Here with the two stars of the show, at least two of the many stars. Mike Olson, CEO of Cloudera, Ping Li, XL Partners. You guys are up on stage today. Big announcement, $100 million fund, very exciting. Congratulations to both of you on that and congratulations on the third Hadoop World. Yeah, you bet. Thanks very much. It's great to be on theCUBE and to have a chance to catch up with you guys again. It was great last year. This show, as you can tell, is a bigger deal by far. The number of companies, the number of people, the momentum, the energy in the room, just fabulous. We are thrilled. Mike, you were on stage giving a great keynote, a lot of good buzz on Twitter. We were watching it, taking some photos. You mentioned years ago, kind of did you walk down memory lane? You did kind of like where we've come from and what's going forward. You mentioned three years ago, speeds and feeds, past three years, getting all the code going and now it's kind of growing like crazy. And now going forward, it's about business and the tools and the platform. Can you just share quickly that view? Yeah, no, I think that's exactly right. You know, when the platform was brand new, evangelizing the platform was important. How does it work? Why is it different? What can it do for you? But we've seen adoption now, not just in the web space where Hadoop was born, but in financial services and Telco and big old guard brick and mortar retail in a bunch of vertical markets where business problems matter. The users we see rolling this software out now are focused on driving revenue, on understanding customers, on solving real mission critical problems for themselves. It's now about analytics. It's about insight you can garner from the data. It's no longer about is Hadoop cool? That question I think is settled. Hadoop's pretty cool. Hadoop is now transforming the way that people can operate their businesses and those applications are going to drive the next round of adoption. You guys just closed $40 million in new fresh financing. Congratulations. Thanks very much. We had the lead investor on Frank Artali here on theCUBE earlier right before you guys came on and he was so excited and he said, quote, I love the platform play of Cloudera and platforms are good business, and but ultimately you're judged by what sits on top of the platform. So I'd like to bring Lee into the conversation with you here because you guys announced on stage the big news this morning which was Excel partners is doing a hundred million dollar big data fund. And people are seeing these kinds of purpose-built funds. We saw our client at Perkins with the iPhone fund very successful and the iPad fund and the social fund that they got or what is called. So one, tell us one, is it because there's a whole new set of new ventures that are creating on top of the platform? And talk about the fund if you could, Mike if you could talk about the stuff that's going to be on this platform that you're seeing that's new venture creation oriented and you can then talk about the fund specifically. Yeah, I'll be glad to talk about some of the innovation we see happening but actually let me let Ping talk a little bit about Excel's motivation, what it saw happening in the market that made this a sensible, a no-brainer thing to do. Sure, and as you know, John, Excel has always been very thesis-driven in terms of the sectors and categories you want to spend a lot of time in. So the big data fund, although it's a new fund, it's not a new area of focus for Excel. Obviously our investment Cloudera predates this fund by quite a number of years and we have a lot of other companies, Couchbase and others that are innovating up and down the big data stack. And the fund was really an opportunity to capture and stimulate innovation on top of the Duke platform and other big data platforms. It's very rare to have a platform company that comes around every decade or so whether it's the iPhone, it's Facebook, it's Google, it's VM where these platform companies are really create a bow wave of innovation behind them and around them, right? And I think that's what a lot of the big data fund is about. Through our involvement in Cloudera, we have seen how Hadoop has been deployed at enterprises far and wide and for applications that we probably didn't realize was possible before Cloudera got going. So I think this is to further that and complete that cycle with more applications built on top of Cloudera and actually underneath Hadoop as well. We asked Frank, why did it take so long? And because it's kind of obvious, but you're first, give us some insight there. You know, I think it takes a while for these technology platforms to slowify and become fertile enough and mature enough for entrepreneurs to really realize that there's value to be built on top of them. And I think it takes time for startups to kind of emerge. And I think just in the last six months, I've seen an increased level activity in the ecosystem. That's kind of what drove it. We were kind of not, you never want to be too far ahead of the curve. You kind of want to time it when the entrepreneurs are ready to go. Just a couple quick questions on the fund. Is it a new fund? Is it like limited partners separate fund or is it part of another fund? It's very much going to be managed by the general partners that