 Okay, we're back here live in San Francisco at Oracle Open World 2012. This is theCUBE, SiliconANGLE.tv's exclusive coverage of Oracle Open World. We go out to the events, to extract the signal from the noise. I'm John Furrier. I'm here with my co-host Dave Vellante. And we're here with Billy Bosworth, the CEO of Datastax, the rising star in big data, big data, obviously mainstream. Anytime Oracle's doing anything that's overt and publicly talked about means that it basically admitted full mainstream adoption. Billy, welcome back to theCUBE. Again, CUBE alumni, supporter of SiliconANGLE. CUBE, I appreciate that. Couple big news. So share some of the big news. You guys got some great financing from real big players, real validation. Tell us the news. Well, thanks for being back, first of all. You guys are great, love the show, love to be here. And yeah, we just closed our C round, $25 million with Meritech, who is the leader of that round. We had inside our participation from Lightspeed and Crosslink, who have been with us now through the A and the B round. And we're pretty excited about it. It gives us an opportunity to put the hammer down now on our growth. We've got a lot of customers to start servicing better. And we're looking forward to applying this back into product and obviously into expansion in sales and marketing. There's a lot of really fun, exciting stuff going on right now. And we were just thrilled to get Meritech. They're just solid quality guys. And that matters a lot in terms of running the business. Great reputation of taking emerging companies and going, taking them to the whole next level. We were excited to broadcast Cassandra Summit live. You guys put on, as stewards to that community, did a great job. And Laura was fantastic to work with in your team. But I want you to share with the folks out there because there's a really big trend that we all see inside the ropes here is that around the big data trend. And just over the past 24 months or so, give or take a few months here or there, it went from these communities that were evolving, somewhat competitive, this and that. I got Cassandra, I got Hadoop. And all of a sudden, big data just exploded onto the scene. The growth has just been fantastic. Just the massive growth across all industries. Kind of changes the dynamics a bit around the emerging companies and the approaches. So there's been kind of this elimination of any kind of civil war going on between the different communities too. Oh my God, this is a massive mega trend. There's so much opportunity. And that's pretty much been documented by theCUBE here in SiliconANGLE. But I want you to share with the folks out there, what is happening? I mean, why is the growth happening? And what does that mean to the Cassandra community and the other communities out there? Yeah, well, clearly we too believe for a long time that this was a very big tide that was going to raise a lot of boats. And we kind of would laugh a little bit internally when everybody would try and put all those new players in a box together and say, fight it out. Tell me why you're better than them. Tell me why you're better than them. We used to say, guys, this market is a lot more nuanced than that. And that's what's happening is now things are settling down. The technology wars are settling down. People are understanding the use case is better. And the biggest thing I think is the success of companies like us who are allowing the open source technology to be adopted in a way that, A, we facilitate education in the community. So we have to make sure that there's a lot of documentation and meetups and groups and opportunities to learn. And then B, when a company decides that they're ready to take this and do something real with it in production, they have to know that there's a commercial entity behind that. And so without that in the ecosystem, I think that that slows adoption. But when you bring those two things together, a very rapidly growing open source ecosystem combined with technologies that have companies behind them that they can trust, combined with better understanding of use cases. And fortunately in open source, people are willing to talk. Like when you guys were at our conference, you saw some of the great customers who would get up there and tell everything. They would tell their entire story. Well, that breeds more adoption. It says, oh, look, this isn't something that is only for the elite few. I can do this. I can do this in my company. And that's exactly what's happening. So it's ironic to see all the momentum, Billy. And I want you to juxtapose that to where we are now, Oracle Open World. And my specific question is, what's your relationship with Oracle? And we watch them and we say, open, you know, your world open and growing and you look at Oracle, but what is your relationship with Oracle? Yeah, well, first you got to remember, I'm a 20 year relational guy. So the 20 years of my career were all about relational technologies. I know it inside and out. I know it from a technical perspective. I know it from a business perspective. And a big part of the reason why I joined this company was because I could see the change that was happening in the architectures of a lot of these applications today. One big thing, the sensational news that everybody wants to hear is, you're ripping this out and replacing it with this different technology. So