 Live from New York City, it's The Cube at Big Data NYC 2014. Brought to you by headline sponsor, Juan Disco, with support from EMC, MarkLogic and Teradata. Now here is your host, Jeff Kelly. Welcome back everybody, this is The Cube, we're live at Big Data NYC, we're in the heart of Times Square on 42nd Street, we are getting close to the end of the broadcast here, day two, we've had a lot of great interviews, a lot of good conversations around Hadoop, Big Data. We've got a good friend, Cube alum Lawrence Schwartz, VP of Marketing at Attunity joining us here. Welcome Lawrence. Thanks for having me back Jeff. Absolutely, you're here a long time at Cube alum and great to have you back. So tell us a little bit about the show, what's going on down at the Javits Center, the vibe you're seeing down there. I spoke with some members of your team and they said like they're getting a little worn out from all the traffic at their booth and some of the things are going well. It is an amazing show, I mean I've been going to these shows for a couple years now and I remember when it was Hadoop World and Stratton there were seven things and they were kind of in a room in a hotel somewhere and there was a couple of little stands and now it's turned into a real show. So going into this we didn't know what to expect this year especially being at the Javits, but the traffic is phenomenal, there's a lot of people with real problems as I think you mentioned with your last guest, there's thousands of people there, so it was great. You can see that Hadoop is kind of getting out of that proof of concept studies and really getting into deployment and people have real questions, they really want to understand what they need to do next. Yeah, and we're seeing that too in our data. We recently released our big data adoption survey and you're seeing it's still, the majority are still either in evaluation or POC phase, but there's over 30% of these early adopters are doing things in production with Hadoop and no SQL and some of these other big data technologies, so it's really moving along and one of the interesting findings of our survey was that the top technology in use of these big data projects is data integration, which speaks to the fact that this is still, this is going to be a challenge no matter whether it's big data, traditional data, small data, whatever you call it, is getting data to the right place at the right time is still always a challenge for practitioners. So talk a little bit about what you're seeing in your customer base. Do you see that move from some of these big data POCs starting to move into production? I know you guys just made an announcement around Hadoop, so I'm sure you're seeing some action there. Sure. What's it look like on the ground with your customers? Yeah, no, it's amazing. You know, we've been dealing with a replication, so moving data between, you know, SQL Databases, Oracle SQL Server to know the newer data warehouses out there, everything from Teradata stuff to Vertica to PDW from Microsoft. So we've been doing that for years. And so we know that there's always challenges just, you know, going with the non-Hadoop activity and customers come to us with that problem. And as they started to adopt Hadoop, right, and they started to look at ways to implement it, they keep coming back to us, either they know us and they work with us and they say, okay, you help us make that whole process simple, and can you help us do that with Hadoop? And that's what helped us, you know, to launch this product, getting that feedback and thought. And then we hear from people who we haven't worked with before, maybe they've gone out and tried some of the other tools, that's just scoop, right? And they have a little bit of experience and they say, well, that was entertaining for development, but now I need to go into production and scale this up and I need to figure something out fast. So we're hearing it from those two angles. And I was surprised at the show. You know, it's not just kind of the edge, right? These are, you know, large financial institutions coming over. They want to put some of our travel stuff, they want to create that data lake, there's all sorts of uses for it. So I was, it was nice to see and a little bit surprised just to see, you know, how many big places are talking about it. And for us, when we talk to accounts, even if they haven't started anything with Hadoop yet, it's always something that we have to show them and talk about what we do because they're thinking about it, right? They might not do it this year, they might not, they might do it next year, right? So it's that coming together that was interesting to see at the show and hear about. Yeah, well, it's interesting. You're seeing this, you know, the different kind of waves of adoption. Of course, you know, the early adopters, we had the Web 2.0 companies and we kind of had the e-commerce companies. And now we're seeing, I agree, I'm definitely seeing a lot more activity in the financial services sector and the banks. And I think they're really starting to recognize the power of technologies and solutions like Hadoop and when they can get a much broader view of their data assets and they can really drill into more specific insights that's going to help them make better decisions around fraud, around customer upsell and things like that. So it's not surprising that, you know, bank, that's where the money's made and that's where the money is stored and that's not surprising. They tend to be a little bit earlier adopters, some of the analytics type technology with CEP and some other things they've done. So let's talk a little bit about some, you know, customer uptake. What's it been like in terms of, you know, new customers, different industries? Are you seeing any trends in terms of who's coming to attunity? You mentioned kind of the use cases. Are there any verticals beside banking