Media and its ecosystem sponsors. >> Coverage to theCUBE in New York City for Big Data NYC, the hashtag is BigDataNYC. This is our fifth year doing our own event in conjunction with Strata Hadoop, now called Strata Data, used to be Hadoop World, our eighth year covering the industry, we've been there from the beginning in 2010, the beginning of this revolution. I'm John Furrier, the co-host, with Jim Kobielus, our lead analyst at Wikibon. Our next guest is Christian Rodatus, who is the CEO of Datameer. Datameer, obviously, one of the startups now evolving on the, I think, eighth year or so, roughly seven or eight years old. Great customer base, been successful blocking and tackling, just doing good business. Your shirt says show him the data. Welcome to theCUBE, Christian, appreciate it. >> So well established, I barely think of you as a startup anymore. >> It's kind of true, and actually a couple of months ago, after I took on the job, I met Mike Olson, and Datameer and Cloudera were sort of founded the same year, I believe late 2009, early 2010. Then, he told me there were two open source projects with MapReduce and Hadoop, basically, and Datameer was founded to actually enable customers to do something with it, as an entry platform to help getting data in, create the data and doing something with it. And now, if you walk the show floor, it's a completely different landscape now. >> We've had you guys on before, the founder, Stefan, has been on. Interesting migration, we've seen you guys grow from a customer base standpoint. You've come on as the CEO to kind of take it to the next level. Give us an update on what's going on at Datameer. Obviously, the shirt says "Show me the data." Show me the money kind of play there, I get that. That's where the money is, the data is where the action is. Real solutions, not pie in the sky, we're now in our eighth year of this market, so there's not a lot of tolerance for hype even though there's a lot of AI watching going on. What's going on with you guys? >> I would say, interesting enough I met with a customer, prospective customer, this morning, and this was a very typical organization. So, this is a customer that was an insurance company, and they're just about to spin up their first Hadoop cluster to actually work on customer management applications. And they are overwhelmed with what the market offers now. There's 27 open source projects, there's dozens and dozens of other different tools that try to basically, they try best of reach approaches and certain layers of the stack for specific applications, and they don't really know how to stitch this all together. And if I reflect from a customer meeting at a Canadian bank recently that has very successfully deployed applications on the data lake, like in fraud management and compliance applications and things like this, they still struggle to basically replicate the same performance and the service level agreements that they used from their old EDW that they still have in production. And so, everybody's now going out there and trying to figure out how to get value out of the data lake for the business users, right? There's a lot of approaches that these companies are trying. There's SQL-on-Hadoop that supposedly doesn't perform properly. There is other solutions like OLAP on Hadoop that tries to emulate what they've been used to from the EDWs, and we believe these are the wrong approaches, so we want to stay true to the stack and be native to the stack and offer a platform that really operates end-to-end from interesting the data into the data lake to creation, preparation of the data, and ultimately, building the data pipelines for the business users, and this is certainly something-- >> Here's more of a play for the business users now, not the data scientists and statistical modelers. I thought the data scientists were your core market. Is that not true? >> So, our primary user base as Datameer used to be like, until last week, we were the data engineers in the companies, or basically the people that built the data lake, that created the data and built these data pipelines for the business user community no matter what tool they were using. >> Jim, I want to get your thoughts on this for Christian's interest. Last year, so these guys can fix your microphone. I think you guys fix the microphone for us, his earpiece there, but I want to get a question to Chris, and I ask to redirect through you. Gartner, another analyst firm. >> Jim: I've heard of 'em. >> Not a big fan personally, but you know. >> Jim: They're still in business? >> The magic quadrant, they use that tool. Anyway, they had a good intro stat. Last year, they predicted through 2017, 60% of big data projects will fail. So, the question for both you guys is did that actually happen? I don't think it did, I'm not hearing that 60% have failed, but we are seeing the struggle around analytics and scaling analytics in a way that's like a dev ops mentality. So, thoughts on this 60% data projects fail. >> I don't know whether it's 60%, there was another statistic that said there's only 14% of Hadoop deployments, or production or something, >> They said 60, six zero. >> Or whatever. >> Define failure, I mean, you've built a data lake, and maybe you're not using it immediately for any particular application. Does that mean you've failed, or does it simply mean you haven't found the killer application yet for it? I don't know, your thoughts. >> I agree with you, it's probably not a failure to that extent. It's more like how do they, so they dump the data into it, right, they build the infrastructure, now it's about the next step data lake 2.0 to figure out how do I get value out of the data, how do I go after the right applications, how do I build a platform and tools that basically promotes the use of that data throughout the business community in a meaningful way. >> Okay, so what's going on with you guys from a product standpoint? You guys have some announcements. Let's get to some of the latest and greatest. >> Absolutely. I think we were very strong in data creation, data preparation and the entire data governance around it, and we are using, as a user interface, we are using this spreadsheet-like user interface called a workbook, it really looks like Excel, but it's not. It operates at completely different scale. It's basically an Excel spreadsheet on steroids. Our customers built a data pipeline, so this is the data engineers that we discussed before, but we also have a relatively small power user community in our client base that use that spreadsheet for deep data exploration. Now, we are lifting this to the next level, and we put up a visualization layer on top of it that runs natively in the stack, and what you get is basically a visual experience not only in the data curation process but also in deep data exploration, and this is combined with two platform technologies that we use, it's based on highly scalable distributed search in the backend engine of our product, number one. We have also adopted a columnar data store, Parquet, for our file system now. In this combination, the data exploration capabilities we bring to the market will allow power analysts to really dig deep into the data, so there's literally no limits in terms of the breadth and the depth of the data. It could be billions of rows, it could be thousands of different attributes and columns that you are looking at, and you will get a response time of sub-second as we create indices on demand as we run this through the analytic process. >> With these fast queries and visualization, do you also have the ability to do semantic data virtualization roll-ups across multi-cloud or multi-cluster? >> Yeah, absolutely. We, also there's a second trend that we discussed right before we started the live transmission here. Things are also moving into the cloud, so what we are seeing right now is the EDW's not going away, the on prem is data lake, that prevail, right, and now they are thinking about moving certain workload types into the cloud, and we understand ourselves as a platform play that builds a data fabric that really ties all these data assets together, and it enables business. >> On the trends, we weren't on camera, we'll bring it up here, the impact of cloud to the data world. You've seen this movie before, you have extensive experience in this space going back to the origination, you'd say Teradata. When it was the classic, old-school data warehouse. And then, great purpose, great growth, massive value creation. Enter the Hadoop kind of disruption. Hadoop evolved from batch to do ranking stuff, and then tried to, it was a hammer that turned into a lawnmower, right? Then they started going down the path, and really, it wasn't workable for what people were looking at, but everyone was still trying to be the Teradata of whatever. Fast forward, so things have evolved and things are starting to shake out, same picture of data warehouse-like stuff, now you got cloud. It seems to be changing the nature of what it will become in the future. What's your perspective on that evolution? What's different about now and what's same about now that's, from the old days? What's the similarities of the old-school, and what's different that people are missing? >> I think it's a lot related to cloud, just in general. It is extremely important to fast adoptions throughout the organization, to get performance, and service-level agreements without customers. This is where we clearly can help, and we give them a user experience that is meaningful and that resembles what they were used to from the old EDW world, right? That's number one. Number two, and this comes back to a question to 60% fail, or why is it failing or working. I think there's a lot of really interesting projects out, and our customers are betting big time on the data lake projects whether it being on premise or in the cloud. And we work with HSBC, for instance, in the United Kingdom. They've got 32 data lake projects throughout the organization, and I spoke to one of these-- >> Not 32 data lakes, 32 projects that involve tapping into the data lake. >> 32 projects that involve various data lakes. >> Okay. (chuckling) >> And I spoke to one of the chief data officers there, and they said they are data center infrastructure just by having kick-started these projects will explode. And they're not in the business of operating all the hardware and things like this, and so, a major bank like them, they made an announcement recently, a public announcement, you can read about it, started moving the data assets into the cloud. This is clearly happening at rapid pace, and it will change the paradigm in terms of breathability and being able to satisfy peak workload requirements as they come up, when you run a compliance report at quota end or something like this, so this will certainly help with adoption and creating business value for our customers. >> We talk about all the time real-time, and there's so many examples of how data science has changed the game. I mean, I was talking about, from a cyber perspective, how data science helped capture Bin Laden to how I can get increased sales to better user experience on devices. Having real-time access to data, and you put in some quick data science around things, really helps things in the edge. What's your view on real-time? Obviously, that's super important, you got to kind of get your house in order in terms of base data hygiene and foundational work, building blocks. At the end of the day, the real-time seems to be super hot right now. >> Real-time is a relative term, right, so there's certainly applications like IOT applications, or machine data that you analyze that require real-time access. I would call it right-time, so what's the increment of data load that is required for certain applications? We are certainly not a real-time application yet. We can possibly load data through Kafka and stream data through Kafka, but in general, we are still a batch-oriented platform. We can do. >> Which, by the way, is not going away any time soon. It's like super important. >> No, it's not going away at all, right. It can do many batches at relatively frequent increments, which is usually enough for what our customers demand from our platform today, but we're certainly looking at more streaming types of capability as we move this forward. >> What do the customer architectures look like? Because you brought up the good point, we talk about this all the time, batch versus real-time. They're not mutually exclusive, obviously, good architectures would argue that you decouple them, obviously will have a good software elements all through the life cycle of data. >> Through the stack. >> And have the stack, and the stack's only going to get more robust. Your customers, what's the main value that you guys provide them, the problem that you're solving today and the benefits to them? >> Absolutely, so our true value is that there's no breakages in the stack. We enter, and we can basically satisfy all requirements from interesting the data, from blending and integrating the data, preparing the data, building the data pipelines, and analyzing the data. And all this we do in a highly secure and governed environment, so if you stitch it together, as a customer, the customer this morning asked me, "Whom do you compete with?" I keep getting this question all the time, and we really compete with two things. We compete with build-your-own, which customers still opt to do nowadays, while our things are really point and click and highly automated, and we compete with a combination of different products. You need to have at least three to four different products to be able to do what we do, but then you get security breaks, you get lack of data lineage and data governance through the process, and this is the biggest value that we can bring to the table. And secondly now with visual exploration, we offer capability that literally nobody has in the marketplace, where we give power users the capability to explore with blazing fast response times, billion rows of data in a very free-form type of exploration process. >> Are there more power users now than there were when you started as a company? It seemed like tools like Datameer have brought people into the sort of power user camp, just simply by the virtue of having access to your tool. What are your thoughts there? >> Absolutely, it's definitely growing, and you see also different companies exploiting their capability in different ways. You might find insurance or financial services customers that have a very sophisticated capability building in that area, and you might see 1,000 to 2,000 users that do deep data exploration, and other companies are starting out with a couple of dozen and then evolving it as they go. >> Christian, I got to ask you as the new CEO of Datameer, obviously going to the next level, you guys have been successful. We were commenting yesterday on theCUBE about, we've been covering this for eight years in depth in terms of CUBE coverage, we've seen the waves come and go of hype, but now there's not a lot of tolerance for hype. You guys are one of the companies, I will say, that stay to your knitting, you didn't overplay your hand. You've certainly rode the hype like everyone else did, but your solution is very specific on value, and so, you didn't overplay your hand, the company didn't really overplay their hand, in my opinion. But now, there's really the hand is value. >> Absolutely. >> As the new CEO, you got to kind of put a little shiny new toy on there, and you know, rub the, keep the car lookin' shiny and everything looking good with cutting edge stuff, the same time scaling up what's been working. The question is what are you doubling down on, and what are you investing in to keep that innovation going? >> There's really three things, and you're very much right, so this has become a mature company. We've grown with our customer base, our enterprise features and capabilities are second to none in the marketplace, this is what our customers achieve, and now, the three investment areas that we are putting together and where we are doubling down is really visual exploration as I outlined before. Number two, hybrid cloud architectures, we don't believe the customers move their entire stack right into the cloud. There's a few that are going to do this and that are looking into these things, but we will, we believe in the idea that they will still have to EDW their on premise data lake and some workload capabilities in the cloud which will be growing, so this is investment area number two. Number three is the entire concept of data curation for machine learning. This is something where we've released a plug-in earlier in the year for TensorFlow where we can basically build data pipelines for machine learning applications. This is still very small. We see some interest from customers, but it's growing interest. >> It's a directionally correct kind of vector, you're looking and say, it's a good sign, let's kick the tires on that and play around. >> Absolutely. >> 'Cause machine learning's got to learn, too. You got to learn from somewhere. >> And quite frankly, deep learning, machine learning tools for the rest of us, there aren't really all that many for the rest of us power users, they're going to have to come along and get really super visual in terms of enabling visual modular development and tuning of these models. What are your thoughts there in terms of going forward about a visualization layer to make machine learning and deep learning developers more productive? >> That is an area where we will not engage in a way. We will stick with our platform play where we focus on building the data pipelines into those tools. >> Jim: Gotcha. >> In the last area where we invest is ecosystem integration, so we think with our visual explorer backend that is built on search and on a Parquet file format is, or columnar store, is really a key differentiator in feeding or building data pipelines into the incumbent BRE ecosystems and accelerating those as well. We've currently prototypes running where we can basically give the same performance and depth of analytic capability to some of the existing BI tools that are out there. >> What are some the ecosystem partners do you guys have? I know partnering is a big part of what you guys have done. Can you name a few? >> I mean, the biggest one-- >> Everybody, Switzerland. >> No, not really. We are focused on staying true to our stack and how we can provide value to our customers, so we work actively and very important on our cloud strategy with Microsoft and Amazon AWS in evolving our cloud strategy. We've started working with various BI vendors throughout that you know about, right, and we definitely have a play also with some of the big SIs and IBM is a more popular one. >> So, BI guys mostly on the tool visualization side. You said you were a pipeline. >> On tool and visualization side, right. We have very effective integration for our data pipelines into the BI tools today we support TD for Tableau, we have a native integration. >> Why compete there, just be a service provider. >> Absolutely, and we have more and better technology come up to even accelerate those tools as well in our big data stuff. >> You're focused, you're scaling, final word I'll give to you for the segment. Share with the folks that are a Datameer customer or have not yet become a customer, what's the outlook, what's the new Datameer look like under your leadership? What should they expect? >> Yeah, absolutely, so I think they can expect utmost predictability, the way how we roll out the division and how we build our product in the next couple of releases. The next five, six months are critical for us. We have launched Visual Explorer here at the conference. We're going to launch our native cloud solution probably middle of November to the customer base. So, these are the big milestones that will help us for our next fiscal year and provide really great value to our customers, and that's what they can expect, predictability, a very solid product, all the enterprise-grade features they need and require for what they do. And if you look at it, we are really enterprise play, and the customer base that we have is very demanding and challenging, and we want to keep up and deliver a capability that is relevant for them and helps them create values from the data lakes. >> Christian Rodatus, technology enthusiast, passionate, now CEO of Datameer. Great to have you on theCUBE, thanks for sharing. >> Thanks so much. >> And we'll be following your progress. Datameer here inside theCUBE live coverage, hashtag BigDataNYC, our fifth year doing our own event here in conjunction with Strata Data, formerly Strata Hadoop, Hadoop World, eight years covering this space. I'm John Furrier with Jim Kobielus here inside theCUBE. More after this short break. >> Christian: Thank you. (upbeat electronic music)