Oracle's being ripped out and you guys are coming in. That's not what's happening. And what's happening today is, it used to be we would write an application and you'd pick a database and that app would talk to that database one to one, one to one relationship. That's no longer the case. It's now a world of both and and and. So it's about taking the technology. So you take something like Oracle and you put it in its place. You put it in the right place in the stack. You take something like DataStacks Enterprise, you put it in the right place in the stack and you end up with a multiplicity of data stores on the back end. So it's no longer one app has one database, one app might have five databases and they could be relational and non-relational and Hadoop and on and on. So the modern architectures now are embracing all these different technologies in a way that says this piece of my app needs this technology, I'm going to make that work correctly. Well, Larry defines modern, but we won't go there a little bit differently. What I want to ask you is, you got the financing, well, maybe we will later. Get 25 million, want to boost the product up. So explain to folks out there the problem that you're solving and you're go to market with the product because you obviously got some new finance, you want to expand on that. And specifically talk about the nuances around unstructured and structured databases because they're not mutually exclusive anymore and that's changing architectures. Yep, yeah, great questions. So I would start with by saying what we do is we are a platform that is a massively scalable NoSQL solution, right? It's based on Apache Cassandra and the typical use cases for us are somebody is striving for a continuously available architecture and with that, they have to have that performance be linear as they scale the system. They want to make sure that that is always available, as we said, for continuously available. They want it to be at low cost and then the real key, the real difference maker is they want it to be operationally simple because if you start letting operational complexity get out of control, your costs are going to go up, your performance is going to go down. Our solution is very operationally simple at scale. Now, you bring up a great question around the structured unstructured data. We actually deal with a lot of structured data and that's something a lot of times people hear and say, whoa, really do you? And we do. The canonical use case for Hadoop would be around unstructured log data but with Cassandra, it's often about time series data and data coming in very, very high velocity but it's a structure that you know and understand as you're coming in. You still have to build your data models. It's not a no schema, it's just light schema. Exactly, exactly. Well, it's a flexible schema and that's the real power is that as my app needs change, I can change my schema literally on the fly. I mean literally, from row to row as I insert data in the database, I can change my schema as I need to. So it doesn't have to be unstructured data, it can be, or it could be a combination and we see that a lot as well. So we have a very rich data model. Our data model is built on something called Google Bigtable. So you can get into very complex data models where you can mix your structured data and your unstructured data together but big data isn't always unstructured. That's typically Hadoop. For our world, we see a lot of- A lot of the analytical solutions are recognizing that you can have all this unstructured stuff and that's a lot of flexibility, operational efficiencies but in the day when you run apps and or run search or API calls, you got to put it into a schema. That's essentially structured database. Yeah, again, going back to my roots. I mean it's not a throwaway. People are building more SQL databases with the advent of more unstructured. Yes, exactly. That's not a common known concept. I remember when I was at my last company quest who just was acquired by Dell and the deal closed on Friday but when we started into this world I remember when I first heard this idea no SQL doesn't have a schema and I thought, well that's just idiotic. I mean if you have no idea, literally what you're telling me is you're just randomly throwing data into a store. That doesn't make any sense for anybody and then we learned over time of course there's a schema, right? It's just that it's flexible. It can be adapted very, very quickly row to row but the one thing I would say for people at Oracle, if you're an Oracle person and you're a relational person you're trying to get your head around this new world start with the data modeling because if you get your head around the data modeling difference everything else will come natural. It's that initial leap from, wait a minute, you're telling me I'm going to have 20,000 columns? Did you say 20,000 columns? You're insane. Why in the world would you have 20,000 columns? Well because you think about things differently, right? The data is different in a no SQL database. If you get past that everything else gets easier. You mentioned Bigtable before and you also mentioned all those rising ties that lifts all ships. What do you make of some of the new entrants into this market around Bigtable? For instance, the Accumulo project focusing on things like cell level security. Is that fit in? Is that a complementary