that are you seeing an uptake in? Sure, sure. We have some great, you know, healthcare companies, you know, coming in and doing work. You know, they might be moving from, you know, again, the traditional data stores that they have and they want to do something else. They want to try one of the new data warehouses and so they come to us for that. Instead of doing it on a nightly batch process or doing a long complicated ETL process, they want to do something, you know, much more streamlined, right, much more fits your architecture, something like an ELT, which I know you're talking about with another guest. So that's another industry that's coming to us. We see people coming for doing, you know, offloading of some of their analytics. So they might have stuff on a traditional store like Oracle. They want to try offloading that in some new projects and getting those new projects started is often, you know, one of the pain points, right? Even though there's a lot of, you know, people have an ETL in their toolkit often, especially as a bigger company, they try applying that and they see that, boy, that's hard to do. It takes a lot of time. It may be automated or so claimed to be automated, but they're not quite there with a nice user interface and an easy way to get started. And we can help shave off, you know, for some customers instead of months of development, time to get going. They can get it down to, you know, hours, days depending on their application. So it's a nice combination of, hey, there's a new technology I want to use, you know, whether it be, you know, the cloud or a new data warehouse or Hadoop. I see the value of it. I want to get there, but that getting there is the hard part, right? It's a data integration problem that you had in your survey. And that's where we can, you know, lower the barrier, right? So when we look at Hadoop adoption, you know, that's where we see our role in this, is how do we lower those barriers to adoption and make this more feasible for people to kind of get started and get going? Yeah, you know, another interesting finding, we asked about kind of ROI on big data and Hadoop. And I'm curious to get your take. And, you know, what we found was, you know, there's big expectations. People are expecting, you know, three, four dollars back on the dollar invested in big data technology. I would argue that, hopefully that number will go up. You want to, I think this is a 10, 20 x possible value creator. But today, on average, they're only realizing about 55 cents on a dollar according to our survey. And, you know, any given survey can, you know, can say up or down a little bit. But I think what it tells you, generally, is that people are not yet getting the full investment. I think part of that's because, you know, you're still in POC, you're not going to get the full value until you kind of move into production. But I'm curious from your perspective, from what you're seeing out there, what are some of the biggest challenges, A, for achieving ROI and is one of the, are one of those challenges just, how do you calculate it in the first place when you're doing a lot of net new types of workflows? Right, right. No, it's a, that's a great question. Actually, the talk I gave at the show was all about, I think the topic was the big data, you know, the elephant and the bear, right? Because everyone knows about big data, everybody knows about elephant. And the bear was a customer that we have, a place called Grizzly Oil. And they are a kind of a very entrepreneurial energy company. They're using a lot of sensor data from a lot of different areas. And they want to pull that information in and kind of figure out their analytics, where they drill next, what's going to happen, and where is it most profitable. And so, they were looking at, you know, traditional tools and they're trying to find a better way to do that. And it goes back to, you know, we were able to get them to kind of a big data solution a lot quicker. And again, they're small, they're not, you know, they're not an Exxon, they're not an enormous company. They're very small DBA resources and small teams, so they want to do that efficiently. And we're able to go back there and working with Nucleus Research, they came and they saw, I think it was a 1500 percent ROI. Their payback period was two weeks and, you know, they were able to cut down their personnel and staffing need dramatically. And they're able to get going in a few days and have to have an army of consultants help them. So, I think you have to pick the right application. You know, that's an interesting cutting edge idea of how do they pull in the data? How do you think about the Internet of Things, right? Or the industrial Internet of Things. So, for the right applications, I think there's a real hunger and a need. And I know you were talking about this in your panel yesterday. I think some people are still a little bit stuck on the, what was the alternative to ROI? Was the reduction? Reduction on investment, yeah. Reduction on investment. I think a lot of people are kind of stuck on that mindset. But you've got to look at, you know, how do these things play out into new value. And that's where you see the big, the really big returns. Yeah, I mean, so you're seeing customers doing things like, you know, moving data from your data warehouse into the long-term storage or some of the transformations. But, you know, that's, you know, so you're reducing your costs in terms of your storage costs. And that's much easier to calculate the ROI on that. But the challenge there is, okay, well, the idea is so you take that savings and hopefully you invest that in new, net new revenue creating projects. Whether it's, you know, something around the Internet of Things or leveraging data you couldn't leverage before. But it's, but when you start with that cost savings, you've got to shift the mindset. Sure. And that's also one