(Rhythm music) >> Narrator: Live, from Munich, Germany, it's The Cube. Coverage, DataWorks Summit Europe, 2017. Brought to you by Hortonworks. >> Okay, welcome back everyone. We are live in Munich, Germany for DataWorks 2017, formally known as Hadoop Summit. This is The Cube special coverage of the Big Data world. I'm John Furrier my co-host Dave Vallente. Two days of live coverage, day one wrapping up. Now, Dave, we're just kind of reviewing the scene here. First of all, Europe is a different vibe. But the game is still the same. It's about Big Data evolving from Hadoop to full open source penetration. Puppy's now public in markets Hortonworks, Cloudera is now filing an S-1, Neosoft, Talon, variety of the other public companies. Alteryx. Hadoop is not dead, it's not dying. It certainly is going to have a position in the industry, but the Big Data conversation is front and center. And one thing that's striking to me is that in Europe, more than in the North America, is IOT is more centrally themed in this event. Europe is on the Internet of Things because of the manufacturing, smart cities. So this is a lot of IOT happening here, and I think this is a big discovery certainly, Hortonworks event is much more of a community event than Strata Hadoop. Which is much more about making money and modernization. This show's got a lot more engagement with real conversations and developers sessions. Very engaging audience. Well, yeah, it's Europe. So you've go a little bit different smaller show than North America but to me, IOT, Internet of Things, is bringing the other cloud world with Big Data. That's the forcing function. And real time data is the center of the action. I think is going to be a continuing theme as we move forward. >> So, in 2010 John, it was all about 'What is Hadoop?' With the middle part of that decade was all about Hadoop's got to go into the enterprise. It's gone mainstream in to the enterprise, and now it's sort of 'what's next?' Same wine new bottle. But I will say this, Hadoop, as you pointed out, is not dead. And I liken it to the early web. Web one dot O it was profound. It was a new paradigm. The profundity of Hadoop was that you could ship five megabytes of code to a petabyte of data. And that was the new model and that's spawned, that's catalyzed the Big Data movement. That is with us now and it's entrenched, and now you're seeing layers of innovation on top of that. >> Yeah, and I would just reiterate and reinforce that point by saying that Cloudera, the founders of this industry if you will, with Hadoop the first company to be commercially funded to do what Hortonworks came in after the fact out of Yahoo, came out of a web-scale world. So you have the cloud native DevOps culture, Amar Ujala's at Yahoo, Mike Olson, Jeff Hammerbacher, Christopher Vercelli. These guys were hardcore large-scale data guys. Again, this is the continuation of the evolution, and I think nothing is changed it that regard because those pioneers have set the stage for now the commercialization and now the conversation around operationalizing this cloud is big. And having Alan Nance, a practitioner, rock-star, talking about radical deployments that can drop a billion dollars at a cost savings to the bottom line. This is the kind of conversations we're going to see more of this is going to change the game from, you know, "Hey, I'm the CFO buyer" or "CIO doing IT", to an operational CEO, chief operating officer level conversation. That operational model of cloud is now coming into the view what ERP did in software, those kinds of megatrends, this is happening right now. >> As we talk about the open, the people who are going to make the real money on Big Data are the practitioners, those people applying it. We talked about Alan Nance's example of billion dollar, half a billion dollar cost-savings revenue opportunities, that's where the money's being made. It's not being made, yet anyway with these public companies. You're seeing it Splunk, Tableau, now Cloudera, Hortonworks, MapR. Is MapR even here? >> Haven't seen 'em. >> No I haven't seen MapR, they used to have pretty prominent display at the show. >> You brought up point I want to get back to. This relates to those guys, which is, profitless prosperity. >> Yeah. >> A term used for open source. I think there's a trend happening and I can't put a finger on it but I can kind of feel it. That is the ecosystems of open source are now going to a dimension where they're not yet valued in the classic sense. Most people that build platforms value ecosystems, that's where developers came from. Developer ecosystems fuel open source. But if you look at enterprise, at transformations over the decades, you'd see the successful companies have ecosystems of channel partners; ecosystems of indirect sales if you will. We're seeing the formation, at least I can start seeing the formation of an indirect engine of value creation, vis-à-vis this organic developer community where the people are building businesses and companies. Shaun Connolly pointed to Thintech as an example. Where these startups became financial services businesses that became Thintech suppliers, the banks. They're not in the banking business per se, but they're becoming as important as banks 'cuz they're the providers in Thintech, Thintech being financial tech. So you're starting to see this ecosystem of not "channel partners", resell my equipment or software in the classic sense as we know them as they're called channel partners. But if this continues to develop, the thousand flower blooming strategy, you could argue that Hortonworks is undervalued as a company because they're not realizing those gains yet or those gains can't be measured. So if you're an MBA or an investment banker, you've got to be looking at the market saying, "wow, is there a net-present value to an ecosystem?" It begs the question Dave. >> Dave: It's a great question John. >> This is a wealth creation. A rising tide floats all boats, in that rising tide is a ecosystem value number there. No one has their hands on that, no one's talked about that. That is the upshot in my mind, the silver-lining to what some are saying is the consolidation of Hadoop. Some are saying Cloudera is going to get a huge haircut off their four point one billion dollar value. >> Dave: I think that's inevitable. >> Which is some say, they may lose two to three billion in value, in the IPO. Post IPO which would put them in line with Hortonworks based on the numbers. You know, is that good or bad? I don't think it's bad because the value shifts to the ecosystem. Both Cloudera and Hortonworks both play in open source so you can be glass half-full on one hand, on the haircut, upcoming for Cloudera, two saying "No, the glass is half-full because it's a haircut in the short-term maybe", if that happens. I mean some said Pure Storage was going to get a haircut, they never really did Dave. So, again, no one yet has pegged the valuation of an ecosystem. >> Well, and I think that is a great point, personally I think, I've been sort of racking my brain, will this Big Data hike be realized. Like the internet. You remember the internet hyped up, then it crashed; no one wanted to own any of these companies. But it actually lived up to the hype. It actually exceeded the hype. >> You can get pet food online now, it's called amazon. [Co-Hosts Chuckle Together] All the e-commerce played out. >> Right, e-commerce played out. But I think you're right. But everybody's expecting sort of, was expecting a similar type of cycle. "Oh, this will replace that." And that's now what's going to happen. What's going to happen is the ecosystem is going to create a flywheel effect, is really what you're saying. >> Jeff: Yes. >> And there will be huge valuations that emerge out of this. But today, the guys that we know and love, the Hortonworks, the Clouderas, et cetera, aren't really on the winners list, I mean some of their founders maybe are. But who are the winners? Maybe the customers because they saw a big drop in cost. Apache's a big winner here. Wouldn't ya say? >> Yeah. >> Apache's looking pretty good, Apache Foundation. I would say AWS is a pretty big winner. They're drifting off of this. How about Microsoft and IBM? I mean I feel in a way IBM is sort of co-opted this Big Data meme, and said, "okay, cognitive." And layered all of it's stuff on top of it. Bought the weather company, repositioned the company, now it hasn't translated in to growth, but certainly has profitability implications. >> IBM plays well here, I'll tell you why. They're very big in open source, so that's positive. Two, they have huge track record and staff dealing with professional services in the enterprise. So if transformation is the journey conversation, IBM's right there. You can't ignore IBM on this one. Now, the stack might be different, but again, beauty is in the eye of the beholder because depending on what work clothes you have it depends. IBM is not going to leave you high and dry 'cuz they have a really you need for what they can do with their customers. Where people are going to get blindsided in my opinion, the IBMs and Oracles of the world, and even Microsoft, is what Alan Nance was talking about, the radical transformation around the operating model is going to force people to figure out when to start cannibalizing their own stacks. That's going to be a tell sign for winners and losers in the big game. Because if IBM can shift quickly and co-op the megatrends, make it their own, get out in front of that next wave as Pat Gelsinger would say, they could surf that wave and then tweak, and then get out in front. If they don't get behind that next wave, they're driftwood. It really is all about where you are in the spectrum, and analytics is one of those things in data where, you've got to have a cohesive horizontal strategy. You got to be horizontally scalable with data. You got to make data freely available. You have to have an abstraction layer of software that will allow free movement of data, across systems. That's the number one thing that comes out of seeing the Hortonwork's data platform for instance. Shaun Connolly called it 'connective tissue'. Cloudera is the same thing, they have to start figuring out ways to be better at the data across the horizontal view. Cloudera like IBM has an opportunity as well, to get out in front of the next wave. I think you can see that with AI and machine learning, clearly they're going to go after that. >> Just to finish off on the winners and losers; I mean, the other winner is systems integrators to service these companies. But I like what you said about cannibalizing stacks as an indicator of what's happening. So let's talk about that. Oracle clearly cannibalizing it's stacks, saying, "okay, we're going to the red stack to the cloud, go." Microsoft has made that decision to do that. IBM? To a large degree is cannibalizing it's stack. HP sold off it's stack, said, "we don't want to cannibalize our stack, we want to sell and try to retool." >> So, your question, your point? >> So, haven't they already begun to do that, the big legacy companies? >> They're doing their tweaking the collet and mog, as an example. At Oracle Open World and IBM Interconnect, all the shows we, except for Amazon, 'cuz they're pure cloud. All are taking the unique differentiation approach to their own stuff. IBM is putting stuff that's relate to IBM in their cloud. Oracle differentiates on their stack, for instance, I have no problem with Oracle because they have a huge database business. And, you're high as a kite if you think Oracle's going to lose that database business when data is the number one asset in the world. What Oracle's doing which I think is quite brilliant on Oracle's part is saying, "hey, if you want to run on premise with hardware, we got Sun, and oh by the way, our database is the fastest on our stuff." Check. Win. "Oh you want to move to the cloud? Come to the Oracle cloud, our database runs the fastest in our cloud", which is their stuff in the cloud. So if you're an Oracle customer you just can't lose there. So they created an inimitability around their own database. So does that mean they're going to win the new database war? Maybe not, but they can coexist as a system of records so that's a win. Microsoft Office 365, tightly coupling that with Azure is a brilliant move. Why wouldn't they do that? They're going to migrate their customer base to their own clouds. Oracle and Microsoft are going to migrate their customers to their own cloud. Differentiate and give their customers a gateway to the cloud. VVMware is partnering with Amazon. Brilliant move and they just sold vCloud Air which we reported at Silicon Angle last night, to a French company recently so vCloud Air is gone. Now that puts the VMware clearly in bed with Amazon web services. Great move for VMware, benefit to AWS, that's a differentiation for VMware. >> Dave: Somebody bought vCloud Air? >> I think you missed that last night 'cuz you were traveling. >> Chuckling: That's tongue-in-cheek, I mean what did they get for vCloud Air? >> OVH bought them, French company. >> More de-levering by Michael. >> Well, they're inter-clouding right? I mean de-leveraging the focus, right? So OVH, French company, has a very much coexisted... >> What'd they pay? >> ... strategy. It's undisclosed. >> Yeah, well why? 'Cuz it wasn't a big number. That's my point. >> Back to the other cloud players, Google. I think Google's differentiating on their technology. Great move, smart move. They just got to get, as someone who's been following them, and you know, you and I both love an enterprise experience. They got to speak the enterprise language and execute the language. Not through 19 year olds and interns or recent smart college grads ad and say, "we're instantly enterprise." There's a dis-economies of scale for trying to ramp up and trying to be too heavy on the enterprise. Amazon's got the same problem, you can't hire sales guy fast enough, and oh by the way, find me a sales guy that has ten 15 years executive selling experience to a complex strategic sales, like the enterprise where you now have stakeholders that are in multiple roles and changing roles as Alan Nance pointed out. So the enterprise game is very difficult. >> Yup. >> Very very difficult. >> Well, I think these dupe startups are seeing that. None of them are making money. Shaun Connolly basically said, "hey, it used to be growth they would pay for growth, but now their punishing you if you don't have growth plus profitability." By the way, that's not all totally true. Amazon makes no money, unless stock prices go through the roof. >> There is no self-service, there is no self-service business model for digital transformation for enterprise customers today. It doesn't exist. The value proposition doesn't resinate with customers. It works good for Shadow IT, and if you want to roll out G Suite in some pockets of your organization, but an ad-sense sales force doesn't work in the enterprise. Everyone's finding that out right now because they're basically transforming their enterprise. >> I think Google's going to solve their problem. I think Google has to solve their problem 'cuz... >> I think they will, but to me it's, buy a company, there's a zillion company out there they could buy tomorrow that are private, that have like 300 sales people that are senior people. Pay the bucks, buy a sales force, roll your stuff out and start speaking the language. I think Dianne Green gets this. So, I think, I expect to see Google ... >> Dave: Totally. >> do some things in that area. >> And I think, to you're point, I've always said the rich get richer. The traditional legacy companies, they're holding servant in this. They waited they waited they waited, and they said, "okay now we're going to go put our chips on the table." Oracle made it's bets. IBM made it's bets. HP, not really, betting on hardware. Okay. Fine. Cisco, Microsoft, they're all making their bets. >> It's all about bets on technology and profitability. This is what I'm looking at right now Dave. We talked about it on our intro. Shaun Connolly who's in charge of strategy at Hortonworks clarified it that clearly revenue, losing money is not going to solve the problem for credibility. Profitability matters. This comes back to the point we've said on The Cube multiple years ago and even just as recently as last year, that the world's flipping back down to credibility. Customers in the enterprise want to see credibility and track record. And they're going to evaluate the suppliers based upon key fundamentals in their business. Can they make money? Can they deliver SLAs? These are going to be key requirements, not the shiny new toy from Silicon Valley. Or the cool machine learning algorithm. It has to apply to their product, their value, and they're going to look to companies on the scoreboard and say, "are you profitable?" As a proxy for relevance. >> Well I want to keep it, but I do want to, we've been kind of critical of some of the Hadoop players. Cloudera and Hortonworks specifically. But I want to give them props 'cuz you remember well John, when the legacy enterprise guys started coming into the Hadoop market they all said that they had the same messaging, "we're going to make Hadoop enterprise ready." You remember that well, and I have to say that Hortonworks, Cloudera, I would say MapR as well and the ecosystem, have done a pretty good job of making Hadoop and Big Data enterprise ready. They were already working on it very hard, I think they took it seriously and I think that that's why they are in the mix and they are growing as they are. Shaun Connolly talked about them being operating cashflow positive. Eking out some plus cash. On the next earnings call, pressures on. But we want to see, you know, rocket ships. >> I think they've done a good job, I mean, I don't think anyone's been asleep at the switch. At all, enterprise ready. The questions always been "can they get there fast enough?" I think everyone's recognized that cost of ownership's down. We still solicit on the OpenStack ecosystem, and that they move right from the valley properties. So we'll keep an eye on it, tomorrow we'll be checking in. We got a great day tomorrow. Live coverage here in Munich, Germany for DataWorks 2017. More coverage tomorrow, stay with us. I'm John Furrier with Dave Vallente. Be right back with more tomorrow, day two. Keep following us.