excel and it's going to be part of our existing funds. Well, you know, as you know, we have multiple billion dollars in capital worldwide and this is really a global initiative. So our Europe, China and India partners, we're working closely with the US partners. So we'll invest out of those funds but really dedicated to forming a coherent portfolio of companies around big data. Are you guys, is Cloudera part of the fund? Are they going to be advisors? Is there a set of advisors? Cloudera predates this fund but obviously along that thread of big data that we've been lucky to be on. ClickView is another company we took public last year that was invested years ago but is also part of that thread. Infusion.io is another example but these are going to be for new companies that have yet to be funded. So essentially the Excel family, whoever's in the Excel family essentially is an advisor. Pretty much what you're saying. That and also we brought together some industry advisors as well. I think, you know, not just industry but also data scientists, open source experts like Doug Cunning. We have research, we have a professor from Stanford, Computer Science, Jeff here who's a thought leader in data visualization. So we've had a lot of kind of external resources to help really drive innovation, really articulate where the opportunities are. Hilary Mason, who's the CUBE alum. Yeah. Oh, those names on the slide you presented. Yeah, Hilary Mason at Bitly, so. Yeah, that was a good list of folks up there. Yeah, I'll say from Cloudera's point of view, we're tremendously excited about this fund. We believe that Hadoop adoption is going to be driven by applications, analytics, visualization tools. In order to do what I told you guys earlier, in order to build business value, we really need innovation in the applications. Now the platforms arrive, we need apps that make it accessible. So while this isn't a Cloudera initiative, it's one that Cloudera is enthusiastically excited about. I'm thrilled that Excel has done this. I think that the venture community generally realizes the potential of big data. I think that it's only now that we're starting to see interesting application analytic biz tool investment opportunities come into existence. And that's because the platform had to achieve some ubiquity outside the web space before that could happen. Yeah, we were talking to Kirk earlier about the competitive stretch. Obviously the competition that's entered the space, you guys have been untouched for years and then all of a sudden that's extremely positive validation. And we talked about, okay, what do you guys do and how does he spend that $40 million to compete? And we kind of brought the analogy of Google, right? Google was number one in search and how much more market share can you grow unless you add more users. And their whole goal was to add more users. So he was pointing out, by adding more Hadoop roles, Cloudera's role will increase. So that seems to be the competitive strategy and this fund plays into that. Is that correct? Yeah, absolutely. And I actually think the rise of the data scientists is probably one of the most amazing transformations that have been happening over the last couple of years and people like Hilary Mason and Jeff Hammerback are really pioneering a whole set of thinking around how to use big data for new applications. And I think it's going to be more than just data analytics and data visualization. I think collaboration, CRM are all traditional software categories that are going to be reinvented on top of Hadoop. So I think it's exciting. What kind of investments are you looking at up and down the stack across the board? Anything in particular hot? I mean, I think the charter is to really anything in the ecosystem. So we are obviously looking at things that are more kind of infrastructure like cloud storage and areas around automation and data center technologies that can make the data platforms be more effective, but also the applications on top and that's going to be as broad as a project management tool to a collaboration tool to a mobile application that uses all these technologies. I think we're only going to start to see new opportunities as big data becomes ingrained in our thinking. The explosion of data from mobile devices, from sensors, from systems in the world, machines talking to machines, has really driven the need for storage capture analysis. The question is once you've got that data, what kind of questions can you answer? To Ping's point, data scientists can help us answer those questions. On the other hand, the expertise, the insight of those data scientists needs to be rendered into tools and to applications into the next generation of infrastructure that ordinary mortals can use. I don't think I'll ever be a data scientist. I love hanging out with them, but I want them to reduce their expertise to software that I can use to run my business, to run my life. Mike, you're technical, though. I mean, it's cool to hang out with data scientists because they're smart, it's always cool to hang out with smart people, but they're really changing the game and we heard that as Kirk was saying, you know, it's disruptive when it's hard to find people to hire when they don't exist, unlike the DBA market, which you're familiar with, but what do you envision the enablement above the platform to be? I mean, technically speaking, is it what's the big opportunities for startups and entrepreneurs? Is there any glaring white space that you see that, because Cladier is an operating system, essentially a platform, a lot of come-on machines, you got to tie all that together. What are the white space opportunities that you see? If you've got a deep understanding, if you, in your gut, understand a data type, a data source for some particular vertical market, from some particular device or class of devices, if you know where that stuff comes from, what it can tell you. That knowledge, I think, applied to this platform, to big data in general, rendered into useful applications is gonna be the big opportunity.  