 Okay, welcome back. We're here live in Las Vegas. This is SiliconANGLE in Wikibon's theCUBE, our flagship program, we go out to the advanced district of SiliconANGLE as I'm John Furrier, the founder of SiliconANGLE. I'm Joe, my cohost, Dave Vellante. Our next guest, Bruno Kurtik, founding VP, product strategy for Sumo Logic. Welcome to theCUBE. Thank you very much. Pleasure to be here. So, you guys are in the same hot market right now. Splunk went public. We just covered the Splunk conference. They're competitive viewers. There's plenty of beach for everyone, right? The fruits are coming off the tree, as they say in the paradise they'll call big data. So, you guys really are in a hot market. You know, getting data out of logs, whether they call it data waste, data exhaust, whatever you want to call it. There's data flying around everywhere, certainly more than ever, certainly with cloud. Again, more connected devices, internet of things. You name it, it's thrown off data. Machine data, people data, log data. So, hot market. So, tell us about what you guys are doing as a company and then the role here at AWS. Sure. So, we're focusing on the machine data. So, as you say, the exhaust of IT infrastructure and other things such as the internet of things, machine to machine data, things like that. So, we have a different angle on this data set, right? We fundamentally believe that human and a query language simply can't conquer this data set. That the data growth is so big and exponential in nature that in order to actually mine it for insights and actually be able to solve availability and performance issues, security issues as they emerge, you need to have more than just a human looking at the data. So, we've invested heavily into machine learning capabilities essentially to augment what a human does by applying a very smart algorithm to pre-process the data, to boil the ocean and really try to find areas of focus for a human so to dive deeper on. So, you're not taking the human out of the equation. No, we're not. You're trying to dramatically compress the time it takes a human to get to the right decision. That's right. We believe actually that it is, you know, you can have a very smart algorithm, but that smart algorithm isn't going to do anything without really a human expert input. So, we believe that ultimately it's the combination of human and an algorithm that will enable enterprise to find insights in this data set. Somebody said to me the other day, I wonder if Bruno, you could comment on this. We're down in the Hadoop world, we had our own event, Big Data NYC. Abhimeta, I think, John was on the Cube and he basically said, look, algorithms are free. Really, it's what you do with that algorithm. It's how you integrate them. It's the applications that you write. Do you agree with that? Oh, I absolutely do. So, the, you know, sort of what did Google do, you know, years ago when they came up with their page rank algorithm, right? It was a great algorithm, but it's the fact that they harvest the human feedback and merge that with the algorithm to actually makes them the most effective search engine, right? And so, we fundamentally believe that, you know, algorithms are a dime a dozen. It is how you use it. It's how you collect the data. It's how you collect the insight and the feedback to the algorithm that actually makes it more powerful. So, internet of things, obviously, is, you know, all the rage, industrial internet. That's a trend that is wind at your back, obviously, because you're saying there's so much data and that's just going to create ridiculous amounts of data. Talk about your play there and what that means to you. So, one of the reasons why we are actually built in AWS and why we decided that we're going to make this as a cloud offering was not because we wanted to bake a cloud version of a non-premise software. It's because the cloud fundamentally offers us the architectural methodology or architectural possibility to actually keep up with the volumes of data that we're facing today. So, we are in AWS and we run our footprint across multiple physical locations. We're actually able to scale our footprint on demand as our data growth goes up. We can expand our footprint and as it winds down, we can contract it as we need. So, it's an on-demand compute and it's all about the architecture and how you actually try to leverage that compute. So, we've architected the product in order to keep up with the data volumes that are, you know, for the next decade. So, how do you guys deal on the platform? So, one of the things that we find interesting in this market is that it's kind of a use case where it's a tool, it's a hammer, everything looks like a nail, that's the old expression, but in reality, the initial use cases that we've seen in this market is it's a big aspirin, pain relief. But really, it's about not just that it's enablement. So, talk about what you guys are enabling because what ends up happening in all these cases, we talk to your customers and Splunk customers is like, hey, this liberates me, I get to find stuff faster, I get to do something else. And so, really, it's not just solving a problem, it enables them. Can you talk a little bit about how you see that evolving? What is that enablement? What's that next step? What are customers bolting on top of your platform? So, that's actually a great point. You know, we obviously start with those core use cases that every customer of ours wants to satisfy. We start with availability and performance, making sure that they reduce downtime, reduce the