>> Live from Orlando, Florida, it's theCUBE. Covering .conf2018 brought to you by Splunk. >> Welcome back to Splunk's .conf2018. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host Stu Miniman. We're here in Orlando. Day one of two days of wall to wall coverage, this is our seventh year doing Splunk .conf, Stu amazing show, a lot of action, partnership is growing, ecosystem is growing. And we're going to to talk to one ecosystem partner, Gemini Data. J.R. Murray's here as the vice president of technical services. Welcome to theCUBE, thanks for coming on. >> Happy to be here. >> Yeah so when we first started this, Splunk ecosystem was really tiny and it's just sort of growing and growing and now is exploding. But tell us about Gemini Data what are you guys all about. What's your role? >> Sure, so my role is VP of technical services. I manage our sales engineers and professional services consultants as well as our managers services practice, based in the United States. So what I do is I go through and help make sure all the operations go pretty smoothly. And in terms of the company and what we do we've got a couple different things that we work on. Primarily our focus is around big data platforms and making them easier to deploy and manage. We offer a hardware appliances as part of that package and we also have an investigate software platform that we feed data into and it helps analysts jobs be a little bit more easier and quicker to do investigations. >> And you guys started the company three and a half, four years ago, is that right? >> That's right, that's right. >> Back when big data was and kind of still is a mess. >> That's right. >> Doug even said that in his conversations today. He said that we live in a world filled with change. The messiest landscape is the data. >> That's right. >> The bigger, the faster, the more complex the data, the messier it is. So you guys kind of started to solve a problem. Why did you start the company? What was the problem you were trying to solve? >> So really where we started is we focused on there's a problem with deploying big data platforms, customers have poor experiences in terms of it's too complicated, there are a lot of very technical details you have to worry about. And if you're a little bit lower on the maturity curve of technology solution implementation you might need some help along the way or if you are a little bit further along in the technical maturity curve you may actually need some help in getting something that's more turn-key in order to alleviate a lot of the challenges that go along with IT bureaucracy. You've got maybe something that you need that's purpose built because you've got something that's very central to your security strategy. You need to make sure that it's up and running, and reliable, and dependable. So that's where we come in. We have a platform that we allow you to implement. It's a turn-key solution, multiple systems get your Splunk deployment up and running. >> And when you do that on your website looking at, you support various technologies, I see Splunk on there, FireEye, Cloud Era, Service Now, Amazon, Azure, so those are sort of systems, RSA. I mean they've got a lot of products and a lot of cases it's cloud or, they've got a platform like Splunk. Will you actually do like bottoms up stuff with Hadoop and pig and hive or are you really focused on sort of that higher level helping customers integrate those platforms that they brought in. >> Right. >> Kind of helping them be a platform of platforms if you will, is it the former or the latter? >> Yeah so that's kind of the idea right? We come in and we go through and we say what are your actual goals here do you just want to go through and install Splunk or do you actually have a big data strategy that we can help you execute on. So it's kind of a cohesive holistic approach in terms of, what you need to deploy and how we help you get there. So if you need to deploy Splunk we help you install Splunk. If you want to do Splunk and have a Hadoop data role for example you can have hadub just alongside your Splunk all on the same platform. You can go through and manage that centrally and make it a little bit easier to manage via policy push out jobs centrally all the automation and orchestration is there and the under pendings for all those solutions. >> Yeah J.R. who who are you typically selling to? One of the things we look at data is pervasive in the company in companies but who owns it, I've talked to a number of people at this company that are like well I've got Splunk and everybody comes and asks me questions right now. So where do you fit in in the organization? >> So we've got a few different things going on. So in terms of who we sell to and where we focus, its kind of across the board we've got very large enterprises who are pushing tens of terabytes into the deployment, and we help them out with getting a solution that's going to be something that's a little bit more manageable. You've got a limited staff, the knowledge of Splunk is hard to hard to actually cultivate and then actually keep and retain folks that know Splunk. They are generally very well paid. So its easy for them to find opportunities elsewhere. You've invested a lot in these people, your success is very critical and they're a critical part of it. And it's important to keep those people around. So we've got a manage service to help with customers like that. We call it Gemini Care. We come in and we are actually able to have an automated monitoring and break fix type of resolution service that factors into those types of deployments. And as part of that we go through and offer some services and touch points throughout the month to make sure they're getting what they need from a value standpoint. I mean its one thing to have the platform and the deployment, and the data but in fact if you're not getting any value out of that what good is it? So if you don't have the talent the skills you're able to go through