type of innovation? What's your take on that? Are you actually building that type of security into your offerings? Right, well I think with Accumulo specifically from what I know about it is the cell level security was the big deal. The problem with cell level security was and is you take a massive performance hit and even some tests I saw that are published from a public resource talked about a 40% hit, 40, 4-0 as soon as you turn on cell level security. So you better have a pretty niche need to say why am I going to give up that much performance. Because in my world, yeah my world performance is everything and so to give up 40% you better have a really specific reason. So I think you'll see some of these projects emerge when they do have these niche needs and somebody will come along and say let's solve that particular problem. But other than that, I think that you're still looking at the momentum of the bigger players and saying hey we're looking to you to help solve some of these problems down the road and that's up to us as vendors to address those needs. We're here with Billy Boswell, the CEO of DataStacks. Let's change gears a little bit and talk about the business side of it. You said you, you know, it's a great guess because you got the grade A content on the tech side as well as the business. You're the CEO of a company that's crossing the chasm. You guys are doing great, the finance and the validation, Merit-Tech, I mean all the great leaders they've brought into the promised land in terms of liquidity and growth. But really the reality is that there's disruption going on coming out from the emerging companies like DataStacks and other companies, threatening Oracle and these other guys around the database, right? So let's talk about Data Warehouse and Business Intelligence. That business is going through a complete transformation. Could you just share with the folks of your perspective, given your experience around the landscape of Data Warehouse and Business Intelligence? Critical success factors that they need to do, things they, if they don't do, they're going to be toast. And what do they need to do to stay ahead? Because they have that nice structured front end, they got analytics, they have install-based clients. What do they need to do to stay ahead and be relevant? BI is a topic that's very near and dear to my heart. As a user, as a technologist, I've studied it for a long time. I've got good friends in the industry who run companies that are very successful. And it's a fascinating market to me. The one thing that people need to understand is these technologies have very, very long tails, longer than any of us ever suspect. And so as soon as you're pronouncing something dead and buried, you look up 15 years later, guess what? It's still around. Sometimes it's still around at a flat rate, maybe not growing as fast. But I think what these guys are doing is by incorporating and looking at it and you see what they're doing, you see a lot of the acquisition strategies have been around trying to bring more of like a map-reduced technology particular, which is a Hadoop thing, into the data warehousing world. How do we take these data warehouses that have this great ecosystem, a great set of tools around it, a huge knowledge base of people who know how to access them, and how do we bring in some of the new world? I think that goes so far. I think it's a great plan. I would do it if that's what I was doing, but it's hard to wholly change your DNA. It really is difficult. And you see this over and over, over again, history of technology, and now it's a great time, by the way, to go back and reread. If you guys haven't read it in years to all your viewers, Innovator's Dilemma. Go back and reread that because you are seeing that exact kind of disruption model taking place around this world of big data, and it's a tough challenge. What would you do if you were the CEO of those companies? Because they have Innovator's Dilemma, then you got everyone's a Monday morning quarterback, everyone has a view of the elephant in the room, no pun intended. What do they do? Do they acquire? Do they ground up as real time? Real time is so dangerous to them because they're just not used to what that means in terms of response time. But you got the performance coming in on the hardware side. Yeah, so I think if I was CEO of one of those companies, I'd go coach football for a living. I'd go retire and go coach high school football again. But no, it is a tough problem. And yes, you do get second gas. Everybody comes in and says, we'll see, you should've done this, you should've done that. No, I think they're not quite worried about guys like us just yet going out of business. But I think that they are very concerned about how do they stay relevant? How do they stay hot? How do you attract a 20-something? That's always one of the things I used to think about a quest was how do we continue to attract a 20-something guy or gal coming out of college and they want to do this new and innovative stuff? And I think they do that through acquisitions. I think it's too hard to do it organically. My personal take, it takes a long time to do that stuff right. So I think that acquisition tends to be a faster path and you saw that with some of the acquisitions. Think about the columnar. You had Natesa, Aster, Green Plum, Kickfire, Vertica, those all went within 18 months