 From Orlando, Florida, it's theCUBE, covering .conf18, brought to you by Splunk. Hello everybody, welcome to Orlando. This is theCUBE, the leader in live tech coverage and we're here at splunkconf.conf 2018. The hashtag is SplunkConf18. My name is Dave Vellante, I'm here with my co-host, Stu Miniman, Stu, it's great to be in Orlando again. Last year we were in D.C. This is our seventh year covering Splunk.conf and we've seen the company really move from essentially analyzing log files on-prem in a perpetual license model to now a company that is permeating all of IT into the lines of business, security, IT performance, application performance, moving into IoT, really becoming a mature company. It's a company with $1.7 billion in revenue forecast for this year. We're talking about a $17 billion market cap, they're growing at 36% and they're a company, Stu, that is in the process of successfully going from a perpetual license model to a renewable model. Splunk set the goal of being 75% renewable by 2020. Sounds like renewable energy, but repeatable renewable from a subscription standpoint, they're already there. So you're seeing that in the execution. This is your first .conf or conf, as they like to say. You were at the ESPN Wide World of Sports Center. You saw what, what's the number, 8,000 people? Yeah, I think 8,000 at the show this year. It's a bit of a strong growth and Dave, I've been hearing from the team for years the excitement of the show, the passion of the show, saw like right over near where we were sitting, there's the whole group of, you know, that was the Splunk trust they've got, the Fes is on, a lot of them have, you know, superhero capes on, and you know, it's what you expect from a passionate, you know, technical, maybe even say geeky audience that things like, you know, we're announcing the S3 API compatible storage and everybody's like, yay, we're so excited for this. It's, you know, hardcore techies. Well, what was the other big, big, big clap? Screen? Yeah, yeah, that's right, dark mode. We're going to go to dark mode. I don't have to play with the CSS. I mean, anybody that's a bit played with a website, it's, you know, changing these things is not trivial, but yeah, right, I click a little button and right, the joke was, you know, this is the kind of bright one for the executives, but when I'm down in the gamer center, you know, I don't want this glaring screen here, so I can switch it over to dark mode and yeah, people were pretty excited about that. So again, the roots of Splunk, they took log data and analyzed it. You know, Doug Merritt, the CEO, talked today, talked about making things happen with data, and I thought he did a really good job of laying out sort of the past, putting the past behind us in terms of, he said, I've been to, I can't tell you how many master data management classes, trying to optimize the database, trying to codify business processes and harden those business processes. The problem is data is messy and data is growing so fast, business processes are changing so fast, the competition is moving so fast, customers are changing. So you have to be able to organize your data in the moment. So, you know, the whole idea that, even go back to the early big data days in Hadoop, the whole idea was to bring five megabytes of compute to a petabyte of data and no schema on write or what some call schema on read. And Splunk was really part of that, put the data, you know, get the data organized in a way that you can look at it in a moment, but then let the data flow. And so that has definite implications in terms of how you think about data. It's not trying to get the data all perfect so you can use it, it's trying to get the data into your data ocean, as we like to say, and then have the tooling to be able to analyze it very, very quickly. They announced Splunk 7.2 today, which is a big deal, some things like, we'll talk about a few of the features, obviously focused on performance, but one of the things they talked about was basically being able to split storage and compute. So previously you had to add essentially a brick of storage and compute simultaneously. We've heard about these complaints for years in the converged infrastructure space. It's obviously a problem in the software space as well. Now customers are able to add storage or compute in a granular fashion and they're cozying up to Amazon doing S3 compatible store. Yeah, I mean Dave, I love that message that he put out there. You said life is messy. You can't try to control the chaos. You want to be able to ride those waves of data, take advantage of them, not overly make things rigid with structure because once you put things in place, you're going to get new data or something else going to come along and your structure is going to be blown away. So when you need to search things, you want to be able to look at them at that point in time but be able to ride those waves, flow with the data, live the way your data lives. And that's definitely something that resonates in this community. And Dave, something I've, watching this space as an infrastructure guy and watching the cloud movement, there were a lot of reasons why traditional big data failed and I kind of never looked at Splunk like most of those other big data companies. Yes, they had data. Yes, they're part of the movement of taking advantage of data but they weren't, oh, well, we have this one tool that we're going to create to do it all. It's like some of the Duke players. They're playing with all the latest things. You want TensorFlow, you want to do with the AI, the ML, Splunk is ready to take advantage of all of these new waves of technologies and they've done a couple of acquisitions like VictorOps in the space that they keep growing and the goal as you mentioned the revenue but Splunk today has, I think it's 16,000 customers. They have a short-term goal of