it and use us to implement some of those used cases and things like that. >> Yeah yeah one of the other things that changed a lot in the last 3, 4 years is the on the premises of course is where a lot of the customers are and a lot of data is but partner with the cloud, you partner with the Ager's and Amazon's in the world even if you start talking about edge that diversity of where my data lives. How how is that playing into your solution? >> So it's funny you mention that we came to arka we led with and applied base solution and we said customers that are having problems either getting hardware common thing is you want to put a box in or 10 or 20 boxes but you've got the storage team saying hey we need to hook up to our our sand we spent millions of dollars on this, we're going to get some use out of it and guess what Splunk you're going to be our biggest consumer of all of our storage internally on this brand new sand we got. A lot of times its not attractive to a lot of interim customers. You've got IOPS requirements, you've got all these other requirements. Folks don't understand you've got hard requirements for CPU's and and the band width there. So if you're using virtual solutions which a lot of customers are forced into doing you actually have a very difficult time getting reserved resources on those virtual hosts. So you get a bare metal box in there, you get a platform on it you have none of those issues. So in terms of where we pivoted from there the industry is obviously going towards cloud. So what we're trying to do is actually, we have a solution in the market today. Customers are really interested in us helping them on that journey so we've got plenty of customers who are on premise today they have a cloud strategy they want to get out of the data center business and they need to get into cloud. So what we're doing is we're helping them we've got equipment who in a code located data center and what we're doing is migrating customers over to that infrastructure as more of a subscription basis. So it's the same platform but now it's in the cloud. There are benefits to that. >> So I want to I want to actually let me follow up now, so the subscription basis >> Right. How does that work? So it used to be what sort of an upfront perpetual license and then here you go and then we'll you when there's another upgrade. >> Right >> And now how's it work I know 75% last quarter of Splunk's bookings or revenue I'm not sure which one. Were subscription based irratible and there was a big long discussion about whatever it was 606 and all the Wall Street guys trying to part through it. What does it mean for the customer? What does that transition like? >> Okay >> Is it like hey good news. >> Right >> We're not going to go through the spike cycles we're going to smooth things out for you. But what's that conversation like? >> We've got a lot of flexibility with customers. We've got the ability to do OPX or CAPX, we've got the ability to ship as an appliance kind of as an all in one solution. However what we've really migrated to as what the market has demanded is customer feedback. Is, "hey we can buy this box anywhere" and we're like, "you know what you're right. If you want to go right ahead here's the software subscription. So now we have the option to sell the appliance and the software subscription together as one package that's also partially subscription but what happens when you migrate that into the cloud, is now you've got a cloud based subscription infrastructure and that software license is sort of included in that. >> I want to ask you about use cases. You were talking a little bit before but if you pre go back before the term big data came to fruition, you kind of had the EDW was the so called data big data used case and you had maybe a couple of analysts that knew the decision support systems and could build a cube and they were like the data gods. So big data comes in and you had used cases like a cheaper EDW that was kind of a really popular one. Certainly fraud detection was one, precision marketing, ad serving, obviously Splunk and the security and IT operations base although Splunk never really used the term big data so its only sort of more recent and line of business analytics. So you see all these sort of new uses for data very complex as you pointed out. You guys started the company to sort of help squint through some of that complexity and actually build solutions. So the brief history of big data by Dave Vellante. So given all that how has your customers use of data changed over the last since you guys have started and where do you see it going? >> So we originally started, originally we had some customers that came over into this new business venture existing relationships and what not they were using a different sim platform. You one of our primary objectives were to was to get them all in to Splunk and that's something that we were able to do successfully. So they were doing security analysis, log retention, those were their primary goals and that's it. Maybe compliance, okay. So their really focusing on that. Now today we're doing entirely different things. We're focusing on as you mentioned anti-fraud. Huge opportunity in the space there with Splunk the tools in that space today are prohibitively expensive, very complex and we come in with Splunk we're able to take in data from all sorts of places and technologies really know really know understanding of the data at that point required yet and then we convert that into business value for the customer by means of services. Because there's very little in the way of precan used cases for that and frankly when it comes to the fraud space a lot of customers their requirements are all different. There aren't really many shops that are very much alike at all. So you've got to sort of manage around that. Now that's one way but we're also seeing folks who want to do executive reporting out of their Splunk data. You're talking about being