 Okay, we are back here live at the Fluent Conference in San Francisco, California in the heart of tech, tech geeks and the alpha developers here. This is SiliconANGLE's exclusive coverage of Fluent Conference. This is theCUBE, our flagship program and we'll go out to the advanced trick to see the noise. Our next guest is Paul Sand for GM Products, developer platforms for Splunk. Welcome to theCUBE. Thanks, great to be here. Splunk, obviously we love you guys, been following you guys since you were started up when public. We did the dot conference in Vegas last year. We'll be doing it again according to Jeff and looking forward. You guys really built an amazing technology. Love the backstory, which is on SiliconANGLE, but essentially you guys had a great product and then your use case kind of testing became the product and big data exploded and huge traction, growth, big vibrant community. You went public. So big data is obviously the central theme to the marketplace, but for developers in DevOps, they use cloud, they want to use big data. Big data is now part of the development process. I wrote a post in 2008 saying data will be the development kid of the future and it has arrived. So talk about that. How is data now part of the developer environment as a raw material? Well, so it just depends on what kind of data you're working with and what we're doing with our developer platform efforts is to make it as easy as possible for Splunk developers or any developer who has knowledge of standard technologies, whether that's languages, frameworks or querying capabilities for them to hook in and build applications on top of our data store. And we see the use of Splunk in DevOps, you mentioned DevOps as a scenario, we see that all the time. In fact, Splunk is such a great product where you can just take it, download it for free, put it on your laptop and start to put your application logs inside of Splunk. And we see developers and test organizations using Splunk today for that very purpose, to accelerate the throughput on their development cycles. So increased velocity by having QA people and test people use Splunk to find out how the applications are behaving and performing. So then bridging that with IT environments as well. So you guys offer a REST API, JSON, all these formats for developers, which is normal stuff for any JavaScript guys. So the key thing that's changing is the access to low latency real-time information. So mobility, you got Geo data, plus apps are throwing off data. Which you guys built a business on. Now it's going back the other way. So you guys see tying that two together. What's the core offering that you offer developers to kind of bring that and kind of connect them together? Right, well Splunk has always been optimized around low latency high throughput scenarios. So what we're doing is we have a REST API, as you mentioned, JSON as an option. We have nearly 200 endpoints now on that REST API. Anything that you can do via the web UI that we ship with the product, you can do programmatically via the REST API. So that's foundational. We have to have an API strategy if you want to be able to interface with developers. Second thing that we're seeing is we're building out a layer of abstraction on top of that with SDKs. So we have a set of six SDKs today. And when I first initially said we have to have a comprehensive set of SDKs across static, dynamic and client side technologies, people said that's too many. But the fact is that there are myriad languages that are being used to build applications today and developers want to hook into a language based on the problem they're trying to solve and we're meeting that need for them. And then we're also building frameworks using standard JavaScript technologies like Backbone, like jQuery, require to make it even that much easier to build applications on top of Sploom. So Paul, for the folks that weren't here for your last session, tell them a little bit at home what you guys covered, what was kind of the key topics in the session that you just finished. Yeah, it essentially boils down to a simple point of to do big data development or any kind of data development you should be able to hook into the technologies that you already are using. And to get leverage on that investment. So we showed how you can Splunk Minecraft data. And so in our office in Seattle, we're Splunking Minecraft. So it's been a little bit of a time sink for some of my engineers, which I was at first. Keeps them motivated to work. Keeps them at the office a little bit later. Yeah, so I just had a directed energy into demoware, which has been a lot of fun. So Splunk, historically, as you know, has been very strong with log data. I like to say Splunk was big data before big data became big data. Because log data demonstrates all of the characteristics of big data. But you can put any kind of data you want into Splunk. And what they did is they took the Minecraft activity, put it into Splunk, hooked it in with a Minecraft visualization technology and built some very interesting views of the world where you can look at real time where people are going in the world and see what's happening. So we talked about that in the session. We also just talked about how you can have some interesting 3D visualizations, looking at FAA, airport delay data, and hooking into external APIs as well, all from within this Splunk development environment. It's definitely a geek show when the demos are constantly games. We had the shoot them up game and the keynote and you guys getting into it. What are you guys going to do next? Do Bitcoin manage all the Bitcoin data? Well, Splunk can take any kind of data. So if it throws off, if it has an API or if it throws off any kind of data, we can Splunk it. So Splunk is a verb. You should definitely Splunk it. What is the craziest, most coolest thing you've ever seen? Crazy in a good way. Most amazing thing you've seen people using Splunk for. Yeah, I think it comes down to one use