because people wanted to get those technologies and absorb them into that ecosystem. So they were somewhat, excuse me, they were somewhat evolutionary toward that space. Whereas you guys are now fulfilling, you and your colleagues, their upstart colleagues, are fulfilling the original vision put forth by a lot of the VI vendors. And that 360-degree view of your business, but more importantly, predictive analytics. And space at VI was, in many respects, saved by the Enron debacle. It became a reporting and compliance and very nitty-gritty, boring, but not a bottom-line productivity impact, which is what you're having. We just had opera solutions on. I mean, it's a huge productivity impact. That's what's really disruptive in game-changing and why we should go back and read the innovators to what moment. So what's your perspective on that? Well, data beats theory. And that's one of the biggest challenges around predictive, right? Is how do you get the theory, enough confidence level to use the statistical term, the mathematical term? How do I get my confidence level and my prediction higher? Well, I think at this point it's kind of a foregone conclusion. Everybody realizes that the more data you have, that actually beats any theory that you can propose on the prediction. And I remember the first time this became real to me was when I saw a study on the member of the West Nile that couple years ago when it became popular and then they did a mapping of Google searches versus the CDC. And the Google searches outpaced it by almost two weeks, if I remember right. And so that's when you saw your first indication of, wow, data really does trump any theoretical projection that we can map. So can we get the data fast enough? That was the problem. Can we keep up with the velocity of it? Can we catch it quick enough? And so you're exactly right. I mean, some of this stuff is new and innovative. And I think it's the fact that now you can operate so intimately with your data at such an incredible pace and incredible speed that that is the dream. You're right. That's the dream of the eye. But like companies like ours, you got to remember where I like to say we're behind the drywall. We create the infrastructure of the plumbing. And now there needs to be an emergence of a whole ecosystem on top of that, whether that'll be the traditional guys or some new players, probably a combination of both. But that is absolutely necessary. Believe me, the plumbing is hard enough. So getting the plumbing right and making sure that these tools can plug into this is really our aim as a company. Billy, let's talk about some of the things you mentioned. You're trying to find that 25 year old. You're seeing Oracle kind of an older brand, Larry, the captain of industry, still kicking it at his age. And really he's like one of the left guys left standing of his legendary status. He got Joe Tucci out there kind of still kicking over the EMC, fighting the hard battle. But database has changed. You're a relational guy. You know, the DBA, right? Classic position. How is that role changing? Because looking at the hardware, it's not getting less, it's less complex in silos, but it's still a big complicated matter to operate at scale. DevOps, NoOps, all that being kicked around. What's the new job? What's the new title? I mean, DBA is a little bit narrow. You're not a database administrator anymore. You're a operations guy. What's out there? What's the new job? And what should people be looking to train themselves on? So for me, I think right now the hottest one that I would strive for, if I could go back in time and lose my gray hair and get back to being in the early 90s, I would probably focus really hard right now on thinking about architect. You know, these application architects hold a lot of decision making power in the organizations. And they're the ones that have to ensure that, you know the old saying, just because you can do a thing doesn't mean you should do a thing. Well, that's going to happen a lot with these new technologies. People are just going to start implementing because they can and they're easy, but they're not going to have that wisdom to look down the road and say, but what's going to be the long-term impact of this? And the architects are the ones that we look to for some of that sage advice on, where's this going? What's the long-term goal of this? How is it used throughout the entire organization? How is this going to grow in scale with our business? So that would be the first step. The second step I think that developers, and I was a developer, we love building stuff. We're not so hot at maintaining it over time. You know, we just like to move on to the next thing and always stay hot and functional. So I think DBAs bring, in particular Oracle DBAs, bring a particular wisdom of the organizational, institutional knowledge combined with a lot of the internals on the operating system. Most Oracle DBAs knew a lot about UNIX and really knew how to do that stuff. And I think they are in a position to bring that wisdom to the table in this big data environment to work with the architects and to say, well, let's think about this long-term. When is the right time to use this technology versus when is the right time to use that technology? They can become more of the data management experts instead of just the database systems administrators. So if they start thinking more