>> Announcer: Live from Las Vegas, it's theCUBE! Covering Splunk .conf19. Brought to you by Splunk. >> Okay, welcome back, everyone. It's theCUBE's live coverage here in Las Vegas for Splunk's .conf 2019. It's the 10th year of .conf and we have two great guests, Rick Fitz, senior vice president, general manager of groups at Splunk, and Karthik Rau, vice president, area GM of SignalFx. The big story is SignalFx acquired by Splunk. Rick, you sponsored that. Guys, welcome to theCUBE, great to see you guys again. >> Yeah, great to be here, Jeff. >> Great to be here. >> They just broke a world record for the bike on intro there. >> Rick: They did. >> Pretty exciting what's going on here, a lot of records being broken. Splunk just continues to move the needle on capabilities, product, platform, brand messaging. SignalFx coming, we've been reporting on it since their founding, really in your wheelhouse, you guys bought them for a good number, a big number? >> Rick: Yup. >> Why? What's going on? Why the interest in SignalFx? >> You know, for a long time, we've been watching, I would say, perhaps, patiently, watching the market and the trends, and we were really waiting for a time where the new application architecture was really going to kind of start to take hold, where this cloud native trend that we've been seeing where people are building applications, where people are actually delivering applications to market in quite a different way, would finally get some escape velocity, and we've been watching patiently for that to occur. And as we saw that last year start to accelerate, really, we went out and surveyed the entire market and, of course, at the end of that survey, resulted in the acquisition of SignalFX, and also of Omnition. And so we bought those two companies, and have combined them to deliver on our vision of what we've trying to do for DevOps. >> Rick, you and I had a conversation in 2015 here in theCUBE at the .Conf at that time, you were on the IoT, you saw this wave, again, you've been patient. What about IT operations that's happening now that makes this so critical for Splunk? 'Cause IT operations, we know what automation's doing, machine learning toolkit, getting a lot of rave reviews. People love to automate things, but more apps are coming. What's the motivation now? What was the critical linchpin for you to make this happen? >> Yeah, exactly. What we're seeing is, in traditional IT operations is this world where developers build these monolithic applications, hand 'em off to operations, and they operate it. And then in the same conversation, you'll get handed over to somebody running, if you will, developer engineering or cloud engineering or they have various different levels for it but you're really dealing with an engineering organization and they're being tasked with digitization of their enterprise and very strategic investments are being made there, but they're also being asked to build things at high availability, high scalability, and highly reliable with lots of change. So it's kind of the competitive advantage of the enterprise. And as I was seeing that occur more and more I just saw the distance between IT operations and development, kind of, separate, and I said, wow, that's interesting 'cause it's being driven by this new application architecture, or cloud native architecture. And I didn't want to be left behind. I wanted to actually be able to build a bridge for IT operations into this future. And I think this future trend is something that's going to be lasting for the next 10, 15 to 20 years. So I think this is very strategic to Splunk and very important for us to get right for the long-term, but I also see my role as part of Splunk, is to make sure that we take IT operations into this new world, because these new worlds, and if you will, the existing worlds, those operating models are quite different. >> John: Yeah. >> They operate differently. They think differently. They, in one they own their code, they're on call. In another one they're waiting for something to fix so then they try to, you know, we're waiting for something to break and then they fix it. So we're trying to actually help enterprises across that entire gambit with some pattern. >> And certainly with security the theme here, at this event, this is a security event too, on top of everything right? So, this is what it's turned into. >> Rick: That's right. >> Data is driving a lot of security polemetry and data's important for security, so. >> Yeah. >> I mean, that's operations. >> That's right. And your apps have to be secured, in both worlds. >> Yeah. >> So, I think Splunk has a role to play in helping in this transformation for all of IT as it becomes much more developer centric. And, of course, as I said, that is really one of the strategic reasons why we led the acquisition Citadel FX in Omni. >> Well, we're looking forward to seeing how you handle the acquisition, of course, we were fans of the deal. Karthik, I got to ask you, every single company in observability space is going public. So, why, you could have gone public, why Splunk? Why sell to these guys? What made it a fit for you? >> Well, ultimately, we look at a number of things, or we looked at a number of things in making the decision and we wouldn't have done this with anyone other than Splunk. Just a strategic fit was just so great on so many levels. You know, when we started the company our goal was to solve the modern dream observability challenges for anyone building a cloud native application, and we knew that was going to be a long road. They're going to be a lot of things we needed to invest in and develop. And so we started on the metric side. We layered on distributive tracing and we took a philosophy that we wanted to build an enterprise great, scalable, robust, feature-rich set of technologies. We weren't in the market to build, you know, SMB, kind of very simple, limited type of a product. We're really focused on the larger, more sophisticated customers. And so, as we looked at continuing to extend our portfolio, one of the things that we needed to invest in was in the logging space because, when you think about the trifecta of monitoring data types that you need, you know, logging is a big part of it. And we knew that we wouldn't be able to go and build a logging system from the ground up that would be robust enough to support enterprise use cases, and so we started a partnership conversation with Rick and team, and it just became very clear through that process that there was a tremendous amount of product fit, vision fit, culture fit, values fit. Just everything was so aligned that we realized that we could do so much more together as one company. So, we rounded out the solution portfolio, or the technology portfolio quite substantially over night by becoming a part of Splunk and then the other part of it too is, you know, we saw as we were dealing with customers, we were dealing mostly with native cloud native, cloud first customers. But a lot of the customers that we were, that were prospects, that we were talking too were more traditional enterprises who were not 100% of the way there yet. Some of them weren't even 10% of the way there yet. And it was difficult for us to really engage in conversations early with them, to help them understand what does it mean to shift from traditional IT ops to DevOps because we didn't have a relationship with them on the IT ops side of things, and so, the other thing that we were really excited about being a part of Splunk is we can be a part of that conversation from the very beginning when the customer, you know, maybe they're just beginning to think about it and they don't have the urgency of doing it today but we can be there with them from the very beginning and help them get there on their timelines. >> This is an interesting discussion point because what you're highlighting and we've had conversations about your company about being a platform, not just a tool. So, you're getting at is that as you guys started getting more market share, you're platform needs, you needed logging. And meet the market leader, right here right? >> Yeah. >> That's right. >> So, you guys need them, so, partnering's hard when you're trying to build a platform. Now, you can have a platform that enables partners to build on top of it, but components of a full baked platform, it's hard to partner. Rick, what's your thoughts and reaction to that, because that's my statement, but do you agree with it? It's hard to partner in the platform, it's core competency. Look it, he struggled with logging 'cause he'd have to build out a boat load of new investment and you guys are already, just to catch up. >> Yeah, that's right. And I think the thing that needs to be stated here is in your large scale enterprises, they are truly looking for the best to breed, highly scalable environments, right, that we're talking about here. And, they want, they encouraged us to take a step in this direction. It was an obvious choice and I think that has been the reaction that we've kind of heard universally. Like, this is a great idea. This is a really strategic thing that you've Splunk folks have actually done. And so that's really encouraging and so I would agree with you. Partnering, and we were talking through it, but as we were talking, it's like, this is better not to partner in this case. >> John: Better together. >> One of the things that's really important is that logs, you know, that's what were all about. We've actually spent a lot of time in trying to invest into this streaming world of dealing with things in stream. And these guys have perfected it for Metrix, which is, that's the strategic aspect of this. And then combining what they had already done with Tracing, with Omnition, it just doubles down on this future of this application architecture that I mentioned. >> Some MMAs have a couple flavors to them. You buy a company, you throw them under a general manager, an executive, they kind of live there. Founders lead, you get the core tech, some team. The other scenario is full team comes in, hits the ground running. They're building out. They're going to own the build-out. It's seems to me based upon the Omnition acquisition, you're giving Karthik and team, kind of some reign here. >> Rick: Yeah. >> To go build this out. Is that how you guys see it? >> Yeah, that's exactly right. And so, both Speros and Karthik report to me. I'm their onboarding czar, as it were. But were really what we're going to focus on is customer success and achieving our business case. And really capitalizing on the opportunity. These guys were running a hundred miles an hour and we got to get them to got a thousand miles and we're only going to make adjustments to the business case in order to achieve that. And that's what we're here to do is to shepherd this organization in its entirety to the greatness that I think we all see out there. We're going to do that in a very careful, cautious way. >> Karthik, Omnition is a acquisition stealth company. Kind of a commitment saying hey, here's some more horsepower. Talk about how that happened and what's the purpose behind that acquisition. >> Well, I can let Rick talk to how it happened. And I'll talk about the other plans, so. >> When we surveyed the market we actually found that people have certain strengths. These guys that actually started their journey into tracing. I guess their first release was last December and so they've made some strides. And we kind of found Omnition through this discussion and we went like, oh my gosh. And we were in the process of doing the acquisition, doing due diligence. And we set everything on their roadmap is what these guys have done and vice versa. This is another combination that we can't pass up. This is, and what I told him the day we closed, I said, if you had the capital you would have done this, and he's like, yeah I would've. (chuckles) >> One of the things that Rick had asked me during our process was, what are the top three things that you would invest in if you had Slunk resources behind you. And I said Microservices APM, Microservices APM, Microservices APM, and so. >> And I got a big grin 'cause I obviously couldn't disclose what we doing but.. >> You know, the Omnition team, they're still in stealth so there's not a whole lot out there on the web about them. It's a phenomenal team. They've got people who are committers on some major open source projects, deeply technical, very, very shared philosophy to what we had a SignalFx in terms of open instrumentation, not having any proprietary lock in how you collect an instrument data. Very similar philosophies around leveraging the power of analytics and monitoring. And we just actually focused on different parts of the problem because we're both relatively early in this effort. So, we effectively doubled up the teams capacity over night and accelerated our roadmap by several quarters, so, I'm really excited about what we can do together with them. >> Well, are they the Bay area or they from.. >> They are Bay area base, yes. >> Okay cool. Well, I want to get your guys' thoughts on the keynote today. Feedback was authentic, kind of very cool keynote. As you guys bring this together, Rick, Karthik team, the optics, the messaging, what's the core positioning? What's, as you guys look at wholistic view now that you've invested in and are building out for customers, what's the posture? Take us through the keynote positioning. What's the marketplace, customer message around the future here? >> Yeah, I think it's really clear that what we're trying to do for IT organizations and application development organizations is build solutions that are modern and helpful to their core mission. And, by the way as I mentioned, in the world of new development, it's different, it's a different solution set. It's a different approach, a different operating model than it is in current IT operations. And so, one of the key messages we wanted to resonate is that we have the right solutions in both these worlds for you and that we're trying to develop an operating model of reactive response, a quick response, or engaging the right person in the problem, through our use of VictorOps for example, and using that as a way to be very intelligent about how we educate the people that are engaging in resolution process. So, we are trying to create a bridge to both worlds so that they can both be successful. And then under pit that, of course, with automation that can be leveraged in both worlds as well. So, that's what we're trying to convey. We know it's early days, by the way, these guys have been with the company for three weeks, so, it's kind of like, wow. >> Culture shock. >> Culture shock. >> Throw into deep water. Yeah, let's throw you out on stage in front of 11,000 people and see if you can swim and they did phenomenal, by the way. But that was kind of the key message and we're so excited because we just, we feel like were just in the first inning of perhaps a 19 or 20 inning game, 'cause I think it's going to be a lot of fun. >> Karthik: Yeah it is. >> And it's going to be close out here but we're really excited to be able to bring this to market. >> I mean, it's amazing coming in now three weeks in to see the breath of technology that's available and that's going platform. And, you know, what struck me today watching the keynote was just, you know it's such a feature rich and such a broad platform from everything in the, with the core, indexing capabilities that everyone's known about a long time. All of the ML, the additional capabilities we're going to bring in on the metric side. >> Yeah. >> And then the use cases just across every persona, there's just so much that we can do. >> What do you think of the culture? Are they run hard? They a playful company? They like to work hard, play hard? >> Yup. >> But they also are focused on real customer value. They got great engaged communities. What's your take of the culture so far? >> Yeah, absolutely. I mean culture fit was a really important part for us if we're going to be acquired by a company and be a part of a larger organization. Their kindred spirits I feel to the way we ran SignalFx. It's a very customer focused organization, great technology and engineering culture. And it's hard to find both, right? It feels like every organization is very important and very well respected. It's not like heavily skewed to it's just all about engineers, it's all about sales, it's very balanced culture and it's very customer focused. >> Guys, congratulations. Big deal. They don't see these kind of mega deals, they come along once in a while. It's a big bet. Good luck with everything, Rick. Thanks for coming on. Final question for both of you, what's the big take-a-way to take back to the office as you leave .Conf this week? What's going to resinate the most with you guys that you're going to take back as feedback? >> For me its, you know, I get my energies from customer conversations. We all do here at Splunk. If you're having a bad day, go talk to a customer and then they walk you and stop you in the hall and say, you know we really thank you again doing what you do. And so it just, I take back from this always that what we do matters and is important and just keep chugging along at it because we're doing some really good work out there that's really helping lives. And that's really important. >> John: That's good therapy. >> Yeah. >> When a bad day, talk to a customer. >> Go talk to a customer. >> I love you guys. (laughs) What's your take-a-way? >> I'm just, I'm thrilled at the number of customers who are coming up to me and saying how excited they are about the acquisition and working with us. You know, that's really re-affirming for me and it's just super exciting to see what we have ahead of us. >> You guys have a great tech following. A lot of tech leaders who knew you guys, knew you had good stuff so congratulations. Great Validation. >> Yup. Thank you. >> John: Good job >> Thank you John. >> Thanks you guys for coming on theCUBE. Great insight. Thanks for sharing all that data. (laughs) Data to everywhere here on theCUBE. I'm John Furrier, more coverage after this short break. (upbeat music)

