 From San Jose in the heart of Silicon Valley, it's theCUBE covering Big Data SV 2016. Hey, welcome back everybody. Jeff Frick here with theCUBE alongside of George Gilbert from Wikibon. We are live in downtown San Jose, California at the Fairmont Hotel, the gold room for Big Data Week. It's our third year doing Big Data SV concurrent with Strata, a dupe world that's happening over at the convention center. And it's really an important show for us. Big Data continues to get more and more important as it kind of also kind of goes under the covers is really an infrastructure play. And of course we keep hearing over and over and over that cloud has been such a huge and abler to move the Big Data story forward. So we're really excited for this next segment. We've got two great guys from Zoloni to give us an update. Ben Sharma, the CEO and Tony Fisher, SVP of Business Development. Welcome to theCUBE. Thanks guys, great to be here. Ben's a CUBE alumni. We saw you, I think last in Manhattan, right? So why don't you give us kind of a quick update on Zoloni? What's been going on since last we saw you? Sure, so we have a lot of news to share. Big thing is that we raised our first round of funding. So that's something that we had discussed last time. Very excited to have Sierra Ventures as the lead investor in our series A round along with Beard Capital out of Chicago. And also along with that, we have expanded our management team. So Tony came on board a month back and also we brought in a few other folks to kind of run different functions. So Tony manages strategy and business development. Congrats. And who's the lead investor at Sierra? Mark Fernandez. Awesome. Congratulations. Not easy to raise money in this kind of the last couple of months. Tony, so what did you see? Why did you decide to jump on board? Oh, that's a loaded question. I guess there's two major reasons. One of them is interesting to me, but maybe not to you guys. It's the people. I mean, the people that are there are extremely solid. I've known several of them for a while. Good, solid people. So that's good. But the other is the technology. I have been in this industry for years and years and years more than I'm gonna admit to right now, but I've only been at Zoloni for a month. So I have seen the industry mature. What I see, and one of the most important things I see in the big data world is that organizations are beginning to mature their big data environments. We've seen a lot of trial sandbox types of things and now we see organizations evolving into production environments. And that requires more rigor, it requires more quality, it requires automation. And these are the things that Zoloni brings to the big data world. And you came in as a business development, Vice President. So kind of what's your charge? You've got some fresh powder in your musket and what are you, what hills are you gonna go take down? Yeah, I think that if you look at the job, then I'm gonna do the things that are most needed at the time. Right now, one of the things we need the most is business development. We need to get our name out. We need to get our brand out. We need to meet more people and have more partners. So that's priority one. Excellent. So tell us about, when you're talking about the market maturing, if we look at it as a, if we look at sort of data lakes and how they're being used as some sort of journey, is there, are there commonalities? Is there a pattern to how you see customers traversing them and sort of where do you see the leaders and the laggards right now? Yeah, I mean, as Tony mentioned, right? So then we see the market maturing and the maturity of the customers in terms of onboarding big data platforms. And one of the things that we see is that they may have gone through a POC or a pilot or whatever, but now as they have use cases that needs to be deployed in production, what are some of the things that they need to care about, right? In terms of governance, data management, data quality. Also elasticity of the platform. I mean, we see a lot of onboarding of cloud platforms along with on-prem type of environments. So how do you now manage a hybrid type of infrastructure where some data may come into the cloud first, some data may be on-prem and you need to have a complete view and automation and management across those different environments? Yeah, these are things that we've been really good at in the legacy world. And we've had years and years, decades to refine that, but we haven't been very good at it in the big data world. So that's the real evolution we see. So what type of value were customers looking at when they were dipping their toe in the water? You know, from the research we conducted both by surveys and then sort of more formally with lots of vendor interviews, over the last couple years the data we had suggested that POCs and pilots extended roughly two years before customers got into production and that's now accelerating rapidly. Were they just getting their feet wet knowing that they needed a big data initiative and without necessarily something that was measurable in the end and has that changed now? I think the way we see it is that customers definitely feel confident with the technology so that it's not so much of like a science project that they're trying to do. And they know that for some of the use cases they have to have these platforms in place. So for example, we're working with one of the large telco operators in Middle East region and what they're trying to do is take the data that is being generated within the telco operators environment from smart phones and other devices and be able to make it available to the smart city so that the smart city can use the data from the telco operator more efficiently to drive their services better for their citizens, right? And they knew from the get go that they couldn't do this kind of use case in a traditional data platform where the data is coming in from network elements and phones and other devices that their subscribers are using. So they had to put in place a big data environment where they could ingest streaming data from switches and routers and all the things that you see in a wireless network along with customer data and other things so that they can find out location density for a given location. They can provide that in a geo-fenced way to the- A congestion. Yeah, they can see how many people are traveling on the highway from point A to point B so that they can provide that information to the smart city based on the data that we are collecting from cell towers, from handheld devices and others, right? So that now the smart city can dynamically control the number of lanes they have in the highway going in one direction from point