about data management expertise, they can become a very necessary and wise voice inside of the organization that I can tell you right now is desperately needed. You need people who have seen changes before and understand how these shifts happen. So what about the data scientists? So this is a new emerging role. I mean, it's not really a new role, but certainly emerging is reemerging, if you will. A lot of experimentation, a lot of latitude to go off and do things. Where do they fit into this whole equation? Do they perpetuate the problem or are they, I mean, obviously they're helping solve amazing issues, but what's your take on that? Data scientists, first of all, I love the quip from Merv Adrian from Gartner that he did a while back jokingly, but he said a data scientist is a data analyst who lives in San Francisco. And I thought that was great. But no, they don't perpetuate the problem. They actually bring a lot of clarity and vision to the problem because, again, developers are excellent at the nuts and bolts, the architecture, the schemas, the design of the app to make it scream and perform, but sometimes the developers don't have the best answer to the so what? And the world that you're going to see very often happening now in big data is so what? Great, I'm capturing three petabytes. So what? What's it helping me do? Am I saving money? Am I making money? Am I changing lives? Am I curing diseases? Am I bringing water to build? What's the so what? That's the role of the people who really do understand the data. They have to come in with the end goal in mind and say, how do we get there? And then, by the way, back to my earlier point about the data modeling, this is where they can come in and help bring some clarity in the beginning on why we're capturing what we're capturing, what we're going to do with it down the road, that helps the developers architect a better system. So I think that the role of the data scientists or the data analysts is 100% prevalent and applicable to where we're going. So what's the competition look like for you now? Obviously we talked earlier about how in market growth, everyone's happy, high-fiving each other. There's not a lot of arrows being fired at each other because that's what's happening now. But as you look out on the horizon, out in the valley of 20 miles stair, what's the competitive landscape look like to you? Because it seems to be coming from the big guys too because you're a threat possibly or a partner. So can you talk about competition? Yeah, because now this comes really into our business model which you were asking about earlier and from a business model perspective, one that we don't talk about often that really historically as you think back, as soon as I say it, if you think back to the companies you're going to say, wow, that's right, is open-source itself? Remember, we spend a lot of money making the open-source product really great. So over time as that becomes better and better and better, people start saying, well, do I need that commercial company or can I go ahead and do this on its own? Historical, MySQL, EnterpriseDB, the Eclipse Foundation that didn't have a commercial company behind it. That's problem number one. It's a good problem to have, but it's a business problem as you go forward. Secondarily, what we want to do is as the data starts coming into the organization very, very quickly, simultaneous and different demands get placed upon that data. So you have different constituencies all of a sudden that want different answers from the same data. Well, these systems are typically designed to do one thing and one thing really well. So what if I have real-time demands on my data, but I also have search demands on my data and I also have Hadoop analytical demands on my data, do you want to spend all your time as a customer fighting through all that integration and ETL and transfer? Well, that's what we do at DataStacks. And so our thing is we want to bring integration to that solution. We want to make sure that you don't have to fight and wrestle with your infrastructure and we want to make sure that you can do that in the easiest way possible. So you're going to see us, while we're the Cassandra company, you do see us bring some other technologies into the mix. I want to just capture that point. You said the three demands were real-time, search, what was the third one? Analytics, batch analytics. Yeah, so you have batch analytics like something with a classic query that's going to run. It's an actual program. And that program might have to run for a couple of hours, you know? Because some of these, some of these analysis are incredibly deep. So explain that to the folks out there. I want to just drill down that because that's an important point. People don't realize that the data, and this talks about some of the in-memory stuff that Oracle is promoting as well as HANA. And we talked about this at Cassandra Summit with Solid State and what Flash has done for the business. You guys operate at scale. Big, big, big issue. So the demand on the data, what does that mean? In terms of like behind the, under the hood? Great question. It's a problem that's been with us for a very long time. And if anybody's been around for, for any length of time, you remember this problem called workload isolation. And what happened with workload isolation really just said that systems are designed typically