(groovy music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Everyone, welcome to this special CUBE Conversation here in Palo Alto Studios for theCUBE. I'm John Furrier, host for theCUBE. We're here with special guest, Arijit Mukherji, who's the CTO of SignalFx, hot startup that's now growing very very fast in this cloud native world. Arijit, great to see you. Thanks for coming on. >> Great to see you too again, John. >> So cloud growth is changing the landscape of the enterprise. We're seeing it obviously, it's no real surprise, Cloud 1.0 has happened, public cloud. Cloud 2.0 as we're calling it is changing the game, where you're seeing enterprise cloud really the focus. We're seeing cloud native really move the needle. Kubernetes has kind of created that abstraction, kind of standard, defacto standard, if you will, people getting around. So you're seeing the game changing from how apps are built. >> Right. >> To security and everything in between. So a new set of services, web services at scale has certainly change the game. You guys are in the middle of this with monitoring and observability. And I want you to help us understand the core problem enterprises are having today because they know it's coming. They know it's here. They got investments out there. The cloud has changed the game for the enterprise, what's the problem? >> Yeah, so you're absolutely right, John. So everybody's moving to this sort of the new way of doing things, right. So monoliths are gone. Microservices are in, containers are in. And you're going to have to do that because we know if you don't do that, you're going to get lapped right, by the competition. And so the challenge right now is how do you make that successful? And the challenge there is these new environments are much much more complex as you mentioned. And the question is unless you understand how these systems behave, how will you be able to run them successfully? So the challenge as far as monitoring and observability is concerned is I think it's critical that it be there for it to be able to sort of do this cloud transformation successfully. But it's a far more complex and hard challenge than it used to be. >> We've seen the evolution. Yeah, we've been covering that for 10 years. It's the 10th year of theCUBE. We'll be celebrating that at VMworld this year. You've seen wave one, lift and shift, do some basic stuff, not a lot of heavy lifting. No tinkering with some of the tech in there. Monitoring is great. And then you've got rearchitect. Let's get some cloud native. Let's see some Kubernetes. And then the next path is this complete microservices. This is where everyone's really excited about. >> That's right. >> This is where the complexity is. So I got to ask you, that changes the notion of monitoring and observability. So given that this shift is happening, rearchitecting to full microservices, what is observability in that equation? >> Right, so there's a very interesting difference between I think, monitoring and observability that I think I would like to touch upon here. So you know, back in the days of the monolith, it was what we called classic monitoring. Monitoring is about looking at things, looking for things that you know might happen. So for example, if I know my server might fall down, I will run a probe to make sure that it's up or not, right? But when you move to this new world, I mean, have you, if you look at any cloud native environment with all the microservices and containers, for example Amazon's S3 has 120 different microservices powering it behind it. Now, if you were to go and ask an engineer like what is the map or how are the data flow happening in that environment? I guarantee you, no one person can probably do that well. So then, monitoring doesn't work because I don't even know what to look for. So what's important is I be able to gather telemetry, have the information available so that the unknowns, the kind of things that I'm not expecting because it's just too complex or just unanticipated. Like that data will allow us to figure out what went wrong. So observability is about gathering telemetry and information so that we can deal with that complexity, understand problems as they behave because the world is no longer simple anymore. >> So overall, observability is just monitoring in a dynamic environment, what you're saying. 'Cause monitoring used to be simple. You know it's going on. Static routes. >> That's right. >> Set policy, get some alarms. Network management, basic stuff. >> Exactly like Nagios checks and what not, yup. >> Now, you're saying there's unknowns happening, unexpected things going on around the services. What would that be just as an example? >> Yeah, so for example, again with microservices, why are we doing it? Because we want smaller teams to be able to innovate quicker, faster, right. So instead of my monolith, let's say I have whatever, SignalFx has 50 different microservices powering it. Now each of these teams, they are deploying software on their own because the whole idea of Cloud 2.0 is that we are able to move faster. So what that means is individual chunks of my overall service are adapting or changing over time or evolving. And so that's the complexity, like it's actually a changing landscape. Like my map does not stay the same on an ongoing basis. That is fundamentally a big challenge. The other challenge that I would mention too is that how ephemeral things are getting. So all these microservices that are themselves adapting, they're also being deployed in containers and by Kubernetes. Where these containers, they keep popping up and down all the time. Like even on infrastructure on which we are running it's extremely dynamic, right. Containers, Lambdas, sort of serverless is another great example of that. So it's a very shifting sands is what we're standing on, in some sense, right. >> And a lot of times, we cover a lot of real time. And you can't just throw in logs, you got to have that in there. This begs the question, okay, so I get the complexity. I'm a customer or I'm someone who wants to really go down this observability track with you. Why is it important? What's in it for me? >> So in the end, without it, how will you succeed? So it's almost like will a pilot with blinders on, will he be able to fly an aircraft? The answer is no. Similarly, I mean we may want to move to this modern awesome environment, which lets us move fast but unless you have visibility into it, unless you can find when problems are happening, unless you can, when those problems happen, be able to find the root cause and remediate them quickly, you're not really going to be successful. And so that's really why observability is important because it allows us to not only sort of run this well but it also allows us to understand the user experience because in the end, we are all service providers, we have users, right. And so understand what the user's experience is like. So that's important. Understand the key business metrics. If you look at a lot of the talk track that's been going around in the circuit around error budgets, and SLIs, and SLOs, which are sort of important things. The whole idea is that we want to measure and monitor what's important to the business, to the user. And that's kind of what observability allows us to get. >> You know gone are the days of a few application servers and a database. >> That's right. >> So on the why is it important, I got to follow up and say from an operations perspective, what is the new reality, okay? Because we know there's going to be a lot of databases out there, and a lot of different applications. You mentioned some of the containerization, dynamic microservices. But what's the impact and what's the importance to the operation side of the equation with observability? >> So what's happening now is again, back in the monolith days, the operators, the IT staff, who were running those infrastructure, they were the ones who would implement monitoring, right. But if you see the way now these environments are structured, these organizations are structured, it's the developers who are building tools. They are the ones who are also running them. And in order for an organization to be able to move fast, they need to give powerful tools to their developers to do their job. And because there is no one person who knows the right way of doing things. So it's really about sort of democratizing that capability. So you will need to give powerful observability tools to the developers, the operators, who are also the new operators, to sort of make with it what they will, in the sense that they are ones who best understand the meaning of the data that's being collected. Because it's all very specific to individual microservices. So that's really a powerful observability platform is one that allows you to easily collect a lot of information, allows you to analyze, visualize it, and sort of treat it in a way to sort of it helps you answer the questions you want to answer. >> So you're saying that okay, ops gets monitoring and observability. But a new persona, user is a developer. >> That is correct. >> And what do they care about? 'Cause they just want it to be abstracted away. They're not really probably wake up and say, "Hey, I can't wait to look at observability." So is it more of a use, so talk about the developer dynamic 'cause this is, that seems like a new trend. >> Yes, it totally is. So things are becoming less about black box testing, and more about sort of observability being an end-to-end process. So let me tell you what I mean. So back in the day, let's say, I implemented, I deployed a monolith. It was a Java server. There were standard ways to check them as a black box. I could run probes, et cetera, to run a health check end point and whatnot, life was great. But now, that's obviously not good enough. Because as I mentioned, because of interactions, because of complexity, a black box testing doesn't even work because like I said, the whole environment is very dynamic. So what the pattern now is that as I said, observability is an end-to-end process in the sense that developers care about observability when they're writing the software. It is not an afterthought anymore. So as I'm writing, as I'm developing software, I think about well, when this thing goes out into the wild, how will I monitor it? What are the things that I care about as a developer? Because I understand the system the best. And so you instrument, you build systems for observability is I think a big change that's happening. And once that happens, when you are the one person, who also is able to best read that data. >> So while they're developing, they get these benefits inherently right there on the spot. >> That is correct. >> This is kind of consistent with the live programming trend that's really popular in some languages. Rather than doing all the debugging, post event, coming back to it. >> That's right. >> So making it very efficient seems to be a use case. >> Yes, you are absolutely right. It's one of the things I'll actually talk about a lot actually is you know, observability, what is it for? Is it just for telling me when my production is not working well? The answer is no. Even when I'm developing, I may want to know well, did I have a performance degradation? How do I know that the code that I wrote is good? So I use again the same telemetry that I'm going to use later, even during the development process to make sure that the code that I wrote works well. We do the same thing during deployment. Again, I deploy a version or a canary or a few of them. Are they running well, right? So it is not just about what's happening in production, it's about end-to-end from development and deployment up to production. >> And that's what developers want. They want it in the moment, right when they're coding. >> That's right. >> Taken care of. >> It's instant gratification, like everybody else wants. >> And more efficiency. They know it's going to break, they know the consequences, they can deal with that. >> Yes. >> This is awesome, so the next question I have for you is how do you implement observability? >> That's a great question. So you can think of it as sort of in two ways. One is the means through which you get it. So you get observability through metrics, through logs, through traces, through probes, et cetera. That's one way. Another one is I think I alluded to a little bit earlier is what are your goals? Because everybody's goals are different, right? And if you think about in that sense, then the sort of the purpose of observability are a few. A, is it allowing your teams to move faster? So I spoke about some of the process just about earlier. Are they able to deploy code with confidence, faster? When problems happen, how quickly are we able to then triage them? So the whole incident review process. That's kind of important, observability better help me with that. The user experience is also something very important. As I mentioned, observability is going sort of more up the stack, so to speak. And so being able to understand what the user experience is, is very important. Similarly, understanding from the business point of view, what does the business care about? For example, when I had that outage, how much loss did I have? How many eyeballs did I miss on my side? So I think one way to think about it, you need to have good processes, good tools. At the same time, you need to be clear about what your goals are, and make sure that sort of whatever you're implementing, sort of furthers those to some extent. >> I'd like to play a little CUBE game here with you, and walk through the observability myths and reality. >> Sure. >> I'll say the myth, and you can tell me the reality. Myth number one, having observability reduces incidents. >> No, actually it might increase it. Let me put it that way, I'll tell you why. So it's almost like in a human, I may be measuring someone's temperature or pulse rate like every day. Does that make the person less or more prone to health problems? Chances are it's going to be the same. I might actually find things that I was not aware of, right. So in that sense, just having observability does not necessarily change anything about the process. But what it will do though is when a problem does happen, it I have this treasure trove of data, which I can then use to quickly isolate the problem. So what it does is it shrinks the outage time, which is in the end, what's very very important. So while it may not reduce your outages, it will definitely make them better from the end user point of view. >> Second myth, buying a tool means you have observability. Reality? >> No. Having a doctor doesn't mean I am healthy. In the sense that I think a tool is very important. It's a very very important step but the question is how well adopted is the tool? What kind of data am I sending to the tool? How are the users, my engineers, how well versed are they in using it, right? And so there's a lot of other stuff associated with it. So tool selection is very