of the challenges I think is making sure you're looking at this from both a cost savings and a revenue producing point. Absolutely. I think you're missing, you're missing a huge part of the value of something like Hadoop if you're just looking at it as a long-term storage platform. Yeah. And the thing is, it's challenging because I've done ROI studies, you know, where I am now and other places I've been at. And it's very easy to look at the infrastructure savings and costs, right? That's a, okay, I save this much space, you know, this much less power. It's very hard to quantify the value in, you know, healthcare or energy because it's very, very specific to that energy, right? If, you know, a big retail place can transact on things, you know, a tenth of a second faster, that could be huge, right? You know, people won't abandon stuff in their card or, you know, they'll stick around. And so it varies by industry. So you really have to go in there and understand those different industries. But once you do that, then you show the value to that one and then it gives ideas to others in the industry, okay, this place did it, you know, maybe I ought to think of these things. But yeah, if you just come in there on the cost side, you're only getting a small piece of it. It's more easy to understand and more universal piece. But you don't kind of get those niche uses. Yeah. So we'd love to get your take on something we were talking about last night on the panel and kind of been talking about it on the Cube the last two days, kind of the state of the, kind of the Hadoop market. And so we've, you know, we've got, everyone is kind of interested in one of some of these Hadoop companies going to go public. Are they overvalued, undervalued? Yeah. We had a pretty spirited debate last night on the panel about that. You know, Clutter's got a four billion dollar plus valuation. What did you take on the big data, the Hadoop market for those kind of companies? Is it a little frothy? Do you think where you think the valuations are too high? I mean, what's your take on where we're going in that market? Yeah. It's, you know, it's hard to say. It's like, you know, any investment vice, I give anyone, I'm not investor, right? We'll put that disclaimer at the bottom. Exactly. Exactly. But, you know, it's, it's, you know, there's a ton of money going into it, investment money and, you know, hundreds and millions of dollars and whatnot. There's a lot that they have to prove. But at the same time, when you look at the, you know, the billions of dollars being made at the traditional vendors, right? The ones that they're kind of oftentimes, you know, co-op, you know, co-op, what's the word, co-optition with, right? Yeah. Co-optition with. With that, they might try to displace or figure out their place in the world. I mean, it's a huge, huge value from that end. So, you know, will there be consolidation, you know, with so many vendors out there? I'm sure if we go back and look at the show list, you know, three years from now or five years from now you'll say, okay, this guy went with that guy and that one got that guy. So I don't think everyone can survive. But it is a new area and it is a new frontier and, you know, I don't know if I can comment either way. It's, there's a lot of value to be had. Yeah, it's hard to put a value on these companies because it's not just that the, one of the complicating factors, of course, is not just that the argument that the technology put to good use is going to create a lot of value. But it's, you know, the business models are so different from the different players. You've got the open source component. Right. How is that going to impact the market? So it's not a traditional enterprise software market. I think that's confusing people. And there's definitely, you know, some interesting, you know, overlaps, right, even on the technology pieces. So if you look at trying to fill in the whole ecosystem and obviously we're part of that, there's the open source, right, which is, you know, I think you use the term, you know, it's, you know, it's a free software, it's like a free puppy, right, or something like that. So, but we see that even in our world where we're talking about data movement, there's some tools out there like scoop and they're open, they're, you know, people can get started quickly. But that's kind of like saying, you know, okay, you want to drive somewhere, here's the wheel, here's the chassis, go build it and have fun. That's one end of the spectrum. On the other end of the spectrum is you've got, you know, these big established ETL vendors, you know, in our area, and they're like the Hummers, right, you know, they do everything, they can, you know, bring you on the back country mountain road. But face it, at the end of the day, most people are happy with, you know, a Toyota Prius or a, you know, Corolla or something like that, for their everyday job, simple, gets the job done, I don't have to do any amount of work. So I think there is an interesting value point between, you know, the pure open source, you know, whether you're adding services or adding some capabilities on top of that, and the established players which are, you know, oftentimes, you know, the big, you know, SUVs that are doing everything for everyone that are very, you know, expensive and hard to use and maintain. So, yeah, it's the kind of, I think of, you know, not necessarily the data integration space, but in the database space, you think of exadata, and it's like, it's like a Ferrari, the thing hums, it performs, but you're also going to pay a lot of money for it. And so, do you need that much power? Does that allow you the flexibility you need to invest in other areas as well? So, you know, they're in an interesting position where they're trying to adapt to this world, and as well, you know, the big ETL vendors are trying to do the same. So I've been hogging the