 We're back. This is Dave Vellante. We're here in Las Vegas live. We're at .conf, the Splunk user conference. This is the second year for theCUBE at .conf. theCUBE is a live mobile studio. We go out to the events. We extract the signal from the noise. Sanjay Mehta is here. He's the vice president of product marketing at Splunk. He's joined by Manish Kalra, who's the director of product marketing. The big news at the show, one of the pieces of the big news is Splunk 6. It's really targeted at the enterprise. We're going to talk about Splunk 6. We've got a demo. Gentlemen, welcome to theCUBE. Hey, thank you. Thank you. So Sanjay, let's start with you. What are the goals and objectives of Splunk 6? Give us the background. Yeah, so our mission statement at Splunk is, and by the way, thanks for having us and it's really great having you here at the event. Our mission statement for Splunk is to make machine data accessible, usable, and valuable to everyone. And so we are constantly progressing on that mission. And Splunk 6 is really all about how do you take data and put it in the hands of everyone? And so what we've done is we've basically delivered three breakthrough innovations in this product. And together, they are game-changing. And that's not my words. That's the words of our customers. That's from our customers when we've interviewed them and we've spoken to them. They really genuinely believe that these are game-changing features. What the features are are firstly, the pivot interface. And what that does is it really provides the ability to analyze data without writing or learning a query language. Secondly, we have the data model. Again, a concept that's made perhaps well understood. We've implemented it entirely differently. For those of you who know Splunk, it's a late-binding approach. It means it's very, very flexible and essentially enables you to add meaning to underlying raw machine data. Underneath all of that, we have a patent-pending technology called a high-performance analytics store. What that does is it improves the performance of analytics kinds of operations by up to a thousand times. And so we're really excited about what this can deliver for our customers. There's a lot more in the release, by the way, but those are the three things that we're leading with. So this is the holy grail of, I've said, big data. Being able to put analytics in the hands of not just the power analysts, not just the technology geeks, but the everyday line of business users, the CMO, the head of logistics, the salespeople, and the like. And a lot of people are trying to go for it. So you've described sort of three capabilities. It's sort of fast, easy, and flexible. It's really what these deliver. And if you don't have any one of those, you're not going to succeed. Talk about the types of people that are going to be accessing this platform. Yeah, so Splunk's very well used in IT today and by security teams. They'll use Splunk to secure their infrastructure. They'll use it to keep systems running, to troubleshoot issues, and to gain analytics from all of their systems. Now, what we've seen over the last few years is that these analytics are all the insights that are available from machine data are really valuable to business users. If you've got a senior title, to like a CEO or CIO, to lines of business users, because they contain a categorical record of all activity and behavior of customers, of users, of transactions, of all these kinds of things. So we've seen this migration of people in IT taking this information and putting it on the desk of the C-level person or the business user. Now, they're consuming that data. They're looking at it on a dashboard. It's real time. They are looking at reports. They're sent to them via PDF. They can use it on their, look at it on their iPads, iPhones, and on their laptops. Now, what we've done with Splunk 6 is we've enabled them not just to consume and look at the data, but to interact and analyze the data. You'll see that in the demo in a second. And what that means is we're really empowering self-service. So we're enabling them to look at that information, to analyze it and to get insights that are genuinely game-chart. I mean, they help them get a much richer sense of their digital services, of their websites, their infrastructure, their products, everything that they're doing as part of their company. They get a much richer sense of what's happening in real time using Splunk. So this vision is a promise that's been out there for a long, long time in our industry. When it was called decision support, it was a promise of putting it into the hands of the users and it never materialized, but we're on the cusp today. So let's take a look at Splunk 6 and see why. Okay, great. So for our demonstration today, what we're going to do is we've got a fictional company here called Splunktel. And I'm on the business side of this fictional company. And what we've done is we've launched a new mobile music service. So I'd like to find out what platform our users are downloading the music from and what music is popular because, I mean, I might need to renegotiate my contract with the record label that I'm paying a royalty to. So what we're going to do is let's just start with the familiar experience