 From Burlingame, California, it's theCUBE. Covering SumoLogic Illuminate 2019, brought to you by SumoLogic. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Hyatt Regency San Francisco Airport at SumoLogic Illuminate 2019. We were here last year for our first time. It's the third year of the show. It's probably 800, 900 people, around 1,000. Packed house just had to finish the keynote and we're really excited to have our first guest of the day who's been here since the very beginning is Bruno Kurtick, the founding VP of Product and Strategy for SumoLogic. Bruno, great to see you. Likewise, thank you. So I was doing a little homework and you were actually on theCUBE, AWS re-invent, I think 2013, wow, how far has the cloud journey progressed since that first, I think it was our first year at re-invent as well. That's the second year of re-invent. Right, so what an adventure. You guys made a good bet six years ago, seems to be paying off pretty well. It really has been. We kind of sniffed out that the cloud is going to be a real thing, put all of our bets into it and have been executing ever since and I think we were right. I think it is no longer a question, is this cloud thing going to be real? Our enterprise is going to adopt it. It's just how quickly and how much. Right, right. But we've seen kind of this continual evolution. All right, this jump into public cloud, everybody jumped in with both feet and now they're pulling back a little bit but now we're really seeing this growth of the hybrid cloud, a big announcement here with Anthos and Google Cloud Platform and containers and the rise of Docker and the rise of Kubernetes. So I don't know, as you look at kind of the evolution, a lot of positive things kind of being added to the ecosystem that have helped you guys in your core mission. That's right and look, five years ago which is such a short time but yet in sort of the speed of the technology adoption and change, it's in millennia. What's happened over the last few years technology stacks have changed dramatically. We've gone from, okay, we can host some VMs in the cloud and put some databases in a cloud to we're now building microservices architecture, leveraging new technologies like Kubernetes, like serverless technologies and all this stuff and one of the fastest growing technologies that's being adopted by some of the customer base actually the fastest is Kubernetes and also the fastest customer segment, growing customer segment and some logic is multi cloud customers. Basically that sort of desire by enterprise to build choice into their offerings being able to have leverage over the providers is really coming to fruition right now. Right, but the multi cloud almost it makes a lot of sense, right? Because we hear over and over you want to put your workload in the environment that's most appropriate for the workload. You know, it kind of flipped the bid, it was no longer here's your infrastructure, what kind of apps can you build on it now, here's my app, where should it run? And that may be on-prem, it may be in a public cloud, it may be in a data center. So it's kind of logical that we've come into this hybrid cloud world. That said, now you've got a whole another layer of complexity that's been added on and that's really been a big part of the rise of Kubernetes. That's right and so as you're adopting services that are not equal, right? You have to create a layer that insulates you from those services. If you look at in our continuous intelligence report that we just announced today, you will also see that how customers and enterprises are adopting cloud services is they're essentially adopting the basic and core compute storage network and database services. And there's a long, long tail of services that are very infrequently adopted and that is because enterprises are looking for a way to not get too locked into any one service provider. Kubernetes gives them that layer of insulation with Anthos and other technologies like that. You are now able to seamlessly manage all those workloads whether they're on your on-premise in AWS, in GCP, in Azure or anywhere else. Right, so there's so much we can unpack here. One of the things I want to touch on which you talked about six years ago but it's even more, I think appropriate today is kind of this scale, this exponential growth of data and this exponential scale of complexity. And we as people, it's been written about by a lot of smarter people than I, we have a real hard time as humans with exponential growth. Everything's linear to us. So as you look at this exponential growth and now we're trying to get insights, now we've got IoT and this machine to machine data which is a whole another multiple orders of magnitude. You can't work in that world with a single painted glass with somebody looking at a dashboard that's trying to find a yellow light that's soon to go red. If you don't have analytics, you're hosed. That's right. This is no longer a world of ding dong lights, right? You can't just like say, okay, red, green, yellow. As sort of companies go digital, right? Which is driving this growth in data. You know, ultimately that data is governed by Moore's law. Moore's law says machines are going to be able to do twice as much every 18 to 24 months. Well, guess what? They're going to tell you what they're doing twice as much every 18 to 24 months and that is an exponential growth rate, right? The challenge with that is budgets don't grow at that rate either, right? So budgets are not exponentially growing. So how do you cope with the onslaught of this data? And if you're running a digital service, right? If you're serving your customers digitally generating revenue through digital means, which is just about every industry at this point in time, you must get that data because if you don't get that data, you can't run your business. This data is useful not just in operations and security. It's useful for general business. It's useful in marketing, in product management, in sales. And their complexity and the analytics required to actually make sense of that data and serve it to the right constituency in the business is really hard. And that has been what we have been trying to solve, including this economics and machine data. And I mean, talked about it today in the keynote is we're trying to bend the cost curve of Moore's law, yet deliver the analytics that the enterprise can leverage to really not just operate an application but run their business. Right. So let's talk about this concept of observability. You've written some blogs about it. You know, when you talk to people about observability, what should they be thinking about? How are you defining it? Why is it important? It's a great question. Observability right now is being defined as a technique. Right. The simplest way to think about it is people think of observability. I need to have these three data sets and I have observability, right? And then you have to ask yourself a question. First of all, what is observability and why does it matter, right? I think there's a big misconception in the market and how people adopt this is that they think observability is the end but it isn't. Observability is the means of achieving a goal. And what we like to talk about is what is the goal of observability? Right now, observability is talked about strictly in the DevOps