case for one of our SDKs where a healthcare company was Splunking pacemakers, which was tremendous. Splunking pacemakers. Splunking pacemakers, yeah. So they were taking the data on the health of the device and putting that into Splunk via our Java SDK. So they could get better insight into how that device is performing so they could build a better device. So ultimately saving lives. So that one I thought was great. Splunk saving lives, you know. That's a real practical example. People always talk about techniques, the visualization of data. That's all great, especially for the developers here, but like, big data has transformed the tech alpha geek circles to real world. And the pacemaker's won. Any other examples that you guys have use cases of real world examples of big data kicking butt out there? Well yeah, I mean, it can be very prosaic too. I mean, what we see as a fundamental tenant that we have is you don't have to be a data scientist in order to derive the benefits from big data. And what we see a lot among our enterprise customers is customer service agents using Splunk to find out what's happening with a particular device, let's say. One of our larger customers is Splunking their next generation DVRs so that in providing that data back to their customer service rep. So they can get proactive information prior to the customer even knowing there's a problem with the DVR. They can troubleshoot it. Or if they're on the phone with a customer working real time to more quickly solve that question. Paul, the developer questions are interesting because obviously it's a whole brave new world. I mean, you got analytics, converging in the data, you got server side, Node.js has really leveled, created a lot of headroom for developers, especially on server side. Not just a front end tool anymore. So some real software engineering chops going into this market of front end guys. I'm going to call it that. It's full stack. I mean, you can do full stack and job is good now. Yeah, so that's going on great. So what do you guys see as the challenges right now for developers? So right here we've been hearing about developers, really excited, but we're trying to tease out what are the challenges, hence the opportunities for the folks that are interested in fluent the conference and the tools and the tooling and the platform stuff that's going on in the developer community, from IDE support all the way into other things. Yeah, I mean, I think with the intersection of big data and existing tools that are already out there, it is challenging to even just filter signal through all the noise, right? So what we're doing is trying to invest in different ways to plug in. So let's say, if there's already an IDE that exists, let's say for Java or .NET, we're going to provide plug-in capabilities for that. If it's something where you need to do development in a browser, we're going to provide that capability through tooling that we offer in a way that allows for development across multiple languages, but then even bringing together disparate data sets. So that's a challenge where I need to have some data in a relational data store, I need to have some kind of data in a no-SQL big data store. How do I bring that all together into one coherent whole? That's a challenge, not to get too much on a vendor pitch, but it's a native capabilities via our search language to really bring together disparate data sets. I know Jeff wants to ask a question. I always try to monopolize the conversation that gets geeky like this, especially on big data. But machine learning's not being talked about much here. You guys evolved a lot of machine learning, obviously with Splunk, I'm just imagining right what's going on. What's the status of the machine learning state of the business right now? Because there's a lot of machine learning and semantics that goes on underneath the covers to be Splunked. You guys work on, how is that going to evolve and how is that going to enable developers? Can you shed any insight on machine learning trend? It's interesting, definitely. I think that one aspect is looking at being able to predict future behavior or future outcomes. So predictive analytics is something that is an interesting aspect that you can learn from machine data and then derive some scenarios and algorithms around what could potentially happen in the future. That gets to a core value prop that we have in IT organizations where we start to see some of those scenarios around what could potentially happen with certain service thresholds in your environment. But then even taking- Is it mature? I mean, put an elementary school, middle school, high school, where's the machine learning state of the art right now? I think it's early days still. I think it's early days. I think that- I'll say kindergarten. Yeah, yeah. I mean, I think you have to, I think you have to look at applications of it, which is why I just mentioned predictive. Because I think that we've all seen it where you talk about just sort of the base underlying technology, but you really need to focus on the use cases and the applications and customer benefit. Yeah. So you even say predictive, probably even toddler stage, crawling. Yeah. Not even standing out yet. Yeah, not quite solid foods yet. Yeah. But I think very interesting in something that will warrant attention in the future. So Paul, the Twitterverse is lighting up. They want to know where is the Minecraft visualization? Everybody wants access to the visualization. So you've got to have the guys in Seattle. It's in the Seattle office right now. They got to open that up to the people. Okay, well, we can definitely do that, but feel free to stop by the Splunk Seattle office anytime. And we have beverages and you can see it there, but we'll get it on the internet. Okay, so just a follow-up question. I love that Splunk was big data before big data was big data. The next kind of iteration of that as we talked briefly is kind of the industrial internet. What are