 from Union Square in downtown San Francisco. It's theCUBE, covering PagerDuty Summit 18. Now, here's Jeff Frick. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at PagerDuty Summit at the Western St. Francis in Union Square, historic venue. Our second time to this show is about 900 people here talking about kind of the future of DevOps but going a lot further than DevOps. So we're excited to have a couple of CUBE alumni here at the conference from SignalFX. We've got Arjeet Mukherjee. Mukherjee, yeah. Thank you, and Karthik Rao, co-founder and CEO of SignalFX. Gentlemen, welcome. Yeah, thank you very much. So what are you doing at PagerDuty Summit? Well, we've been partners with PagerDuty for a long time now. We've known them since the very early days. We share a common investor, but we both operate very squarely in the same space, which is companies moving towards DevOps development deployment methodologies, leveraging cloud native architectures. We solve a different part of the problem around monitoring and observation, and we partner with them very closely around incident management. Once a problem is detected, we typically integrate in with PagerDuty and trigger whatever incident management paths that our customers are orchestrating by PagerDuty. So it's been a really integral part of our entire workflow since we started the company. So we're very close partners with them. Yeah, it's interesting, because Jen announced having 300 integrations or 300 plus integrations, whatever the number is, and to the outside looking in, it might look like a lot of those are competitive, like there's a lot of kind of workflow and notification types of partners in that ecosystem, but in fact, lots of different people with lots of different slices of the pie. Yeah, absolutely. It's a really big problem space that everyone is trying to solve in this day and age. Some of our competitors are in that list, but we partner very closely with PagerDuty. As I mentioned earlier, our focus really is around problem detection and leveraging the most intelligent algorithm statistical models in real time to detect patterns that are occurring in a production environment and triggering an alert. And typically we're integrating in with PagerDuty and PagerDuty deals with the human elements of once something has been detected, how do you manage that incident? How do you route it to the appropriate people? One of the things that's really interesting as this world is changing towards these DevOps models is the number of people that have to get involved is substantially greater than it was before. In the old days, you would have an alert go into a knock and you'd have a specialist group of people with very specific runbooks because your software wasn't changing very often. In today's world, your software is changing sometimes on a daily basis and it could be changing across dozens of teams, hundreds of teams and larger organizations. And so there's a problem on the detection side because companies like SignalFX have to do a really great job of detecting problems as they emerge across these disparate teams or across a much, much, much larger environment with much larger volumes of data. And then companies like PagerDuty really have to deal with a far more complex set of requirements around making sure the right people get notified at the right time. And so they're two very different problems and we're very happy to have been partnering with them for a number of years now. And the complexity around the APIs and where the app is running. I mean, there's like so many levels now of new complexity compared to when it was just one app running on one system, probably in your own data center, probably that you wrote compared to this kind of API-centric multi-cloud world that we live in today. That is exactly right because what's happening is our application architectures are changing. So we used to have these monoliths. We used to have three tiers and whatnot. And we're replacing that within microservices, like about systems and whatnot. At the same time, the substrate on which we are running those services, those are also changing, right? So instead of servers, now we have virtual machines, we have cloud instances and containers and pods and what have you. So in a way, we are sort of growing below two in some sense. And so that's why it's sort of monitoring this kind of a complex, more numerous environment is becoming a harder challenge. We're doing this for a good cause because we want to move faster. We want to innovate faster. But at the same time, it's also making the established problems harder, which is sort of what requires newer tools, which sort of brings companies like us into the picture. And then just the sheer scale, volume, number of data that's flowing through the pipes now on all these different applications is growing exponentially, right? We see it time and time again. So really begs for a smarter approach. Absolutely. I mean, on two levels, right? The number of minutes of software consumption is up exponentially, right? Since the smartphone came out in 2007, you've got billions of people connected to software now, connected all the time. So the load is up, orders of magnitude, which is driving, even if you didn't change the architectures, you would have to build out substantially more back-end systems. But now the architectures are changing as well, where every physical server is now parceled up into VMs, which are parceled up into containers. And so the number of systems are also up by orders of magnitude. And so there's no possible way for a human to respond to individual alerts happening on individual systems. You're just going to drown in noise. So the requirements of this new world really are, you have to have an analytics-based approach to monitoring and more automation, more intelligence around detecting the patterns that really matter. Which is such a great opportunity for artificial intelligence, right? And machine learning. And that's what we talk about all the time. Everyone wants to talk about those kind of as a vendor led something that you buy. Yeah, that's kind of okay, but really where the huge benefit is, companies like you guys, and PagerDuty using that technology integrated in with what you deliver on your core to do a much better job in this crazy increasing scale of volume. Let's run it through the machine. Yes, because the systems are becoming so complex that even if you ask a human to go and set up the perfect monitoring or perfect alerting, et cetera, it might be quite a hard challenge, right? So as a result of automation, computer intelligence, et cetera, needs to be brought in to bear, because again, it's a more complex system. We need higher order systems to sort of deal with them. You're very, very right, yes. And that's a trend we are starting to see. Within the product, we are actually focusing a lot on sort of data science capabilities with an eye to sort of making them more and more sort of machine learning and automation in the future. We have capabilities