space, right? Basically, how am I going to get observability into an application and it's maybe runtime. How it's running, whether it's up and performing. The challenge with that is that is a pigeonhole view of observability. Observability, if you think about it, we talk about objectives during observability. Observability to an SRE could be uptime and performance. Well, guess what? To a different group, like security, observability is not getting breached, understanding your compliance posture, making sure that you are compliant with regulatory rules and things like that. Observability to a business person, to a product manager who owns a P&L on some product is how are my users using this product? How is my application being adopted? Where are the users having trouble? What are they, where is the user experience for, right? So all of this data is multifaceted and multi-useful. It has multi-uses and observability to us is objectives driven. If you don't know what your objective is, observability is just a tool. I love that, you know, because it falls under this thing we talk about off the two, which is, you know, there's data, right? And then there's information in the data. That's right. And then, but that is a useful information, right? Because it has to be applied to something. That's right. In and of itself, it has no value. Correct. And what you're talking about really is getting the right data to the right person at the right time, which kind of stumbles into another area, which is how do you drive innovation in an organization? And one of the simple concepts is democratization. You get more people, more of the data, more of the tools to manipulate the data than that P&L manager is going to make a different decision based on different visibility than a security person or the DevOps person. So how is that evolving? Where do you see it going? Where was it in the past? And, you know, I think you made an interesting, or Ramin made an interesting thing in the keynote where, you know, you guys let your software be available to everyone. And there was a lot of people talking about giving more people more access to the tools and more of the data so that they can start to drive this innovation. I'll give you sort of an example, sort of one of the, one of the sort of aspects of when we talk about continuous intelligence, what do we mean? So this concept of agile development didn't evolve because people somehow thought, hey, why don't we just try to push code to production all the time, break stuff all the time? What's the reason why that came about? It did not come about because somehow somebody decided it's a better software development model. It's because companies try to innovate faster. So they wanted to accelerate how they deliver digital products and services to their customers. And what facilitates that delivery cycle is the feedback loop they get out of their data. They push code early, they observe the data, they understand what it's telling them about how their customers are using their products and services, what products are working, what they're not. And they're quickly baking that feedback back into their development cycles, into their business cycles to make better products. Effectively, it evolved as a tool to differentiate and out-innovate the competition. And that's, to a large degree, one of the ways that you deliver the right insight to the right group to improve your business. And so this is applicable across all use cases in all departments around a company. But that's just one example of how you think of this continuous innovation, continuous data, continuous analytics, and continuous insights. Don't spend two years doing an MRD and another two years doing a PRD and then another two years shipping a product. When you actually ship it, half of the assumptions that you made two years ago are already old and wrong. So now you've got to go, you've wasted half of your development time and you've only released half of the value that you could have otherwise. Right, right. And your assumptions are not going to be correct, right? You just don't know, right? Until you get that stuff out there. And times change over time. Like two years of Kubernetes with a single-digit percentage adoption technology in Sumo's customer base, now it's a third, right? Which means, no, things have changed, right? If I had made an assumption as of two years ago on Kubernetes, I wouldn't have done this announcement. Right, right. But we did it in an iterative mode and we benefit from that continuous information and continuous intelligence that we do on our own there. Right, right. We've had Joe and the boys on lots of times. So it's a pretty interesting how fast that came and how it really kind of overtook Docker as a form of the container. Even though Docker, according to the report, is still getting a ton, a ton of traction. And it's working in conjunction with Kubernetes, right? Kubernetes allows you to manage those containers, right? And Docker containers are always part of that ecosystem. And so it's like the management layer and the actual container layer, right? So as you look forward to give you the last word, you know, as we're really kind of getting into this IoT world and 5G's coming just around the corner, which is going to have a giant impact on industrial IoT and this machine to machine communications. What are some of your priorities? What are you looking, you know, kind of a little bit down the road and keeping an eye on? Interesting question. You know, we used to think about IoT as a new domain. We should think about IoT and maybe we need to build a solution for it, right? It turns out our biggest customers, IoT customers, and the way that I have at least personally reframed my thinking about IoT is the following. Computational capacity is ubiquitous now. What used to be a modern application three, four, five years ago was something that you access through your laptop or through your mobile app and maybe your smart watch. Now the computation that you interface with runs in your doorbell, in a light switch, in your light bulbs, in the house, it runs everywhere. It runs in your shoe because when you run, it talks to your phone to tell you how many steps you've taken, all this stuff, right? Essentially, enterprises building applications to serve their customers are simply pushing computation farther and farther into our being. Everywhere, there's now IP networks, CPUs, memory, and all of those distributed computers are now running the applications that are serving us in our lives, right? And to me, that's what IoT is. It's just an extension of what the digital services are. And we interface with those and it so happens that when you push computation farther and farther into our lives, you get more and more computers participating, you get more data and many of our largest customers are essentially ingesting their full stack of IoT devices to serve their customers. Right, it's a crazy future. And it's just kind of this continual atomization, too, of compute and store and memory. Well, Bruno, hopefully it will not be six years before we see you again. Congrats on the conference and thanks for taking a few minutes. Absolutely, okay. All right, he's Bruno, I'm Jeff. You're watching theCUBE where it's Sumo Logic Illuminate at the Hyatt Regency San Francisco Airport. Thanks for watching, we'll see you next time.