you guys seeing with your customer base and uses of Splunk as there's more and more, and actually even mobile. I mean, yes, they push things out, but more importantly, now there's remote sensors all over the place feeding data back into the applications. What are you seeing in some of the early adoption of kind of industrial internet applications as Splunk? Right, well, so when I was back at Microsoft, I worked on an RFID product actually, and that was many years ago. It was perhaps a little bit ahead of its time, but you're right about sensors and capturing data. That now is becoming more mainstream. And I'm keying off of industrial because we do have a number of customers who are taking the data from sensors that are available all over the place and being able to look at some interesting in visualizations like in SCADA scenarios, where you can start to look at the topology of particular locations and start to troubleshoot environmental issues in some kind of physical infrastructure. That's very interesting. So it's a bridging of the digital and real-world tangible problems that can be solved. That's interesting. And then with mobile, socialize, have to give a quick mention of socialize. They're one of the startups that we work with, and they're enabling customers to provide analytics on inclusion in mobile applications and social applications. So we see a lot of use around that, we have a vibrant startup community targeting Splunk as a platform and providing value-add analytics to people who use their cloud-based services. So talk a little bit about that. So I think that some of the really interesting things about the times in which we live is people build great companies, but then they use those companies as a platform A for an active development community to expand innovation and new things and breadth of the application outside of the core four walls. And then the other thing is you get people actually building businesses on your application as a platform, which obviously, I think AWS is probably the poster child also in Seattle, I don't know if they come over and look at the visualizations. So talk a little bit about as a company and working with those communities, what that means and how that changes your responsibility to those and then how does that really help the company grow? Well it does, it definitely changes the responsibility. We talk a lot and think a lot about how we can have stability in our APIs, in our interfaces, but still push the innovation curve and derive value. So you have to think about it from that standpoint in terms of people start to build a business on your underlying platform. You do have to think about, you know, maintainability and all of that, which is why we have versioning of our API, which is very important. But the thing that is very interesting that I liked from a product, from an engineering standpoint is once you bootstrap and enable that external community, it creates a very positive self-reinforcing feedback loop, where you can get a lot of great data back from the community, because if something is going wrong, if their needs aren't being met, you'll hear about it, which is wonderful, which is why I love working with developers, because you get that feedback loop going, that allows us to further invest and create better product at a platform level so that they can continue to build businesses and, you know, for our customers, for anybody who, you know, purchases is blunt directly or indirectly through a partner, it just leads to better product all around and a better overall experience when you have that vibrant ecosystem. Yeah, I think it's such an interesting part. You're kind of the open source philosophy, if you will, as opposed to free software. There is this immediate feedback loop, and it's kind of the no BS zone, if I could steal a different O'Reilly's quote, because you hear back from the community, and it does reinforce this quick development, quick advancement, quick attacking of problems. That's right, exactly, and that's why all of our developer assets are up on GitHub, and we've embraced that. I've been at the Seattle office for two years since it opened, we've been from our initial onset, we focused on GitHub and being open source and very transparent. Well, my final question is obviously machine to machine data, user to app data, app to machine, a lot of different dimensions to data. So how do you guys look at that, and what's next for Splunk in terms of your product roadmap? Obviously, an ecosystem of developers, you guys are enabling the foundation, and then with the SDKs that build on top of it. So you're going to be compatible in different environments, let those environments define themselves. The API is really the bedrock, but as the core platform of Splunk, you guys have to look at the different touch points, machine to machine, machine to app, app to machine, user to user. I mean, there's a lot of different exhaust out there. How do you look at that from a product perspective? What's next? Well, in terms of what's next, I highly recommend you could, you'll hear about it at user conference end of September in Las Vegas, and we'll have all kinds of forward-looking statements there around our products. So, I highly recommend. You've been media trained. Damn, we couldn't get you to break anything on theCUBE. I can't do that today, but when we're ready to do a user conference, we'll hear about it first. We'll be there. We'll be there. That's how we look at Florida. Okay, Paul Sanford with Splunk, great company, these guys have done amazing things. Great example of digging in, building a great opportunity, great platform, great technology, hit the big data wave, and just never turn back, congratulations, and looks like you're doing some great things with the developers and the great foundation. Okay, this is theCUBE. Just look at Ankle's flagship program, go out to the events, instruct the students on the noise, be right back with our next guest after the short break.