Absolutely, but really the question is, can we get people with taste and judgment about data, productive with this new generation of platform technology? My bet is there's a lot of money to be made, not just by the investors, but by the entrepreneurs and the people who get those products and apply them to their business. That's a great point. I mean, Ping, I'll ask you a question to riff off Mike there. I mean, in the venture business, it's an old saying, oh, that's a feature, that's not a company. I mean, what he's basically saying, and what we're seeing in the marketplace, is that someone who's actually uniquely aware of data, a feature, if you will, can build a company, because you could actually use the speed, the competitive advantage. You can actually make a lot of money. You didn't have to have the whole platform built if there's a turnkey platform. Does that change the nature of the investment? I mean, that whole feature is kind of like a cliche, it's a feature not a company. But you're seeing these lifestyle businesses that are kind of getting some traction, they could always pivot and expand. I mean, a kid who understands big data could create a reservation system possibly in airlines and start an airline and who knows, right? I mean, this is kind of a new thinking. I mean, it's a mindset. Yeah, I think a lot of, I think it's a great point because a lot of the interesting software application companies feel like features when they first get going. It's something that's very lightweight, easy to use, gets adopted in the consumer or enterprises hand without a big sales force. And before you know it, everyone's using it and it becomes a product slash platform, right? And I think that is something that we're, it's a lot of the consumerization of IT that we are investing behind is really about building products that are consumable and therefore a lot of times like a feature, right? And it seems like it sneaks into the enterprise before it actually, people realize they have to buy it. It's viral, right? It's viral.  When it got going, it was felt like a feature, right? But now it's your filer. FTP client. Now it's your FTP, now it's your filer, right? It's now your storage infrastructure for the cloud. So, you know, I think that's going to be a wave of software innovation that, you know, we have to sell. That's amazing though. But this changes the whole venture game, right? It's like, you know, don't discount these little new innovation ideas to spark at the scene. No, we have, you know, a lot of our investments these days are behind companies that have, didn't raise any venture capital but have gotten a certain set of scale by now looking for growth capital. It's because they're able to get going without a lot of heavy weight, you know, development in order to develop the initial insertion feature. I don't want to, I don't want to minimize the challenge in starting and running a business. Certainly it's a challenge I've lived repeatedly. I do think it's easier to start a company for a bunch of infrastructural reasons today than ever before. But in the big data space, I will also say the ability to work with data at scale, to basically deploy these tools, to get it on a platform that makes it manageable is brand new. And what that means is even some relatively simple ideas are gonna be transformative. I think you're gonna see some really exciting new companies start now with deep, deep insights about how to do processing. But by using brute force across more data than ever before, I mean Peter Norvig at Google famously said, we don't have better computer science, we don't have better algorithms than you guys, we just have more data. And I think you're gonna see important innovations be driven by just scale. Simple algorithms apply to absolutely everything you can eat. I think it's pretty exciting. Dave is a new development kid. I wrote a post three years ago on that. I think that's now come true. Just to switch gears, Mike, you have a lot of experience. You sold your last company to Oracle. We had some guests on earlier in theCUBE. We're talking about the relational database market. How there's some similar parallels to how that developed, very disruptive. Early on, some of the core table stakes stuff was developing, the minimum features and then the replication, all that stuff came in. What do you see right now relative to that? Is it a good analogy and what can you draw on from your past experience to apply to the Duke market around the evolution and the next steps? I'm fortunate. So when I was a young programmer in the 1980s, very early 