 Live from Las Vegas, it's theCUBE. Covering Splunk.conf.19 brought to you by Splunk. Okay, welcome back everyone to theCUBE's live coverage here in Las Vegas for Splunk.conf. It's their 10th anniversary, 10 years of doing their big customer shows. theCUBE's seventh year of covering Splunk. I'm John Furrier, host of theCUBE. Our next guest is CUBE alumni Simahajee, senior director and head of platform and industry for Splunk. Knows the data business, we last talked in 2014. Great to see you. Good to see you again, John. You've been busy. I have, it's been a busy time with Splunk. You have been in the data business, we've been following your career for the years, data stacks, now Splunk and other endeavors. But you've been in the data, you've been swimming in the data business. You've seen cloud scale, you understand open source, you understand kind of the big dynamics. Splunk has a full enabling data platform. Started out with the logs, keeps moving along, they buy companies, they integrate it in. But this platform concept of enabling value, value for customers has been a big part of the success. And it continues to yield success every year. Even when people say no. What is successful data platform? Because everyone wants to own the data layer. Customers just want to get value out of the data. So what, as a product marketer, product person, what is a data platform? So it's a really good question. And you know, you kind of hit the nail on the head when you said we've been talking about the data platform for several years, like decades, almost. So if you think about, you know, a data platform, like way back when, and I'm dating myself, when I graduated from college, you know, people were looking for insights, right? They were like, give me a report, give me a dashboard. We went into databases and data warehouses enabling this. But when you actually think about the data platform, or data to everything platform, as we at Splunk call it, it has five critical elements in my mind. You know, the first is, how do you get all of your information? Like the data that's coming in from networks, logs, applications, people. You and I generate a ton of data. How do we get this all together into a single place so you can get insights on it? One may think that it's pretty easy, but the truth is we've been struggling as an industry with it for decades. So what Splunk, I think, what's super unique is you can actually bring in any of the data. And some of the challenges that customers have had in the past is we force them to structure this data before they can ask questions of it. With Splunk, it's free form. You can bring it in any information and then structure it when you're ready to ask that question. So, you know, a data platform, number one is flexibility in the way you bring your data. Second, and you know this, being in the business, is getting real-time insights. Alerts on your phone, real-time decisioning. And then you have operating in different ways. On cloud, on premises, hybrid environments, that's the third. And I think the fourth and the fifth are probably the most important and interrelated is allowing a good data platform caters to everyone in the org. So from your most non-technical business user to the most technical data admin, IT guys, security analysts, giving them the same information but allowing them to view it in many different ways and ask different questions of it. So we call this, you know, it's Splunk is from a product marketing and a business standpoint, we refer to it as many lenses on your same data and good data platforms do that while allowing and empowering different users. So those are the five in my mind. I mean, I love geeking out on platform conversations. I could, we could talk for an hour but I know you got, you're busy. But I want to ask you, all successful platforms in this modern era of architecture when you got cloud scale, massive data volumes coming in need key building blocks. Take me through your view on why Splunk's been successful as a platform because you got to enable value from the dorm room to the board room, you know. So you've got to have that use case breadth which you do. What are the key building blocks of the Splunk data platform? Great question. And you know, we've kind of figured this out as well as have been working on building out these building blocks that are most critical customers, right? So if you think about it, you start with the core, the index, if you will and that's your place to bring, you know, as Splunk started with all your logs together and it's your single go-to place. Then as you think about it with working with customers they need massive data engines. So what we just announced today, the general availability of data stream processor and data fabric search, it allows you to have those two massive engines from how do I bring my streaming data in to how can I do massive scale processing? The other elements are around AI and machine learning, right? So in a world where we're moving to automation that's super critical to the success. And then you have consuming the way you consume insights, the users consume insights. If you think about you and I the amount of time we spend on our phone how do we make it easy for people to act on their information? So those are your core platform building blocks, your index, you have your data engines, you have AIML, you have your business analytics and then you have your portfolios on top which is use case specific, if you will, for IT, for security and then for DevOps. That's awesome and let's get into the news. You guys had your product keynote today. Yesterday was opening day but I want to read the headline from Splunk press release and commentary. I want to get your reaction to it. Splunk Enterprise expands data access with data fabric search and data stream processor empowers users with context and collaboration. Key words, context in there, collaboration. Obviously search is a hard problem. I mean, e-discovery, we've seen carnage and people trying things. You guys deal with a lot of data, a lot of diverse data, it's been a big theme here. Your customers have grown with more data coming in. Why are these two features important? What's the keys behind the fabric search and the data processor? Is it the real time, is it the data acceleration? What are some of the key value points? What should people know about the fabric search and the processor? So actually, let me start with the data stream processor. You know, with DSP, what we're really doing is looking at streaming data. So when you think about the real time customers, IoT, sensor data, anything that's coming on the wire, data stream processor lets you bring that into Splunk. Now the uniqueness of data stream processor is if you wanted to, you didn't have to bring it in