>> Announcer: Coverage DataWorks Summit Europe 2017 brought to you by Hortonworks. >> Welcome back everyone. Live here in Munich, Germany for theCUBE'S special presentation of Hortonworks Hadoop Summit now called DataWorks 2017. I'm John Furrier, my co-host Dave Vellante, our next guest is Shaun Connolly, Vice President of Corporate Strategy, Chief Strategy Officer. Shaun great to see you again. >> Thanks for having me guys. Always a pleasure. >> Super exciting. Obviously we always pontificating on the status of Hadoop and Hadoop is dead, long live Hadoop, but runs in demise is greatly over-exaggerated, but reality is is that no major shifts in the trends other than the fact that the amplification with AI and machine learning has upleveled the narrative to mainstream around data, big data has been written on on gen one on Hadoop, DevOps, culture, open-source. Starting with Hadoop you guys certainly have been way out in front of all the trends. How you guys have been rolling out the products. But it's now with IoT and AI as that sizzle, the future self driving cars, smart cities, you're starting to really see demand for comprehensive solutions that involve data-centric thinking. Okay, said one. Two, open-source continues to dominate MuleSoft went public, you guys went public years ago, Cloudera filed their S-1. A crop of public companies that are open-source, haven't seen that since Red Hat. >> Exactly. 99 is when Red Hat went public. >> Data-centric, big megatrend with open-source powering it, you couldn't be happier for the stars lining up. >> Yeah, well we definitely placed our bets on that. We went public in 2014 and it's nice to see that graduating class of Taal and MuleSoft, Cloudera coming out. That just I think helps socializes movement that enterprise open-source, whether it's for on-prem or powering cloud solutions pushed out to the edge, and technologies that are relevant in IoT. That's the wave. We had a panel earlier today where Dahl Jeppe from Centric of British Gas, was talking about his ... The digitization of energy and virtual power plant notions. He can't achieve that without open-source powering and fueling that. >> And the thing about it is is just kind of ... For me personally being my age in this generation of computer industry since I was 19, to see the open-source go mainstream the way it is, is even gets better every time, but it really is the thousandth flower bloom strategy. Throwing the seeds out there of innovation. I want to ask you as a strategy question, you guys from a performance standpoint, I would say kind of got hammered in the public market. Cloudera's valuation privately is 4.1 billion, you guys are close to 700 million. Certainly Cloudera's going to get a haircut looks like. The public market is based on the multiples from Dave and I's intro, but there's so much value being created. Where's the value for you guys as you look at the horizon? You're talking about white spaces that are really developing with use cases that are creating value. The practitioners in the field creating value, real value for customers. >> So you covered some of the trends, but I'll translate em into how the customers are deploying. Cloud computing and IoT are somewhat related. One is a centralization, the other is decentralization, so it actually calls for a connected data architecture as we refer to it. We're working with a variety of IoT-related use cases. Coca-Cola, East Japan spoke at Tokyo Summit about beverage replenishment analytics. Getting vending machine analytics from vending machines even on Mount Fuji. And optimizing their flow-through of inventory in just-in-time delivery. That's an IoT-related to run on Azure. It's a cloud-related story and it's a big data analytics story that's actually driving better margins for the business and actually better revenues cuz they're getting the inventory where it needs to be so people can buy it. Those are really interesting use cases that we're seeing being deployed and it's at this convergence of IoT cloud and big data. Ultimately that leads to AI, but I think that's what we're seeing the rise of. >> Can you help us understand that sort of value chain. You've got the edge, you got the cloud, you need something in-between, you're calling it connected data platform. How do you guys participate in that value chain? >> When we went public our primary workhorse platform was Hortonworks Data Platform. We had first class cloud services with Azure HDInsight and Hortonworks Data Cloud for AWS, curated cloud services pay-as-you-go, and Hortonworks DataFlow, I call as our connective tissue, it manages all of your data motion, it's a data logistics platform, it's like FedEx for data delivery. It goes all the way out to the edge. There's a little component called Minify, mini and ify, which does secure intelligent analytics at the edge and transmission. These smart manufacturing lines, you're gathering the data, you're doing analytics on the manufacturing lines, and then you're bringing the historical stuff into the data center where you can do historical analytics across manufacturing lines. Those are the use cases that are connect the data archives-- >> Dave: A subset of that data comes back, right? >> A subset of the data, yep. The key events of that data it may not be full of-- >> 10%, half, 90%? >> It depends if you have operational events that you want to store, sometimes you may want to bring full fidelity of that data so you can do ... As you manufacture stuff and when it got deployed and you're seeing issues in the field, like Western Digital Hard Drives, that failure's in the field, they want that data full fidelity to connect the data architecture and analytics around that data. You need to ... One of the terms I use is in the new world, you need to play it where it lies. If it's out at the edge, you need to play it there. If it makes a stop in the cloud, you need to play it there. If it comes into the data center, you also need to play it there. >> So a couple years ago, you and I were doing a panel at our Big Data NYC event and I used the term "profitless prosperity," I got the hairy eyeball from you, but nonetheless, we talked about you guys as a steward of the industry, you have to invest in open-source projects. And it's expensive. I mean HDFS itself, YARN, Tez, you guys lead a lot of those initiatives. >> Shaun: With the community, yeah, but we-- >> With the community yeah, but you provided contributions and co-leadership let's say. You're there at the front of the pack. How do we project it forward without making forward-looking statements, but how does this industry become a cashflow positive industry? >> Public companies since end of 2014, the markets turned beginning at 2016 towards, prior to that high growth with some losses was palatable, losses were not palatable. That his us, Splunk, Tableau most of the IT sector. That's just the nature of the public markets. As more public open-source, data-driven companies will come in I think it will better educate the market of the value. There's only so much I can do to control the stock price. What I can from a business perspective is hit key measures from a path to profitability. The end of Q4 2016, we hit what we call the just-to-even or breakeven, which is a stepping stone. On our earnings call at the end of 2016 we ended with 185 million in revenue for the year. Only five years into this journey, so that's a hard revenue growth pace and we basically stated in Q3 or Q4 of 17, we will hit operating cashflow neutrality. So we are operating business-- >> John: But you guys also hit a 100 million at record pace too, I believe. >> Yeah, in four years. So revenue is one thing, but operating margins, like if you look at our margins on our subscription business for instance, we've got 84% margin on that. It's a really nice margin business. We can make that better margins, but that's a software margin. >> You know what's ironic, we were talking about Red Hat off camera. Here's Red Hat kicking butt, really hitting all cylinders, three billion dollars in bookings, one would think, okay hey I can maybe project forth some of these open-source companies. Maybe the flip side of this, oh wow we want it now. To your point, the market kind of flipped, but you would think that Red Hat is an indicator of how an open-source model can work. >> By the way Red Hat went public in 99, so it was a different trajectory, like you know I charted their trajectory out. Oracle's trajectory was different. They didn't even in inflation adjusted dollars they didn't hit a 100 million in four years, I think it was seven or eight years or what have you. Salesforce did it in five. So these SaaS models and these subscription models and the cloud services, which is an area that's near and dear to my heart. >> John: Goes faster. >> You get multiple revenue streams across different products. We're a multi-products cloud service company. Not just a single platform. >> So we were actually teasing this out on our-- >> And that's how you grow the business, and that's how Red Hat did it. >> Well I want to get your thoughts on this while we're just kind of ripping live here because Dave and I were talking on our intro segment about the business model and how there's some camouflage out there, at least from my standpoint. One of the main areas that I was kind of pointing at and trying to poke at and want to get your reaction to is in the classic enterprise go-to-market, you have sales force expansive, you guys pay handsomely for that today. Incubating that market, getting the profitability for it is a good thing, but there's also channels, VARs, ISVs, and so on. You guys have an open-source channel that kind of not as a VAR or an ISV, these are entrepreneurs and or businesses themselves. There's got to be a monetization shift there for you guys in the subscription business certainly. When you look at these partners, they're co-developing, they're in open-source, you can almost see the dots connecting. Is this new ecosystem, there's always been an ecosystem, but now that you have kind of a monetization inherently in a pure open distribution model. >> It forces you to collaborate. IBM was on stage talking about our system certified on the Power Systems. Many may look at IBM as competitive, we view them as a partner. Amazon, some may view them as a competitor with us, they've been a great partner in our for AWS. So it forces you to think about how do you collaborate around deeply engineered systems and value and we get great revenue streams that are pulled through that they can sell into the market to their ecosystems. >> How do you vision monetizing the partners? Let's just say Dave and I start this epic idea and we create some connective tissue with your orchestrator called the Data Platform you have and we start making some serious bang. We make a billion dollars. Do you get paid on that if it's open-source? I mean would we be more subscriptions? I'm trying to see how the tide comes in, whose boats float on the rising tide of the innovation in these white spaces. >> Platform thinking is you provide the platform. You provide the platform for 10x value that rides atop that platform. That's how the model works. So if you're riding atop the platform, I expect you and that ecosystem to drive at least 10x above and beyond what I would make as a platform provider in that space. >> So you expect some contributions? >> That's how it works. You need a thousand flowers to be running on the platform. >> You saw that with VMware. They hit 10x and ultimately got to 15 or 16, 17x. >> Shaun: Exactly. >> I think they don't talk about it anymore. I think it's probably trading the other way. >> You know my days at JBoss Red Hat it was somewhere between 15 to 20x. That was the value that was created on top of the platforms. >> What about the ... I want to ask you about the forking of the Hadoop distros. I mean there was a time when everybody was announcing Hadoop distros. John Furrier announced SiliconANGLE was announcing Hadoop distro. So we saw consolidation, and then you guys announced the ODP, then the ODPI initiative, but there seems to be a bit of a forking in Hadoop distros. Is that a fair statement? Unfair? >> I think if you look at how the Linux market played out. You have clearly Red Hat, you had Conicho Ubuntu, you had SUSE. You're always going to have curated platforms for different purposes. We have a strong opinion and a strong focus in the area of IoT, fast analytic data from the edge, and a centralized platform with HDP in the cloud and on-prem. Others in the market Cloudera is running sort of a different play where they're curating different elements and investing in different elements. Doesn't make either one bad or good, we are just going after the markets slightly differently. The other point I'll make there is in 2014 if you looked at the then chart diagrams, there was a lot of overlap. Now if you draw the areas of focus, there's a lot of white space that we're going after that they aren't going after, and they're going after other places and other new vendors are going after others. With the market dynamics of IoT, cloud and AI, you're going to see folks chase the market opportunities. >> Is that dispersity not a problem for customers now or is it challenging? >> There has to be a core level of interoperability and that's one of the reasons why we're collaborating with folks in the ODPI, as an example. There's still when it comes to some of the core components, there has to be a level of predictability, because if you're an ISV riding atop, you're slowed down by death by infinite certification and choices. So ultimately it has to come down to just a much more sane approach to what you can rely on. >> When you guys announced ODP, then ODPI, the extension, Mike Olson wrote a blog saying it's not necessary, people came out against it. Now we're three years in looking back. Was he right or not? >> I think ODPI take away this year, there's more than we can do above and beyond the Hadoop platform. It's expanded to include SQL and other things recently, so there's been some movement on this spec, but frankly you talk to John Mertic at ODPI, you talk to SAS and others, I think we want to be a bit more aggressive in the areas that we go after and try and drive there from a standardization perspective. >> We had Wei Wang on earlier-- >> Shaun: There's more we can do and there's more we should do. >> We had Wei on with Microsoft at our Big Data SV event a couple weeks ago. Talk about the Microsoft relationship with you guys. It seems to be doing very well. Comments on that. >> Microsoft was one of the two companies we chose to partner with early on, so and 2011, 2012 Microsoft and Teradata were the two. Microsoft was how do I democratize and make this technology easy for people. That's manifest itself as Azure Cloud Service, Azure HDInsight-- >> Which is growing like crazy. >> Which is globally deployed and we just had another update. It's fundamentally changed our engineering and delivering model. This latest release was a cloud first delivery model, so one of the things that we're proud of is the interactive SQL and the LLAP technology that's in HDP, that went out through Azure HDInsight what works data cloud first. Then it certified in HDP 2.6 and it went power at the same time. It's that cadence of delivery and cloud first delivery model. We couldn't do it without a partnership with Microsoft. I think we've really learned what it takes-- >> If you look at Microsoft at that time. I remember interviewing you on theCUBE. Microsoft was trading something like $26 a share at that time, around their low point. Now the stock is performing really well. Stockinnetel very cloud oriented-- >> Shaun: They're very open-source. >> They're very open-source and friendly they've been donating a lot to the OCP, to the data center piece. Extremely different Microsoft, so you slipped into that beautiful spot, reacted on that growth. >> I think as one of the stalwarts of enterprise software providers, I think they've done a really great job of bending the curve towards cloud and still having a mixed portfolio, but in sending a field, and sending a channel, and selling cloud and growing that revenue stream, that's nontrivial, that's hard. >> They know the enterprise sales motions too. I want to ask you how that's going over all within Hortonworks. What are some of the conversations that you're involved in with customers today? Again we were saying in our opening segment, it's on YouTube if you're not watching, but the customers is the forcing function right now. They're really putting the pressure one the suppliers, you're one of them, to get tight, reduce friction, lower costs of ownership, get into the cloud, flywheel. And so you see a lot-- >> I'll throw in another aspect some of the more late majority adopters traditionally, over and over right here by 2025 they want to power down the data center and have more things running in the public cloud, if not most everything. That's another eight years or what have you, so it's still a journey, but this journey to making that an imperative because of the operational, because of the agility, because of better predictability, ease of use. That's fundamental. >> As you get into the connected tissue, I love that example, with Kubernetes containers, you've got developers, a big open-source participant and you got all the stuff you have, you just start to see some coalescing around the cloud native. How do you guys look at that conversation? >> I view container platforms, whether they're container services that are running one on cloud or what have you, as the new lightweight rail that everything will ride atop. The cloud currently plays a key role in that, I think that's going to be the defacto way. In particularly if you go cloud first models, particularly for delivery. You need that packaging notion and you need the agility of updates that that's going to provide. I think Red Hat as a partner has been doing great things on hardening