 Live from Boston, Massachusetts, it's theCUBE at the HP Vertica Big Data Conference, 2014. Brought to you by HP with your hosts, John Furrier and Dave Vellante. Okay, welcome back. And when we are here live in Boston, Massachusetts, this is theCUBE, our flagship program. We go out to the events and extract the students with the noise. I'm John Furrier, the founder of SiliconANGLE. Dave Vellante, co-founder of Wikibon.org. Go to SiliconANGLE.tv to check all of our live footage. Go to Wikibon.org to get all the free research. Again, this is theCUBE where we broadcast live from the events that matter. And our next guest is Eric Venzuela, business development, full 360. Welcome to theCUBE. Thank you. Appreciate it. So big data is a discussion. So we were just talking prior to going on about Amazon, the cloud. And you mentioned the word DevOps. And I'm like, oh yeah, we love DevOps. We think DevOps, having developers work with the cloud is the biggest thing that happened in generations in terms of productivity. So welcome to theCUBE. Thanks so much for having me. How are you using the cloud to deliver data and value? Tell us. Well, we're early adopters of cloud and both Vertica with analytics and BI. We've been partnered up with Vertica since a delivery that was given at MIT, our CTO actually participating in that created a relationship. I think this was back in 2007. So we were immediately part of the beta test team at that time. And since then we've been running like crazy. But the interesting thing is that when Vertica was released, that's right around the time that Amazon started coming out with their cloud offering as well. So the two married well together and we immediately started building, started developing and started doing things like that. So what do you think about the Vertica situation? We'll go with Amazon a second. What's so about Vertica? Obviously the results we're hearing from their customers is pretty significant, large scale, big time customers using hyperscale as running with Vertica. You surprised by that? You psyched by that? You've been there from the beginning, kind of present that creation, if you will. What's your take on their success? Well, I'll kind of give you my take on it purely from my base of knowledge, which it's not a huge pool. It's not a deep pool that I dive into. It seems to me that now that there's a place for tremendous amounts of data, that people will continue growing out their data requirements as long as there's no restrictions, that those bits of data, those data sets will continue to grow. And then there will always be a reason to analyze those things. But I'm not at all surprised by that. I think it actually makes sense. I think part of the process getting to this point was the adoption of it. And now that it's easy to, from our perspective, just purely with the cloud, it's really easy to get a stack up and running. It's really easy to get something up and running where you can load some data and actually review your data in real time. And so just based off of that, there's no limitations anymore. So if you have a proliferation of people that can actually do this stuff, yeah, it makes sense to me. They gave a skeptic on self-service BI. So where do you see that going? Because that seems to be, people want business intelligence tools to be less geeky and be like a search engine. But there's a gap between where we are today and where we want to be. So yeah, how are we closing that gap? You know, that's an interesting question. I mean, I think from our perspective, I think we're doing a good job of building the technology, building the infrastructure, making it very easy for more people to get tied to it, to use it, to spin things up. And also, in addition to that, I mean, it's not magic. I mean, there is a level of respect that needs to be given to those platforms to keep them up and running. So I think as you continue building bigger and better platforms, that will eventually close the gap. But if you keep creating tremendous amounts of data, that's also a place where you can put that data and analyze it quickly, irrespective of the size of it. That's also closing the gap. Eric, how much of that is closing that gap as technology versus sort of training and process and the like? Yeah, that's a good question. So early on, I would say even just a couple years ago, a lot of the work that we were doing was purely education. I mean, we were teaching people that building these platforms in the cloud is secure, that it is efficient. And now with a bigger adoption around that, we're not having those conversations anymore. Now it's about, how do we get to that point much quicker? So these solutions are actually created and developed a lot faster than they were in the past. I'm not sure if I answered. So you brought up another point. So you think people are through the knot hole in terms of the pedantic argument of security and privacy. Yes. I mean, I've always said, John, John, you can vouch for this. I think the cloud is more secure than the vast majority of on-premise deployments. I agree. I agree. And so, but yet, we still have people coming on theCUBE saying, no, no, we don't do the cloud. Now, maybe that's industry-specific. Yeah, I don't understand the hesitation. I mean, you start digging into that. You start asking them, what is your hesitation? I mean, it can't be a cost, it can't be cost, right? Because I think the cloud is actually proven to reduce a lot of the operating