the limelight, I know, I know Jeff wants to get a question. The other kind of interesting thing is really, is where do people make their bets? And I think, you know, it was an investor capital markets presentation yesterday because people want to make bets. And in the early days of any technology, you just don't have that many places you can put money directly into that sector if you're being told, hey, we need to, we need to play here. So I think that's really the challenge too when, you know, as you came out with a hypothesis, there's a lot of big data plays actually in the practitioner side. There's a lot of big data plays and actually some of the traditional sides. Yeah. And then, and then, unfortunately, you know, a lot of companies are still private. Sure. So, you know, you're not able as a public market to put money there. So I think you get this kind of ridiculous concentration of an investment for people that want to play in the space in the early days before they really have a broad portfolio of companies to actually spread that money around and make maybe a little bit tighter investment decisions based on more traditional parameters because you just don't have a population to choose from. Yeah. There just aren't that many options out there. But, you know, of course, the TUNI is a public company. So you've got to respond to the public markets. What would you advise would you have, you know, not nothing, no material information. But I'm curious, what advice would you have to some of these companies that are going to go public at some point? You know, you're going to see some of the Hadoop players go public. Sure. How does that change the game for a company that's still in growth mode and, you know, still expanding and doing things that are not traditional? Right, right. How does that, and now you've got, now you go public, you've got shareholders that want returns and, you know, want to see the numbers every quarter. Yeah, sure. How does that change, change the equation? Yeah. No, I mean, I've been at, you know, some private startups and I've been at, you know, public companies what I see here, you know, there's an accountability, right, on the public markets of, you know, quarter to quarter invisibility and whatnot. And while that can be challenged, right, because you've really got to show that and show what's happening, it does keep you honest, right? It keeps you focused on what's important for us when there's a tremendous amount of opportunities that we can go after in interesting new areas. We really focus on the ones of, okay, which ones are not just, you know, experimental, but you know, which ones are people adopting, trying to figure out and can get us, you know, value that we could see over the next year or two. And we still make bets on things that are longer term, but it kind of keeps you open and honest, right? It's hard to go and say, oh, well, I think this technology is a really interesting niche and I'm going to go all in on it for a couple of years and you just, you don't do that as a smaller public company. But at the same time, you focus on what the real pain points are and that's what gets you that kind of return on value, so. Yeah, it's going to be fascinating when we start to see some of these companies go public, Jeff, and the scrutiny they're going to get. I'm really, I can't wait to see those, you know, those S-1s and really. I'll talk to you when they get those huge valuations on the way around, see if that really changes the game significantly. And, you know, it's one thing to talk about revenue and we, you know, we have a revenue forecast, but the other part of that equation is, well, how much are they spending for every dollar revenue? How much does it cost them to create that revenue? Absolutely. If it's a three to one, there's going to be some, it's going to cause a little bit of consternation in the investment world, I think. But Lawrence, we've got time or just one more question I want to give you the last word. Sure. So what's top of mind for you going forward? What are some of your objectives? What's on the road map to the extent that you can share? Right, right. What are you focused on? Well, you know, it's a fascinating time for us. You know, we see a lot of things coming together for our company in this space just in the last, you know, year or two. You know, we've invested heavily in the cloud. I've been very close with Amazon. We've invested, you know, as with Hadoop and we've gotten this, this announcement out there. And so now, you know, we're in a good position to help a lot of our customers figure out how do I manage all that, right? I've got, you know, maybe one system I've been using for a long time and I want to do something a little bit different. Maybe I want to try something in the cloud. Maybe I want to try to do or something else. So we're in a very good position for that. We've done some new tools this year, this Maestro product, which helps kind of orchestrate all that movement. So it's an exciting point of time for us where we can be a good consultant and guide and, you know, for offer guidance for our customers and really help them figure out the problems because we can solve many more pieces of the puzzle than we could, you know, even a year or two ago. So for us, that's a fun place to be. It's a fun place to learn about where the market's going. It's a fun place for us to kind of adopt as we go along and figure out the best solutions. So that's what I'm looking forward to is, is, you know, as people adopt this, they're going to do things we've expected and they're going to do things we had no idea and that's going to be kind of fun. Yeah, it's a fun market to be in right now. A lot of interesting things happening. So Lawrence, thanks so much for joining us again. We are wrapping up day two here live on theCUBE at Big Data NYC. We'll be back after this with our wrap up segment. Stay tuned.