that, making it secure. There's others in the ecosystem as well as the cloud providers. All three cloud providers actually are investing in it. >> John: So it's good for your business? >> It removes friction of deployment ... And I ride atop that new rail. It can't get here soon enough from my perspective. >> So I want to ask about clouds. You were talking about the Microsoft shift, personally I think Microsoft realized holy cow, we could actaully make a lot of money if we're selling hardware services. We can make more money if we're selling the full stack. It was sort of an epiphany and so Amazon seems to be doing the same thing. You mentioned earlier you know Amazon is a great partner, even though a lot of people look at them as a competitor, it seems like Amazon, Azure etc., they're building out their own big data stack and offering it as a service. People say that's a threat to you guys, is it a threat or is it a tailwind, is it it is what it is? >> This is why I bring up industry-wide we always have waves of centralization, decentralization. They're playing out simultaneously right now with cloud and IoT. The fact of the matter is that you're going to have multiple clouds on-prem data and data at the edge. That's the problem I am looking to facilitate and solve. I don't view them as competitors, I view them as partners because we need to collaborate because there's a value chain of the flow of the data and some of it's going to be running through and on those platforms. >> The cloud's not going to solve the edge problem. Too expensive. It's just physics. >> So I think that's where things need to go. I think that's why we talk about this notion of connected data. I don't talk hybrid cloud computing, that's for compute. I talk about how do you connect to your data, how do you know where your data is and are you getting the right value out of the data by playing it where it lies. >> I think IoT has been a great sweet trend for the big data industry. It really accelerates the value proposition of the cloud too because now you have a connected network, you can have your cake and eat it too. Central and distributed. >> There's different dynamics in the US versus Europe, as an example. US definitely we're seeing a cloud adoption that's independent of IoT. Here in Europe, I would argue the smart mobility initiatives, the smart manufacturing initiatives, and the connected grid initiatives are bringing cloud in, so it's IoT and cloud and that's opening up the cloud opportunity here. >> Interesting. So on a prospects for Hortonworks cashflow positive Q4 you guys have made a public statement, any other thoughts you want to share. >> Just continue to grow the business, focus on these customer use cases, get them to talk about them at things like DataWorks Summit, and then the more the merrier, the more data-oriented open-source driven companies that can graduate in the public markets, I think is awesome. I think it will just help the industry. >> Operating in the open, with full transparency-- >> Shaun: On the business and the code. (laughter) >> Welcome to the party baby. This is theCUBE here at DataWorks 2017 in Munich, Germany. Live coverage, I'm John Furrier with Dave Vellante. Stay with us. More great coverage coming after this short break. (upbeat music)

>> Narrator: Recovery. DataWorks Summit Europe 2017. Brought to you by Hortonworks. >> Hello everyone, welcome to The Cube's special presentation here in Munich, Germany for DataWorks Summit 2017. This is the Hadoop Summit powered by Hortonworks. This is their event and again, shows the transition from the Hadoop world to the big data world. I'm John Furrier. My co-host Dave Vellante, good to see you Dave. We're back in the seats together, usually on different events, but now here together in Munich. Great beer, great scene here. Small European event for Hortonworks and the ecosystem but it's called DataWorks 2017. Strata Hadoop is calling themselves Strata and Data. They're starting to see the word Hadoop being sunsetted from these events, which is a big theme of this year. The transition from Hadoop being the branded category to Data. >> Well, you're certainly seeing that in a number of ways. The titles of these events. Well, first of all, I love being in Europe. These venues are great, right? They're so Euro, very clean and magnificent. But back to your point. You're seeing the Hadoop Summit now called the DataWorks Summit. You're seeing the Strata Plus Hadoop is now Strata Plus, I don't even know what it is. Right, it's not Hadoop driven anymore. You see it also in Cloudera's IPO. They're going to talk about Hadoop and Hadoop Distro. They're a Hadoop Distro vendor but they talked about being a data management company and John, I think we are entering the era, or well deep into the era of what I have been calling for the last couple of years, profitless prosperity. Really where you see the Cloudera IPO, as you know, they raised money from Intel, over $600 million at a $4.1 billion dollar valuation. The Wall Street Journal says they'll have a tough time getting a billion dollar valuation. For every dollar each of these companies spends, Hortonworks and Cloudera, they lose between $1.70 and $2.50, so we've always said at SiliconANGLE, Wiki Bond and The Cube that people are going to make money in big data or the practitioners of big data, and it's hard to find those guys, it's hard to see them but that's really what's happening is the industries are transforming and those are the guys that are putting money into their bottom line. Not so much for technology vendors. >> Great to unpack that but first of all, I want to just say congratulations to Wiki Bond for getting it right again. As usual Wiki Bond, ahead of the curve and being out there and getting it right because I think you nailed it and I think Wiki Bond saw this first of all the research firms, kind of, you know, pat ourselves on the back here, but the truth is that practitioners are making the money and I think you're going to see more of that. In fact, last night as I'm having a nice beer here in Germany, I just like to listen to the conversations in the bar area and a lot of conversations around, real conversations around, you know, doing deals, and you know, deployments. You know, you're hearing about HBase, you're hearing about clusters, you're hearing about service revenue, and I think this is the focus. Cloudera, I think, in a classic Silicon Valley way, their hubris was tempered by their lack of scale. I mean, they didn't really blow it out. I mean, now they do 200 million in revenue. Nothing to shake a stick at, they did a great job, but they're buying revenue and Hortonworks is as well. But the ecosystem is the factor, and this is the wildcard. I'm making a prediction. Profitless prosperity that you point out is right, but I think that it has longevity with these companies like Hortonworks and Cloudera and others, like MapR because the ecosystem's robust. If you factor in the ecosystem revenue that is enough rising tide in my opinion. The question is how do they become sustainable as a standalone venture, that Red Hat for Hadoop never worked as Pat Gilson, you know, predicted. So, I think you're going to see a quick shift and pivot quickly by Hortonworks, certainly Cloudera's going to be under the microscope once they go public. I'm expecting that valuation to plummet like a rock. They're going to go public, Silicon Valley people are going to get their exits but. >> Excel will be happy. >> Everyone, yeah, they'll be happy. They already sold in 2013. They did a big sale, I mean, all of them cashed out two years ago when that liquidation event happened with Intel but that's fine. But now it's back to business building and Hortonworks has been doing it for years, so when you see your evaluation is less than a billion, so I'm expecting Cloudera to plummet like a rock. I would not buy the IPO at all because I think it's going to go well under a billion dollars. >> And I think it's the right call and as we know, last year, at the end of last year, Fidelity and other mutual funds devalued their holdings in Cloudera and so, you know, you've got this situation where, as you say, a couple hundred, maybe you know, on the way to 300 million in revenue, Hortonworks on the way to 200 million in revenue. Add up the ecosystem, yeah, maybe you get to a billion, throw in all of what IBM and Oracle call big data, and it's kind of a more interesting business, but you've called it same wine, new bottle. Is it a new bottle? Now, what I mean by that is the shift from Hadoop and then again, you read Cloudera's S1, it's all about AI, machine learning, you know, the cloud. Interesting, we'll talk about the cloud a little later, but is it same wine, new bottle, or is this really a shift toward a new era of innovation? >> It's not a new shift. It's the same innovation that the Hortonworks was founded on. Big data is a categorical and Hadoop was the horse they rode in on, but I think what's changing is the fact that customers are now putting real projects on the table and the scrutiny around those projects have to produce value, and the value comes down to total cost of ownership and business value. And that's becoming a data specific thing, and you look at all the successes in the big data world, Spark and others, you're seeing a focus on cloud integration and real-time workloads. These are real projects. This isn't fantasy. This isn't hype. This isn't early adopter. These are real companies saying we are moving to a new paradigm of digital transforming our companies and we need cost efficiencies but revenue-producing applications and workloads that are going to be running in the cloud with data at the heart of it. So, this is a customer-forcing function where the customers are generally excited about machine learning, moving to real-time classification of workloads. This is the deal and no hubris, no technology posturing, no open standards, jockeying can right the situation. Customers have demands and they want them filled, and we're going to have a lot of guests on here and I'm going to ask them those direct questions. What are you looking for and? >> Well, I totally agree with what you're saying and when we first met, it was right around the, you know, the mid point of the web 2.0 era, and I remember Tim Berners-Lee commenting on all this excitement, everybody's doing, he said this is what the web was invented to do, and this is what big data was invented to do. It was to produce deep analytics, deep learning, machine learning, you know, cognitive, as IBM likes to brand that, and so, it really is the next era even though people don't like to use the term big data anymore. We were talking to, you know, some of the folks in our community earlier, John, you and I, about some of the challenges. Why is it profitless, you know? Why is there so much growth but it's no profit? And you know, we have to point out here that people like Hortonworks and Cloudera, they've made some big bets, take HDSF of example. And now you have the cloud guys, particularly Amazon, coming in, you know, with S3. Look at YARN, big open source project. But you got Docker and Kubernetes seem to be mopping that up. Tez was supposed to replace MapReduce and now you've got. >> I mean, I wouldn't say mopping up, I mean. >> You've got Spark. >> At the end of the day the ecosystem's going to revolve around what the customers want, and portability of workloads, Kubernetes and microservices, these are areas that just absolutely make a lot of sense and I think, you know, people will move to where the frictionless action is and that's going to happen with Kubernetes and containers and microservices, but that just speaks to the devops culture, and I think Hadoop ecosystem, again, was grounded in the devops culture. So, yeah, there's some progress that are going to maybe go out of flavor, but there's other stuff coming up trough the ranks in open source and I think it's compelling. >> But where I disagree with what you're saying is well, the point I'm trying to make, is you have to, if you're Cloudera and Hortonworks, you have to support those multiple projects and it's expensive as hell. Whereas the cloud guys put all their wood behind one arrow, to use an old Scott McNealy phrase, and you know, Amazon, I would argue is mopping up in big data. I think the cloud guys, you know, it's ironic to me that Cloudera in the cloud era picked that name, you know, but really never had. >> John: They missed the cloud. >> They've never really had a strong cloud play, and I would say the same thing with Hortonworks and MapR. They have to play in the cloud and they talk about cloud, but they've got to support hybrid, they've got to support on param, they got to pick the clouds that they're going to support, AWS, Azure, maybe IBM's cloud. >> Look, Cloudera completely missed the cloud era, pun intended. However, they didn't miss open source but they're great at and I'm an admirer of Cloudera and Hortonworks on is that their open source ethos is what drove them, and so they kind of got isolated in with some of their product decisions, but that's not a bad thing. I mean, ultimately, I'm really bullish on Cloudera and Hortonworks because the ecosystem points I mentioned earlier are not high on the I wouldn't buy the IPO, I think I'd buy them at a discount, but Cloudera's not going to go away, Dave. They're going to go public. I think the valuation's going to drop like a rock and then settle around a billion, but they have good management. The founders still there, Michael Olson, Amr Awadallah. So, you're going to see Cloudera transform as a company. They have to do business out in the open and they're not afraid to, obviously they're open source. So, we're going to start to see that transition from a private venture backed, scale up, buy revenue. In the playbook of Silicon Valley venture capital's Excel partners and Greylock. Now they go public and get liquid and then now next phase of their journey is going to be build a public company and I think that they will do a good job doing it and I'm not down on them at all for that and I think it's just going to be a transition. >> Well, they're going to raise what? A couple 100 million dollars? But this industry, yeah, this industry's cashflow negative, so I agree with you. Open source is great, let's ra-ra for open source and it drives innovation, but how does this industry pay for itself? That's what I want to know. How you respond to that? >> Well, I think they have sustainable issues around services and I think partnering with the big companies like Intel that have professional services might help them on that front, but Michael Olson said in his founder's letter in his S1, kind of AI washing, he said AI and cognitive. But that's okay because Cloudera could easily pivot with their brain power, and same with Hortonworks to AI. Machine learning is very open source driven. Open source culture is growing, it's not going away, so I think Cloudera's in a very good position. >> I think the cloud guys are going to kill them in that game, and cloud guys and IBM are going to cream these profitless startups in that AI and machine learning game. >> We'll see. >> You disagree? >> I disagree, I think. Well, I mean, it depends. I mean, you know, I'm not going to, you know, forecast what the managements might do, but I mean, if I'm cloud looking at what Cloudera's done. >> What would you do? >> I would do exactly what Mike Olson's doing is I'd basically pivot immediately to machine learning. Look at Google. TensorFlow it's go so much traction with their cloud because it's got machine learning built into it. Open source is where the action is, and that's where you could do a lot of good work and use it as an advantage in that they know that game. I would not count out the open source game. >> So, we know how IBM makes money at that, you know, in theory anyway it wants. We know how Amazon's going to make money at that with their priority approach, Microsoft will do the same thing. How to Cloudera and Hortonworks make money? >> I think it's a product transition around getting to the open source with cloud technologies. Amazon is not out to kill open source, so I think there's an opportunity to wedge in a position there, and so they just got to move quickly. If they don't make these decisions then that's a failed execution on the management team at Cloudera and Hortonworks and I think they're on it. So, we'll keep an eye on that. >> No, Amazon's not trying to kill open source, I would agree, but they are bogarting open source in a big way and profiting amazingly from it. >> Well, they just do what Amy Jessie would say, they're customer driven. So, if a customer doesn't want to do five things to do one thing this is back to my point. The customers want real-time workloads. They want it with open source and they don't want all these steps in the cost of ownership. That's why this is not a new shift, it's the same wine, new bottle because now you're just seeing real projects that are demanding successful and efficient code and support and whoever delivers it builds the better mousetrap. In this case, the better mousetrap will win. >> And I'm arguing that the better mousetrap and the better marginal economics, I know I'm like a broken record on this, but if I take Kinesis and DynamoDB and Red Ship and wrap it into my big data play, offer it as a service with a set of APIs on the cloud, like AWS is going to do, or is doing, and Azure is doing, that's a better business model than, as you say, five different pieces that I have to cobble together. It's just not economically viable for customers to do that. >> Well, we've got some big new coming up here. We're going to have two days of wall-to-wall coverage of DataWorks 2017. Hortonworks announcing 2.6 of their Hadoop Hortonworks data platform. We're going to talk to Scott now, the CTO, coming up shortly. Stay with us for exclusive coverage of DataWorks in Munich, Germany 2017. We'll be back with more after this short break.