cost for on-premise, right? So I kind of think that part of it might be from when you look at on-premise and how to manage and run and build on-prem, there's a different shift in the way that you think in order to get that up and run in the cloud. It doesn't directly translate, right? The way that you do things on-premise is certainly not going to be the way that you're going to do things in the cloud, right? So that, it could be an unknown, right? It could be, maybe there's just a gap there. I've kind of found that. I mean, once I've started educating people on certain things that you can do in the cloud and cannot do, you know, that sort of lifts a little bit. Oh, good. I think that makes DevOps so powerful is the fact that speed is a game, right? So like, right now the big trend that you're seeing in the startup community and in the data community is what they're calling the full-stack startups or integrated stacks or integrated value chains, if you will. The old model of siloed approaches is kind of getting busted down. We're really busting down the silos for the first time in history and changing that market. So like, if that's the case, things like clouds should make BI as easy as using Google Search. Right, and you know, building those infrastructures, I mean, like you were mentioning with DevOps in general, I mean, with a click of a script, I mean, now you've got an entire stack up and running and it's tuned, right? You know, you've worked with the operating system to tune it as best possible to operate with this, you know, vertical instance, right? So you know exactly how that's gonna work. And so what we've been able to do is that as changes or additions or updates or things that are needed to be done around Vertica, we just have to, we leverage DevOps and we only have to make that change or make that addition to our scripts just one time. And we know that we can now provide this to all our customers, right? So it's not as if we're starting from scratch or from ground zero each time, building up from there. So, I mean, I tend to agree with you and John, I'll tell you again, I'm sort of a cloud bigot, but I wanna play devil's advocate. Why is renting cheaper? Why is it less expensive than owning? You would think at some scale, Zynga tested this. I'm not sure it worked out the way they wanted to. Uber seems to be going in a similar direction. But is it just better? Is it more sort of getting out of that what AWS calls the non-differentiated heavy lifting? Can you replicate what you're doing in the cloud on premise? I mean, you're from a, you're a long time consultant, right? You got good background there. If you were one of these enterprise guys trying to hang on to the past, could you replicate that capability? It's much easier going from on-prem to the cloud, going back from the cloud on-prem. Not, no, that's a huge challenge. I mean, that's not an easy thing. Because of the mindset, the DevOps culture. Yeah, but there's also this little limitation, right? I mean, the environment that- Scales there, the environments are going to be completely different too. One thing you can almost guarantee is that Amazon's going to be exactly the same every time you work there. Different on-prem is always going to be kind of a challenge. Taking one of our scripts, just bringing this as an example, is that if we've developed a script, we know exactly that that's going to run. We know 100% of the way that it's going to run and operate and behave inside the cloud. In order to take that and apply it to an on-prem, we would have to then understand how that on-premise network and how all of that works modify this to make it work, right? So, once you go forward, I mean, there's no point in going back, but... Although some have tried. Yeah, some have tried, yeah. I mean, and I think if you look back a couple years ago, there was probably some companies that started in the cloud to do development, to do test dev, because it was really cheap plays. They didn't have to procure hardware. They didn't have to have these big CapEx purchases in order to just prove a concept to do some test dev. And I think they started those things with full intention of coming back in on-prem. But as that's, the cloud stuff has actually grown, it makes more sense for them just to... When we talk to our customers, we always throw that out as an option from the very beginning. If we're going to build in the cloud, you might want to consider keeping it in the cloud, taking that off the table of going back to on-prem. Let's just keep it in the cloud. I want to ask you a question that I asked a lot of folks. If you could ask, this is more for you to think about, I'm a customer standpoint, you're a customer. What is the biggest thing that they're looking for? And then the specific question is, if they could ask their data anything, what would be the number one thing they'd want to ask their data? Ask the data, what's the top level, the number one thing that most customers want to ask of their data? So we cross a lot of verticals, I mean, we're in, we have some airlines, we have some big games, we have quite a few banks. They're all going to ask a different question, but I mean, I think it's just about getting down to the nitty-gritty, right? I mean, it's about making an educated decision with intent that's based off of fact and data. I think everybody's going to be asking that question, how they digest it and how they use it at that point. You know, I guess