important but I think adoption and making it a success within the organization is also very very important. >> Okay, final myth, observability is free or cheap. >> I wish, so. >> Well you're a for-profit company. >> That's exactly right. No, I think, I really feel that observability is almost like a, it's an associated function. As I mentioned earlier, if I'm going to be successful flying this plane, I need commensurate amount of other services that sort of help me make that successful. So in a way, one way to think about it is it is it scales up as complex and as large as your environment gets and justifiably so. Because there's various other reasons too because in a way, adopting new technologies all the time. So tools are getting just more and more complicated. My requirements are getting more complicated. And then another thing I would add is the quality of my service like the level, the quality of service that I provide, the higher bar I want, I probably am spending more on observability. But it's a justified cost. So I think it's not a fixed cost obviously. It grows with your complexity and the kind of quality that you want to provide. >> Well, it's also, I mean I think the observability challenge with the complexity is there's a hidden cost though if you're not observing the right areas. >> Yes. >> The cost of not having that visibility, as a blind spot, could have business benefits, I mean not benefits, but consequences in a sense of outages, security, I mean there's a lot of different things that you got to have the observation space being enterprise. >> You totally have to do that. It's actually one thing we like to say is that instrument first, ask questions later. Now, coming from a for-profit vendor, it may sound sort of self-serving. But it's kind of not so too in the sense that I mean hindsight is 20-20. If I am stuck in a bad situation, I have no telemetry to sort of fall back on. Then where do I go with it? So I think we should be more conservative and sort of try and instrument the things that we think might be applicable, the kind of questions I may want to ask when a rainy day comes. So I think you're absolutely right. So it has to be something. It's a philosophy that developers, engineers, should sort of imbibe and they should then practice it as part of their sort of own workflows. >> I want to get into where company should invest in observability but I want to just throw a wildcard question at you, which is when you look at the big data space, even go back 10 years ago to now, cloud, there's always been they're talking about tool versus platform. And anything that's been data centric tend to be platform like conversations, not a tool. Tool can be like okay, it does a thing, does it really well, a hammer, everything's a nail with the hammer. But there's more dynamic range required 'cause you're talking about observation space, talking about cloud, horizontally scalable, hybrid on-premises. >> That's right. >> So again, it kind of feels like a platform technical challenge. >> You're absolutely right. So I think two factors at play if you ask my opinion. One is if you were interested in monitoring, a tool is perfect, right? Because you kind of know what you want. If a tool does that well, there's more power to it. But if you don't know what you want, if you are basically collecting stuff and you're depending on it as a way to answer questions on the unknown unknowns as Charity Majors like to say, then you do need a platform. Because that platform needs to be sort of inclusive. It must have data of different types, all be able to come into it. It's not really meant for a specific purpose. It's meant to be a generic tool. So we do see this trend in the industry towards more sort of a platform approach to this. Obviously, they will have tool-like capabilities because they're answering sort of particular use cases, et cetera. But the underlying platform, the more powerful it is, I think the better it is in the long run. >> Yeah and the argument there could be if it's an enabling platform, you can create abstraction layers for visualization. >> That's correct. >> Or APIs, other services. >> That's exactly right, yes. >> Okay, so now, I'm interested. I want to get started. How do I invest in good observability? I'm crossing the chasm, I'm going to full microservices. I've done a rearchitect, my team has got cultural buy-in. We're hiring, we're building our own stack, we're going to have on-premise, we'll be in the cloud. In some cases, fully cloud. What do I do, what do I invest? What's my play book? >> Sure, so I think we talked about the first one a little bit. So you have to choose the right tool. And the right tool in my opinion is not the one that does the best job now. When maybe I'm small, I'm not fully there yet. We have to think about what's the right tool or the right platform for when I'm, where for where I do want to go. Now, that may be commercial, that may be open source, that's not the point. But the point is that we need to have a very considered thought about what is it that we're betting the farm on, right. So that's number one. But that's not good enough. So as I mentioned, we need to make sure that there is a usage and understanding of the tool within the organization. So a big part of it is just around the practice and the culture of observability within the organization. So for example, good habits like every time we have an incident, you speak about these are the things that we measure, this is how we use our observability tool, here are the dashboards that we depended on. Sort of reinforcing those concepts over and over again so that those who are on board, they are obviously doing it right. Those who are not, they see the value and they start sort of using those good practices. That's kind of very important. The third thing I would say is start moving towards more higher level monitoring, observability. So measure the user experience, measure what's important to the business. Stuff like that are important. The fourth area that's sort of very very key is around sort of the whole incident management process. This is actually a very active topic. A lot of discussion going on out there right now is you know, it's great that I have this great tool, I have all this telemetry. I found there was a problem. But when that incident happened, there are a lot of again good practices. This is part of the whole culture and process of observability is how do we make that process smooth and standardized, and sort of become more efficient at it, right? And let's say you were to do that, the final sort of end goal is as we like to call is a self-running or a self-automated cloud, where do we really need humans in all of this? How can we sort of run remediation in a more and more automated fashion? At least for the stuff that maybe does not require a human intelligence, right? Actually, you'll find that 80, 90% of issues probably don't. If you think hard about it, don't require human. So I think this move towards automation is also another sort of very fantastic trend. I've seen that being very successful in the past. Some of my old companies. And I think that's going to be a trend in later stage. >> Yeah, for known processes, people and process. >> Yup. >> That's where problems come. Bad process or code and people mistakes. You can automate that. Mundane tasks or on differentiated kind of heavy lifting. >> That is exactly right because you know, there was this interesting study done that was commissioned by Stripe, where they found that among the CXOs, they value engineers and developers more than money. Because getting them, because they are such a scarce resource. So if you do get them, you probably don't want them to sort of run and do mundane things. That's not what you hired them for. So you want them to do the work of the business, and the more you can isolate them from sort of the mundane and they have automation come into play, I think it's just better across the board. >> Oh, they're investing more from the CSOs and the CIOs we talked to. >> Right. >> There's more investments in-house now for real development, real software development, real projects. And those top talent, they want to work on the toughest problem. >> That's exactly, that's why we're moving to a SASS future right? Because all the function that are not core to my business, I want to farm off and have somebody else take care of them. >> So you got me sold on observability, I'm a big believer in the observation space. And I certainly think cloud as they're horizontally scalable, elastic resource, and certainly, the Kubernetes trends with service measures and all the stuff going on, Kubeflow, and a bunch of other things. More and more services are going to be very dynamic. >> Yes. >> So you're going to have a lot of unknown and unusual patterns. >> That's right. >> That's just the way the internet works now. So you had me sold on that. Now, I need to get my team to the next level. They bought into devops. How do I take the temperature of where our IQ is in the life cycle of observability? Because I got to know where I am. Is there a way I can track my maturity or progress? >> Yes, yes, it's a topical question because I go out and I meet a lot of prospects and customers. And it's part of my job. And a lot of times, because sort of we are sort of in the leading edge, they will ask us, they look to us to tell them like are we doing it right? Or how did you guys do it? So just so that sharing of information, how can we get better? So as part of that, we actually, at SignalFx, we actually built a maturity model. It's a way for us to sort of evaluate ourselves across various dimensions to see how well are we doing? Not only, it's not just the score, it's also about how well are we doing? But how can we improve? Like if you wanted to go to the next level of maturity for example, like what are some of the things that we can do? And it spans multiple dimension. It starts with how are we even collecting data, right? How easy is it, in other words, for somebody starting a new service or using a new software to get to the business of observability? That's kind of important. You got the data, how will am I able to visualize it? Because effective visualization is as you can understand, very important. The next part comes with alerting. So well, things are running. I know how they're going. How well can I detect when problems are happening? How soon can I detect when problems are happening? What kind of items can I monitor? Can I monitor the low-level things? Or can I monitor the higher-level constructs? When the problem happen, let's talk about remediation. How quickly can I triage the problem to find out where it was? What kind of tools and slice-and-dice capabilities do I have? That's an important part of it. Let's say I did it. After that comes things like remediation. So I found the problem, how well can I remediate so like we talked about automation. So there is multiple different categories where we sort of, we talked about what, we've seen in the field in terms of what people are doing as well as some of the best practices. >> So you're going to make this tool available to customers? >> Yes, we have, it's actually available on our website. And if you come to our website, you'll be able to sort of run the assessment, as well as sort of see all of it yourself. >> Well, we've been following you guys since you launched. You've got a great management team, great technical chops, we've covered that. And this observability is a real trend as it moves into more complexity as we talked about. Most customers that are getting into this are trying to sift this from the signal, from the noise, and trying to think, decide who is the leader, and who is not. So how would you describe what a leader in enterprise observability looks like from a supplier's standpoint. You guys are one, you want to be the leader. You're the market leader. >> Right. >> What does a leader look like from a customer's standpoint? What are the things that have to be in place? What are the table stakes to be that leader? >> Sure, that's a great question. And yeah, so we did, SignalFx, we did build SignalFx to be a leader in this space, frankly. And there's a lot of different aspects that goes behind like what creates a good supplier in this space. One is I think you have to be open and flexible. Like you have to be, it's a platform play. You better be able to collect data from all the systems that are out there. The kind of the quality of the integration is very important. Another big thing we're finding is scale. A lot of these systems might not work when you move to sort of large numbers. And the problem that we are seeing is while I may have a hundred servers, I may be running 10,000 containers in those hundred servers. So now, everybody is a scale player, right. So the question is will your platform really be able to handle the complexity and the load? So that's an important one. Analytics as we mentioned is another very very important capability. I'll like to say that the ability to do analytics is not just good enough. How easy is it to use? Like are you developers and engineers, are they even using it? So the easy and the capability of analytics is important because that's kind of what allows us to measure those KPIs, those SLIs, those business metrics. And so that's kind of important. Slice-and-dice capability like how fast is the tool? Because when that outage happens, I don't want to run a five-minute query to sort of find some suspicion or you know to. And so the question is how quickly will it answer these ad hoc questions for me when the problem happens? So sort of the whole triage process that I talked about. The ability to support automation is one. The ability to, as I mentioned to take in different types of data, traces, metrics, be able to play with logs. All of those are sort of important aspects of it, yeah. >> Final question for you. If a customer says, "I'm going to cross the bridge "to the future with SignalFx." What's some of the head room? What's some of the futures that you would expect the customer to imagine or expect down the road as observability becomes more scalable? I can imagine the metrics are going to be all over the place. >> Yes. >> A lot of unusual patterns. New apps could come in that could be hits. And new data comes in. So as you take them today in observability, what's the next level, to cross that bridge to the future? >> Sure, sure. >> What's the next expectation? >> I think one thing will be to expect the unexpected. Because the world is changing so fast, I think you would probably be running things that you won't expect later. But a few things, I would say yes. So I think the proliferation of metrics and traces is like a big trend, where we see there used to be this dependence on sort of these monoliths and APM that sort of is transforming a little bit. There is this also this concept of using data science and artificial intelligence to come to bear on this space. So that's actually an interesting trend we see, where the idea is that it's hard because it's a complex system. It's hard for humans to define exactly what they want. But if the system can help them, can help identify things, that's actually really fantastic. Another one that we sort of briefly touched upon is automation or self-automated systems, where I think well, the time you're going to see that platforms like ours are going to help you automate much of this in a safe manner, because these are controlled systems, where if you, things can go awry and that's not a good position to be. So these are some exciting areas, where I think you will see some development down the road. >> And we've been seeing a lot of conversation around correlation and causation, and the interplay between those as these services are being stood up, torn down, stood up, torn down. You can look at the numbers all day long but you got to know causation, correlation. >> You bet, you bet because I think a lot of times, we naively think about this as a data problem, right? Where I find the kink in the graph, and if I go looking, I'll probably going to find a hundred different things that were sort of also correlated. Some of them may or may not be related to it. So I think a good tool is one that sort of gives you a sense. It sort of creates a boundary around the data set that it needs to look at, that is sort of relevant to your problem, and able to give you clues to causation. That you're exactly right because again, complexity is a hard problem to deal with. And anything that we can do to sort of help you short-circuit some of the pain is awesome. >> And I think you're on the right track with this developer focus because devops has proven that the developers want to code, build apps, and abstract away the complexity. And certainly, it's complex. >> That's right, that's right. It's fairly complex. >> Arijit, thanks for coming on. Arijit Mukherji, the CTO of SignalFx here inside the special CUBE Conversation breaking down the future of observability, where monitoring is going to the next level, certainly with cloud, impact to enterprise cloud. I'm John Furrier, here on theCUBE. Thanks for watching. >> Thank you. (groovy music)