to do one type of thing. So think about a race car. Race cars designed to go very fast and handle very well, right? It's not designed to pull a four ton load up a mountain. That's a different problem, right? It's a different vehicle. And technologies happen the same way. So when you get multiple demands placed on your data, think about it in that analogy. Do you want your car to go really, really fast? Or do you want your car to be able to pull a lot of weight? Well, that's a mixed workload problem. And what we do is we try and solve that problem by allowing you to do those multiple demands of your persisted real time environment plus your search, plus your batch analytics. But we keep those workloads separated. And that's always been a bit of a holy grail. And it's not easy to do. Cassandra's architecture allows us to do that. So the multiple demands on the data is something that the architects, again, going back to the architects, they kind of have to solve that problem. And the developers have to solve that problem of same data, but different consistencies want different things out of it. Well, we're pushing it up on time. I'm going to ask you one final question. Dave, if you have one question, we'll get it in real quick. Financing, great validation, markets exploding. Obviously, there's a modern infrastructure, software-defined infrastructure, ops and DevOps, all that's part of it. It's very relevant. You guys are in a great path. Congratulations. So I want to ask you, what's your outlook for the next year? And we'll see you guys at Strata in New York for the Hadoop World and essentially the big data summit, if you will. What's your outlook for the year? What's your goals and how's your team organizing themselves? Yeah, crossing the chasm to use another popular business book from Jeffrey Moore. Crossing the chasm is really what the next year is all about. And that means as you move from the innovators and the early adopters and you move into what he calls the early majority, things have to change in the way the product is delivered. It's got to be easier to install. It's got to be more intuitive to use. It's got to be easier to upgrade. It's got to be better documented. It's got to have the feature set that enterprises are going to demand and expect as they move into the middle of the curve. And that's where we have, all of us I think in this space have a lot of work to do to continue to make this more accessible and adoptable from what we would consider more mainstream customers. And that's happening now. I just did a webinar the other day where I showed this progression pyramid. I'll send it to you guys later. We had the early edge cases and then we had the next ones. And then now suddenly it's starting to emerge in companies that everybody knows and understands. And we have to make the product more consumable and more digestible for those type of corporations who aren't living on the bleeding edge, who really are a little more conservative in their adoption patterns. So that's sort of a business priority question. My last question is more of a futures question. Well, we have you here. We love to tap your brain. There's a discourse around analytical and transactional applications coming together. Cassandra came out a big table. You just saw Google announced, well, disclose Spanner. What's your take on that? And do you see those two worlds really, you know, coming together and colliding in any way? And which two worlds specifically? So the analytical and the transactional applications coming together. I yes and no. I think that they will come together through companies like ours bringing them together. I don't think they'll come together quickly anyway in the foreseeable, certainly not the next year or two in the individual technologies themselves because sometimes we lose appreciation for how difficult some of these problems are that are being solved. So to take something that was designed for pure batch analytics and suddenly say, let's make it become a jack of all trades, that is a really non-trivial engineering problem. And so the demand for what the system was built for continues to increase. It's going to have to stay focused on that. I think when these things start defocusing, then you're going to run into the problem somewhat that the relational databases have run into which is trying to serve everybody in the same way, equal priority. That's a bad formula for continuing to propel your technology. So I think that the commercial companies will work on the integration of it, but the individual technologies and the open source projects will stay very laser focused on what they do extremely well. Yeah, it's unclear that Spanner will even go open source in the near term anyway. And there's a lot of proprietary hardware even in there. So interesting, great perspective, Billy. I always love you as a guest. Great guests, great aid content as we say, great signal from the noise on both sides, technical and business, congratulations. You got the tiger by the tail with your new company, data stacks is moving to the next level. It's exciting to watch and be part of this past couple of years, Dave, watching all of our friends who are doing all the work really start to grow and take advantage of the marketplace. So congratulations, takes great products. This is siliconangle.com, the Cube. We'll be right back with our next guest after this short break.