that depends on what they're talking about. We were talking earlier, yes, in the previous interview with Peter Fishman from Yammer about A-B testing, and you know, we were kind of kicking it around. Obviously, depending on how you look at it is good. We had Eric Schwartz on Twitter just comment to me. The vast majority of A-B testing I've seen done is utterly useless, mostly low base rate problems, sample size, and then Peter's talking about other analytics. Are you seeing the myth of big data and this intelligence being skewed by the hope that there's something there? Or are you seeing real, actual insights with the staff and the technology in place to make that happen? Where are we in that continuum, being nirvana of the holy grail? The people are peaked in talent. The things are fully scalable. The data's all ready, all the tools there. Versus kind of the dream, are we being sold the dream? I don't think so. I think infrastructure's there. I think that's available. I think that we can fully leverage that. I think the expertise is there in terms of what it is that you wanna do with that, with all those, with that huge volume of data. I think we're definitely getting a lot further away from a pipe dream and vapor than we think, but that's definitely moving quickly. I think by this time next year at this conference, we'll definitely see a different landscape. I think we're definitely gonna see a lot more people getting into the big data to either consulting or infrastructure in some way or another. So the name Full 360 is interesting, particularly because back in the day of the Heartland of BI and Enterprise Data Warehouse, a lot of the marketing was around a Full 360 degree view of your business. And most people would argue that that never lived up, that reality never lived up to the expectation. And that's fair, right? Yet you guys chose that name, and you talk about BI and DW for the cloud. So we talked about DevOps, you're clearly doing things differently. But are you finding that you're adding more business value, that it's really not sort of traditional BI and EDW? Or is it just you're doing that better? Well, I think we're definitely taking a different approach to how to do it. And I think that, I think conceptually, we're doing it better, right? I think we're fully using the technology out there to its maximum. Vertica, for example, the cloud, for example, I think we're pushing the limits as to what those two technologies can do independently and then actually together, right? So I don't know, that's kind of a tough answer. Well, so essentially, are you fulfilling the promise that was laid out there? I think we're getting, yeah, I think we are. I mean, I think we're getting close. I mean, I think we're listening to our customers in a way and building what it is that I think that they're asking. I think once they get closer to it and more knowledgeable on what big data is or how to manage it or how to use it, that's where things really start to grow, right? So you mentioned a couple of industries that are traditional airlines and banks and then the new sort of emerging big games industry. Taking the airlines and banks, when we talk to people in our community about what they're doing with data warehousing, and even if you go back sort of before the big data meme, they would say things like, oh, it's our infrastructure, it's just so painful. We're chasing chips every time Intel comes out with a new chip, we got to buy it, because these things are so slow. Ingesting data is like a snake swallowing a basketball. It's just they just describe this painful patchwork of endless hell, right? There's another expression I won't say. It's one of my favorites. Some of those are the pumpkin. So what I'm asking is, is that changing because of your processes and because of the cloud and maybe a little bit of vertical in there? Or has something else changed? Is it this big data? Is it Hadoop that you're injecting into it? Is it, you know, have cost just hit a tipping point? I'm trying to understand what's different besides just the cloud and your excellence, your expertise and squeezing as much as you can out of the technology. I think it's a combination of all those things. I think if you view it in a certain way and you're open to expanding upon it and the infrastructure is there and it's somewhat limitless with the cloud, like I said, that's a really good partnership. That's a good marriage there. But then when you add on expertise and a lot of things that are broken that we've had to fix over a period of time, I think when you combine it all, now you're just focusing on how to digest that data. If you have the pipes big enough, if you create them big enough, now you can actually get the data loaded in and then it's about digesting it. Now, if you have a columnar type database like Vertica that can do the analytics very quickly, that part is now taken care of, right? And then if you have the expertise and the knowledge to put it all together, I mean, I don't think it's just one thing. I don't think Vertica in and of itself is solving all the problems or the cloud is solving all the problems. I think it's definitely a combination of everything put together. I wonder if we could talk a little bit more about the sort of, we'll be talking a lot about the technologies and sort of dig it in and playing devil's advocate on some of the cloud stuff and that's all well and good. And it sounds like most of your customers are way past