1990s, I got to watch the development of the relational market play out firsthand, not as a company leader, but as a participant in the market. And as you say, there was this broadly useful horizontal platform that could solve a whole bunch of different problems with, let's be honest, in the early days, not that many apps, you had to kind of roll your own. As soon as a core group of vendors, IBM, Sybase, Ingress back in the day, later Oracle, invested in making it consumable, manageable, monitorable, they created an ecosystem of partners of other vendors that built value on top and look at the size of that market today. I think we're in exactly that spot with big data in general, with Hadoop in particular. The Apache Hadoop community has created something tremendous. The opportunity for newly funded businesses, for established companies to move into this space, to deliver that innovation on top, and to participate in that ecosystem, to be one of the companies that makes value, that makes a living, I think it's gonna drive this market to every bit as big as the relational market. I will say that I don't see them as competitive in particular right now. The applications that relational systems have solved for 30 years were, they grew up alongside those systems, they co-evolved. So it's unlikely that Hadoop would outperform those on old guard workloads, but big data, complex data, sophisticated analytics, Hadoop's game changing there. So to follow up on that, I mean, everybody loves to put Hadoop into an analytics bucket. You guys forecasting that that is gonna change and evolve into maybe even transaction oriented workloads and how do you see that shaping up? I think there's plenty of homework for us to do on the Hadoop platform, making it more scalable, more reliable, more consumable today. I don't think we need to be eating anybody else's lunch for the time being, and I think the green field opportunity of big data is so enormous that there's plenty of room for innovation. Over the course of five years, who knows what happens? In general, technology fractures, explodes into lots of variability for a short while and then begins to coalesce for the single platform again. I mean it's like you had side bay oracle, you had all the early informants. Yeah, well and there were network databases and I mean there were lots of different data structures. The relational market, over time, offered the critical features to its customer base but it did take some time and in the meantime, lots of innovative thinking, lots of new value creating. So right now I think we're in that explosion of Darwinian variability. And over time, we'll see who wins. Certainly it's a leader. And I think the relational database won the war in many cases because the application developers embrace them and they completed the value proposition to the user. And I think that's really what is exciting if I look forward in the next couple of years. Who's gonna, I mean there's gonna be a role of people who are building applications that don't bring up Hadoop because it's assumed to be a part of the platform. And I think that will be when it's become truly mainstream. Yeah, I think that's right. Nobody bought side base or ingress back in the day because it was easy to manage. Likewise, nobody buys cloud-era enterprise because it's easy to manage. They want the Hadoop platform for analytics. Yeah, we need to make it easy to manage, but that's merely to enable the application of value or job. I mean it needed to work as solving obvious problems that could never have been solved before. Okay, quick question. Then I have the Linux questions so you can start noodling on Linux and Hadoop, that big comparison. We're gonna drill down on that. But my quick question is Mike and Ping, what surprised you between last year and this year? Is there anything that's jumped out at you besides all the trend data? Other than the fact that Oracle's here. And actually, Oracle owns the promoted tweet keyword, HW2011. I'm sorry, yeah. Thank you, Oracle, that was awesome. Yeah, we didn't get any money out of that. So Oracle sponsored SiliconANGLE, we need the sponsorship. No, seriously, I mean, outside of some of the trend data, what couple things, or if there's a few things that really surprised you this year that you said, wow, I didn't really think that was gonna happen. We always believed at cloud-era that Hadoop was a big deal and that the rest of the market would recognize that. It has been breathtaking how quickly that realization has dawned. Large established vendors, Oracle and others have made announcements about Hadoop adjacency, or in some cases, Hadoop platforms, Hadoop analytic offerings, Hadoop applications. And not just emerging startups, but big established vendors. That pace, that explosion of interest at scale has been wonderful, a little surprising, but wonderful. For me, the only thing I would add is it's been completely energizing to see how it's not a Web 2.0 phenomenon. Hadoop has always been tied to Web 2.0, it's about Google, it's about Yahoo, it's about Facebook, and... Doesn't apply to us. It doesn't apply to us. And now, when I talk to enterprise CIOs and IT folks outside the valley, I don't have to explain Hadoop anymore. They almost say, yeah, we know Hadoop. Give us a data scientist so we can build these four applications. That has been an incredible shift in the last 12 months. It's not a valley thing anymore. It's mainstream. Well, I'm surprised.  