(electronic music) >> Announcer: Live from Austin, Texas, it's The Cube. Covering South By Southwest 2017. Brought to you by Intel. Now, here's John Furrier. >> Hey, welcome back, everyone, we're here live in Austin, Texas, for South By Southwest Cube coverage at the Intel AI Lounge, #IntelAI if you're watching, put it out on Twitter. I'm John Furrier of Silicon Angle for the Cube. Our next guest is Alison Yu who's with Cloudera. And in the news today, although they won't comment on it. It's great to see you, social media manager at Cloudera. >> Yes, it's nice to see you as well. >> Great to see you. So, Cloudera has a strategic relationship with Intel. You guys have a strategic investment, Intel, and you guys partner up, so it's well-known in the industry. But what's going on here is interesting, AI for social good is our theme. >> Alison: Yes. >> Cloudera has always been a pay-it-forward company. And I've known the founders, Mike Olson and Amr Awadallah. >> Really all about the community and paying it forward. So Alison, talk about what you guys are working on. Because you're involved in a panel, but also Cloudera Cares. And you guys have teamed up with Thorn, doing some interesting things. >> Alison: Yeah (laughing). >> Take it away! >> Sure, thanks. Thanks for the great intro. So I'll give you a little bit of a brief introduction to Cloudera Cares. Cloudera Cares was founded roughly about three years ago. It was really an employee-driven and -led effort. I kind of stepped into the role and ended up being a little bit more of the leader just by the way it worked out. So we've really gone from, going from, you know, we're just doing soup kitchens and everything else, to strategic partnerships, donating software, professional service hours, things along those lines. >> Which has been very exciting to see our nonprofit partnerships grow in that way. So it really went from almost grass-root efforts to an organized organization now. And we start stepping up our strategic partnerships about a year and a half ago. We started with DataKind, is our initial one. About two years ago, we initiated that. Then we a year ago, about in September, we finalized our donation of an enterprise data hub to Thorn, which if you're not aware of they're all about using technology and innovation to stop child-trafficking. So last year, around September or so, we announced the partnership and we donated professional service hours. And then in October, we went with them to Grace Hopper, which is obviously the largest Women in Tech Conference in North America. And we hosted a hackathon and we helped mentor women entering into the tech workforce, and trying to come up with some really cool innovative solutions for them to track and see what's going on with the dark web, so we had quite a few interesting ideas coming out of that. >> Okay, awesome. We had Frederico Gomez Suarez on, who was the technical advisor. >> Alison: Yeah. >> A Microsoft employee, but he's volunteering at Thorn, and this is interesting because this is not just donating to the soup kitchens and what not. >> Alison: Yeah. >> You're starting to see a community approach to philanthropy that's coding RENN. >> Yeah. >> Hackathons turning into community galvanizing communities, and actually taking it to the next level. >> Yeah. So, I think one of the things we realize is tech, while it's so great, we have actually introduced a lot of new problems. So, I don't know if everyone's aware, but in the '80s and '90s, child exploitation had almost completely died. They had almost resolved the issue. With the introduction of technology and the Internet, it opened up a lot more ways for people to go ahead and exploit children, arrange things, in the dark web. So we're trying to figure out a way to use technology to combat a problem that technology kind of created as well, but not only solving it, but rescuing people. >> It's a classic security problem, the surface area has increased for this kind of thing. But big data, which is where you guys were founded on in the cloud era that we live in. >> Alison: Yeah. >> Pun intended. (laughing) Using the machine learning now you start with some scale now involved. >> Yes, exactly, and that's what we're really hoping, so we're partnering with Intel in the National Center of Missing Exploited Children. We're actually kicking off a virtual hackathon tomorrow, and our hope is we can figure out some different innovative ways that AI can be applied to scraping data and finding children. A lot of times we'll see there's not a lot of clues, but for example, if we can upload, if there can be a tool that can upload three or four different angles of a child's face when they go missing, maybe what happens is someone posts a picture on Instagram or Twitter that has a geo tag and this kid is in the background. That would be an amazing way of using AI and machine learning-- >> Yeah. >> Alison: To find a child, right. >> Well, I'll give you guy a plug for Cloudera. And I'll reference Dr. Naveen Rao, who's the GM of Intel's AI group, was on earlier. And he was talking about how there's a lot of storage available, not a lot of compute. Now, Cloudera, you guys have really pioneered the data lake, data hub concept where storage is critical. >> Yeah. >> Now, you got this compute power and machine learning, that's kind of where it comes together. Did I get that right? >> Yeah, and I think it's great that with the partnership with Intel we're able to integrate our technology directly into the hardware, which makes it so much more efficient. You're able to compute massive amounts of data in a very short amount of time, and really come up with real results. And with this partnership, specifically with Thorn and NCMEC, we're seeing that it's real impact for thousands of people last year, I think. In the 2016 impact report, Thorn said they identified over 6,000 trafficking victims, of which over 2,000 were children. Right, so that tool that they use is actually built on Cloudera. So, it's great seeing our technology put into place. >> Yeah, that's awesome. I was talking to an Intel person the other day, they have 72 cores now on a processor, on the high-end Xeons. Let's get down to some other things that you're working on. What are you doing here at the show? Do you have things that you're doing? You have a panel? >> Yeah, so at the show, at South by Southwest, we're kicking off a virtual hackathon tomorrow at our Austin offices for South by Southwest. Everyone's welcome to come. I just did the liquor order, so yes, everyone please come. (laughing) >> You just came from Austin's office, you're just coming there. >> Yeah, exactly. So we've-- >> Unlimited Red Bull, pizza, food. (laughing) >> Well, we'll be doing lots and lots tomorrow, but we're kicking that off, we have representatives from Thorn, NCMEC, Google, Intel, all on site to answer questions. That's kind of our kickoff of this month-long virtual hackathon. You don't need to be in Austin to participate, but that is one of the things that we are kicking off. >> And then on Sunday, actually here at the Intel AI Lounge we're doing a panel on AI for Good, and using artificial intelligence to solve problems. >> And we'll be broadcasting that live here on The Cube. So, folks, SiliconAngle.tv will carry that. Alison, talk about the trend that, you weren't here when we were talking about how there's now a new counterculture developing in a good way around community and social change. How real is the trend that you're starting to see these hackathons evolve from what used to be recruiting sessions to people just jamming together to meet each other. Now, you're starting to see the next level of formation where people are organizing collectively-- >> Yeah. >> To impact real issues. >> Yeah. >> Is this a real trend or where is that trend, can you speak to that? >> Sure, so from what I've seen from the hackathons what we've been seeing before was it's very company-specific. Only one company wanted to do it, and they would kind of silo themselves, right? Now, we're kind of seeing this coming together of companies that are generally competitors, but they see a great social cause and they decide that they want to band together, regardless of their differences in technology, product, et cetera, for a common good. And, so. >> Like a Thorn. >> For Thorn, you'll see a lot of competitors, so you'll see Facebook and Twitter or Google and Amazon, right? >> John: Yeah. >> And we'll see all these different competitors come together, lend their workforce to us, and have them code for one great project. >> So, you see it as a real trend. >> I do see it as a trend. I saw Thorn last year did a great one with Facebook and on-site with Facebook. This year as we started to introduce this hackathon, we decided that we wanted to do a hackathon series versus just a one-off hackathon. So we're seeing people being able to share code, contribute, work on top of other code, right, and it's very much a sharing community, so we're very excited for that. >> All right, so I got to ask you what's they culture like at Cloudera these days, as you guys prepare to go public? What's the vibe internally of the company, obviously Mike Olson, the founder, is still around, Amr's around. You guys have been growing really fast. Got your new space. What's the vibe like in Cloudera now? >> Honestly, the culture at Cloudera hasn't really changed. So, when I joined three years ago we were much smaller than we are now. But I think one thing that we're really excited about is everyone's still so collaborative, and everyone makes sure to help one another out. So, I think our common goal is really more along the lines of we're one team, and let's put out the best product we can. >> Awesome. So, what's South by Southwest mean to you this year? If you had to kind of zoom out and say, okay. What's the theme? We heard Robert Scoble earlier say it's a VR theme. We hear at Intel it's AI. So, there's a plethora of different touchpoints here. What do you see? >> Yeah, so I actually went to the opening keynote this morning, which was great. There was an introduction, and then I don't know if you realized, but Cory Booker was on as well, which is great. >> John: Yep. >> But I think a lot of what we had seen was they called out on stage that artificial intelligence is something that will be a trend for the next year. And I think that's very exciting that Intel really hit the nail on the head with the AI Lounge, right? >> Cory Booker, I'm a big fan. He's from my neighborhood, went to the same school I went to, that my family. So in Northern Valley, Old Tappan. Cory, if you're watching, retweet us, hashtag #IntelAI. So AI's there. >> AI is definitely there. >> No doubt, it's on stage. >> Yes, but I think we're also seeing a very large, just community around how can we make our community better versus let's try to go in these different silos, and just be hyper-aware of what's only in front of us, right? So, we're seeing a lot more from the community as well, just being interested in things that are not immediately in front of us, the wider, either nation, global, et cetera. So, I think that's very exciting people are stepping out of just their own little bubbles, right? And looking and having more compassion for other people, and figuring out how they can give back. >> And, of course, open source at the center of all the innovation as always. (laughing) >> I would like to think so, right? >> It is! I would testify. Machine learning is just a great example, how that's now going up into the cloud. We started to see that really being part of all the apps coming out, which is great because you guys are in the big data business. >> Alison: Yeah. >> Okay, Alison, thanks so much for taking the time. Real quick plug for your panel on Sunday here. >> Yeah. >> What are you going to talk about? >> So we're going to be talking a lot about AI for good. We're really going to be talking about the NCMEC, Thorn, Google, Intel, Cloudera partnership. How we've been able to do that, and a lot of what we're going to also concentrate on is how the everyday tech worker can really get involved and give back and contribute. I think there is generally a misconception of if there's not a program at my company, how do I give back? >> John: Yeah. >> And I think Cloudera's a shining example of how a few employees can really enact a lot of change. We went from grassroots, just a few employees, to a global program pretty quickly, so. >> And it's organically grown, which is the formula for success versus some sort of structured company program (laughing). >> Exactly, so we definitely gone from soup kitchen to strategic partnerships, and being able to donate our own time, our engineers' times, and obviously our software, so. >> Thanks for taking the time to come on our Cube. It's getting crowded in here. It's rocking the house, the house is rocking here at the Intel AI Lounge. If you're watching, check out the hashtag #IntelAI or South by Southwest. I'm John Furrie. I'll be back with more after this short break. (electronic music)

>> Announcer: theCUBE presents On the Ground. (light electronic music) >> Hello everyone, welcome to this special exclusive On the Ground Cube coverage here at Oracle's Headquarters. I'm John Furier the host of theCUBE, I'm here with my guest, Brad Tewksbury, who's the Senior Director of Business Development for the big data team at Oracle, welcome to On the Ground. >> Thank you, John, good to be here. >> So big day, Brad, you've been in this industry for a long time, you've seen the waves come and go. Certainly at Oracle you've been here for many, many years. >> Yeah. >> Oracle's transforming as as a company and you've been watching it play out. >> Brad: Yeah. >> What is the big thing that's most notable to you that you could illustrate that kind of highlights the Oracle transformation in terms of where it's come from? Obviously the database is the crown jewel, but this big data stuff that you're involved in is really transformative and getting tons of traction. With the Cloud Machine kind of tying in, is this kind of a similar moment for Oracle? Share some thoughts there. >> Yeah I think there's many, if you look at the data management path from going back to client server to where we are today, data has always played a pivotal role, but I would say now every customer is going through this decision making process where they're saying, "Ah-ha data I'm being disrupted by all different companies." Before it was you know, okay I got my data in a database and I do some reporting on it and I can run my business, but it wasn't like I was going to be disrupted by some digital company tomorrow. >> Cause the apps and the databases were kind of tied together. >> They were tied together and things just didn't move as fast as they do today. Now it's in these digital-only companies, they realize that data is their business, right? I think one of the pivotal things that we've been doing some studies with MIT is that 84% of the SMP value of some of these companies comes from companies that have no assets, right? Just data, so like UBER doesn't own any taxis. Airbnb doesn't own any hotels, yet they've got massive valuation, so companies are starting to freak out a little bit and they're starting to say, "Oh my god, I got to leverage my data." So the seminal moment here is saying, "How do I monetize my data?" Before it wasn't this urgency, now there's a sense of like I got to do something with this data, but the predicament they're in is, especially these legacy companies is they've got silos of stuff that's not talking to each other, it's all on different versions and different vendors. >> Well, Oracle's always been in the database business, so you made money by creating software to store data. >> Brad: Right. >> Now it sounds like there's a business model for moving the data around, is that kind of what I'm getting here? So it's not just storing the data software, store the data, it's software to make the data. >> Brad: Yeah. >> Accessible. Yeah, it's three things, I think it's three things. It's ingesting the data, right, from new sources outside of the company, so sensors and social media, right that's one thing. Secondly, it's then managing the data, which we've always done, and then the third thing is analyzing it, so it's that whole continuation and then what's happened here is the management platform is expanded. It's gone from just a relational base to this whole SEQUEL world and this Hadoop world, which we completely support. By no means is this relational a zero-sum game, where it's relational or nothing at all, it's we've expanded the whole data management platform to meet the criteria of whatever the application is and so these are the three data management platforms today, who knows what's going to come tomorrow, we'll support that as well, but the idea is choose the right platform for the application and what's really becoming about is applications, right? And this data management stuff is obviously table stakes, but how do I make my applications dynamic and real-time based on what I have here? >> Four years ago, and CUBE audience will remember, we did theCUBE in Hadoop World, that's called back then before it became Strata Hadoop and O'Reilly and Cloudera Show, but Mike Olson and Ping Lee said, "Oh we have a big data fund," so they thought there was going to be a tsunami of apps, never really happened. Certainly Hadoop didn't become as big as people had thought, but yet Analytics rose up, Analytics became the killer app. >> Brad: Yeah. >> But now we're beyond Analytics. >> Brad: Yeah. >> The use of data for insights, where are the apps coming from now? You had Rocana, here we had Win Disk Scope providing some solutions, where do you guys see the apps coming from? Obviously Oracle has their own set of apps, but outside of Oracle, where are the apps? >> So yeah, it's an interesting phenomena, right? Everyone thought Hadoop is the next great wave and the reality is if you go talk to customers and they're like, "Yeah, I've heard of it, but what do I do with it?" So it's like apps are like what's going to drive this whole stack forward and to that end, the number one thing that people are looking for is 360 view of customers, they all want to know more about customer. I was talking with a customer who represents the equivalent of the Tax Bureau of their county and instead of putting the customer, it's the taxpayer or the customer's at the center and all the different places that you pay taxes, so they want to have one view of you as the taxpayer, so whether you're public entity, private, the number one thing that the apps that people are looking for is show me more about customer. If I'm a bank, a retail, they want to cross-sell that's the number one app. In telcos, they want to know about networking. How do I get this network? I want to understand what's going on here so I can better support my Support Center, but secondary to that we're in this kind of holding pattern. Now what are the next set of apps and so there's a bunch of start-ups here in Silicon Valley that are thinking they have the answer for that and we're partnering with them and opening up a Cloud Marketplace to bring them in and we'll let customers decide who's going to win this. >> Talk about Rocana and their value proposition, they're here talking to us today, what's the deal with Rocana? >> So Rocana is an interesting play, what they have found is that customers, one of the ways they talk about themselves, is they offer a data warehouse to IT. So if I'm the IT guy, I want to go in and have basically a pool of all kinds of log analysis. How's my apps running, do I need to tune the apps? How's the network running, they want a one bucket of how can my operation perform better? So what we've seen from customers is they've come to us and they've said, "okay, what have you got in this new space "of Hadoop that can do that?" Look at log analysis and all kinds of app performances from a Hadoop perspective. They were one of the people, the first persons to answer that, so they're having great success finding out where security breaches are, finding out where network latencies are, better like I said, looking at logs and how things co6uld run better, so that's what they're answering for customers is basically improving IT functions, right, because what's happening is a lot of business people are in charge, right, and they're saying, "I no longer want "to go to IT for everything, I want to be able to just go to basically a data model and do my own analysis of this, "I don't want to have to call IT for everything." So these guys in some way are trying to help that manta. >> Talk about Win Disk Scope, what are they talking about here and how is their relationship with Oracle? They're speaking w6ith us today as well. >> Yeah, so you know, in this big data world what we're seeing a lot of is customers doing a lot of what we call a lab experiment. So they got all this data and they want to do lab experiments, okay great. So then they find this nugget of okay, here's a great data model, we want to do some analysis on this, so let's turn it into a production app. Okay, then what do you do, how do you take it to production? These are the guys that you would call. So they take it into an HA high-availability environment for you and they give you zero data loss, zero down time to do that. One of the things that Oracle's, we're touting is the differentiator in our Cloud is this hybrid approach where you have, you know, you could start out doing test-dev in the Cloud, bring it back on Primm, vice versa, they allow you to do that sync, that link between the Cloud and on Primm. We work today with Cloud Air, we OEM them in our big data appliance, if the customer has Hortonworks, but they also want to work with our stuff, their go-between with that as well. So it's basically they're giving you that production-ready environment that you need in an HA world. >> Brad, thanks for spending some time with us here On the Ground, really appreciate it. >> Yeah. >> I'm John Furier, we're here exclusively On the Ground here at Oracle Headquarters, thanks for watching. (light electronic music)