getting to 20,000 but with what they started talking about in the keynote today, Splunk next, they really want to be able to do an order of magnitude more customers and when you get great customer examples like Carnival Cruises, the CEO, I thought, talked about the sea of data, lots of good puns in the keynote there but mobile cities floating around and lots of data that they want to be able to get the customer experience and make sure the customer gets what they need and make sure that Carnival knows what they have to make sure that they're running better and optimizing their business too. So, great example, looking forward to talking to them on theCUBE. Well, and they have many dozens, I think it's in last quarter, it was like 60 plus deals over a million dollars, they have many 10 million dollar plus deals. That's an outcome of happy customers. It's not like they're trying to engineer those deals, I'm sure some of the sales guys would love to do that but that's a metric that I think was popularized by the likes of Anil Boucheray at Workday, certainly Frank Slutman at ServiceNow. It's one that Wall Street watches and Splunk, it's an indicator, Splunk is doing some very, very large deals that underscores the commitment that many customers are making to Splunk. Having said that, there are many more that are still smaller users of Splunk. There's a lot of upside here and they're going into a serious TAM expansion. That's something that we're going to talk to to Doug Merritt about making acquisitions of a company, Victor Offs is their most recent acquisition of security, orchestration and management. They're doing, the ecosystem is growing, they're doing bigger deals or partnerships with the likes of Accenture, Deloitte is here, EY. Accenture actually has a huge space at this event and those are indicators. I want to go back to something you said earlier about the failure of big data. Certainly big data failed to live up to the hype in many ways. You didn't see a lot of wholesale replacement of traditional databases and EDWs. You did see a reduction in cost. That was the big deal. But clearly enterprise data warehouses and ETL, they're still a fundamental part of people's data strategies despite what Doug Merritt's saying, hey, just the data is messy and you just got to let it flow. Essentially what he's saying. There is still need for structured data and mixing sort of interacting structured and unstructured data, bringing transaction data and systems of intelligence together, analytic data. But the one thing that big data did do in the Hadoop movement, it did a couple of things. One is architecturally it pushed data out and back in the day you had to get a big UNIX box and stuff everything in there. It was your God box of data and you had Oracle licenses and sun microsystems boxes and it was very expensive and you had a couple of people who knew how to get the data out. So the goal of democratizing data, what it did is it is messy. Data went out to the distributed nodes and now the edge, but it brought attention to the importance of data and the whole bromide of data driven companies. And so now we're in a position to make a new promise and that promise is AI, machine learning, machine intelligence, which seems to be substantive. We talk a lot on theCUBE, is this old wine, new bottle and we had an event in New York last month and the consensus from a lot of practitioners and others in the room was, no, there's something substantive here. The data substrate is now in place. Now it's all about taking advantage of it. Tooling is still complex but emerging or evolving and I think the cloud to your point is a huge part of that. By integrating data pipelines in the cloud it dramatically simplifies the deployment model and the complexity of managing big data. Yeah, Dave, Dave, as you said, used to be you had these giant boxes and some of these initiatives, I needed 18 months, millions of dollars and a large time you either need to be a country or a multinational company to be able to put this thing together. I remember one of the earliest case studies that David Fleuer did when we were looking at big data was how do I take that 18 month deployment and drive it down to more like a six week deployment and when you talk about AI, ML and deep learning the promise is that a business user should be able to get answers in a much, much shorter window. So actionable on that data, be able to do things with it, not just looking backwards but you hear the teams. I want to be able to be proactive. I want to be able to be responsive. I want to even really predict what my client is going to need and be ready for it. So as Doug Merritt said, the digital and physical worlds, they're coming together, they don't stop evolving, they're organic, your data model has to be flexible. It's a sea of data, it's an ocean of data, it's not a confined data lake, as John Furrier and others like to say. And so I was happy to hear Doug Merritt talking about a sea, we use the term oceans because that's really what it is. And oceans are unpredictable, they're sometimes really harsh, they can sometimes be messy, but they're constantly evolving. And so I think that kind of metaphor works in this world of Splunk. We got two days here of coverage, a lot of customers coming on today. In fact, Splunk is one of those companies that puts many customers on the cube, which we love, we love to dig into the case studies. We've got some ecosystem partners, some of the big SIs are coming on. And of course, we're going to hear from some of the product people at Splunk that go to market people, Doug Merritt will be on tomorrow. And a number of folks, I'm Dave Vellante, at Dave Vellante on Twitter, he's at Stu, Stu Miniman. Keep it right there, everybody, we'll be back with our next guest right after this short break. You're watching day one from Splunk Conf 18 in Orlando. Right back.