A to point B versus from point B to point A. From the telco. From the telco operators, yeah, exactly. So those are some of the use cases that we see and that is very exciting to kind of see the maturity of the whole ecosystem where we are able to build those use cases with the proper data management. And how can you accelerate that process for a customer in that situation and then translating that more generally? Yeah, so I mean, whatever we are doing for this particular telco operator is very repeatable for other telco operators because if you think about this, there are probably around 500 different operators in the world, but they all buy equipment from five or six equipment vendors in very specific formats with very specific KPIs that needs to be collected for how their network is behaving and things like that, which are defined by 3GPP standards and things like that. So now you can kind of take something and make it very repeatable and take it to different operators and those operators could now create smart city or IoT type of services that they can provide to a smart city initiative in a repeatable way. Is it the demand for those types of applications that's really driving your opportunities or are people kind of understanding now this different way that I can stitch together my data, open data, proprietary data, cloud data in a new way? Are they really starting to think through some of the application possibilities? Are they still kind of, how can we really do this? I think that we see more and more bona fide business problems. People can do things they couldn't do just a few years ago. The technology's better, the hardware's faster, the software's better. So there are new business problems that are being solved and then when we think about IoT and the just massive amounts of data, that data needs to be managed and we sort of saw the, we saw kind of analytics moving back into the data management, but now as these analytic problems are becoming more to the forefront and organizations are being more successful in solving different business problems, this whole idea about data management and the whole idea about much more rigor in the data environment has come into the forefront. Are you seeing, keying off that telco example, I mean is that something then that is repeatable enough where you and referenceable enough where you would go now target telcos and try and stamp out solutions like that? Yeah, I mean we are already seeing, I mean it manifests in different forms. But we are already seeing where other, we are having discussions with other telcos where they're saying that how can I use all this data coming in from my network equipment and my devices to have a better view of my network. How can I provide better service to my customers? So Smart City could be one of the use cases but then there are other use cases that are internal to these operators themselves because if you think about the competition in this space, it is fierce, right? Telco operators in US may be not as competitive as telco operators outside US, especially in Asia and people are carrying cell phones with two SIM cards so that they have two operators at the same time, right? So how do you retain customers and provide better quality of service to the customer so that they don't switch over to the other operators? So one telco implements your solution to help make a Hadoop solution more compelling and accelerate the time to value. But their competitor sees them do it, they go for the same opportunity. It seems like the arms vendors make money but it's not really, it seems hard to see how the customer gets lasting differentiation. Yeah, so there's always some secret sauce that is very specific to the operator and that is part of the analytics, right? So as you build the analytics, you're not just taking the generic kind of platform but you're adding, customizing it to the things that may be more applicable to you in terms of how you want to provide these products and services to your customers versus the competitor in the telco space. And are you helping them with that customer-specific implementation and if so, who sort of owns that IP afterwards? Yeah, I mean we do in some cases but we also work closely with partners and also some of our customers are also standing up their own data science teams so that they are able to do these things themselves. So I mean telco is just one of the use cases, right? I mean we also, one of the verticals, we also see the same repeatability in healthcare, for example. So we just deployed our solution in one of the healthcare providers in Midwest and they're using it for very generic use cases like population health but like taking in data from their EMR systems and correlating with other data sets so that they can come up with better outcomes for their patients and things like that. So that is again very repeated. And now even peers are getting into that space where they're able to bring in EMR data and correlate it with claims data and other things. Yeah, Ben can give you a fascinating use case in just about every industry sector. So there's new problems being solved every day. Well, it's amazing, you're still relatively small. You just did your A-round and yet your logo slide would do any NASCAR proud of that. I mean you've got automotive, you've got consumer, you've got waste management fly by. I mean, how have you been able to achieve such success so early on and how have you kind of cracked that code? And then again, kind of what's your land and expand once you start to secure some of these really nice names? Sure, so I mean we have been in the space for a while and we have been doing these use cases with different customers for a while and we're very fortunate to be able to do that. And we're very fortunate to have these customers that are referenceable. So what we see is that being able to solve some of the mundane problems of data management and data governance so that you're focused on the business use case kind of helps them from a time to market perspective and accelerates build out of these use cases and being able to deploy these in production. And when you're able to show that kind of model where customers are getting value out of their investments in the big data platform, then that kind of multiplies in terms of use cases and different business units now getting onboarded in the platform. So are you often then kind of a component in a larger project because of the specific functionality that you can roll you can fill? Is that what you've got? We have done both. So we work with a wide variety of our partners where we would bring in, we would work with the Hadoop distribution, we would work with the infrastructure partners. In fact, we are building