>> Narrator: From San Jose, in the heart of Silicon Valley, it's The Cube, covering Big Data SV 2016. Now your hosts, John Furrier and Peter Burris. >> Okay, welcome back everyone. We are here live in Silicon Valley for Big Data Week, Big Data SV Strata Hadoop. This is The Cube, SiliconANGLE's flagship program. We go out to the events and extract the signals from the noise. I'm John Furrier, my co-host is Peter Burris. Our next guest is Ritika Gunnar, VP of Data and Analytics at IBM and David Richards is the CEO of WANdisco. Welcome to The Cube, welcome back. >> Thank you. >> It's a pleasure to be here. >> So, okay, IBM and WANdisco, why are you guys here? What are you guys talking about? Obviously, partnership. What's the story? >> So, you know what WANdisco does, right? Data replication, active-active replication of data. For the past twelve months, we've been realigning our products to a market that we could see rapidly evolving. So if you had asked me twelve months ago what we did, we were talking about replicating just Hadoop, but we think the market is going to be a lot more than that. I think Mike Olson famously said that this Hadoop was going to disappear and he was kind of right because the ecosystem is evolving to be a much greater stack that involves applications, cloud, completely heterogeneous storage environment, and as that happens the partnerships that we would need have to move on from just being, you know, the sort of Hadoop-specific distribution vendors to actually something that can deliver a complete solution to the marketplace. And very clearly, IBM has a massive advantage in the number of people, the services, ecosystem, infrastructure, in order to deliver a complete solution to customers, so that's really why we're here. >> If you could talk about the stack comment, because this is something that we're seeing. Mike Olson's kind of being political when he says make it invisible, but the reality is there is more to big data than Hadoop. There's a lot of other stuff going on. Call it stack, call it ecosystem. A lot of great things are growing, we just had Gaurav on from SnapLogic said, "everyone's winning." I mean, I just love that's totally true, but it's not just Hadoop. >> It's about Alldata and it's about all insight on that data. So when you think about Alldata, Alldata is a very powerful thing. If you look at what clients have been trying to do thus far, they've actually been confined to the data that may be in their operational systems. With the advent of Hadoop, they're starting to bring in some structured and unstructured data, but with the advent of IOT systems, systems of engagement, systems of records and trying to make sense of all of that, Alldata is a pretty powerful thing. When I think of Alldata, I think of three things. I think of data that is not only on premises, which is where a lot of data resides today, but data that's in the cloud, where data is being generated today and where a majority of the growth is. When I think of Alldata, I think of structured data, that is in your traditional operational systems, unstructured and semi-structured data from IOT systems et cetera, and when I think of Alldata, I think of not just data that's on premises for a lot of our clients, but actually external data. Data where we can correlate data with, for example, an acquisition that we just did within IBM with The Weather Company or augmenting with partnerships like Twitter, et cetera, to be able to extract insight from not just the data that resides within the walls of your organization, but external data as well. >> The old expression is if you want to go fast, do it alone, if you want to go deeper and broader and more comprehensive, do it as a team. >> That's right. >> That expression can be applied to data. And you look at The Weather data, you think, hmmm, that's an outlier type acquisition, but when you think about the diversity of data, that becomes a really big deal. And the question I want to ask you guys is, and Ritika, we'll start with you, there's always a few pressure points we've seen in big data. When that pressure is relieved, you've seen growth, and one was big data analytics kind of stalled a little bit, the winds kind of shifted, eye of the storm, whatever you want to call it, then cloud comes in. Cloud is kind of enabling that to go faster. Now, a new pressure point that we're seeing is go faster with digital transformation. So Alldata kind of brings us to all digital. And I know IBM is all about digitizing everything and that's kind of the vision. So you now have the pressure of I want all digital, I need data driven at the center of it, and I've got the cloud resource, so kind of the perfect storm. What's your thoughts on that? Do you see that similar picture? And then does that put the pressure on, say, WANdisco, say hey, I need replication, so now you're under the hood? Is that kind of where this is coming together? >> Absolutely. When I think about it, it's about giving trusted data and insights to everyone within the organization, at the speed in which they need it. So when you think about that last comment of, "At the speed in which they need it," that is the pressure point of what it means to have a digitally transformed business. That means being able to make insights and decisions immediately and when we look at what our objective is from an IBM perspective, it's to be able to enable our clients to be able to generate those immediate insights, to be able to transform their business models and to be able to provide the tooling and the skills necessary, whether we have it organically, inorganically, or through partnerships, like with WANdisco to be able to do that. And so with WANdisco, we believe we really wanted to be able to activate where that data resides. When I talk about Alldata and activation of that data, WANdisco provided to us complementary capabilities to be able to activate that data where it resides with a lot of the capabilities that they're providing through their fusion. So, being able to have and enable our end-users to have that digitally infused set of reactive type of applications is absolutely something... >> It's like David, we talk about, and maybe I'm oversimplifying your value proposition, but I always look at WANdisco as kind of the five nines of data, right? You guys make stuff work, and that's the theme here this year, people just want it to work, right? They don't want to have it down, right? >> Yeah, we're seeing, certainly, an uptick in understanding about what high availability, what continuous availability means in the context of Hadoop, and I'm sure we'll be announcing some pretty big deals moving forward. But we've only just got going with IBM. I would, the market should expect a number of announcements moving forward as we get going with this, but here's the very interesting question associated with cloud. And just to give you a couple of quick examples, we are seeing an increasing number of Global 1,000 companies, Fortune 100 companies move to cloud. And that's really important. If you would have asked me 12 months ago, how is the market going to shape up, I'd have said, well, most CIO's want to move to cloud. It's already happening. So, FINRA, the major financial regulator in the United States is moving to cloud, publicly announced it. The FCA in the UK publicly announced they are moving 100% to cloud. So this creates kind of a microcosm of a problem that we solve, which is how do you move transactional data from on-premise to cloud and create a sort of hybrid environment. Because with the migration, you have to build a hybrid cloud in order to do that anyway. So, if it's just archive systems, you can package it on a disk drive and post it, right? If we're talking about transactional data, i.e, stuff that you want to use, so for example, a big travel company can't stop booking flights while they move their data into the cloud, right? They would take six months to move petabyte scale data into cloud. We solve that problem. We enable companies to move transactional data from on-premise into cloud, without any interruption to services. >> So not six months? >> No, not six months. >> Six hours? >> And you can keep on using the data while it is in transit. So we've been looking for a really simplistic problem, right, to explain this really complex algorithm that we've got that you know does this active-active replication stuff. That's it, right? It's so simple, and nobody else can do it. >> So no downtime, no disruption to their business? >> No, and you can use the cloud or you can use the on-prem applications while the data is in transit. >> So when you say all cloud, now we're on a theme, Alldata, all digital, all cloud, there's a nuance there because most, and we had Gaurav from SnapLogic talk about it, there's always going to be an on-prem component. I mean, probably not going to see 100% everyone move to the cloud, public cloud, but cloud, you mean hybrid cloud essentially, with some on-prem component. I'm sure you guys see that with Bluemix as well, that you've got some dabbling in the public cloud, but ultimately, it's one resource pool. That's essentially what you're saying. >> Yeah, exactly. >> And I think it's really important. One of the things that's very attractive e about the WANdisco solution is that it does provide that hybridness from the on-premises to cloud and that being able to activate that data where it resides, but being able to do that in a heterogeneous fashion. Architectures are very different in the cloud than they are on premises. When you look at it, your data like may be as simple as Swift object store or as S3, and you may be using elements of Hadoop in there, but the architectures are changing. So the notion of being able to handle hybrid solutions both on-premises and cloud with the heterogeneous capability in a non-invasive way that provides continuous data is something that is not easily achieved, but it's something that every enterprise needs to take into account. >> So Ritika, talk about the why the WANdisco partnership, and specifically, what are some of the conversations you have with customers? Because, obviously there's, it sounds like, the need to go faster and have some of this replication active-active and kind of, five nines if you will, of making stuff not go down or non-disruptive operations or whatever the buzzword is, but you know, what's the motivation from your standpoint? Because IBM is very customer-centric. What are some of the conversations and then how does WANdisco fit into those conversations? >> So when you look at the top three use cases that most clients use for even Hadoop environments or just what's going on in the market today, the top three use cases are you know, can I build a logical data warehouse? Can I build areas for discovery or analytical discovery? Can I build areas to be able to have data archiving? And those top three solutions in a hybrid heterogeneous environment, you need to be able to have active-active access to the data where that data resides. And therefore, we believe, from an IBM perspective, that we want to be able to provide the best of breed regardless of where that resides. And so we believe from a WANdisco perspective, that WANdisco has those capabilities that are very complementary to what we need for that broader skills and tooling ecosystem and hence why we have formed this partnership. >> Unbelievably, in the market, we're also seeing and it feels like the Hadoop market's just got going, but we're seeing migrations from distributions like Cloudera into cloud. So you know, those sort of lab environments, the small clusters that were being set up. I know this is slightly controversial, and I'll probably get darts thrown at me by Mike Olson, but we are seeing pretty large-scale migration from those sort of labs that were set up initially. And as they progress, and as it becomes mission-critical, they're going to go to companies like IBM, really, aren't they, in order to scale up their infrastructure? They're going to move the data into cloud to get hyperscale. For some of these cases that Ritika was just talking about so we are seeing a lot of those migrations. >> So basically, Hadoop, there's some silo deployments of POC's that need to be integrated in. Is that what you're referring to? I mean, why would someone do that? They would say okay, probably integration costs, probably other solutions, data. >> If you do a roll-your-own approach, where you go and get some open-source software, you've got to go and buy servers, you've got to go and train staff. We've just seen one of our customers, a big bank, two years later get servers. Two years to get servers, to get server infrastructure. That's a pretty big barrier, a practical barrier to entry. Versus, you know, I can throw something up in Bluemix in 30 minutes. >> David, you bring up a good point, and I want to just expand on that because you have a unique history. We know each other, we go way back. You were on The Cube when, I think we first started seven years ago at Hadoop World. You've seen the evolution and heck, you had your own distribution at one point. So you know, you've successfully navigated the waters of this ecosystem and you had gray IP and then you kind of found your swim lanes and you guys are doing great, but I want to get your perspective on this because you mentioned Cloudera. You've seen how it's evolving as it goes mainstream, as you know, Peter says, "The big guys are coming in and with power." I mean, IBM's got a huge spark investment and it's not just you know, lip service, they're actually donating a ton of code and actually building stuff so, you've got an evolutionary change happening within the industry. What's your take on the upstarts like Cloudera and Hortonworks and the Dishrow game? Because that now becomes an interesting dynamic because it has to integrate well. >> I think there will always be a market for the distribution of opensource software. As that sort of, that layer in the stack, you know, certainly Cloudera, Hortonworks, et cetera, are doing a pretty decent job of providing a distribution. The Hadoop marketplace, and Ritika laid this on pretty thick as well, is not Hadoop. Hadoop is a component of it, but in cloud we talk about object store technology, we talk about Swift, we talk about S3. We talk about Spark, which can be run stand-alone, you don't necessarily need Hadoop underneath it. So the marketplace is being stretched to such a point that if you were to look at the percentage of the revenue that's generated from Hadoop, it's probably less than one percent. I talked 12 months ago with you about the whale season, the whales are coming. >> Yeah, they're here. >> And they're here right now, I mean... >> (laughs) They're mating out in the water, deals are getting done. >> I'm not going to deal with that visual right now, but you're quite right. And I love the Peter Drucker quote which is, "Strategy is a commodity, execution is an art." We're now moving into the execution phase. You need a big company in order to do that. You can't be a five hundred or a thousand person... >> Is Cloudera holding onto dogma with Hadoop or do they realize that the ecosystem is building around them? >> I think they do because they're focused on the application layer, but there's a lot of competition in the application layer. There's a little company called IBM, there's a little company called Microsoft and the little company called Amazon that are kind of focused on that as well, so that's a pretty competitive environment and your ability to execute is really determined by the size of the organization to be quite frank. >> Awesome, well, so we have Hadoop Summit coming up in Dublin. We're going to be in Ireland next month for Hadoop Summit with more and more coverage there. Guys, thanks for the insight. Congratulations on the relationship and again, WANdisco, we know you guys and know what you guys have done. This seems like a prime time for you right now. And IBM, we just covered you guys at InterConnect. Great event. Love The Weather Company data, as a weather geek, but also the Apple announcement was really significant. Having Apple up on stage with IBM, I think that is really, really compelling. And that was just not a Barney deal, that was real. And the fact that Apple was on stage was a real testament to the direction you guys are going, so congratulations. This is The Cube, bringing you all the action, here live in Silicon Valley here for Big Data Week, BigData SV, and Strata Hadoop. We'll be right back with more after this short break.