>> live from London, England. It's the queue covering a ws summat. London twenty nineteen Brought to you by Amazon Web services >> Welcome back to London Summit Everybody, this is David Lamont and you watching the Cube, the leader in live tech coverage. We loved to go out to the events. We extract the signal from the noise. This is our one day coverage of a WS summit London, and it's packed house twelve thousand people here. The twenty six thousand people registered, which is just outstanding. Chris Bunches. Here's the general manager of a MIA for cloud reach, and he's joined by Mark Randy, whose CEO of signal FX. Gentlemen, welcome to the Cube. >> Thank you. >> Okay, let's start with signal effects. What's going on at the show? What's the buzz like? >> Very busy. Dozens deep. A lot of demonstrations feature in our massively scalable metrics platform and distributed tracing platform. So we've had a very good show. Good showing in London. >> Good. We're going to get into some of that. Chris, tell us about cloud reach. What you guys do? >> Sure. So Cloud Reach was founded in two thousand and nine. So quite a long time ago in the history of cloud confusing, at least >> was right after the Cloud City with >> quite a pure vision around helping complex organizations to adults public cloud computing technologies to doom or faster and better. That's all we've ever done. It's all we ever intend to do way work these days with enterprise organizations across the cloud lifecycle starting with adoption, helping them to understand White Cloud. How am I going to do this? How am I going to move my data center's into the cloud? How am I gonna build new services moving on through the life cycle? We help them with that. At that migration, we helped them to shut down their data centers on rebuild them in a WS. We helped build New Cloud native Services. Using the latest offerings from from Amazon and other cloud providers, we worked with him on Data analytics, helping them to generate insights from their data. Data flows in an ever faster pace from across the across the world into their organization. On all of that is wraps with an MSP manage service twenty four hours a day, seven days a week. >> So, Mark, I gotta ask you so back back in the day, the narrative was that the public law was going to kill every man, his service provider out there. It's been nothing but a tailwind for your business. Business is booming. What's what actually happened to give you that? Left >> on the signal effects side I look, the big trends are the move to the cloud number one. The second piece is just a change in the architecture's you know, the move to communities, the introduction for elastic burst e type use cases of things like Lambda and and that even more importantly, just the process of developing software movement from, you know, waterfall, Dad, agile and the Whole Dev ops movement in introduction of micro services. So that's it's It's just a lot of a lot of these ways been going on for quite some time, but they're really starting to hit the shore to shore right now, and I think it's been a great great opportunity for companies like Cloud Reach Tio to take advantage of were very excited by the partnership. >> Well, it has. It has ripple effects on the rest of the business, doesn't it? I was saying earlier in a segment that it used to be the business of No, we can't do that because and now you look around this audience, it's all doers and builders, and, you know, it's it's actually great marketing because it works, doesn't it? So clouded has been a fundamental component of >> Yeah, I mean, our whole businesses around making t v enabler helping businesses to innovate. Once upon a time, the message was all around. Cost saving is the reason to move to the cloud, and there's still an element of that. Nobody wants to pay Mohr, but actually, increasingly, what we're seeing is organizations moving to Amazon because they want the agility, they want to move faster. And they don't want to be the the culture of no and have a process that takes six months to deliver a new service to the business. They want to be out of deliver things in hours or minutes in the some cases, and they want to do so quickly on they want to innovate, a pace that they've never been able to before, partly from a competitive threat perspective and partly from a market opportunity. There's so much, but we can deliver to customers if we put our minds to it and use the primitives, the Amazon providers, as building blocks to enable new >> services. You know where you live in the Bay Area. I spent a lot of time out there, were based in Palo Alto and use a vortex that unique that sometimes I think way think that that's where all the action is. You come to London and you see all these startups. Every business is becoming a software company. And you know, we don't in Silicon Valley in America have a monopoly on innovation anymore, >> not even close. So there's a lot of great innovation all over Europe. Uh, here in the U K. All the way to Northern Europe, Doc, uh, Paris Way we see it across the board. So >> So what are people doing? They building new cloud native APS in the public loud. Are they doing a lifted shift and trying to get more agility out of those traditional APs? What's the landscape? Looks like? >> It's ah, combination of the two. The startup organizations, of course, is starting with no legacy. There's nothing to my great and they are building cloud native and they're doing so far, >> we have no I d >> no. Yeah, technically, before nine years, four hundred on eBay test migrations. But that's the only hardware for the museum. Exactly the larger organizations. They have huge volumes of legacy infrastructure, some of it dating back to the seventies. In the case of financial institutions or public sector, then all of that is an opportunity to modernize, and not just for the agility and innovation but in some cases just to reduce risk. There is huge business risk in these old, untouched, dusty, cobweb ridden servers that nobody understands anymore. And there's a really opportunity to move that to the public cloud, reduce and remove that risk. And while you're there, take advantage of the new technologies and innovative deliver a better service to you or in consumer whoever that may be >> so prik uber, Netease and micro services, even though containers have been around for a while. But the modern doctors ascendancy. You know why? To K was the year of the decade of modernization. It was like four or five years leading up to y two K at some I T shop said, Okay, we're going to modernize, but but none of these micro services existed, so it really was. It was about dates, maybe some application portfolio rationalization. What's different today that I could take those apse that were written in the seventies with a lot of custom code? How am I able to modernize, though >> I think it's the maturity of the services. You look at something a platform like Amazon. There's one hundred twenty hundred thirty, or Mohr. It grows almost every week. Building blocks primitives, the Amazon are providing, and its a rating on it. At an incredible rate on DH, there's almost a service for everything. And when you think they've run out of services to introduce, a new services is created. And, you know, we talked about micro services. They introduced Lambda back in two thousand fourteen, which was there. Serve Elice environment driving event based micro services architectures, and it's ahead of the game. It's ahead of the curve. It's causing people to think very differently about what's even possible from a night perspective. And there's no way. In most organizations, you, Khun, build that kind of infrastructure on that kind of platform that is build and costs you on a Microsoft microsecond basis. I mean, it's it's >> incredible. It was amazing. I remember the first virtual machine. It would be anywhere that I saw spun up like, Wow, this is going to change the world. And then the cloud comes along like a while. This is going to change the world. And now survivalists. I don't even have to deploy servers anymore. It's side by Amazon >> way. See this? Even even in some of the more traditional organizations we we worked with in the UK and in Germany and France and elsewhere, you don't even need to be looking at service. Just the ability toe programmatically spin up a virtual machine without a human touching anything. That's incredible to some organizations, right? They're used to it, taking six months to provision of infrastructure to deploy an application. Now they can click a button, and by the time they've made a cup of coffee, it's it's up and running, and it's It changes the way people >> think So much Talk about Cloud Region signal effects. What's the partnership like between you two and what's your partnership like with eight of us? >> Um, on the cloud reach side, we went through an extensive evaluation by cloud reach, and over several months they evaluated all the alternatives on the market and ended up selecting us to be their standard for their many service provider business. It's We're super excited about that. On the go for it, we're rolling that out with them there. Current customer based on DH. We were hoping that, uh, using signal effects, that cloud reach that will help them be the point of spear on all cloud native. You know, in their marketplaces, they go pursue other customers, so it's pretty excited about. >> So it's not a pressure release deal, not a Barney deal. Like we like to say that >> they're up there, They're a paying customer. And, you know, I made a big bet on signal effects going forward. >> So why the choice to go with manage service provider? You have You could have built it yourself and take us through that. >> Yeah. I mean, the nature of the business we're in is very much predicated on the fact that you don't build it yourself. You know, you look at the market and if somebody is already doing it well and provides excellent service as a commodity, you use it. We've been in the MSP space since round about twenty ten very soon after the the company was was founded, and we know it pretty well. We have a large customer base. We are one of the top tier MSP for along the major cloud vendors in the world, lots of large organizations. However, as we look to refresh our tooling with a view on Maura, an application centric approach, which is what all of our customers want and expect a CZ we look to micro services and the very latest platforms and technologies he's being released by the hyper scale cloud vendors. We recognize the need for a newer, more modern tooling on DH. After a thorough evaluation, a CZ mark says signal effects came out on top. Why is that? Partly it's the cloud native element. You know, some of that sounds a little bit like a marketing buzzword, but in reality, what it means is the company was founded relatively recently and as a result, was geared towards modern technology. So out of the box they support doctor, they support containers, they understand, and they're orchestrated around micro services. It deals with scale on volume, and we we want to low test things in a big way. We only serve large scale and surprise customers. And they are going to throw tens of thousands of containers on micro services at their tooling, and it has to be able to track tto handle that massive volume of transactions. >> It's a complicated picture, actually. You know, sometimes micro services aren't so micro. Yes, and you've got to secure all these containers. Got spinning up of'Em is easy. >> Well, >> you see multiples. So how do you guys deal with that? I mean, you're obviously experts at it, but But give us the sales pitch >> on. Yeah. So I think you kind of you covered it earlier with, You know, all these great new technology with introduction of micro services. I mean, developers in our writing it the running it, they're pushing code directly into production environment. You know, you went from releasing code once or twice a year, a few years back now toe several releases and you know your people lifting shift. They're starting with a few micro services. Someone we're getting up into the hundreds, even thousands in our most advanced deployments. It it it ends up being worth a situation Where Alright, all this innovation is great, but it also introduces a ton of complexity. And based on the way we've architect of our system, really time streaming like within seconds, you're going to need to see it, to react to it, whatever the use cases. And that's what differentiates signal FX is this massively scalable streaming architect we built for from a Metrix platform standpoint and then from an Eastern West standpoint for your from your custom code are Micro Services, a PM solution on top of that to go help measure what those transactions air how they're performing across the entire complex environment. So we feel like we're just purpose built for today to help in the lift and shift crowd and or for the more advanced customers, they're intothe point dozens, if not hundreds of micro services. >> Tell me more about this metrics platform you mentioned a couple times. What is that all about? >> Well, we start with essentially, you know, the three big pillars are logs, metrics and eight p. M. And you know, our company was found it. We have deep roots. Back in the two thousand seven ranges, our founders were you know, they built the monitoring stack at Facebook and so had several years, you know, kind of earning and learning that secret. You know, in the early days, they didn't call it Dev Ops. Back then they called it move fast, break things, didn't call >> it. They didn't call it >> a micro services. I mean, and then twenty, twenty, thirteen, early, two thousand fourteen. That's when the founders got together and started. The company is also the same time frame. Doctor came out. Were just purpose built for this for this environment. >> Final thoughts. Yeah. Thie event where you guys were headed. Maybe little road map, if you could. >> The event has been incredible. Every year it gets a little bit bigger. It gets a little bit more exciting. There's, ah, bigger range of organizations, different industries. And it changes a little bit over time. This year, financial services has been particularly of interest for us, but this event is a lot of large large banks, investment houses, those kind of companies here on DH. That's been really exciting for us. I think trend I'm most excited about is really around machine learning. Amazon talked about it in the keynote this morning and democratization of very, very complex technology bring it to the masses is a as a manage service that can be provisioned in minutes and seconds. And to me that something that's that's really exciting and using the signal FX platform, we're now in a position to provide manage service wrappers around the machine learning based solutions that we build for our >> customers. Yeah, the financial services. Interesting. Back in two thousand nine when you started, a lot of the banks in New York thought they could scale and compete essentially with KWS >> world. The world changes very quickly. Absolutely >> final thoughts for you. >> Yeah, I think they think we're moving past that point. You know, even the later adopters. I think we're moving past that point and look at that name there getting pressure from the startup community, whether it's intact or or any industry's gonna have that type of pressure. You talked about that y two k moment. I think in any vertical out there, it's that you know those cloud native type companies the companies are becoming software companies were going toe transform yourself or you're going to have some pressure from the start up going forward. We're >> guys. I'm thrilled that you could make time to come in the queue. Thank you. Thank you. Thanks for having us. All right. Keep it right there. But it is. Dave Alonso will be back with our next guests right after this short break. You watching the Cube from London? Eight of US Summit right back.