Splunk. You can actually process that live on the wire and it works just as well. Now with data fabric search, you alluded to this earlier. It's how do you search across your massive data lakes, warehouses that exist, without having to bring it all in one place. So in the product keynotes demo today, we showed a really cool demo of a business analyst user really solving a business problem while searching across S3 Hadoop and data that's sitting in Splunk. And then with data fabric search, you can also do massive federated global size searches. On the context and collaboration, that's really once you have all this data in Splunk, how do you let your users consume it, right? And that's the mobile, the connected experiences, as well as phantom and VictorOps, like really activating this data and automating it. I want to get your thoughts on something that we've been saying on theCUBE and I've been kind of promoting for about a year now. And it really came back, when you go back to the early days of Hadoop and big data and you know those days, getting diverse data is hard, right? And so, because it's in different formats. And the database schemas and or unstructured define that the databases in a way hamper, hinder that capability. But we've been saying that diverse data gives better machine, makes machine learning better. Machine learning is AI and AI provides business benefits. This flywheel is really important. And can you give an example of where that's playing out in Splunk? Because that seems to be the magic right now is that getting the data in together, knowing what data is, no blind spots, as much as possible, and that has been possible. But getting that flywheel going, better diverse data, better machine learning, better AI, better business value. I think it comes down to the word diverse, right? So, when you're looking at data coming in from many different sources, you also get a holistic perspective on what's going on in your business. You get the insight on what your customers may be doing and engaging with your business. You get insight on how your infrastructures are performing and where you can optimize both for the business from your day-to-day ops and operations to how customers are working and interacting with your business. The other piece is when you think about machine learning and AI, as you automate this, it's a lot easier when you have the holistic context, right? So, you know, diverse data means more context, more context means better insight into what you're trying to get to. And it just kind of rounds out the perspective. I often refer to it as it's adding a new dimension to something you already know. And it opens up a whole nother conversation around what is the practitioner's role, not just a database administrator setting up databases because you're getting at context is important. What's the data about the data? What do I keep? What should be addressable for an application? Is this relevant content for this? Some data is more valuable than others at any given time. So, addressability becomes a big thing. What's your vision around this idea of data addressability for applications? So, you know, just going back to what you said about the administrators and the doers, we call them the doers, they're the innovators, right? They're the people building the cool stuff. And so, when you actually can bring these elements in for them, you really are giving them the ability to innovate and do better and have that accessibility into the information. And really kind of like, you know, like build the best that they could, right? So, you know, we've been saying turn data into doing. And it really is true. Like these are, again, the architects of what's happening. And they're the people like taking all this diverse data, taking the machine learning, taking the technology and the building blocks and then turning it into like all the doing that we do. It's interesting. The market's changed from, it actually changed the role of the database person. It makes them broader, more powerful. Yes. And because, you know, they're the ones fueling the business. Well, thanks for coming on. I really appreciate the insight. I wish we had more time. On a personal question, what's exciting you in the industry these days? Obviously, you're at Splunk, companies continue to grow from startup to IPO, to massive growth, now to a whole nother level of market leadership. You got to defend that. So you got to put some good products out there. But what are you getting excited about these days from a tech standpoint? You know, I think it's, we're finally getting it. We're finally getting what, you know, being a data to everything platform is. For example, right after the keynote, I had more than a few people come up to me and say, well, you know, that made sense, right? Like when we think about Splunk as the data to everything platform, and what data platforms are meant to do, and how they should operate. So I think the industry is finally getting there. What's exciting me next is, if you look behind us, and all the industry traction that we're seeing. So, you know, taking technology and data beyond, and really enabling businesses from financial services, to healthcare, to manufacturers, to do more. You know, the businesses that traditionally, like maybe have not been adopting technology as fast as software companies. And now we're seeing that, and that's super exciting. You know, I always get into these kind of philosophical debates with people either on theCUBE or off theCUBE, where, you know, what does a platform success look like? And, you know, I always say, and I want to get your reaction to this. I always say, if it's got applications or things being enabled of value, and a healthy ecosystem. So, do you agree with that statement? And if so, what's the proof points for Splunk on those two things? What is defining that, what a successful platform looks like? You know, that's, I do agree with you. And when I think about a successful platform, it's, if I look around this room, and just see how, you know, like New York Presbyterian is using Splunk to, to like, we heard from Dell today and Intel. So, when you see the spectrum of customers using Splunk across a variety of successes, it's, that's super exciting to me. That tells me that, you know what, it is everything when you say data to everything. All right, it sounds like you've got a fun job these days. I do, it's fun to be here, so it's great. It's been great to see you. Thanks for coming back on theCUBE and looking forward to catching up. Likewise, good to see you as well. I'm John Furrier here in theCUBE. It's Deema, she's awesome. CUBE alumni from 2014 now at Splunk, leading the product efforts and marketing. I'm John Furrier, you're watching theCUBE. Be right back after this short break.