. >>Okay, welcome back. This is live in Las Vegas. This is the end of day one. This is our wrap up segment of the cube at Splunk conference dot conference 2013. I'm John furrier with Dave Alante, my cohost and Jeff Kelly making an appearance in this segment has been scouring for stories, talking to all the folks, talking to the CEO, talking to all the people on the team, customers scouring the web. Guys, welcome to the wrap up. Thank you John. John guys, I gotta I gotta say I'm really impressed with what Splunk's done here. Um, and with post IPO you kind of see what people are made of when they have to do transitional things day. We know we do and I've seen companies pivot, turn on a dime. You guys certainly have helped companies, you know, get into that, into the, into the thermal growth and um, but here a companies succeeding, um, they hit a rocket ship growth. >>They go public. A lot of challenges could be distraction, but certainly, uh, my impression is no distraction here. Splunk certainly is hitting cruising altitude only getting better and stronger. Certainly the customer acquisition numbers as strong and their partner ecosystem is great. Their keynote and fan based or customers are loyal. All in all, Dave, I've got to say, you know Splunk's looking really good. Yeah, John. I mean I think you see a lot of different models. This is too broad models. I guess in the, in the it business one is the safe bet. It's, it's IBM, it's, it's HP, it's, it's EMC, it's Oracle, it's Cisco. I mean you're going to do business with those companies because you know they're going to deliver a product and they're going to stand behind it and they're going to service you and then you got the 10 X value proposition companies, that's companies like Tableau service now Workday, Splunk, these are the companies that are really transforming their irreverence. >>Steve Cohen said disruptive, they're disruptive so they got a little mojo going and I'm gone. But at the same time, customers are willing to take a chance because the value proposition is so compelling and so transformative to their business and they can't get that from their traditional it suppliers despite what the traditional it suppliers are telling them. So I love that kind of mojo at a, at a, at an event like this. Jeff Kelly, I want to go to you for a second. Let's talk about what you're finding us. Show us who you are on the, on the, uh, we had a crowd chat today w you know, preparing them for Hadoop world and big data in New York city. A quick programming note. Um, we, the Q will be in New York city for strata conference. Had duper world covering that in con in concert to the big data New York city event going on as well that week. >>Um, but you're out, you did a chat this morning about big data with Hadoop ecosystem. A lot of had doopy we had cloud era MRR Dhalla on, they have a relationship also with Hortonworks. Um, what did you find out there? What stories did you dig in? What observations did you find? Well, very much like a, the last show we were at a Tableau's customer conference. It's a really excited, uh, customer base here. These, these customers, uh, you know, are, are clapping and cheering during the keynote. It's something you don't necessarily see more than excited. They're giddy, right? I mean, right. They're there, they're getting yapping, they're hooting or hollering, right. And, and there's really a sense of community around the, around the customer base. They love to trade stories. They love to trade best practices. The hackathon, last night I was at, uh, you know, just rooms filled off the, off the corridors here at the, uh, the cosmopolitan. >>They were there till 11 o'clock at night. They were in there, you know, they had, uh, some, some, some, some TV going at, I saw a rerun of Alf playing on the big screen for some reason. I guess that's a popular with the group here. But anyways, these guys were up there all night. You know, they're coding the drinking beers, they're having a good time. Uh, they really enjoy this. You didn't, it's not something you see at eight. At one of the, a larger events, some of the mega vendors we see. Um, you know, the other thing, you know, Mike coming into this Splunk I think was really early on, uh, recognizing the, the value that providing applications that allow you to really manipulate and understand data. Really they saw the value of that very early. Obviously that's, they base their whole premise of their organization on that. >>Oh, they have re, you know, kind of written this wave, uh, of big data, all things big data. And they're one of the few companies out there that are actually selling and providing applications that allow people and make sense of, um, in this case, machine generated data, but they're expanding to other data types. Um, the key for them I think going forward is to continue innovating. You know, they've kinda got that lead, uh, I think because they were the, one of the first out of the gate to recognize the value in this. They gotta keep innovating. And I think you saw with the announcements today, clearly they are, uh, the cloud, uh, option that they unveiled today was very popular. Um, and it's going to help them, especially against some of the more nimble startups. It's funny, it's, Splunk is now kind of a kind of a big established company in a sense in this large, in this big data world, there are companies like om Bogley and Sumo logic who are coming at Splunk doing similar things, but doing it from a cloud perspective, well sponsored down. >>Got an answer for that. Why would I want to ask you guys about that? Because you know, John, Jeremy Burton, we, you know, made, we were there when cloud met big data and so people have been putting those two together. But you take a company like Splunk and a couple of like Tableau, not big cloud plays. What about that cloud meets big data? Is that, is that a misconception on the industry's part or not? Or is it a fundamental requirement that cloud meets big data? I think it's a fundamental requirement as you know, we were, you know, close to EMC when they put that together and we had the first cloud mobile social editorial. You guys had the first real research around those three pillars. Um, and big data just became a, came out of social and cloud and since the cloud era, you know, pun intended with Cloudera, the company, um, but you know, Dave, we saw this from day one. >>This is a fundamental economic wealth creating inflection point, meaning new companies, new brands going to emerge that are going to change the game and this is where all the chips are on the table and you're seeing the incumbent vendors like EMC changed their game and go cloud meets big data and go in there. And EMC, I give Ian, Jeremy Burton a lot of credit. He saw the work we were doing. He saw the marketplace, he came fresh into EMC and said cloud and big data. Those are the two pillars. He bet the ranch on that and the beds coming home. Jeremy is making more money than any, even not a CMO anymore. He's the executive vice president doing great just on the stock options. He made a good bet that's playing out who's also a great executive with some product shops. Absolutely. Table stakes in my opinion. >>Um, that the application market is going to be enabled by that. So, Jeff, Kelly, so I've got to ask you, there are forces that you mentioned you've got open source. Uh, you've got some new players that are or have seen the opportunity that Splunk has created, the, they're going to have to Splunk. So, so what's your prediction here? I mean, you've got, you've got a public company now, they've got more resources. They're clearly a leader in the, in the business, but you got other companies coming after him. Not only start us, you know, we were at, um, we were at HP, uh, the, the Vertica user group, they were talking about, you know, their Splunk killer. Uh, you hear it all the time. Oh, we can do that. We can do that. What does that all mean for Splunk? Well, the good news for Splunk is they're, they're, they're ahead of everybody in this game because they've been doing this for longer. >>Uh, you know, they, they, they have a, a more generally accepted among the customers, uh, you know, a better application for VMware, for instance. So they're actually ahead of a lot of these other vendors, VMware itself trying to claim Oh yeah, it'd be where it says, well now we've got a tool for monitoring that's just as good as Splunk. Well, you know, if you talk to some of the people using the Splunk app for VM ware, they'll disagree with that. So bottom line is, you know, this is a little bit simplified, but people really like the Splunk user interface in the application. It's very easy to use and that's something that you can't necessarily replicate. So, you know, it'll take, it'll take some time for some of these players to catch up. But you know, back to the point John was making this whole idea of cloud and big data and you're asking, you know, is that really, is that really the, the, the two mega trends here? >>And I think absolutely when we start talking about, uh, industrial internet, internet of things, whatever term you wanna use, we're, we're years away from that really being a, a reality I think in terms of it's an interconnected world, but clearly the two key enabling technologies are going to be big data, making sense of all those connected devices and cloud being able to connect them in a way that that makes sense. Um, where you can't do that in an on premise situation if you've got isolated data centers. Now the other thing, this company who started in 2005, it's yet another Silicon Valley success story. John, I mean it's just Silicon Valley is just running the table. What's your take on the Valley action going on here? I think Silicon Valley is going to continue to do well and, and um, and rule the road here and on IPOs and success. >>Silicon Valley is the ecosystem that drives a lot of wellness to wall street of startups. However, there are, there are a lot of successes outside of Silicon Valley. This is just another string of, of successes. Um, but Dave, this is an absolute poster child in my opinion, of a venture that could have gone the wrong way. I mean, Splunk was not a shining star when it got funded. It took two visionary venture capitalists, Nick and David Hornick, Nick from, uh, he'd know the ignition and uh, David Hornik from August capital made the bet. They bet on technical founders, they bet on the right product guys. It was in small tools and it was at the time it was, wasn't the trendy thing. This is pre big data. This is log files. They saw a problem, they saw a good team. Now this thing could've gone off the rails, right? >>If you look at today's market, this is what I worry about all this startup environment is that all the different funding dynamics, all of this crowd sourcing this, that you've got to have good investors. This is a great example of great investors back in their guys back on their team because this thing could have been off the rails in the fourth year. Okay. Product strategy, debate, board room dynamics, people not paying attention, uh, asleep at the switch as we say. And this is, this is an example of a company done right. They hit the growth curve, big data swooped in, they had a great product, happy customers and incrementally move the ball down the field. And finally, you know, scored the big long ball with the touchdown with big data. And I think, you know, it's classic. These are football analogies, you know, first down, first down, first down, and then big data comes down. >>They throw the ball in the end zone, touchdown home run. There it is. That's the IPO. That's the success story. There's a fine line between. Good and great here. Isn't there though? I mean, like you say, I mean who even Steven Cohen was saying, uh, uh, uh, not, not Steve Sorkin, sorry. Steven. I was saying that he didn't, could've never predicted, you know, where they'd be today, the IPO, et cetera. So there is a fine line. You could go, well, this is the thing, this is my point. If you look at Splunk, right? Dave, they could have, no one was buying their stuff initially. Right, and so except for some tech geeks, no one was kind of get it, but the recession hit and people weren't spending in 2008 that was a big surge and you saw the spending and Splunk became a great solution because for very little cash you can come in and create business value. >>That was a really, really important moment in the company's history, David, and what's also happened is they believed in their own product. You heard from the people here culture, they're Everett, they're disruptive, they use their own product and they focused on the customer. Those two things, good timing still is, you know, comes to people who are prepared. I mean it's not an, I mean, it's not enough to just have a big market. It's not enough to just have a lot of capital behind you. You need other ingredients obviously to succeed. I'm afraid the younger generation doesn't understand the startup world is you can't just magically put pixie desk and get the home run. You got some times really be in a good position as they say in basketball and be ready for the rebound off the rim. In this case it log file tool with good technology moves into the big data world and hello, they're got an enterprise customers. >>Part of, I think part of it is, look, you've got to admit, part of it is luck and timing. You've got to have that on your side. But they've also got a really good product and they're smart enough when that, when those opportunities present themselves to take them. I think they are. Again, timing is fantastic for them right now. We've been talking about the, uh, the year of the big data application and we're really still waiting for that. They are in a really good position right now to really take advantage of all the interest in, you know, SQL on Hadoop, interactive analytics on Hootsuite. Well guess what, they've got a product and hunk a cute name, but a good product that allows you to get right in there as a business user and start analyzing, searching data using a circular base. I gotta tell you it's a very good looking product and people are looking for this. >>People are like, well, how am I going to get all that value product? I'm going to get all that value out of Hadoop sense bugs in answer hunk. You got the naming convention, interesting names, but nevertheless they've got a, they've got a play right now in an area that's got a lot of interest and they've got, they've got the track record in the log data to actually show they've got, they know how to, they know what they're doing. I don't remember Mike Olson to cloud Hadoop worlds ago, announced the the application tsunami. That kind of never came the way they said. We said the analytics was a killer app. In the meantime, as the market kind of catches up, we still haven't seen that application framework, but yet still analytics is the killer app, right? It's definitely the killer app. I think. Well, the analytics for the masses is the, is the killer app and that's the Holy grail that everybody's going after. >>And I'm not, I'm not declaring Splunk is there. I don't think Splunk is there. I don't think anybody's there yet. You talk to a Tableau customers, you talk to Splunk customers, they're not there yet, but they're closer than the BI crowd ever was. They're certainly closer than the traditional BI players. And they, and then that's because they don't have that legacy architecture to deal with. But there's also a cultural issue. It's not just the technology of the products, it's getting business users to understand how to look at data and look at it as, as an asset and something that you can actually drive. Timing's right for that. Absolutely. So I want to wrap up and ask you guys some follow up questions at the close, the segment out, first impressions of day one and what are you looking for for day two? Jeff, we'll start with you. >>I am first impressions. You know, like I said, very excited, uh, base of customers here and you know, 18,000, 1800, excuse me. Plus customers, 18,000. That'll be a few years. But, uh, nevertheless a good showing here. Uh, I think tomorrow, you know, on the cube, we're going to look for certainly some more customer stories. Um, you know, it's always interesting to hear from customers because they are on the front lines. They're using the product every day. So I expect to see a lot more of that. Um, and really tomorrow I think is going to be a lot about, a lot about uh, these customers networking with one another and I'm hoping to get out there. Let's add on the question to, uh, to you then, then to Dave. Same thing. What's the challenges for Splunk as well? I think the challenge for me is from, from my perspective is to continue and make the, make the cloud play real, continuing to invest in that, uh, and that product and that approach. >>Um, as we met, as I mentioned a minute ago, I think cloud and big data are critical to really leveraging industrial internet, the internet of things. And if Splunk wants to be a key player there, they've got to really fill out that portfolio of cloud based capabilities. I know you said David, go first. Sorry for me. For me, John, we heard from the executives today, very strong story. We heard very solid product lineup. It's very clear in talking to customers that there's, there's passion here, there's real traction. Um, it's substantive. To me. The big thing is ecosystem. I feel as though the ecosystem here at Splunk is, is, is good, but I feel like it's not been as deliberate as it can be. I think Splunk has a ways to go there. I think that is one of the leverage points that this company really has to focus on. >>Because like today we talked about earlier, 45% of Splunk sales goes through the channel. I think it's gotta be way, way, way higher than that. Now they're making great progress, but I think that they've got to have a goal of getting to 70% and that comes through the ecosystem. It's gonna take some time. It's going to take some investment. That's really where, to me, the big upside is for this company and my impression is I'm very impressed with Splunk. I'm very impressed with the ecosystem. I'm impressed by the rabid fan base of their customers who are proud of the private name getting exemplifies my point about startups having a great product focus products will win. Again, you know, the four P's of marketing, they teach you in marketing one Oh one one of those products. Um, but the challenge is, Dave, I would, I would agree with you. >>The ecosystem is a challenge. Good news is they have a great turnout here. Um, you're not, there's no lightweights out there, all heavyweights in terms of what they're doing with tech and their value proposition. So, you know, gray star for the ecosystem. So I think it's looking good off the tee to use the golf analogy, um, landing in the fairway. So, so that's one. My big, my big thing on the challenges for Splunk and that I'm watching is the cloud. I think moving to the cloud is not as easy as it appears, although that's the value proposition. So to move the DNA of the company with the pressure to drive revenue, luckily the market's kind of moving to them right now. So it might be a, a rising tide floats all boats. Moving to the cloud is very, very difficult. And I think that's gonna be a key challenge. >>We're going to keep watching them look at what SAP has challenged the cloud. They've had multiple restarts and misfires. Now they've seen them get their groove back with HANA. I think this could be a big challenge for Splunk and we're going to, I'm going to watch their cloud and that's going to be my focus then tomorrow. I would agree with that. I would just say on the ecosystem point, um, I, I think they would actually, I think they do have more work to do Dave, but I think they're in a really good position because some of the Hudu players, for instance, knees, Splunk, I think more than Splunk means to them right now. Okay. We're going to close down what the government is closing down right now. So, you know, that's, uh, that's, uh, we'll be back tomorrow because we work for free open source content, um, programming node. >>Next weekday we're gonna talk about big data and internet of things. I'll be interviewing the CEO of GE. Um, I'm really proud of you, John, for, uh, being selected out of the zillion people that they could choose. They chose you to, to host this panel. Yeah, that's fantastic. It might be my last, but we'll see. Moving some Q mojo to the GE event, industrial internet next week in Chicago. Minds and machines, another player to watch. Guys. Great day and great wrap up here. And that's day one. Wrap in the books tomorrow here when we go to the party tonight, find out what's going on here at, at, uh, inside the cube, inside a Splunk conference. Dot conference. 2013. I'm John furrier with Dave Alante and Jeff Kelly Wiki bond with back tomorrow. Goodnight. And, and join us tomorrow.