>> Narrator: Live from San Francisco, it's theCUBE, covering Google Cloud Next 19. Brought to you by Google Cloud and it's ecosystem partners. >> Hello and welcome back to theCUBE's live coverage, here in San Francisco, the Moscone Center. This is theCUBE's live coverage of Google Next 19, Google Cloud computing conference. I'm John Furrier, Dave Vellante my cohost. Stu Miniman's here as well, he'll be coming on doing interviews. Our next guests are the founder and CEO of SignalFx, Karthik Rau, and Leonid Ingolnik, EVP of engineering. SignalFx has been a great company, we've been following for many, many years. Pioneer in a lot of the monitoring and serviceability of applications, now prime time, the world has spun to their doorstep. Karthik, congratulations on your success. It's prime time for your business. >> Ya, thank you, John. >> John: Welcome back. >> Great to be on, we're on again. >> I'm glad that you're on because we talked six years ago about some of the trends, we saw early. We saw the containers, Docker movement, and also Kubernetes got massive growth. You had the visibility of what these services are going to look like, cloud web services, kind of the next level. It's kind of here right now. >> Yeah, absolutely, there are two things that we predicted would happen. One was that architectures would get a lot more distributed, elastic, and it would require a more low-latency monitoring system that could do realtime analytics. That was one of the key changes. And then the other thing that we predicted was that developers would get more involved in operations. Which is the whole DevOps movement. And now both of those are very much in the mainstream, so we're really excited to see these trends. >> And looking at the Google keynotes today, obviously we're starting to see the realization of true infrastructure as code, you're starting to see the beginning signals of, look at, we can actually program the infrastructure, and not even have to deal with it. This is key, and you guys have some hardcore news, so let's get that out of the way. You guys got some updates, let's get into the news, and then we can get into the conversation around what you guys are doing in the industry. >> So, today we're bringing three things to the conference, to boost customers and prospects, starting with announcing our support for cloud functions. Cloud functions are great technology that we're seeing adopted by retail. For spiky workloads, things where you have a flash sale and you need to understand what's happening, it may be lasting minutes, where our platform really shows off the best, which is the one second resolution data. Some of our flash sales we see from existing customers don't last a minute, right, so looking at this in a minute resolution of being able to react to this in a machine time rather than human time, is something that our customers now expect. The second thing we are focusing on is Istio, and Istio on GKE specifically. We're seeing service mesh adoption continuing to go both in new, modern application, as well as taking legacy workloads and unlocking the potential of taking those legacy workloads to the cloud. And with Istio, and specifically on Microservices APM, it's not just applicable to Microservices, we see a lot of our customers realizing a lot of value from tracing abilities that a service mesh like Istio provides, an ability to understand you topology and service interactions for free, out of the box, whether it's on-premise with Istio or on the Google environment. And then lastly, so we see customers and prospects adopt Kubernetes, we're also starting to see the next layer above Kubernetes coming in. And, with Knative, getting the support out of the box, whether it's the dashboard, the tracing of the metrics, and that, that's the third announcement we have today. We're fully integrated with Google's offerings, and we're able to monitor and provide you with some actionable content, just in a flick of a switch. >> So support of Knative out of the box. >> Leonid: Out of the box. >> Full SignalFx, with Knative on Google Cloud. >> That is correct. So those three things. >> Karthik, I wonder if you could give us some insight as to what's going on in the marketplace. A multicloud is obviously a tailwind to you, but multicloud, to date, hasn't really been a strategy, it's sort of been an outcome of multi vendor. So, is multicloud increasingly becoming a strategy for your customers, and what specific role are you playing there to facilitate that? >> Yeah, absolutely. I think particularly most of the larger enterprise accounts tend to have a multi vendor strategy, for almost every category, right? Including cloud, which typically is one of their largest spends. Typically what we see is people looking at certain classes or workloads, running on particular clouds, so it may be transactional systems running on AWS. A lot of their more traditional enterprise workloads that were running on Windows servers, potentially running on Azure, we see a lot of interest in data intensive sorts of analytics workloads, potentially running on GCP. And so I think larger companies tend to kind of look at it in terms of, what's the best platform for the use case that they have in mind. But in general, they are looking at multiple cloud vendors. >> So we heard some customers onstage today, talking about their strategy, I think Thomas asked one retail customer, how'd you decide what to put where? And essentially he said, well, it's either going to go into the cloud, lift and shift, we're going to refactor it, reprogram it essentially, or we're going to sunset it. What he didn't say is, we're going to leave some stuff on-prem. Which somewhat surprised me, 'cause of course, especially into financial services you're going to get a lot of stuff left on-prem. So what's your play, with regard to those various strategies, and for the legacy stuff, I know you're cloud native, that's your claim to fame, but can you help those legacy customers as well? Talk about that. >> Yes, absolutely. >> So I think, what we've seen is it's a given now, that organizations are going to move to cloud. It's a question of when, not if. And the cloud form factors are just, are fundamentally different, they're software-defined. Right, a traditional data center, you're monitoring network equipment, storage devices, you're monitoring disks and fan failures on individual servers. When you're running in a cloud, it's a software-defined infrastructure, and it's far more elastic. And so even if you're just lifting and shifting, how you think about monitoring and observing this new cloud infrastructure's fundamentally different. So we're there for the very first step of the journey for an organization, to get the visibility they need into the new architecture, and many times we're also helping them understand the before and after, so how do I compare my performance in my on-premise data center to what it looks like in the cloud? That's step one. Step two is, they start chipping away at those monoliths, or they have new initiatives, that are digital initiatives, that are running in Kubernetes, or container based architectures, microservices based architectures, and that is a fundamentally different world. How you observe and monitor, deploy, not just monitor, the entire supply chain of how you manage these systems is different. So there, they have to look at different solutions, and we're obviously one of the key players, helping them there. >> Leonid, we've been doing theCUBE now for a decade, and I think John, it was a decade ago we said, we made the statement that sampling is dead. So I love your approach, you're not just taking small samples to do your performance monitoring. What's the architecture that enables you to do that, could you talk about that a little bit? >> So I think the most interesting thing with more modern architectures, especially with microservices adoption, is the complexity of how the transaction flows through the system. And then, basically tossing the coin, like we used to be able to do, in previous generations, to capture some traces and get the data you need. Doesn't work anymore, because it's very tough to predict at the beginning of the trace where the transaction's going to go. We're taking a completely different approach on the market. We look at every single transaction, at scales, we have prospects that are talking at us about volumes of giga span in minutes, so one billion spans observed a minute, and with some of the interesting tech we've built, we are able to pick the interesting things. And the interesting things have a couple categories, transactions that occur infrequently, transactions that are maybe above P90, right, the slow ones, because when look about performance and the understanding of how the application performs, you really want to know what's slow, not what's normal. But you also have to capture enough of what's normal. So with some of our tech, we're still able to keep about 1% of transactions, but the right ones, and that's the biggest differentiator with what we put together for the APM product. >> One of the things I want to talk about with you guys is how you relate to some of Google's announcements. The key things, I'm oversimplifying now, but they got a server list kind of announcement, got Cloud Run environment things, the regions, which is global, and then obviously open source commitment. You mentioned functions, you mentioned Knative, obviously open source. You're seeing open source being much more of a production IT capability, so you guys obviously hit that with these solutions, so the question I have for you guys is, how hard is it for you guys to provide that real time monitoring, because Google needs to build an ecosystem, that's what they're not talking about, they didn't really talk about on stage, their ecosystem. So you guys are a natural fit into service mesh, which they showed onstage, Jennifer Lin showed a great demo. So Google has to build an ecosystem, you guys are clearly positioned, through your announcements, that you're deeply integrated with Google. Cisco announced and integration, obviously they have an integration, so integration seems to be the secret sauce, (laughs) with cloud, to play in this ecosystem. Could you guys elaborate on that dynamic, because it kind of changes the old formula for ecosystems? >> Yeah, it's very different, right? In the old days, you had proprietary systems, so the only way you could actually build an integration is, you had to get your product managers in a conference room with the vendor and get visibility in the roadmap, access to everything, and that's why there were, it just took a lot longer to get things done. I think what you're seeing with Google is, they've taken a very standards based approach to everything, right? So, whatever technologies that they're releasing, they're trying to build it as a standard, you can run it on any cloud. Instrumentation is a core part of their philosophy of any technologies that they're releasing, such that, you have a new platform, it has a metrics library, other standards based mechanisms to collect metrics, traces, events. What that does is it makes it easy for the ecosystem to just pick it up, right? Our belief has been, you know, in the old days monitoring was all about proprietary instrumentation and collection. Today it's all about analysis. So the fact that all of this is openly available, in open source or standards based mechanisms, is great for us, it's great for the customers, it's great for the ecosystem. >> That's their one-to-many way of building integration systems. >> And that's why you guys are supporting Knative, as an example. >> Yep. >> That's really kind of supporting the open source ecosystem, ties it to Google cloud. >> Yeah, I mean, we generally support, our customers are running in every single configuration (John laughing) and type of technology you can imagine, so it's our work philosophy to just be everywhere they are, and to support all of the tech that they might be running. But in general we're big supporters of open source, in that, you know, developers are now running most software. That's the world of web services and SaaS. And developers have a preference for understanding the stacks that they're running on, and being able to control it and so that is obviously why open source has just taken off the way it has. >> I think the other dynamic of embracing open-source and standards is it allows us to focus, not on the meetings with product managers and getting an insight into the roadmap, but on getting the standards based integrations deeply configured with some of, for example, content we provide out of the box for use to your own Google versus for use to your own premise or use to anywhere else. And that's where the differentiation and the value for the customer is, not in kind of getting together on the roadmap and figuring out what to build next. >> You guys should move fast to take advantage of the lift that they get. I'd love it if you guys could just take a minute each to explain SignalFx value proposition 'cause you guys I think are perfectly positioned now as this becomes infrastructure as code with cloud. When should a customer call you guys? When are guys needed? When do guys get called in? Where are you winning? Take a minute to explain when and where you guys fit into the customer environment. >> I would say as soon as a customer starts to leverage a cloud infrastructure, whether that's public cloud, private cloud, open shift, to open stack, pivotal cloud foundry, or a public cloud, how you monitor your infrastructure will be fundamentally different, and we can help you with that. And then along your journey, once you've moved to cloud and you start thinking about how do I build modern application architectures, modern web services, devops, then we are necessary. You cannot get to the cloud native stage where you're releasing software every week unless you have a monitoring system like SignalFx. >> Great, just great. I want to also get your pick your brain on some dynamic that I saw in the keynote, it might not be obvious to the folks that are in the mainstream, but Jennifer Lin gave a demo of taking a workload, and porting it over with a small script, no code modifications, running it on a container. >> Dave: The cloud vMotion >> Anthos migrate was the product but basically migrating workload into containers in the Kubernetes engine automatically with no re-writes, she said what you, where you want. So that kind of, I can see what she did there and that's very cool and that's a game changer that's infrastructureless code, but then she moves to a conversation around services meshes. 