today. What we have here is the standard search window. Users are very familiar with this, the 1800 or so users at the conference here today. They're used to working in this, in this environment right here, our standard search page. As you can see, what we have is our search bar. I'll type in a search. And I've got this information. Now, as a Splunk administrator or knowledge worker, this is very useful to me. It's very familiar. I'm used to working in this raw machine data format to really get pinpoint that problem. But now I'm paying the the analyst persona, the business user. This does not make any sense to me. So the first concept we've introduced is what Sunja just talked about, as part of our powerful analytics story, is data models. And what data models allow here is they allow me to build this structure, provide a meaningful relationship to that machine data. Instead of seeing this, what I get to see is a data model. And what the data model done is that in our, in a customer's environment, you can have lots of machine data coming from all over the place. But I need to look at something very specific. So what this model is, is that specific, consistent view of that data. Now, what that allows me to do is data models power this new pivot interface. So I'm just going to go in and manipulate this data using the new pivot feature. Now, pivot is something that a lot of people on the business side are very familiar with. If they've been using any type of pivot technology previously, something that Microsoft Excel has had around for the since the year 2000. What it allows me to do now is add fields. I'm going to really quit create a very quick report here. I'm going to come in and look at device names. So what are the mobile devices that people are using for to access my mobile service? Done. I'm going to come in. It's going to load. So I can see there's iPhones, Android devices, iPads. Next I want to see what's the actual artist, the music they're downloading. I'm going to come in and download the artist title. I'm noticing that it's not a drag and drop interface. We're going to get there. It's it's okay. So you've got that. I've got more. I've got more. I almost feel safer though that it's not drag and drop. I'm always messing up my pivot tables. Well, we'll help you there. See what happens. The reason the drag and drop is actually very important is I've got this view. I know someone used an iPhone. They download Bruno Mars. They had about so many downloads. The reason it's not the number is increasing is because we're still going through the data right now we're processing it. But this is a little bit of a messy view. You see iPhone, Android, iPhone, Android, Android. I need a better view of it. So that's where the drag and drop comes in. I'm going to split it by column. I'm going to take this device name and I'm going to bring it over here. And now it's going to restructure that view that makes a little more sense in that information. Now I know by artist's name. Now I know per device now, which is a little more it's better information. So you completely reorganized the data as you would expect it at a pivot table, like you said. Now I'm missing one small thing here. Totals. Very easy to go back in now and say, you know what? Total it. I have very easily using drag and drop using the new pivot interface created a report on my machine data, which is very useful for me now. I can share this report. I can save it to a PDF. But you know what? I'm not done yet because some people like me. I'm a visual person. I like visualizations, pie charts and tables. I can come back here and then look at the data in a very different way by simply just selecting a chart here. Now it's loading. Here's that same information in a different view. Still may not make sense to me. I'm going to come here and stack that. I've done all this in the pivot interface. So just to show you what's going on in the background though is that in the background Splunk Enterprise 6 is actually generating queries. And so what Splunk has done. I did all this via a drag and drop interface in the background. What you can see here is full syntax that was generated. Now as a business user now, I did not need to know any of that information. All I needed to know was how to use a mouse. You're a programmer. Okay. I mean the thing with the thing with this product is everything we do is as philosophically in our product is all late binding or schema on the fly is another way of describing the fact that you put structure on the data at the time you query the data. So what that gives you is an incredible amount of flexibility. So what you want though is flexibility. Normally the penalties performance were delivering flexibility with performance. This product is phenomenally fast. Well I was going to say. That's the thing you noticed. So what's the corpus of data that we're operating on here? Machine data. Yeah I mean is it. Yeah I mean today in Kino we had a couple of terabytes and we were able to in fact, Divani when she did the Kino she was able to show a query which was an aggregate type analytics query running across a billion events in a second. Yeah so it's not the it's not the lightweight demo corpus of data. It's a real data set. We're talking terabytes, billions