And we really kind of felt that, but again, I agree with you, it was a massive surprise, and we're psyched to be in the front row to see to it. But when we talk to people out there, they like to put things in little buckets. Oh, it's like Linux, the red hat of this. When it's actually not actually shaping this, a little bit maybe some comparisons. And Almer was breaking this down before he was going to come back at 2.30 to kind of break that down further. Can you elaborate more? Some technical similarities, but differences. Business model similarities, but differences. Sure, so let me begin by saying one striking similarity to me is the Hadoop platform and the Linux platform. At the heart of what we call Hadoop is the Apache Hadoop project. It is surrounded by complimentary open source projects for data loading and for query and for coordination and so on. All of it from the Apache Software Foundation or Apache licensed, all of it integrated. That collection of software together is consumable. Just like the Linux kernel from kernel.org all by itself is of no use. You need Red Hat, you need Debian or someone else to surround it with the apps and with the tools that make it useful. So that's a very striking similarity. Hadoop as a platform is broader than just the Apache Hadoop project.  Our business model is different from Red Hat's. We're concentrating on management and monitoring administration and not on a pure services and support subscription business. We do have a diverse product line that's got proprietary stuff and open source stuff. But I think that we're committed to the open source platform to making sure that analytics and data storage at the heart of the platform are open source and that's because it's the best way to collaborate it's the best way to drive adoption. And frankly it's what customers want. They don't want to be locked into a single vendor for the long term. Maybe another way that we've looked at it is you know Linux was really about commoditization. It was taking existing open source, sorry operating systems and commoditizing them to save money and create an ecosystem around that. That's not what Hadoop's about. Hadoop to me at least is about value creation. It's about enabling new applications to solve new problems and help enterprises make more money. Or analyze their data in a way that can develop new features. So I think it's very different from a kind of paradigm shift in terms of what they're trying to achieve. And therefore the business models and I think the ensuing ecosystem will be quite different as well. She's saying people weren't getting a sustainable competitive advantage from Linux. There were maybe cutting costs and lowering TCO. People are getting a sustainable advantage from data. That seems to be the new source of competitive data. I think that's right. And in fact I would say if you look at open source historically, MySQL and Postgres we totally understood relational databases. We just built some free ones. Linux operating systems were not rocket science. We just built a free one. JVOS, look, middleware was well understood. We just built a free one. Hadoop, we're not knocking anything off. This is an innovative platform. It is chasing nothing at all. It's creating this new capability, this new market. It's a pretty exciting place to be. That's awesome. Well said. I think that was really well put. That's exactly what it was doing. It's creating lift to kind of like glass sealing that the web hit us with. It brings more to it. And that impacts everyone. We were talking last night at dinner. It's like, it's not just the web 2.0 companies. The web is everybody. It's mobile. The cloud is going to be a big part of that. The more connected you are, the more data there is. Okay guys, we really appreciate your time. I know you're super busy. Mike, congratulations on the funding. Pink, congratulations on the $100 million fund. Congratulations on all the success. It's been great to be a part of it. I want to thank you personally for the collaboration this year and your office year and a half. We've been in your office. We're moving on to find some new space. Appreciate you guys are growing out. Growing so fast and it's been great. You've enabled us to have an opportunity to be part of it and we're excited, thanks. Well, we appreciate the chance to be here, to be on theCUBE and also I want to thank everybody who managed to get to Hadoop World this year. We look forward to seeing you next year. It's going to be a great show again, I'm sure. Thanks.