'Cause once you get these things on a containerized, inside the Kubernetes engine, you're kind of enabled for using service meshes. This is like the Holy Grail of microservices. This is a big growth area. Can you guys explain what this means, what does this service mesh mean, 'cause once these workloads start to be containerized you're going to see much more migration to this new model. Where does service mesh kick in and why is it important and what should people pay attention to? >> Well I would say one of the fundamental challenges of microservices is what people are calling more and more, observability, right. Because you have so many systems, like a single application or a single transaction, what is an application anymore? A single transaction can flow through dozens, hundreds, of individual microservices. So, and you're changing your applications all the time. So figuring out when you've introduced a problem very quickly is a big challenge. And so one of the big benefits service mesh brings is it provides automatic instrumentation of your applications and requests in a way that makes it very out of the box to get visibility across your entire environment. So that is step one, getting that visibility. The next step is then you obviously need to analyze this corpus of data and its massive, and that's where a solution like SignalFx comes in we can collect all this data and help you really T-signal for noise. Then the last step really is how do you take action on that data, how do you automate responses? Whether it's rolling back a canary release, or shifting a load balancing strategy so that if there's a bad node you stop sending traffic to that. All of that can be automated. And so what service mesh is doing is it's providing the sub street to allow you to really provide that closed loop automation, that infrastructure is code, you know that's the movement that everyone is really focusing on right now. It's a key technology to enable that. >> Tell me about the observability trends, because this has been a hot venture funded area. We hear trace, dynamic tracing, these are techniques, there's a variety of different mechanisms for observability. How does Kubernetes, and now service mesh's impact observability, where is the puck going to be, if you're going to skate to where the puck is, what's the state of the situation? >> Well I think what it does is it makes instrumentation a lot easier. So typically a challenge when you're running a old Java application from 10 years ago, getting visibility into the app, it's a monolith. You to get the full visibility and the full call stack, that's harder to collect. When you're in a microservices world with service mesh, you're getting that visibility automatically. And what becomes more important is understanding the east/west latencies across all these different microservices. So because instrumentation is so much easier with all these new technologies, what it means for monitoring is it really shifts the focus to who can make the most sense of this data, who can provide assistance to the operators to really help them pinpoint when there is a problem, what is the potential cause, and to triage it very quickly. So again, the whole value proposition is shifted to the analysis. >> So Leonid given that, what are your engineering priorities, maybe share a little road map if you could? >> Sure, so if you think about what we just talked about, adoption of Kubernetes, or service meshes, the challenges that those environments bring both the femorality of the environments on which you now deploy compared to what most of the operators and application developers are used to, as well as the constant motion in the system, right. Kupernetes will move the workload several times an hour and the amount of data those systems tend to generate becomes fairly difficult to cope not just to a monitoring system, but to a human, right? So how can you take about what Karthik talked about all this noise and get it into an actionable intelligence across tens of millions times series an hour possibly in the middle of the night, how do you get the operator to the root cause very quickly? And what kind of technologies do we need to have as a vendor, and that's where we spend a lot of time thinking about, how do we provide actionable insight for those highly femoral environments that are getting even more femoral? >> One of the themes that's here, and already we're seeing it pop out of Google Next, and we've seen it in the other cloud shows we've gone to is, complexity is increasing, and the business model that seems to work well is taking complexity and making things simple. >> Mhm >> Right >> Whether it's extraction layers or other techniques, how does a customer, who's got all these new suppliers, new dynamics, new shift in the marketplace, new business models, how does a customer deploy IT, deploy cloud, and move the complexity to a simplicity model? This is a hard challenge. >> Well, I think that's one of the fundamental mental model shifts that an organization needs to make. Complexity was your enemy in the old days. Right, because you were releasing software once a year, twice a year and so you don't want it to be complex. But if your goal is speed and innovation, you're going to have to accept some complexity to get that speed and innovation. You just have to decide where is that complexity acceptable and how do you change your processes and your tooling to minimize the impact of that complexity. So I think I would disagree with that sentiment because I think organizations have to start thinking about things differently if they really want to move quickly. >> So embrace complexity. >> You have to embrace complexity and you have to think about what are the mitigating factors I need to take in my organization structure, my processes, my tooling, to compensate for the additional complexity I'm creating, but still release software as quickly as I used to. >> I would add, I think in a lot of ways you're shifting the complexity from infrastructure management more up the stack. >> That's, ya. >> In many ways IT is getting more complex, to your point Karthik. >> Ya, I mean all of these extractions make perhaps the underlying infrastructure less complex to manage but you're absolutely right Dave, the applications will become more complex when you move to microservices and you've got 50 pizza box teams working on a bunch of microservices, there's an organizational dynamic as much as there's a tech dynamic, right. How do you get these 50 teams to communicate with one another if there's a issue, an incident. >> And the data pathways, the data pipelines, the journey of that data, is much, much more complex. >> Ya absolutely. >> Final question, as the developers and operators come together, that seems to be a big trend. Developers want frictionless environment, programmable internet, they're going to be spitting up these services and then the operators have to run it. Those worlds are coming together. What's your thoughts on the operations side and developers coming together? >> I think they're two peas in a pod. They're two parts, they're two necessary parts. I think you will see more and more automation move up the stack. I think the place to start is really in the infrastructure layer and it will make the lives of operators of these cloud environments simpler. And then I think that automation will move up the stack as well over time. >> What's the most important story coming out of Google Next, if you can just kind of read the tea leaves, get a sense of what's going on here? 2019, whole new year, whole new game changing. What are your guys' thoughts on what's kind of going on in the cloud business this year? What' going on at Google Next? What's the big story? >> Well I think from my perspective it's very clear they're focused a lot on multi cloud, cloud agnostic and where the right ones run anywhere and run on Google. That seems to be a big push. And then the other is they're just behind on go to market and they seem to be focusing quite a bit on investing in all of the other elements, non-technology elements, to make organizations successful. >> Leonid, on the tech side, what do you see as the big in story here? >> I think Google was always found on the tech and they're continuing to deepen it. I think more interesting for me the story is about the go to market and embracing the complexity of the enterprise. >> Right >> And recognizing that not every application that will come to Google Cloud will be architected in a modern way. The thousands upon thousands of applications that have to lift and shift still and surviving some of the announcements around the service mesh are great enablers for those customers to start embracing the cloud technology. >> Tech geeks love service mesh, I'm a big fan. Guys, thanks for sharing the insight. Give a quick plug for what's going on for SignalFx. What's going on in the company? What are you guys looking to do? Are you hiring, are you expanding, what's going on? >> Ya we're in rapid growth here as a company. We're really excited about microservices APM product that we introduced late last year and what that does is it brings distributed trace analytics to our core monitoring platform. So what that allows you to do is get bottoms up visibility into each individual component through our metrics system, but also a transaction oriented view through our micro services APM product. Bringing the two together, super excited about the level of sophistication and analytics that it's going to bring our customers. >> What's the head count? What's the head count now, roughly? >> We're about 250 people right now. >> 250 okay, and you've raised over nine figures, I think? >> Over a hundred million dollars yeah. >> That's great, congratulations. >> So Karthik as a founder, what's it like to have the vision early and seeing it, and staying the course? And you've stayed on the right wave. >> Yeah. >> And now the wave's gotten bigger, what's it like to be the founder and be where you are now? >> It's terrifying at first because you don't know if the markets are going to move in the direction you need them to, but it's very gratifying when that actually happens and we're very fortunate that the world is moving very squarely into cloud based architectures, and not just cloud but all of these modern run times that are exactly what we predicted the world would look like for the last six years now. >> And you had a great team, engineering team was solid, you've got great chops. Any advice for entrepreneurs out there who are now getting into this world, maybe younger entrepreneurs coming out, building some applications? What's your advice to other founders that are... >> I could spend hours on that topic (laughter) >> I think >> Dave: Ship early and often >> You just have to continue to have faith and conviction in your beliefs and stick it out because there are lots of twists and turns, especially in the early days if you're betting ahead of the curve, you need to be patient and continue to have belief in yourself and your ideas. >> Well congratulations the world has right spun to your doorstep, congratulations with SignalFx. Thanks for coming on theCube. We're in San Francisco for theCube's coverage. Day one of three days. I'm John with Dave Vellante. Stay with us for more live coverage after this short break. (light electronic music)

>> Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Welcome back here at the Sands, as we conclude our coverage here of day one of AWS re:Invent, we've been live on theCUBE, we'll be back with you again on Wednesday and Thursday, but glad you're here with us on Tuesday for our coverage, along with Justin Warren, I'm John Walls, and we're joined now from two executives from SignalFx, Karthik Rau, who's a CEO, and Arijit Mukherji who's the CTO >> Hi. >> At SignalFx, gentlemen thank you for being with us. >> Oh, it's a pleasure being on. >> Alright, so just tell us a little bit about what you do, and why you're here, and then we'll dive in from there. If you would. >> Sure, SignalFx is a cloud monitoring service, designed for operators of applications and infrastructure that might be running in the cloud. Our origins came out of Facebook, so Arijit and much of our technical team are responsible for building the monitoring systems in Facebook, back in the mid 2000's when they had their famous move fast and break things culture. >> Right. >> Which today everyone calls devops, and so what we've really focused on is building a far more analytics centric monitoring approach, that focuses a lot on identifying the patterns that are really meaningful and we believe that's a far more important problem to solve in today's distributive environments. >> And you made some news not too long ago, you've unleashed a new product into the marketplace, Arijit, if you would. >> Yes, yes, we are very excited to launch our, what we call a SignalFx microservices APM product. And it's really aimed at giving customers visibility into their transaction flow that's happening in their microservices environment. As you know, we're moving to microservices, the individual pieces are becoming smaller, and they're growing in number, and so the complexity of those interactions becoming harder and harder to manage, and this product is aimed, basically to help our customers make sense of those, and monitor them effectively. >> A theme that's come up a couple of times today on theCUBE, is that the complexity of the modern way of doing things, in cloud native services, microservices, it's beyond human comprehension >> That is exactly right. >> You need to have the assistance of tools like IPM, and I think we were talking just before we went live, that this is a distributive tracing type approach to microservices, is that correct? >> That is correct. So the goal is to have a lightweight approach, where you can very easily generate the spans, and traces from all your microservices, the whole environment, and the value that we provide is to sort of take them, baseline them to give you a sense of how performance is happening overall in the environment, but more importantly to your point earlier, is how can we help the customer using data signs to help them guide them towards the problem when it is happening, where it is happening, so that you know you can reduce the MTTR, which is sort of the key part of all of this, so that's been much of the focus of the product, yes. >> Okay, so for customers who are looking to re-platform onto microservices, or some of these newer ways of doing things, what is it about SignalFx that helps them to understand how to change an application from one way of doing things, you know monolithic type application, into something more microservices driven? How does SignalFx actually help them with that journey? >> Well our customers who are early in the cloud journey, are doing a number of things. One, they are able to get complete visibility into the old, right, so you typically want to look at a side by side, so you're able to leverage in our smart agent, collect information about your monolithic stack, get full visibility into what the performance looks like in that particular environment, but then what we do better than anyone else, is give you comprehensive visibility into the new stack, and give you the analytics that will allow you to really compare one versus the other, so one of the things that's very different about SignalFx, is we have a very rich analytics capability in the backend, so, collecting metric data across your environment, whether it's your old stack or your new stack, we're able to provide very sophisticated analytics to identify meaningful patterns, outliers, anomalies, and to look across all of your metadata to be able to identify whether those patterns are specific to a subset of machines or a particular version of code, and that's typically very helpful to customers as they're