able to go through and do year to year reporting how are we doing from a risk management standpoint. These are the things you are starting to see trickle up to the Csuite in terms of what does that mean for us and the way we need to make these business decisions. >> So I understand that. So really started out kind of hard core IT and certainly security used cases. What I'm hearing is Splunk is expanding into lines of business actually using data in in ways that perhaps others were trying to do in the past but not really succeeding. >> That's right >> What is it about Splunk that allows you to do that. We heard a lot about 7dot2 today, performance improvements, some efficiency in your granular storage and compute. I'm sure that Csuite doesn't know or care about that but being able to analyze more data is something that they probably would care about, mobile is probably something that they care about. >> Absolutely. So what is that Splunk's doing that maybe others aren't doing or can't do, architecturally or technology wise? >> Now a couple things stand out right off the top. So you've got the ability to scale, you've got horizontal distribution of data which means you can spread that load across many many nodes. We're able to go through and distribute that load and it makes things actually perform. So we get an acceptable user experience and that means everything to a customer, right? So that's one thing. The second thing with Splunk you've skemead read you're able to pull in as much data as you want for as long as you want without having to understand that data. You can actually come back through later and and parse, interpret, report on, and get value out of that data historically without having to necessarily having to understand it upfront. That's in my personal experience been a huge impediment right up front to onboarding data with other we'll call them legacy solutions. But there still some in the market today that require and depend on that is knowing the data upfront. We can't pull in this data unless we know exactly what its supposed to look like and can sanitize it and parse it into fields. >> So Stu I want to follow up if I may. So a lot of people in the big data world talk about no scheme on write or scheme on read >> Sure >> And what they do is they toss everything into a data lake. The big joke is the lake becomes a swamp, they got to go and clean it up. Why is that not the case with Splunk? What's different about Splunk and that they're able to, I forget exactly how Doug said it but essentially structure the data when you need it. >> That's right >> In the moment >> So the difference with Splunk is that you're able to you're able to foster and really pull together the community resources more or less crowdsourcing how to parse all these data sources. You no longer have individuals at every given company with a very specific data source say Windows event logs that might be universal to many other applications and organizations, needing to roll their own. So you're able to socialize and share those things on a place like Splunk base and then suddenly everyone's able to really capitalize on the data, so I see that as more like a force multiplier. You've got the entire community behind you helping you parse your data because they have the same data and that's really what I think makes the difference. >> Whereas the so called data lake would be like the big data metaphor for a god box where only a few people know how to get to the data, right? >> Basically yeah, thats right? And the amount of skill required, okay, that's another big piece when you're in Splunk everything is very well documented so if you need to write a search and its there are plenty of resources you've got the Splunk community, you've also got all of the documentation, you've got the quick reference sheets. Its not hard to get into its hard to become an expert but if you just need to do something very quickly it's not that difficult. >> Well if we look at where Splunk is going next you talk a lot about the AI and the ML and one of the tensions you hear out there is, "how much am I willing to let the system just take that action?" So I'm curious on your product line and working with Splunk what you hear how real people are, the advances that we're getting with AI, ML and deep learning and are users ready to embrace that yet? >> Yeah so that's a technology that's truly made leaps and bounds even over the past five years. Right. So what we're seeing is customers are able to use machine learning to go through and do predictive analytics and to be able to have the machines to sort of speculate as to and you can say predict but its really I think speculation more like what a given categorical value might be. Is it yes or no, maybe for the answer to a question based on what those events say, or is it is there an outage coming up that potentially you could predict based on different values. And there all sorts of applications for that and all sorts of platforms that are trying to do that. Now what Splunk's done is sort of bring that to the masses with machine learning toolkit and made that a little bit easier to really digest for the common person. What they haven't done at least until very recently from what my understanding is that they're doing is that they're actually taking more of that function out and making it more intuitive helping customers understand the most common challenges I'll say. So you're really lowering the bar in terms of the amount of information or knowledge rather and skills to be able to leverage some of these more advanced algorithms and computing resources to go through and get the types of results you expect out of machine learning. >> Well J.R. Murray thanks so much for coming to theCUBE. Really appreciate your time. >> Pleasure. Thank you >> Great to meet you. Alright everybody keep it right there Stu and I will be back with our next guest. You're watching theCUBE from Splunk .Conf18 in Orlando. We'll be right back.