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Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017, brought to you by Quortonworks. Hey, welcome back to theCUBE, live from the DataWorks Summit day two. We've been here for a day and a half talking with fantastic leaders and innovators, learning a lot about what's happening in the world of big data, the convergence with the Internet of Things, machine learning, artificial intelligence, it go on and on. I'm Lisa Martin, my co-host is George Gilbert, and we are joined by a couple of guys. One is a CUBE alumni, Itamar and Corey, and the CMO of Atunity, welcome back to theCUBE. Thank you very much, good to be here. Thank you for joining us. And Arvind Rajagopalan, the director of technology services for Verizon, welcome to theCUBE. Thank you. So we were chatting before we went on, and Verizon, you're actually going to be presenting tomorrow at the DataWorks Summit. Tell us about building the journey that Verizon has been on, building a data lake. Well, Verizon, as you know, over the last 20 years has been a large corporation made up of a lot of different acquisitions and mergers, and that's how it was formed in 20 years back. And as we've gone through the journey of the mergers and acquisitions over the years, we had data from different companies come together and form a lot of different data silos. So the reason we kind of started looking at this is our CFO started asking questions around being able to answer one Verizon question. It's as simple as having a day's payable or working capital analysis across all the lines of businesses. And since we have a three major ERP footprint, it is extremely hard to get that data out. And there was a lot of manual data prep activities that was bringing together those one Verizon views. So that's really what was the catalyst to get the journey started for us. And it was driven by your CFO, you said. That's right. Very interesting, okay. So what are some of the things that people are going to hear tomorrow from your breakout session? I'm sorry, say that again. Sorry, what are some of the things that the people, the attendees from your breakout session are going to learn about the steps in the journey? So I'm going to primarily be talking about the challenges that we ran into and share some around that and also talk about some of the factors such as the catalyst and what drove us to move in that direction, as well as get into some architectural components from a high level standpoint, talk about certain partners that we have worked with, the choices we made from an architecture perspective and the tools, as well as to kind of close the loop on user adoption and what users are seeing in terms of business value as we start centralizing all of the data at Verizon from a back office finance and supply chain standpoint. So that's kind of what I'm looking at talking tomorrow. Arvin, it's interesting to hear you talk about sort of collecting data from essentially back office operational systems in a data lake. Were there, I assume that this data is sort of more refined and easily structured than the typical stories we hear about data lakes. Were there challenges in making it available for exploration and visualization? Or were all the early use cases really like just production reporting? So standard reporting across the ERP systems is very mature and those capabilities are there. But when you look at across ERP systems and we have three major ERP systems for each of the lines of businesses, when you want to look at combining all of the data, it's very hard. And to add to that, to your point around self-service discovery and visualization across all three data sets, it's even more challenging because it takes a lot of heavy lift to normalize all of the data and bring it into one centralized platform. And we started out the journey with Oracle and then we had SAP HANA where we were trying to bring all the data together. But when we were looking at systems, non-SAP ERP systems and bringing that data into SAP kind of footprint, one, the cost was tremendously high. Also, there was a lot of heavy lift and challenges in terms of manually having to normalize the data and bring it into the same kind of data models. And even after all of that was done, it was not very self-service oriented for our users in finance and supply chain. Let me drill into two of those things. So it sounds like the ETL process of converting it into a consumable format was very complex. And then it sounds like also the discoverability, like where a tool perhaps like Elation might help, which is very, very immature right now or maybe not immature, it's still young. Is that what was missing? Or why was the ETL process so much more heavyweight than with the traditional data warehouse? The ETL process is a lot of heavy lifting there involved because of the proprietary data structures of the ERP systems. Especially SAP is the data structures and how the data is used across clustered and pool tables is very proprietary. And on top of that, bringing the data formats and structures from a PeopleSoft ERP system and which are supporting different lines of businesses. There are a lot of customization that's gone into place. There are specific things that we use in the ERPs in terms of the modules and how the processes are modeled in each of the lines of businesses complicates things a lot. And when you try and bring all these three different ERPs and the nuances that they have over the years, try and bring them together, it actually makes it very complex. So tell us then, help us understand how the data lake made that easier. Was it because you didn't have to do all the refinement before it got there and tell us how attunity helped make that possible? Oh, absolutely. So I think that's one of the big things where we picked the Hortonworks as one of our key partners in terms of building out the data lake. Schema and read, you're not necessarily worried about doing a whole lot of ETL before you bring