 From San Francisco, it's theCUBE, covering PagerDuty Summit 2019. Brought to you by PagerDuty. Hey, welcome back, everybody. Jeff Rick here with theCUBE. We're at PagerDuty Summit in downtown San Francisco. It's about 1,000 people, fourth year of the show, third year of theCUBE is here. Happy to be back. Ironically, a couple of weeks ago, we were at SimaLogic, Illuminate down the road by the airport, and we're excited to have somebody from Sima here talk about how these platforms work together. So, returning, again, is Jonathan Rennie, SVP of products at PagerDuty, and joining us is Bruno Kurtick. He's the founding VP of Product Strategy for SimaLogic. Bruno, great to see you. Jonathan, welcome back. Thanks for having us. All right, so, Bruno, we're just at your show, now you got to take it a little bit easier, probably not quite as many responsibilities. We'll talk a little bit about the relationship between the two companies, because from the outside looking in, it looks like there's some redundancies, it looks like two platforms, it looks like where's my single pane of glass, but in fact, there's a real synergistic opportunity to work together. A good question. So, there are two platforms, but it's entirely synergistic. You know, between the two technologies, PagerDuty and SimaLogic, we sort of help our customers who run mission critical products and services that serve their customers, in fact. Number one, get information from their systems and applications to understand what's happening in them, and then leverage our two platforms to resolve those issues, make sure those applications are running, that their customers are happy, that they're delivering the services that they are there to live with them. And I know, Jon, you got a long list of great companies that you guys worked for, and you said it's a really key part of the company strategy. Yeah, the ecosystem that we work with, one of our favorite partners, SimaLogic, we use Sima, we're a big customer of SimaLogic, as well. And it's really important, all of the telemetry, all the machine information that's coming in, again, the part that we play in that is, how do we orchestrate people to get work done when things go south? Right. And how do we get the right people on and give them some information about what they're doing to help triage what they're doing? Right. So, the two work together really, really well. So, one of the themes at both keynotes, it remains keynotes, as well as Jennifer's data, and the fact that you guys both have a giant proprietary data set of both machine activity and people activity from running these businesses, I've teased it on Twitter and overnight sensation, 10 years in the making, that you can leverage to deliver more value. So, as we look forward, data's been important, but now, all the hot topic is machine learning and artificial intelligence. How are you now taking this kind of next gen technology and applying it to these giant data sets to offer kind of proprietary insight to your customers? I'll start with you, Bruno. Sure. So, there's a massive amount of data, right? And it's growing at the rate of Moore's Law. So, there's more data that any human can cooperate. And so, our task at Sumo is in figuring out what is that data trying to communicate to you? So, we spent a lot of effort on machine learning, pattern detection, advanced analytics to help our customers short through that massive amount of data to understand whether their services are available, whether they're performing, whether they're secure, whether they're compliant. And we boiled that up into a set of insights that we then feed downstream or upstream, in this case, to Pedro duty, to help those people who are responsible for those services do the work to make sure they're restored and working well. And I guess to compliment what Bruno's saying, one of the things that we're doing is we're also ingesting a lot of data, a lot of machine data from monitoring products and from service desk products, other kind of sources of data, because that also informs who needs to get engaged when a system goes down. And then what do they need to do in order to fix it? And so, it's all context, it's all data, and how we can help narrow that down. We had a really interesting statistic, this was earlier this year, where we were looking at per responder how is this growth of interruptions, kind of alerts, how is that trending? And now, compared to just a couple of years ago, it's about three times the amount of noise that's coming at them now per responder than three years ago. So clearly, the people on the end of this are getting overwhelmed if we don't do something intelligently to make sense of it for them. Right, that's interesting, because it's really a lot, I don't know, false positives, I don't know if that's the right characterization, but certainly too much to prioritize and an overwhelming amount of data for a person to try to filter. So you're really trying to add that intelligence on the front end, so hopefully the right problems are getting surfaced and not just as broad base of false positives or minor positives. Yeah, it's funny you say false positives, because one of the concepts that we have is there are alerts and