of events, that kind of thing. And Divani also had a little there was, you had fun on the stage this morning with I guess it was Steven with sort of the traditional interface versus the pivot interface and Divani was consistently outperforming just in terms of the user's ability to get back an answer. Right now some of that might have been her expertise I don't know. Well Steve actually him and his team wrote the search language. So he's probably the expert in the world on our search language and so that's why it was an interesting comparison. But he also wins because as well as writing the search language his team also built the data model, the pivot table. So actually the whole goal of all of this is how do you take this data and put it in the hands of more people. They can analyze and get value from it. It's like the command line interface, right? There's still people out there who love the CLI. They like to get the hands of it. But there's so many more users that could benefit from something like this. So thinking across your 6,000 customers today the vast majority obviously. Well nobody, you're just starting. When do you ship Splunk 6? So ship this morning I've got some great news. We've had 5,000 downloads and it shipped this morning. 5,000 downloads? Yeah, it's phenomenal. I mean people can't wait to get their hands on it. We had over a few hundred beta customers where we actually put a thousand beta downloads and a few hundred beta customers. And they gave us fantastic feedback. Many of them love the features in terms of being able to bridge the divide, in terms of being able to take data that usually is used by IT folks and put it into the hands of business users and really bridge that divide. So they really like that. They like the performance. What we haven't spoken about, which we just don't have time for right now, but there's a lot of features in there for the power user as well. So we've got visual interfaces for managing the products. We've got visual interfaces for managing forwarders, the clustering of Splunk for mission-critical environments. We've got a phenomenal, we've done a lot of work for our developer platform. So we've had a strong platform now for developers for over a year with RESTful APIs and SDKs and Java, JavaScript, C-Sharp, Python, et cetera. And what we've done in this release is delivered what's called a web framework. What that means is as a developer, you can now build and innovate on Splunk, like building any regular web applications. So simple XML, Django, JavaScript again, things like that. So when I think about your total available market, you would think there's many more people out there that are non-customers or non-users, I should say, than users. Both are probably true. So today you have virtually everybody, except for the beta customers on the search interface. How do you expect the adoption of the pivot approach to go? Do you expect, let's pick a number so that you don't have to hold you to a prediction. But 10 years down the road, is the vast majority going to be using this new type of pivot-like interface? I know it'll evolve from where we are now. Or do you see more of a mix? Well, I think for now, what we really want to do is to put this analytics in the hands of more people. And what we find is that our customers themselves are extraordinary in terms of how they innovate on the products. They do some amazing things. I mean, we couldn't have even imagined the kinds of things that they do with Splunk. And so the pivot interface is a step in the right direction for sure. We want to enable them to do more. I think what we have heard from our customers is when they use Splunk, they used to, for example, in a traditional business analytics system, you have to predetermine what questions you want to ask. Then you have to build your system, and then you have to wait for the answers, and then perhaps they might have been the wrong questions. We forgot you. Yeah, then you have to buy a bottle of scotch for the power the guy's going to build the queue. Yeah, and that can take many months. Whereas with Splunk, all of this, what Manish showed you, we did in just minutes, that changes how you ask questions. It changes the kinds of analytics you do with your data. And so I think to answer your question, we don't know where it's going to all end up. I think what we do know, though, is that the machine data is incredibly valuable. We want to put it in the hands of more people. We're going to get fantastic feedback from our customers. We already have, and we can't wait to hear from them some more over the next months and year or so, and then we'll feed all of that into the next version. We've already got some great ideas, and you have to wait for those. But right now, this is in the market as of today, and it's had a great reception. Outstanding. Well, gentlemen, thank you very much for sharing the demo. Good luck. Congratulations on getting it out. I know how much work goes into these things. Thank you. And thanks very much for having us here. So we really appreciate it. Thank you. All right, keep it right there. We're back with our next guest. This is Dave Vellante. John Furrier will be back. We're live from the .conf event here in Las Vegas. We're live at Splunk. We'll be right back.