in the product that can look at populations and identify outliers, look at cyclical problems and identify outliers again. So the idea is to make it easy for users to monitor a complex system without having to get into the guts. And to do it on various sorts of data, right? I think you have an interesting use case that we've been experimenting with recently. That's right. Yeah, so I actually have a talk tomorrow. It's quite an interesting one. It's about monitoring social signals, monitoring humans. So we have these systems, we have these metrics platforms, and they are quite generic. The tools that we have nowadays in this sort of available to us are quite powerful. And the set of inputs need not be isolated to what the computers are telling me. Why not look at other signals? Why not look at business signals? In my case, I'm going to talk about monitoring what the humans are doing on Slack as a way for me to know whether there's something of interest that's going on in my infrastructure, in my service that I need to be aware of. And you will be shocked how surprisingly accurate it tends to be. And it's just an interesting thing. And it makes one wonder like, what else is out there for us to sort of look at? Why, why confine ourselves? Right. It's funny, because we hear about sentiment analysis and social media all the time, but more in the context of Pepsi or a big consumer brand that's trying to figure out how people feel. But to do it inside your own company on your own internal tool like a Slack, that's a whole different level of insight. You'd be surprised at the number of companies that monitor Twitter to understand whether they have an advantage. Yeah. Because in this day and age, users are on Twitter within seconds of something is perceived to be slow or something is perceived to be town. You know, they're on Twitter. So they're all sorts of other interesting signals to potentially pull from. Oh, and guess what? We were just at AT&T Spark yesterday and the 5G is coming and it's like 100x more data be flown through the mobile. So the problem's not gonna get any smaller anytime soon. So what else have you guys been up to since we last spoke? Continue to grow, making some interesting moves. Absolutely, we've been very, very busy. One of the big areas of investment for us has been international growth. So we've been investing quite a bit in Europe. We have just introduced an instance of our service that's based in European data center for a lot of our European based clients. They prefer to have data locality, data residency within the European Union. So that's something new that we just introduced last month continue to have a ton of momentum out in EMEA. They're very much on the cloud journey and embracing cloud and embracing DevOps. So it's really great to see that momentum out there. And clearly with GDPR and those types of things you have to have a presence for certain types of customers, certain types of data. Anything surprising in that move that you didn't expect or no, I don't know, I'll let the view. Not in that move, but it's just interesting to see how quickly some of these modern technologies are getting adopted and how, one of the things that we talk about a lot in our trade is FMR, right? So how things are short lived nowadays. And you used to lease these servers that used to stay in your data center for three years. Then you went to Amazon and you leased your instances which probably lived for a few months or a few days. Then they became containers and the containers sometimes for a few hours or for a, you know, and then if you think about serverless and whatnot, it's at a whole different level. And the amount of FMR that's going on, especially in the more cloud native companies, was a little bit of a surprise in the sense that it actually poses a very interesting challenge and how do you monitor something that's changing so fast? And we had to have a lot of engineering put into sort of make that problem more tractable for us. And it continues to be an area of investment. That to me was something that was a little bit of a surprise when we started off. Much of this, you know, dojoization and coordinating was not yet in place. And so that was an interesting technical challenge as well as a surprise. Yeah. Well, I'm curious too, as instances, right? So there's the core instances that are running core businesses that don't change that much, but as it's, you know, it's a promotion. It's a, it's a this or that, right? It's a spin up app and a spin down app. You know, are those even going up on the same infrastructure from the first time they do it to the second time they do it? I mean, how much are you learning that you can leverage as people, you know, are doing things differently over and over again, you know, as their objectives change, their applications change, they're going to go to market around that specific application. That's changing all the time as well. Yeah. So I think the challenge there is to sort of build, at least from a technical point of view, from single effects point of view, is build something that is versatile enough to handle these different use cases. Because new use cases, new ways of doing things are going to continue to happen, probably going to keep on accelerating. So the challenge for us is good and bad, is how do we make a platform that is generic, that can be used for anything that may come down the pike, not only just now. At the second time, how do we innovate to continue to be, you know, up to speed with the latest of that's what's going on in terms of infrastructure trends, you know, software delivery trends and whatnot. Because if you are not able to do that, then that puts us sort of behind. Right, right. So it's a, you know, sort of lot of frenetic innovation, but it's also exciting at the same time. Right. And just the whole concept too, where I think, you know, what's best, practice quickly becomes, you know, expected baseline. That's correct. Really, really fast. I mean, what's like cutting edge, innovative, now unfortunately, or fortunately, that becomes the benchmark by which everything else is measured overnight. That's a thing that just amazes me. What was magical yesterday is just expected boring behavior today. All right, good. So as we get to the end of the year, a lot of exciting stuff, you guys said you're going to be at re-invent. We will see you there. Anything else that you're looking forward to over the next couple of months? We're just, we're really excited about re-invent, it's a big show for us, and we'll have some good announcements around the show. And yeah, looking forward to just continuing to do what we've been doing and deliver more value to our customers. Love it, just keep working hard. All right. RG, I hope your throat gets better before your big talk tomorrow. That's right. All right. Thanks for stopping by. Great to see you. Great to see you. I'm Jeff. You're watching theCUBE. We're at Page of Duty Summit at the West in St. Francis in San Francisco. Thanks for watching. See you next time.