that, right? That's kind of what you told us before. So where are they? I mean, take us through a typical engagement. Somebody comes to you, they got a problem, they see an opportunity. Talk about sort of the business drivers and maybe even give an example. A typical customer is somebody that has, is very on, everything is on-prem and they're interested in the cloud, right? So they know that there's this technology out there, right? And then they have a requirement for managing their data. Either it's in disparate silos or maybe it's just growing so much that so quickly that they just can't get a hold of it and they see moving into the future that it's just going to continue, right? It's just going to continue to get bigger and bigger. That is a, that's the typical customer. So they would generally come to us and we would understand their business, understand their data. We offer a product where what we call, what we call it is a quick start. And what that means is that it's similar to a proof of concept, but we're not really proving any concepts at this point. Everything's really been proven. We're just quickly trying to get them up and running and started on the cloud or with Vertica or with big data. So what we do is we take a sample set of their data, load it in and then give them some reports so they can start reviewing their data. It makes sense at this point because it is their own data. It's not some canned made up data, right? So from there, once all of that groundwork is done, that's when all the creativity starts to come out from the customer. Okay, now all of these things have been done. It's easier for us. Now we can see our data. It's not as complex as we thought. Remove in the unknowns as quickly as possible. That's generally- Then they're hooked. Yeah, exactly. So if you have a customer that has quite a few unknowns and you can remove those, then you're pressing forward. It's just opening up to a different way of thinking. And some of your customers are putting so-called personal information into the cloud? Personal information for their customers. Yeah, yeah, yeah, it's up to them, all right? I mean, they have to decide what data they want to track on their customers, but of course. And I have a sort of a question about AWS. So the Vertica is in the AWS marketplace, or is it kind of bringing your own license, or how does that typically work? It is in the marketplace. There's a community edition that's there that will allow you to spin up Vertica in the cloud. Our stack, the ones that we've developed is different from what is available on the marketplace. So if you were to come to us, we would use our own scripts in order to stand up that stack for you. So it's been through months or years of development in order to get a finely tuned operating system, tuned Vertica, and connected on AWS. It's everything. So I transacted that through full 360. Right, right. And you take care of the, you're my heat shield to AWS. I don't need to hire developers, right? Exactly, exactly. I don't get a monthly bill from Amazon. I get a monthly bill from you guys. You'll get a monthly bill from Amazon for their services as well, just for the usage that's transparent pass through. Yep. We charge for the managed services on a month-to-month basis. But the way to look at it is that we're not only taking over the managed aspect of keeping Vertica up and running in the cloud and all of that. It's really an insurance policy, right? I mean, if you really think about it. We guarantee that that is always gonna be available that it's always gonna be up and running. It's always gonna be up to date. You know, that if there are some, you know, data lags, latency, query issues, all of those things we address for you. So you have a very intimate relationship with Amazon, right? Presumably, right? Pretty close. What do you think about things like auditing and, right, they're not gonna let you go on site, but do you send your auditors in or do you interact with their auditors and do your customers say, well, we trust their security or their security's different than ours? How do you deal with that squaring that circle? Well, I mean, obviously Amazon spends quite a bit of money on making sure that those audits pass, right? That they pass muster. So we don't really focus on that too much. I mean, if our customers wanna know about, you know, Amazon specifically, you know, we reach out to our partner network and have them answer those questions and we'll point them over to the website. Okay, and that's not an issue when you're in your customers. It hasn't been. It used to be, it used to be. Early on, it used to be, but now, not so much. Okay, Eric, thanks for joining us on theCUBE. Appreciate it. Obviously, Amazon Web Services really showed the way for innovation startups. Certainly IBM is trying to match that with EMC and VMware and Pivotal, trying to build their cloud. Everyone's building their cloud, so it's just the beginning of all the action. So appreciate your insights here on theCUBE. Business intelligence, big data is driving ton of innovations and enabling entrepreneurs to be successful, not just big companies. So we're excited to bring you that action. I'm John Furrier with Dave Vellante. Go to crowdchat.net, check out the live chat we're having in our engagement container. And on Twitter, I'm at Furrier and Dave's at atd, Vellante. Check us out, we'll be watching. So we'll be right back with our next guest after this short break.