>>Come back here. This is Silicon Valley coverage of ADU Summit. I'm John Fur, the founder. We're, we're pleased to have a friend inside the cube. It's rare to have such luminaries, Ama Aala, good friend and also co-founder of Cloudera. Really the pioneer in the space that helped build this industry that we're living here at at Hadoop Summit. I'm with Dave Ante from wiba.org. Amour, welcome back to the Cube Cub alumni. Thank you for having me here. Wow, what a journey. Are you co-founded Cloudera? I remember when you in Stealth Mo, I really can't talk about it. And, and then of course the history of Silicon Angle being, you know, founded and kind of built in in your office when you only had like 20 something employees. Yep. We owe a great deal of gratitude to you and, and congratulations to you Michael Olson, the team for building an industry. So I just wanted Thank you. Thank you. And welcome to the Cube. >>Thank you. It was great to be here. >>So what do you think, what's your take on the current Hadoop ecosystem right now? I mean, obviously a lot's happened. I mean it's big now. It's growing up fast. Yeah. The word enterprise grade is out there. You're seeing it move from, you know, trying to change the world. Our first interview, you said, I've seen the future, I want to bring it to the mainstream. It's here. Yeah. It's hitting mainstream right now. Yeah. What's your take of the current situation of the ecosystem and it's, and its value? >>Yeah, so I, I have a quick question first. Should I look to you or look to the camera? Look to >>The camera or both? Whatever you, whatever you'd like. >>So I think it's, the ecosystem is definitely growing, which is very, very healthy. However, there is a side question there, which is what do you think of all the competition coming into the space? So five years ago when Cloudera was started was just Cloudera. There was no other commercial vendor trying to support or enable Hadoop in the, in the industry for enterprises. And today there is at least 10 of them trying to compete with us, right? And that includes big companies, established companies that decided, hey, we gonna start addressing the space, but includes many, many newcomers who like Hortonworks, who were founded over the last couple of years. That's a healthy thing. I mean, that's absolutely a sign of a growing market. If the market wasn't growing, if there wasn't money in the market, if there wasn't, if it was just hype, there wouldn't have been all of these new companies and new ventures showing up. That said, I never look at competition as something that worries me, that I'm afraid now or what's gonna happen to me, or that's normal. That's exactly what happens to successful companies. If you look at Red Hat, when Red Hat was launching with the Linux, they had 25 competitors or even more 30 competitors. That's when Red Hat was forming out. And today, even of these 25, 30 competitors, they still have six or seven still left. So I think it's a very, very healthy sign of the graph of this market and the maturity that's reaching. >>What do you think about some of the, the white spaces that are evolving? You guys have obviously been involved in a lot of deployments at Cloudera. Again, you're doing a lot of, lot of work with the top, top names and the clients that you have aren't usually disclosed cuz you really can't disclose them. What, what are you seeing right now as the white spaces for things to do in the Hado platform? >>It's a very, very good question. So first I can't talk about future, future roadmap. Right now we're becoming a big company at that level where we can't comment on future roadmaps. >>Ah, that's sinus sign of the >>Time. You're well media train, good to see they're doing a good job keeping you >>A, You want more information on that? I can connect you with a pt, >>Please. No, no, no, we're good. We're good. We'll get it outta you. But, >>But our vision, our vision for Cloudera from day one, like you were saying earlier, we saw the future, right? So our vision from from day one was really to build this data system where we can have detail of any type, whether that data is structured or unstructured or images, it doesn't matter. And then on top of that data run any type of workloads. That workload could be the initial genesis of Hado, which is map use, which is batch processing. But now as as we made many announcements through the last few years, we also now have Impala for interactive analytics as a workload. We have a very, very strong partner partnership with SaaS for doing machine learning and statistics as a workload. And a few weeks ago we announced search as another workload. So you have multiple types of workloads that can handle different types of problems that you have within your organization and bring all of these workloads to all of your data regardless of type. And that's the vision that we'll continue to deliver on. That's exactly what we're building going into the >>Future. So how's that fit in with yarn, right? We're hearing a lot at this conference about yarn, the ability to, you know, do more with less in a lot of the things that you typically hear with the enter within the enterprise. And, and so talk about that a little bit. >>Yarn is a very core part to our platform. In fact, yarn has been part of CDH four for more than a year now out in the, in the markets. So we did bring, we were one of the, I think we were the first vendor who brought yarn into a distribution of Hado out there. It's very, very fundamental to us because that is how we're gonna coordinate. We are gonna be using yarn to coordinate launching all of these different type of workloads. You're gonna have the map produce workload, which is very batch oriented. The Impala workload, which is very latency sensitive. The, the search workload, which is also very latency sensitive. The machine learning workload, which is more batch oriented, et cetera, et cetera. And yarn is a very, very central piece to helping us coordinate all of these different types of workloads onto the >>Platform. Cloudera has been a great citizen in the community also. You, you mentioned and, and we witnessed that your team create the industry. You guys were there, you took the chance, you were the first ones commercially funded by the venture capitalists, you know, then others will follow and I'll see huge ecosystem here. Yes. A lot of noise. A lot of people trying to get attention. So I got to ask you, because I want you to address this because I know it's been talked about in some of the other blogs is there's a lot of fud going on around who's doing what? Who's doing what, and in some cases maybe flat out, you know, misinformation and that happens in a growing market, you know, the elbows get sharp. Yes. So I want you share with the audience anything that you want say about the fud around what people say about Cloudera or about others or what you're doing. Just to clarify, cuz there has been, I mean I've gotten back channel information around, you know, not sure the committers this, and it's been, it's been well documented. There's a lot of fu out there. What, what would you say to the folks out there to clarify >>That? Yes, I, I would say that our focus should be to continue to work as a community, to push the platform forwards. I would say that at Cloudera we do a lot of contributions. Horton works definitely is one of the top contributors out there as well. I'll acknowledge that. So as many, many, many other companies and we wanna continue to see the platform evolve. I will stress though that at Cloudera we do have a number of the original project founders working at the company. So it's not just the, the contribution that we bring, but the fact that we have the founders of these projects working at Cloudera. And some of these projects actually were created at Cloudera from day one as opposed to created in some other company. And then you hire the employee and they work for you. So I gave you what examples from Cloudera dot cutting. >>He is the creator of Hudu dot Cutting is also the creator of Luine, which became solar, which is part of the search project that we launched recently. Dot Cutting wasn't with Cloudera from day one, right? So, so when he created these technologies, he actually was at Tia for example, when he created had he was at ta, wasn't at Cloudera. However, he now works for Cloudera. So we get that because now that cutting works for Cloudera. So that's one example. On the flip side, there is projects like Flume and Scoop that are now part of every single distribution out there. And flu and Scoop were both created at Calera. They were actually created inside of Cloudera. Yeah. So the key point is, and and that's what I would like all of the vendors out there that are trying to leverage had and get benefit about out Hadoop is please don't be just takers. >>There are some vendors out there who are just takers. Just wanna take from the open source, take from the open source and don't give back. Right? I'm not gonna name them, but there is a few of them out there. Please, please, please. I mean that that, that is very, very a selfish behavior. It's not gonna help the ecosystem in the long term. We would like to see you both take and give at the same time. So that would be my core message. And that's for example, like I thank Hortonworks because that's exactly what Hortonworks is doing. They're both giving and taking at the same >>Time. You guys have always been clear on that. Nobody, I mean here contribution to open source has been well documented and there's, there's no question about that. John and I have talked about it a lot that you guys help get it all started. And even Haak when we had 'em on a couple years ago, when Horton Works came to the market said, Hey, the more people work on an open source, the better. >>Yeah, >>Exactly. So yeah, it's always been, been your posture. You're not playing games there. Anyways, having said that, you you, you have a strategy to layer on top of that open source some of your own proprietary code. And so you have choices to make Yes. In terms of how you allocate those resources. So as an engineering manager, how do you allocate those resources in terms of, okay, what do we do for the community and what do we do for our own, you know, future because of the business model that we chose? How do you make those trade offs? >>Yes, that's a very, very good question. So first it's important to stress that our core platform, CDH, is open source. Everything we put in the core platform is open source. So for example, in Palo, which we launched very recently as a ga, now we launched beta last year, but now's ga is a hundred percent Apache license, a hundred percent open source search, which we announced very recently is also open source. So the platform itself, we're committing to everything in there to be open source. Now we believe fundamentally just from having lots of history in studying the open source markets from our ceo Mike Olson himself being one of the very first open source people in the world with, with sleepy cats, the company that he sold to Oracle before founding Cloudera from our investors, helping many other open source companies. To have a successful open co open source company, you need to have a very good engine between the business model that generates revenue and between the product that you are creating. If you don't have a good feedback loop there between these two, you won't be able to sustain the innovation to continue to push the, the boundaries of how good the product is. So we strongly believe in that if you are, if your product is literally a hundred percent open source, meaning both the management and every, there is nothing proprietary whatsoever inside of your products. I can't tell what that is. It's >>Taking a picture. >>Oh, sorry, I thought somebody was waiting >>For me. >>Sorry about that. >>It's a cheap signal. >>It >>Was like a's really good. >>I thought it's like a card of paper with some writing. You, >>You, you have a fan fans out there. They're storming the, the concert here. >>Okay, that's, that's good to hear. That's good to hear. Sorry about that interruption. So if, if, if you have everything a hundred percent open source, that creates two problems. First you have no differentiation whatsoever, meaning another big corporation without naming who the big corporations could be, we just can take everything you do, literally every single bit of source code you have and say, Hey, we can do it too. Come to us, don't work with those guys. Right? We have the latest, greatest things that they have. Why do you wanna continue to work with them? So no, no differentiation is number one, which is very dangerous. And number two, when it becomes, if, if it's a hundred percent open source and there is lots of other vendors able to take the art, the open source artifact and work with it, then it becomes now purely about maintenance and insurance on the products, which is a commodity product, which obviously the prices for that will go down to the ground and you won't be able to have this sustain this positive feedback effect between your business model and between your product code map and won't be able to build a long-lasting company. >>So that's why we do have a combination of open source artifacts and proprietary artifacts. Now our pro proprietary AR artifacts is always around the management of the system, right? So how do we manage the security of the system? How do we manage the, the data flow within the system? How do we manage the services inside the, of the system across all layers, right? Not just the Hado player but the edge based layer, the zookeeper layer, et cetera, et cetera. So that's where we focus our efforts going forward and that's how we differentiate ourself from our, from other vendors out there. Cloudera manager, Cloudera navigator are very unique to us. Nobody else has anything close to those capabilities out there. >>So it sounds like the contributions you make to open source are cultural of, of, in nature, I mean DNA of sorts of Right. And so you're, that's something that you guys do cuz you've always done it. Absolutely. And then the, the artifacts that are proprietary are essentially around rationalizing the revenue opportunity with the expense that you're gonna apply there and making a business case decided >>How to balance. That's that's one. And then two, the differentiation from other competitors. So these two things, Yes. >>Okay. >>I believe that's fundamental to business to open source business models. >>Yeah, I mean there are many open source business models, right? You can go pure service, you can go, like you said, you can totally bogart the code. >>There is no, there is no pure service open source model company that was able to build the longlasting surviving public company, never happened in history. They always get acquired because it becomes a commodity. I >>Mean, right. I mean, I mean and even ibm, right? >>Tom or I want to ask you about the storage thing. We were talking before camera, the, the hor and worst announcement storage you, what's your take on that? >>Which one? The Gluster, the one with Red Hats? Yes. Yes. So Red Hats and yeah, there has been recent news about Red Hat with, with Hor Works having a version of the Haddo platform that uses map use for the computation but uses Red Hat for the storage, right? So Red Hat has a new storage offering that was built based off of a company they acquired was called Guster. And that, that news was very, very surprising to me. And it, the reason why it was surprising, it's correlated also with a shift in messaging from, from Horton works. If you look at Horton Works last year at had Summit last year, one of the key messages that they deliver to us is that within the next five years or by 2015, the tagline back then by 2015, and you're doing research right now to see if I'm saying the right thing. By 2015, half the world data data will be on, will be stored in had would be stored in had. Yes. If you look today at the slides, it >>Doesn't say that it says within five years, >>Right? No, no, no. It says, well >>That was the second iteration was within five years. And now they say something >>Different. Now say they say within 2015 by, sorry, by 2015, half the world's data will be processed by Hado and instead of stored by Hado. And that's a very, very fundamental So >>It's a nuance. >>It's a, it's a very important >>Nuance. Well it's a big deal because yes, when I first saw that I said, Hmm, what does this all mean? And then it sounds 2015 sounds a little early. Yes. And now you're saying processed by, Okay that's different. >>Yes, exactly. And and the reason why now is we believe s GFS is very, very core to the had platform. S GFS is very core to had platform, the storage system of had we want. It's really the layer that Mid had with is more than anything else is how scalable, how reliable and how economical the sdfs storage layer is. So we, we really, I mean ask qu works and ask all the companies working in the, in the had community not to fragment at the storage layer. We need the storage for had to stay inside of had and not to fragment that out. That's very, very critical. >>Okay. So but so >>You're saying that they're in indicating through the gesture that, that they're not come out saying we're going to fragment Hgfs, but the way that this is position might signal >>No, no, no. The announcement, the announcement with Red Hat is >>That is the direct signal. It's >>Literally, we, you'll be able to run map produce directly on top of Red Hat storage instead of sdfs. >>Okay. So >>I >>Interpreted it, I interpret it as they were just hortonwork was hedging on its prediction, which I said Okay, I'll give 'em a break on that. You're saying it's something different, >>It's a shift in strategy potentially. Yeah. Which can be dangerous. It's shift in strategy. >>Is that a compliance issue? Cuz you know, the, the Dishon Hads poss Yeah. Red Hat does have a lot of enterprise customers. Yeah. So is that just maybe if >>Then invest in making had poss compliance, which actually by the way, we are as a community investing in that. Yeah. Yes. You must have. Yeah. So we are investing in adding compulsive poss compliance to had, we're investing in adding snapshots into had, which will be coming very, very soon overnight. >>Well, do you think that that pick a year, I don't care if it's 2015 2000, 22,000 whenever that the majority of the world's data will be running into do >>The majority of worse data that has to do with analytics. Yes. Okay. So so there is, >>So that is that >>Is it's very important, the caveat. Yes, exactly. Because there is lots of types of data that are not very suitable for, had at all. For example, that data storage for Oracle systems, for Oracle database systems. No, you wanna store that in an NetApp emc you don't wanna store that in Hao the, the, the, the, the data storage for streaming video files, right? For just streaming lots and lots of video files. No, you don't wanna store that indu. It's >>A huge >>Proportion of the data. Yeah. Which is a huge, huge >>Proportion of data files, in fact that could overwhelm the data. >>Yeah. So the new nuance, like I would say like I agree that the half thing but the half thing within the world of data for the purpose of analysis. >>Yeah. Okay. So that's, that's >>Narrow down the >>Yeah, okay. But it's a more reasonable, But I've, I >>Never, It's still a huge market by the way. It is. Yeah, >>It is. Yes. Okay. So, so what's next for you? A are you, you, you've gone on this, this journey, you start this company. You've, you've been traveling around like crazy working with customers. What's the next phase of aara do's, you know, career? >>What >>Do you want to have happen next? I mean, what, what do you, what excites you? What do you, what are you working on? >>Yeah, it's just to continue to grow cloud there to be the biggest company it can be. I mean, we want to be literally, we want be one of the very few companies that we're able to take an open source model and turn that into a large publicly traded corporation. >>So you've talked about that you guys brought a new CEO on Right. Look at the background of the ceo and it's, you know, clearly it's got some IPO chops. Yes. So that's, that's an aspiration that you guys have put forth. Okay. >>And you're outward facing now. So you're doing a lot of travel. Yes. So what, what, where have, what have your travels taken now? You've been in China, you obviously you've got a European office Yeah. Open. So what's going on internationally? Give us some sound bites of, of what's happening in the field. Yeah, >>So in, in internationally, I mean, Europe definitely is our next big focus right now. And we now have a big operation in Europe and we have an office presence in, in Europe and a big team down there. And it's growing very quickly. I would say Europe is about two years behind the US kind of like that's how the, how the growth usually matters. What's happening here. And yeah, so we, our, our next big market is Europe. We are looking at China. We don't have a big process in China right now. Japan, we have a big presence in Japan. Japan is growing very quickly. So yeah, I mean we're obviously Canada with the US growing very quickly as well. >>Great to have you on the cube again, for me personally and, and for, for Dave. And I wanna say thanks to Cloudera for some great support over the years. You guys have been fantastic. You know, I say it's built a great company. It's so hard to build a company. You guys have done a great job. I gotta ask you the final question because you did bring that first sound bite, which was, I saw the future, this is back when you guys were just in your B round in, in Palo Alto office, just ramping up, just starting to ramp what's next? What do you see as around the corner? Obviously we're on a trajectory right now. A lot of things gonna get done. Positive compliance, a lot of stuff's gonna fill in. The platform's gonna get stronger. Yeah. We think that open source will win. Yeah. Through all the democratization of open source. What's next? What's the, what's around the corner that you're watching personally that you're, that's interesting to you? A or around where this will take us? >>Yeah. So what, what's next is having this, having this vision become true. Having this future vision that, that you refer to become true. Meaning having a single platform that can store all of your data and that can, regardless of the type of that data, and allow you to extract value for different types of workloads, whether that be batch, interactive machine learning or search or more, right? There will be more things that will come to the platform, but how to bring your applications, all of your data applications, how to bring them to your data and all of your data as opposed to have the data go to them. >>And what are the landmines out there that you need to avoid Yes. In the industry and community needs to avoid to make that a reality. >>The, the key landmine, it's, it's a bit technical. The landmine is a bit technical, which is making sure that they, they are vision continues to evolve and that we have the capability to properly have a multi workload resource management system that allows me to run all of these type of workloads without having them step on each other's steps. That's the key key step going forward. And >>Of course, playing well together in the sandbox. And as always, competitive competition is good. And again, Hadup is doing great. Amma Aala, co-founder of Cloudera inside the Cube. This is Silicon Angle and Wiki Bond's exclusive coverage of ADU Summit here in Silicon Valley. Right back with our next guest after the short break.