incidents that need to be managed, but then there are unactionable alerts and incidents, things that really shouldn't be bothering you. So you have to walk that fine line between what do you act on that you should take action on and what are the things that you shouldn't take action on and kind of ignore. And so we use machine learning to do a lot of that work and filter out the bad noise and bring the important information in. Yeah, I wonder if you have any thoughts, Bruno, on how much of that filtration needs to happen to kind of quiet down this tsunami that's coming over the transom? Well, in our terms, it's, you know, every one of our customer centers billions of records per day, literally billions. Billions of records per day. Billions of records and sort of figuring out what matters amongst those billions of records is a hard job. There's a lot of false positives, false negatives that need to be sorted through before it even gets handed up to the upstream technologies like page review, right? So we spend a lot of time doing outlier detection, doing predictive analytics, doing sort of pattern detection, machine learning type of techniques to make sure that the stuff that gets bubbled up has as few false positives and as few false negatives as possible so that the insights that are intelligent, the actions that need to be taken are most appropriate and can be prioritized and handled by a small team of people who own those actions. Right. It's funny to say billions and billions. I have a visualization challenge. I keep throwing out to people and I've yet to get a great response which has shown me a billion piece data set in a visualization that I as a person can look at and comprehend what the heck is going on. Beyond something as simple as, you know, half of them are on this side, half of them are on this side. I mean, we're not wired for that way. We're not wired to be able to take in billions of data points. It's just not going to happen. Just for that context, we actually, we analyze a quadrillion records a day. So, talk about billions and then, you know, many more orders of magnitude than that. It's, those are numbers that are hard to comprehend, right? We don't think in those numbers, right? It's really hard for humans to gross up. So, how do we keep up? I mean, how do we keep up? I mean, it's kind of a bigger problem but you know, as much as anybody, kind of the exponential growth of this data, we're barely getting into IoT and industrial IoT and sensors on everything at the house and on our clothes and our shoes. You scared about keeping up? Can we keep up? What do you, you know, kind of, how do you see this crazy trajectory on the data? We have to kind of gait it somehow? So, from my perspective, there is no sense in being scared of it, right? A digital business generates data without that data that can't run. So, the task is to capture it, analyze it, to understand it and serve up intelligence from it, right? So, our task is to keep pace with that growth and build resilient, scalable systems with the analytics that are required to understand it, right? And so, you know, we can't shy away from it so whether we like it or not, it's not an easy task but we can walk away. Right, right. And then the other just crazy, increasing complexity. No, thank you. On your guy's side really is the variety. I mean, we used to talk about the variety, the old big data, the big three, you know, variety and veracity and velocity. You know, the interconnectivity of all these systems is also the thing that's growing so exponentially and so when something does break, the ability to find what broke amongst this huge potential is really a hard and growing problem. Yeah, it is and that's why sitting in the middle of an ecosystem of a lot of different products that we'll give and send off telemetry that we have to look at is really important. It's almost as if like the information that we're always looking for in the PagerDuty platform, it has to be items that really are actionable by a person which, you know, if you look at the information that is flowing into Sumo Logic, it's even in some ways very broad and so it's a wider funnel. We have a narrower funnel kind of information but they're both very complimentary of each other because one is that humans need to act on in the moments and the other is how do I analyze in a broader sense, even a bigger range of information. So both are so critical as a part of that whole ecosystem as I was saying, we personally use Sumo Logic as a big part of how do we actually triage actual incidents. We built tons of libraries in the Sumo Logic product so we can make sense of even a broader set of information flowing in from all of our logs in some of those critical moments. So it's, yeah, it's great Sumo. Good, good. Well, I'm glad you guys are working on this big data problem because it's a big, hairy one. And the customer's only benefit, right? Yeah. Well, Bruno, Jonathan, thanks again for taking a few minutes. Congratulations on the collaboration. It looks like it's working pretty well. Good guys. He's Bruno, he's Jonathan. I'm Jeff and you're watching theCUBE. We're at PagerDuty Summit downtown San Francisco. Thanks for watching. We'll see you next time.