moving from the old to the new. >> Yeah, can you give me an example then, I mean, in terms of specificity that you've provided, you talk about sophisticated measurements, or stats, just something that would tell us, oh I see, that was kind of an aha moment, maybe for one of your customers. >> Yeah, so the thing that is unique about us is, that because we have a strong metrics product that's backing this, because we have a strong analytics capability that's backing this, when we do distributive tracing, we are tracing and providing you insight not only into your application, like what it is doing, we are actually able to correlate that with your infrastructure, so let's say your application is running in a container, if there is a problem we can actually let you correlate that application in the performance of that to the container, to the host, to the infrastructure, top down as well as sort of left right, so to speak, and that has been sort of key, because what we find is having that capability, really helps our short circuit, the resolution time, because a lot of times the problem may be vertical, other times it may be broad, like horizontal, right? So our goal is to catch both of them. >> Okay then. >> So you're able to identify the root cause of an issue much quicker, so your teams can go and find, that server's failing, I need to go and replace a piece of hardware, or there's a storage issue, and you can just dial it straight in really quickly, is that, is that-- >> Yes well. >> In modern environments, you're far more likely to see performance issues in a small subset of your transactions than you are to see just a massive outage, right? A lot of modern distributed systems are designed to be resilient to individual node failures, for example, in a future that we just launched along with our microservices APM, is something called outlier analyzer, so let's say all your metrics indicate the service is performing fine, but you have point five percent of your users complaining that the performance is terrible. That's where tracing really helps, 'cause now you can look at every transaction, you can understand exactly where, you know, things might be slow, but it's typically a trial and error process, you have to go through every single trace you have to sort of figure out is it a particular version of code, or particular server. Our outlier analyzer feature will automatically look through all of the outliers, identify the over represented dimensions, and guide you to those specific problematic areas, right? So you run our outlier analyzer, it'll tell you, you know, this particular machine is overrepresented in your long tail traces. >> Yeah. >> Or this particular version of code is overrepresented. So, it short circuits the entire troubleshooting process by orders of magnitude. >> Yeah, that kind of intermittent error is always really really hard to find, something which just explodes and catches on fire, that's easy to find. >> And it's extremely difficult for a human to find it by trial and error across a distributed system, that can involve thousands of components right, so you really really have to leverage analytics and that's really what SignalFx is incredibly strong at doing. >> Yeah, so we're basically replacing luck with tools. >> Trial and error and luck, with a more prescriptive trouble shooting. >> Yeah, so for customers who have gone through this journey, and they've actually re-platformed an application, they've converted it into microservices, and they're doing cloud native things, and you've helped some of these customers. What's an example of a customer who's living in that new world, or what's the view like from where they're sitting, where they have all these lovely tools, and they're not relying on luck anymore, what's their sort of daily life like? >> Well I think the biggest difference is, they're now able to automate a lot of remediation, if you can be more intelligent in the signals that you're capturing, apply more intelligent analytics, then you, especially in today's environments, you can automate a lot of remediation, today's frameworks are highly automatable. And so one example of this, we have one of our larger Fortune 500 accounts, they do a number of launches, product launches, where they get massive amounts of load during a product launch and this is not atypical in today's environments. >> Yeah. >> And prior to having the real time data collection analysis with SignalFx, they would have two rooms full of people, supporting every single launch, and very reactively, you know, and something would go wrong they would have to go and figure out what was happening. With SignalFx they are now able to build very sophisticated analyses on the data as they're spinning up containers and instances to support a shoe launch, and they've now actually automated a lot of their remediation, whether it's auto-scaling, or rolling back of you know canary releases and such, and they've gone from having two rooms full of people to having just one on call engineer every time they do a launch, and it's also enabled them to be a lot more aggressive in doing these launches because they just have a lot more confidence in their ability to execute them. You know, that's one example. >> You know Justin was talking about some of the trends, we've heard a lot about today, the one I guess, or one of the constants has been about the pace, the rapidity of innovation, the rapidity of change, and so in your world, what do you think is the next hate to say big thing, but what mountain are you trying to climb now, that you haven't already conquered? >> So in my view, there's some very very encouraging trends that are coming our way actually, there was a talk that I presented earlier today about the concept of service measures and how I feel that they are going to be the next big thing because I think they attack a lot of the core operational challenges that we face in our microservices environment including how well you can instrument your environment, how well do the different types of instrumentation, your metrics, your APM, your logs, how well are they relatable, how tightly coupled are they? Right, how quickly can you make configuration changes within the environment in a more foolproof manner, that's more automated, that is more consistent, and so I feel like technology like that is going to transform how we do software in a few years from now, I see that advancing very very quickly, and something that's very related to that, and something I eluded to, to the talk earlier too was, this concept of feedback driven automation, where now I am no longer just going and just configuring my infrastructure to behave the way I want it to. In fact I'm also observing it as it is running, using high quality monitoring tools like SignalFx, and then using that to create new feedback, because if things sort of diverge from my intent, that I should be able to get it back to where I want to be, and all of this must happen without human interaction, because we work in the order of minutes, while you know automation can do this in seconds. This is absolutely fascinating, I think this is one of those big trends, that are coming down the pipe. >> Karthik anything to add to that? >> No I think Arijit nailed it. (laughing) >> Excellent, alright. Gentlemen thanks for being with us. >> Thank you. >> Good luck with the rest of the show, I'm sure it's been very good for you so far, and for the next two days, have a great time. >> Okay. >> Thank you very much. >> Excellent, thanks for being with us. >> We are concluding our coverage, day one, here of AWS reinvent for Justin Warren, I'm John Walls, we thank you for watching theCUBE. (upbeat music)

>> 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 Westin St. Francis in Union Square, historic venue. Our second time to this show, there's about 900 people here talking about kind of the future of dev ops, but going a lot further than dev ops. And we're excited to have a couple of CUBE alumni here at the conference from SignalFX. We've got Arjit Mukarji. >> Mukarji, yeah. >> Thank you. And Karthik Rao, co-founder and CEO of Signal FX. Gentlemen, welcome. >> Thank you very much. >> So what do you do 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 dev ops development and deployment methodologies, leveraging cloud and 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 really an integral part of our entire work flow since we started the company. So we're very close partners with them. >> Yeah, it's interesting 'cause Jen announced they have 300 integrations or 300+ 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 work flow and notification types of partners in that ecosystem, but in fact, lots of different people with lots of different slices of the pie. >> That is good. >> 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 you know we partner very closely with PagerDuty. As I mentioned earlier, our focus really is around problem detection and leveraging the most intelligent algorithms, 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 router to the appropriate people? One of the things that's really interesting as this world is changing towards these dev ops 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 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 in 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, 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- and have been partnering with them for a number of years now. >> And again, the complexity around the APIs where the app is running, there's 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 'cause we used to have these monoliths, we used to have three tiers and whatnot, and we're replacing that with the micro-services, loosely cabled 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 distances and containers and pods and what-have-you. So in a way, we are sort of growing below too in some sense and so that's why sort of monitoring this kind of 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. >> Right, yep. And then just the shear scale, volume, number of data that's flowing through the pipes now on all these different applications is growing exponentially, right? We see time and time again, so it 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 order sum 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 order sum 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 analytic spaced approach to monitoring and more automation, more intelligence around detecting the patterns that really matter. >> Right. Which is such a great opportunity for artificial intelligence, right, a machine learning. And we talk about it 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 that's run with these machines. >> Yes, because the systems are becoming so complex that even if you asked 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 sort 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 that have dealed with them. >> Right. >> You are 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 which too are sort of making them more and more sort of machine running 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, so to speak. >> Right. >> 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. >> If you want to talk about that. >> Yeah, so I actually have a talk tomorrow, it's called "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 and are 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 things, 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, right? And you'll be shocked how surprisingly accurate it tends to be. It's just an interesting thing, and it makes one wonder what else is out there for us to sort of look at? Why confine ourselves, right? >> Right. It's funny because we hear about sentiment analysis in 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 adage. >> That's right. >> Yeah, because in this day and age, users are on Twitter within seconds if something is perceived to be slow, or something is perceived to be down, they're on Twitter. So there are all sorts of other interesting signals to potentially pull from. >> Right, right. Well and guess what, we were just at AT&T Spark yesterday and the 5G's coming and it's 100x more data'll be flowing through the mobiles, so the problem's not going to get any smaller any time soon. >> No. >> Absolutely. >> So what else have you guys been up to since we last spoke? Continuing to grow, making some interesting moves. >> Absolutely- >> Crossing oceans. >> 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 a 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, outed AMIA, they're very much on the cloud journey, and embracing cloud and embracing dev ops, so it's really great to see that momentum out there. >> Right, 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 you. >> 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 sort of we talk about a lot in our trade is ephemeral, 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 only for a few hours or for- you know. And then, if you think about serverless and whatnot, it's in a whole different level, and the amount of ephemeral 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 in how do you monitor something that's changing so fast? And we had to have a lot of engineering put in to 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 doctorization and coordinating was not yet in place, and so that was an interesting technical challenge as well as a surprise. >> 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 it's a promotion, it's a this or that, right? It's a spin up app and a spin down app. 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 are doing things differently over and over again 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 SignalFX point of view, is build something that is versatile enough to handle these different use cases. We've got 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 up to speed with the latest of that's what's going on in terms of infrastructure trends, software delivery trends, and whatnot. Because if we're not able to do that, then that puts us sort of behind. >> Right, right. >> So it's a sort of lot of phonetic innovation, but it's also exciting at the same time. >> Right, right, right. And just the whole concept too, where I think what's best practice quickly becomes expected baseline really, really fast. I mean, what's cutting edge, innovative now unfortunately or fortunately, that become the benchmark by which everything else is measured overnight. That's the thing that just amazes me, what was magical yesterday is just expected, boring behavior today. Alright 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 Reinvent, we will see you there. Anything else that you're looking forward to over the next couple months? >> Just, we're really excited about Reinvent's 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 rally to our customers. >> Love it, just keep working hard. >> Yep. >> Alright. Arjit, hope your throat gets better before your big talk tomorrow. >> Yeah, that's right. >> Alright, thanks for stopping by Karthik, it was great to see you. >> Great to see you. >> I'm Jeff, you're watching theCUBE, we're at PagerDuty Summit at the Westin St. Francis in San Francisco. Thanks for watching, see you next time.