the data in. And it also provides with the tools and the technologies from a lot of other partners. We have a lot of maturity now where it provides self-service discovery capabilities for ad hoc analysis and reporting. So this is helpful to the users because now they don't have to wait for prolonged IT development cycles to model the data, do the ETL and build reports for them to consume, which sometimes could take weeks and months. Now in a matter of days, they're able to see the data that they're looking for and they're able to start the analysis. And once they start the analysis and the data is accessible, it's a matter of minutes and seconds looking at the different tools, how they want to look at it, how they want to model it. So it's actually been a huge value add from the perspective of the users and what they're looking to do. Speaking of value, one of the things that was kind of thematic yesterday was, we see enterprises are now embracing big data, they're embracing Hadoop. They say, you know, it's got to coexist within our ecosystem and it's got to interoperate. But just putting data in a data lake or Hadoop, there's not the value there. It's being able to analyze that data in motion at rest, structured, unstructured and start being able to glean or take actionable insights. From your CFO's perspective, where are you now on answering some of the questions that he or she had from an insights perspective with the data lake that you have in place? Yeah, you know, before I address that, I want to kind of quickly touch upon, you know, wrap up George's question, if you don't mind. Because one of the key challenges, and you talked about how it really helped, you know, I was just about to answer the question before we move on. So I just want to close the loop on that a little bit. Absolutely, go ahead. So in terms of bringing the data in, you know, the data acquisition or ingestion is a key aspect of it. And again, you know, looking at the proprietary data structures from the ERP systems, it's very complex and involves a multi-step process to bring the data into a strange environment and be able to put it in the swamp and bring it into the lake.  And it's able to bring all the data from the ERP source systems directly into Hadoop, you know, without any stops or, you know, staging databases along the way. So it's been a huge value from that standpoint. And I'll get into more details around that.  So it's very easy for them to consume the data and be able to do ad hoc analysis. So if somebody's looking to, you know, like I said earlier, want to look at, you know, and calculate they're payable as an example or they want to look at working capital, we're actually moving data using a Trinity CDC replicate product of you getting data in near real time into the data lake. So now they're able to turn things around and do that kind of analysis in a matter of hours versus, you know, overnight or a matter of days, which was the, you know, the previous environment. And that was kind of one of the themes this morning is it's really about speed, right? It's how fast can you move? And it sounds like together with a Trinity Verizon is really not only making things simpler as you talked about in this kind of model that you have with different ERP systems, but you're also really able to get information into the right hands much, much faster. Absolutely, you know, that's the whole beauty of the near real time and the CDC architecture. We're able to get data in, you know, very easily and quickly. And at Trinity also provides a lot of visibility as the data is in flight. We're able to see what's happening in the source system, how many records are flowing through. And to a point my developers are so excited to work with the product because they don't have to worry about, you know, the changes happening on the source systems in terms of, you know, DDL and those changes are automatically, you know, understood by the product and pushed to the, you know, destination on Hadoop.  So that's been huge from that standpoint as well. Absolutely. It, Amar, maybe help us understand where, where attunity can, it sounds like there's greatly reduced latency in the pipeline between the operational systems and the analytics system. But it also sounds like you still need to essentially reformat the data so that it's consumable. So it sounds like there's an ETL pipeline that's just much, much faster. But at the same time, when it's like replicate, it sounds like that goes without transformation. So help us, help us sort of, you know, understand that nuance. Yeah, that's a great, great question, George. And indeed in the past few years, customers have been focused predominantly on getting the data to the lake. I actually think that one of the changes in the theme we're hearing, hearing the show in the last few months is how do we move to start using the data, to create applications on the data. So we're kind of moving to the next step. In the last few years, we've focused a lot on innovating and creating the solutions that facilitate, accelerate the process of getting data to the lake from a large, you know, scope of systems, including complex ones like SAP, and also making the process of doing that easier, providing real-time data that can both fit streaming architectures as well as batch ones. So once you got that