amount of time that it takes to repair issues, moving to security to detect security issues and all of that. And so, yes, of course, we start with compressing the amount of time it takes our customers to find those critical events. But it doesn't stop there, right? Part of the whole SaaS offering aspect of our product is that not only do we compress that time and free up your time, free up IT time to actually go chasing the right issues, but we also reduce their dependence or their requirement for them to actually manage the underlying infrastructure to manage those laws, right? And with on-premise vendors, you actually have to spend a significant amount of your team's time to actually wire that technology. And from our perspective, I believe in Adam Smith, right? Specialization, we don't want to rack and stack servers. We don't Amazon do that for us. We don't believe that our customers should learn how to run big data technologies. We will do that, they just get to benefit from it. So we sort of give them that opportunity on a couple of different angles. Let's talk about cloud trails with the announcement here at Amazon. Obviously, Amazon is dipping their toe in the water in a real way. You got VDI and managed desktops with workspaces they announced. The cloud trail also shows you that, hey, we're serious about compliance. We're serious about getting the little things, the minimum table stakes, as we say in theCUBE. What's your relationship with that? Operationally, how does that make organizations behave differently? How does that scale your opportunity? So, I think cloud trail is fundamentally a strategic enabler for AWS. I think it's beyond just providing data for compliance use cases and others. I think the inability or the lack of visibility or the opaqueness, if you want, of cloud infrastructures around the world, not just Amazon, but everybody else's, has been a major roadblock in adoption of these technologies or cloud by large enterprises. And when you run on premise, you can see everything. You get the audit data. Who's doing what? Who's touching with infrastructure components? Who's changing passwords? Who's doing what to our network? You couldn't do that in Amazon or in any other place. So with this announcement today, what they've actually done is they've removed a massive roadblock from serious companies adopting this and gaining a comfort level about what the users are doing and what's being changed in their infrastructure. And everybody in their right mind wants to have that for their production system. How are you guys competing with Splunk? Obviously they're going public. How do you compare against Splunk? How do you talk about the competition? That's my favorite question. So Splunk is a good company, right? They've sort of paved the road for this market. We think that we're fundamentally different than Splunk in a few core ways. First, we're SaaS. We talk about that. Everybody understands that it's, we reduce, dramatically reduce the total cost of ownership that our customers have to sort of spend on running these technologies and gaining the benefits. Second is that we're highly elastic. So for a lot of use cases where there's a lot of bursting, a lot of seasonality, like think about online retail where a company might have 10 times more data that during the Christmas season than during the rest of the year, it's really hard to plan for that. It's really hard to deploy, sort of build out the church for Sunday for the rest of the 10 months of the year. So we don't force them to do that. We provision for the mean and we expand and contract our product footprint and as such translate those cost savings to our customers. But that's ultimately not fundamentally. So it's really SaaS, but they announced their availability of cloud trails thing with this, but they use Amazon machine images. That's different. That's API based. So they're bolting their deal on to the software, right? So that's fundamentally the difference. Your SaaS cloud, their enterprise. Well, you know, it'll be up to the customers to decide what's the right way to SaaS, right? We believe that, you know, we from the get go architect our product to leverage the Amazon services as an API. So to us data center is this virtual thing that we can spin up 10 times as much capacity as we want on demand, right? Porting on-premise technology into the cloud is an entirely different proposition, right? And we've seen how that goes. And you know, we'll let the market decide what the right way. Well, some apps are, you know, go easier than others. So we'll see. We'll see how it's going to go. I mean, but fundamentally the way that we compete with our main competitor Splunk is that, you know, we've invested into the machine learning capabilities, which we believe are fundamental in actually conquering this data center. Humans simply can't get along. The game is just starting. I mean, the game is, to me, it's such a huge market. I always say to people, this is the next Google is in this market, because that's a search paradigm. You guys have discovery, technology, all the contextual behavioral data. I think the next revolution is going to write in your wheelhouse. So be fun to watch. We're tracking you guys Bruno. Thanks for coming on theCUBE. Appreciate it. Tech athlete, another tech athlete. I love talking to the guys who run strategy and founding product theme. That means they know the product and they can talk about the chessboard. Thanks for coming on live here in Las Vegas with CUBE. We'll be right back after this short break for the next guest.