some very close relationship with a couple of infrastructure vendors that are trying to bring in engineered data lake solutions to their customers. But then in some instances, they want us to do everything including selecting the infrastructure, selecting the Hadoop distribution, bringing in our software for doing the data management and governance and also our professional services team to actually build the use cases on top of it. So they're looking to you to be the prime contractor essentially the solutions provider? Yeah, in some cases because they feel that we can build a trusted partner relationship with them. They can rely on us as a trusted advisor where we can tell them what to do, what not to do based on our experience over the years. And they want to leverage that as they kind of start their big data journey. Now you got Tony. So he's gonna go sign up a censure and Jim and I and all the good players. But there are a lot of good logos and part of it is being first to the game having really some Hadoop expertise. It goes way, way back to the beginning of Hadoop. And that's how you get all those logos is being there for the long haul. But it's more than that. And it's a real credit to what you guys have built because a lot of startups, vendor viability is a big issue for big companies taking chances on cool innovative technology. And even if they dip their toe in the water a little bit if you didn't have your first A round in, they're like love what you got but we're concerned about what's gonna happen next month, next year, no discredit. So the fact that they actually took the risk and made infrastructure investments with you guys is a pretty solid vote of confidence. Yeah, and I mean, as I said, we have been early on in the space and we're very fortunate to have customers like Verizon who are publicly recognizable, right? Where we have multiple instances running in terms of use cases that they're building and deploying in production. Is it the key capability that's attracting customers, the fact that you can provide this lineage and this governance? In other words, when they embrace this new type of data lake instead of the highly curated data warehouse, the critical characteristics is, critical characteristic is, where did this come from? How did I change it? Where does it go? I think that is one of the key pieces but also the other thing is that there are so many point products in the market that do one specific thing but then the customer has to stitch all of that together which sometimes they don't know where to start and how to go about doing it, right? Would an example be that like where you have a lineage for the scope of one tool, but you have a tool chain and they have to custom stitch all that together you could provide an end-to-end backbone. That is correct. So like you may have a framework for doing ingestion. You may have another framework to manage the metadata. You may have another platform or a tool to do data quality. So now as a company, you have to figure out how do you kind of make all of them work with each other, first of all, and then get the end-to-end visibility of the lineage and governance and other things that you need to do out of the platform. Okay. Different use cases prioritize different functionality too. So there's also automation. Automation is real big now. The repeatability of the application is something that we see as organizations move into the production environment. The quality of the data is important, the integrity of the data, the security of the data, and access of the data. All of these are important aspects of it as well as lineage. So one last question on this, not just the repeatability, but is it that you are the backbone for potentially best-of-breed tools plug-in with a narrow scope of governance and that you're the glue? Yeah, I mean, there are a couple of things that we have done as part of our product strategy, right? So we want to, we embrace the Hadoop ecosystem. So we are not trying to build something to replace a Hadoop ecosystem component as long as that component is mature enough and used, can be used in an enterprise environment. And then the other approach is that we are very open in terms of the metadata that we capture, the lineage that we capture to be able to provide that programmatically through a REST API to other tools in the ecosystem so that we can integrate well and create this end-to-end kind of structure of what needs to be in place. And then the additional capabilities that we recently added, I think we announced in theCUBE in New York for this new product we came up with called Myka, which is a self-service layer with a catalog with a very business focus so that our business users can now see what's going on in the data lake, what data exists and the metadata in terms of quality, in terms of lineage, in terms of profile of the data so that then they can do self-service enrichments on that data to create the data models that they need for analytics or visualization and reporting. All right, so Ben, unfortunately we're out of time. We're gonna give you the last word. When we see you in New York in six months from now, what are we gonna be talking about? What's on your short-term roadmap? So we see a lot of customers moving to cloud so we are adding new features. We're releasing Release 4.0 this week in Strata. We're adding a lot of cloud features so we will see more cloud adoption. Then we are also adding features that intelligently manage data in a data lake. Life cycle of the data, retention of the data and things like that. So we are going to be talking about a lot of those things as well as security and governance. So security, policy-based security based on value of the data is something that we are... No shortage of things to do. All right, well thank you very much Ben and Tony for stopping by. Great story, again congratulations on your funding. Thank you. Thanks guys. So I'm Jeff Frick with George Gilbert. If you haven't voted yet, go to siliconangle.tv and vote for Cube Madness. I think we've moved to the final four. It's something that we run every year. It's a lot of fun. We start with 64 Cube interviews and have people vote. Not the best, but which one do they think should move on? Which is open to all kinds of fun interpretation. But we're down to the final four. So go to siliconangle.tv, can't miss the Cube Madness banner and vote. We run it concurrently with the Real March Madness so the finals will be over the next, I think three or four days. All the dates are up there. Again, I'm Jeff Frick. We are live in downtown San Jose, big date a week. It's part of big date SV and Strata Hadoop. We'll be back with our next guest after this short break. Thanks for watching.