covered to your questions, what happens next? And one of the things we found, I think Verizon is also looking at it now and Arvin can comment on it later. What we're seeing is that when you bring data in and you want to adopt the streaming or continuous incremental type of data ingestion process, you're inherently building an architecture that takes what was originally a database, but you're kind of, in a sense, breaking it apart to partitions as you're loading it over time. So when you lend the data, and Arvin was referring to a swamp or some customers refer to it as a landing zone, you bring the data into your lake environment, but at the first stage, that data is not structured to your point, George, in a manner that's easily consumable. So the next step is how do we facilitate the next step of the process, which today is still very manual-driven, price-custom development and dealing with complex structures. So we actually are very excited. We've introduced in the show here, we announced a new product by Attunity, Compose for Hive, which extends our data lake solutions. What Compose for Hive is exactly designed to do is address part of the problem that you just described, whereas when the data comes in and is partitioned, what Compose for Hive does is it reassembles these partitions and it then creates analytic-ready data sets back in Hive. So it can create operational data stores, it can create historical data stores, so then the data becomes formatted in a manner that's more easily accessible for users who want to use analytic tools, BI tools, Tableau, Clique, any type of tool that can easily access the database. Would there be, as the next step, whether led by Verizon's requirements or Attunity's anticipation of broader customer requirements, something where there's a, it's not near real-time, but a very low latency landing and transformation so that data that is time-sensitive can join the historical data. Absolutely, absolutely. So what we've done is focused on real-time availability of data. So when we feed the data into the data lake, we feed it into ways, one is directly into Hive, but we also go through a streaming architecture like Kafka and the case of Hortonworks can feed also very well into HDL. So then the next step of the process is producing those analytic data sets or data stores out of it, which we enable. And what we do is designing together with our partners and our customers. So again, when we worked on Replicate, then we worked on Compose. We worked very closely with, Fortune Company is trying to deal with these challenges so we can design the product. In the case of Compose for Hive, for example, we've done a lot of collaboration at the product engineering level with Hortonworks to leverage the latest and greatest in Hive 2.2 Hive LLAP to be able to push down transformations so those can be done faster, including in real time, so those data sets can be updated on a frequent basis. Got it. You talked about kind of customer requirements, either those specific or not, obviously talking to telecommunications company. Are you seeing it tomorrow from itinerary's perspective more of this need to, all right, the data is in the lake, or first it comes to the swap, now it's in the lake to start partitioning it. Are you seeing this need driven in specific industries or is this really pretty horizontal? Yeah, that's a good question. And this is definitely a horizontal need, it's part of the infrastructure needs. So Verizon is a great customer and we've been working similarly in telecommunications, we've been working with other customers in other industries from manufacturing to retail to healthcare to automotive and others. And in all of those cases, on the foundation level, it's very similar architectural challenges. If you need to ingest the data, you want to do it fast, you want to do it incrementally or continuously, even if you're loading directly into a loop. Naturally, when you're loading the data through a Kafka or streaming architecture, it's a continuous fashion. And then you partition the data. So the partitioning of the data is kind of inherent to the architecture and then you need to provide to help deal with the data for the next step in the process. And we're doing it both with Compost for Hive, but also for customers using streaming architectures like Kafka, we provide the mechanisms from supporting or facilitating things like schema evolution and schema decoding to be able to facilitate the downstream process of processing those partitions of data so you can make the data available. That works both for analytics and streaming analytics as well as for scenarios like microservices where the way in which you partition the data or deliver the data allows each microservice to pick up only the data it needs from the relevant partition. Well, guys, it's been a really informative conversation. Congratulations, each of them are on the new announcement that you guys made today. Are been great to hear the use case and how Verizon really sounds quite pioneering in what you're doing. We wish you continued success there. We look forward to hearing what's next for Verizon. We want to thank you for watching theCUBE. We are again live day two of the DataWorks Summit hashtag DWS17 forum. My co-host George Gilbert, I am Lisa Martin. Stick around, we'll be right back.