>> Live from San Francisco, it's theCUBE covering Google Cloud Next 2018, brought to you by Google Cloud and its ecosystem partners. (techy music) >> Hello everyone, welcome back to theCUBE's live coverage here. We're in San Francisco for Google Cloud's major conference, Next 2018. I'm John Furrier, here for three days. Wall to wall coverage on day one. We've got two great guests from SignalFX, Karthik Rau, founder and CEO, and Rajesh Raman, who's the chief architect. Signal's a hot startup in the area. Way ahead of its time, but now as the world gets more advanced, the solution is front and center as the value proposition if cloud moves into the mainstream, devops going to a world at large scale. Not just networking, monitoring, applications, you've got service meshes booming, great topic. Karthik, great to see you, Rajesh, thanks for joining us. >> Thank you. >> John, great to be on. >> So, first of all let's just get it out of the way, you guys have some fresh funding in May, so just quickly give an update on the company. You guys raised-- >> Yeah >> A series... >> A series D. >> Series D, give us, but how much? >> Yeah, so we raised $45 million from General Catalyst leading the round back in May, been building a ton of momentum as a company, close to a couple hundred people today. We're using a lot of that to expand internationally. We've got a team in Europe now, just opened up a team in Australia. So, things have been going great. >> Congratulations, we've had chats before, always been impressed. You guys have a great stable of awesome engineers and talent in the company doing some great work, but it begs the question, I always like to get into the what ifs. What if I could have large scale application development environments with programmable infrastructure, how does that change things? So, Karthik, what's... How as that what if changes, now that is what's happening you're starting to see the cloud at scale for the common masses of enterprises, where old ways of doing things are kind of moving away. It's like horse and buggy versus having a car for the first time-- >> Yeah. >> Jobs are changing, but the value doesn't necessarily change. You still go from point A to point B, you still got an engine, people who care about fixing cars, so people just want to drive the cloud, some people want to get under the hood, whole new architecture. >> Yeah. >> What's the what if of if I could have all these resources, what's the challenges and what do you guys solve. >> Well, I think there are a couple of challenges in this new environment. One is the number of components are just orders of magnitude more than they used to be in a cloud environment, right? We went from having physical machines that live for three years in a data center, divide it up into VMs 10 years ago, now divided up into containers for every process. Not only that, but these containers get spun up and spun down every few minutes or every few hours, and so it's just the number of components in the churn is just massive. So, that in and of itself requires a far more analytics-based approach to understand patterns rather than what's happening on an individual component. The second thing that's changed is the operating model's fundamentally different, because now you're building and running web services, and when you're running web services the people who build the software are the ones who technically are responsible for operating it. And so, you know, you have more updates, you've got more people involved, you've got lots of different components that all need to interact with one another, and so having a communication framework across all of these disparate teams become really, really, really critical. So, those are the two fundamental changes as you move from, you know, for operating these modern, massively distributed-- >> Yes. >> Applications. >> And I'll just add just some observation data that we've seeing in theCUBE is those same folks building aren't necessarily operators, so they want to be in and out fast, right? (laughs) >> They don't want to be running and operating all the time, they want to push some code. Melody Meckfessel here at Google ran a survey with developers and said, you know, "What makes you happy," and it was two things that bothered developers: technical debt and speed for deployments, commits, and the commit number was around minutes. If you can't get something done in minutes then they're onto something else, so the mind share attention of developers and technicos. So, this is a challenge at scale when you have technical debt, which we've seen companies come out of the woodwork, "Oh, yeah, "I'm going to automate something, "I'm going to throw some compute at it with the cloud "with the best monitoring package on the planet "and look how great it is," but all they did was just code some instrumentation and that's it. >> Mm-hmm. >> They weren't dealing with a lot of moving parts. Now as more things come in this is a challenge that a lot of companies face. You guys kind of solved this problem... >> Yeah, absolutely, so maybe Rajesh was a part of the team at Facebook that built the Facebook monitoring system, and that's actually what gave us a lot of the vision to start SignalFX five-and-a-half years ago, so maybe-- >> Tell about the protection, the vision-- >> Yeah. >> And what you guys are doing. >> Yeah, so CICD, you know, it kind of, like, underlies a lot of this vision of, like, moving fast. You mentioned that people wanted, like, you know, push their code in a few minutes... The thing that makes that possible is for you to have observability into what's happening while that push happens, because it's one thing to push very fast, it's another thing to recognize that you might have pushed something bad and to be able to revert it very quickly, too. And so, you'd only need, like, you know, good observability into all the things that matter that characterize the health of your system to be able to quickly recognize patterns, to be able to quickly recognize anomalies, and to be able to maybe push forward or even roll back very quickly. So, I think, like, observability is like a very key aspect of this entire CICD story. >> That's great, and that's great to know that you were over at Facebook because obviously Facebook built, at scale from the ground up, a lot of opensource. Obviously they contributed a lot to opensource, but it's interesting, as they matured and you start to see their philosophy change. It used to be move fast, break stuff. >> Yeah. >> To move fast, be reliable. >> Yeah. >> This is now the norm that's the table stakes in cloud. You have to move fast, you got to push code, but you got to maintain an operational integrity. This is, like, not like an option. This is, like, standard. >> Absolutely. >> How do you guys help solve that problem? >> So, I think there are a few different aspects to it. So, the first is to, you know, people need to ensure that they have observability into their application, so this is ensuring that you have the right kind of instrumentation in place. Thankfully this is kind of becoming commoditized right now and getting metrics from your system. The second part, and the more key part, is then being able to process this data in a real time way. You know, have high resolution, very low latency, and then to be able to do real time streaming analytics on this data. In highly elastic environments when things come and go very quickly, the identity of any individual, like, component is less important than the aggregate system behavior, and so you really need the analytics capability to kind of, like, go across this data, do various kinds of aggregations, compare it against past data, do predictive analytics, that sort of thing. So, analytics becomes the very key concept of, you know, how you operate these environments. >> It sounds so easy. >> Yeah, well one thing I'll add to that, so you know, to your point a lot of big companies sometimes are scared by this. You know, "How do we," you know... "We can't move quickly and break things," and everything that they've designed is around having process and structure to check and make sure everything is clean before they push changes out, and now we're in this world where, you know, an intern or a developer can push directly on a production, how do you manage that? The key thing in this modern world when you're trying to release software quickly, Rajesh hit on this earlier, you need the magic undo button. >> Yeah. >> That is the key to this entire process. You need to design your software, you need to design your process, and you need to design your tools so that if you introduce something bad you catch it immediately and you can roll it back. So, lots of devops practices are oriented around this, right? The idea of a canary release, I'm going to roll out an update to one percent of my systems and users, test it out, observe all the metrics, make sure everything is clean before I roll it out to everyone else, and the ability to roll back quickly is also important. But in order to do all of this you need the visibility, you need the metrics, and you need to be able to do analytics on it quickly to identify the patterns as they emerge. >> That's a great point and I'd love to just double down on that and get your thoughts because some of the Google Cloud people who are operating at this scale, I put them on this whole service-centric architecture, because they're services. We're talking about services, managing sets of services, having analytics, observation space, the reverting back and the undo button, the magic button do-over, whatever you want to call it, but the interesting thing is clean. Having a clean service whether it's an API, message queue, or an event, this stuff's happening all over the place in the new services world. How do you guys help there, is that where you guys get involved? Do you see up in that layer, how far up are you guys looking at some of the instrumentation and the insights? >> Yeah, you want to take that? >> Yeah, sure, so you know, the one thing that we really like about SignalFX and we were very keen on when we built the platform is that we are very agnostic about metrics. We're happy to accept metrics from anywhere, we'll take instrumentation-- >> (chuckles) You don't discriminate against metrics. >> We'll take instrumentation from cloud environment, we'll take, you know, metrics from opensource systems and premier applications, so you know, some of these systems are already kind of built in to get metrics from. You know, we talk to the Kafkas and Cassandras of the world, for example. We can also talk to GCP and AWS and grab metrics from their system. I think the interesting question is like when people really are taking the devops philosophy of, like, so how do you instrument your own application, what questions do you want to ask from your environment that answer the critical questions that you kind of have, and so you know, that's the one, that's the next step in the hierarchy of needs is for people to ask the right kinds of questions, and you know, instrument their applications properly. But like having done that, we can go up and down the stack in terms of, like, insight into whether all the way from your cloud environment through opensource systems, all the way up-- >> So, you guys'll take data from anyone, just stream it in-- >> Yeah. >> Normal mechanisms there, what's the value added, where's the secret sauce on SignalFX? >> So, I think value, it's all about analytics. We are all about analytics, so we are able to look at patterns of the data, we can go up and down the stack and correlate across different layers of software, look at interactions across components in your microservice, for example. You know, one really interesting thing that's happening, as you might be aware, like the whole service mesh aspect of it, which lets us, gives us insight into interactions between components-- >> Yeah. >> In a microservices architecture, so you know, we are able to get all that data and give you insight into how your whole system is working. >> So, you guys, you can see in the microservices layer? >> Absolutely. >> Yeah. >> That's powerful. >> And the key point is monitoring really has become an analytics problem, that's what we keep saying, right, because what's happening on an individual component is no longer as interesting as what's happening across the entire service, so you have to aggregate the information and look at the trend across the entire service, but the second thing that's really important is you need to be able to do it quickly, and this is where our streaming real time system really mattes. And people might ask, "Why does it "matter to do something real time." Like, "Seconds versus minutes, can a human actually "process something in seconds versus minutes?" Perhaps not, but everyone's moving towards automation, right? >> Yeah. >> So, if you want to move to a system where you have a closed loop, you have automation, and guess what, all of these modern systems, all the stuff that Google's talking here is all about automation. >> Yeah. >> And in that world seconds versus minutes, it means a tremendous amount of difference, right, where if you can find signals that will tell you there's an emerging problem within seconds and then you can revert a bad code push or you can auto-scale a cluster or you can, you know, change your load balancing algorithms all within seconds, that is what enables you to deliver, you know, 4.9s, 5.9s type of availability. >> And the consequences of not having that is outages-- >> Yeah, outages. >> Performance. >> Performance degradations, unhappy customers. I mean the cost to a brand now of having any kind of a performance issue is enormous, right? People are on Twitter before your team knows about it. (chuckles) >> Actually, you guys have a lot of the things you're solving, what is the core problem that you solve, what's the value proposition if you narrow it down that's high order bit for SignalFX? What's the corporate problem you solve? >> Well, we're solving the monitoring and observation problem for people operating cloud applications, so what happens is when you use SignalFX you have the confidence to move quickly, right? It gives you the safety net to be able to deploy changes on a daily basis, to have the shared context across a distributed team, so if you've got hundreds of two pizza box teams working together we give you that framework, the communication framework and the proactive intelligence to find issues as they emerge and proactively address them. And bottom line what that means is you can move as quickly as a Google or a Facebook or a Netflix even if you're a traditional Fortune 500 company that's regulated, and you know, you think you may not be able to do it but you really can. >> You give them the turbo charge, basically, for the analytics. All right, here's a question for you, what are the core guiding principles for the company? You guys obviously have a lot going on so you've got a core tech team, I mentioned it earlier. >> Mm-hmm. >> What are some of the guiding principles as you guys hire, build product, talk to customers, what's the key DNA of SignalFX? >> Yeah, I would say we are a very impact-driven company, so I'm, you know, very, very proud of all the people that we have on the team. We've got a lot of entrepreneurs who are focused on solving big problems, solving problems that customers may not necessarily know they need at the time, but as the market evolves we're there to solve it for them. So, we're a very customer-centric company. We have fantastic, we invest aggressively in technology, so it's not just about wrapping a pretty UI around, you know, Bolton Tech. We have real differentiated technology that solves real problems for people, and you know, I think we've in general just tried to skate to where the puck is and understand where the market's headed as a company. >> What are some of the customer feedback that you're getting? For folks that don't know SignalFX, what are some of the things that you're hearing from customers, why are you winning, what are some of the examples, can you share some color commentary? >> Yeah, I'll give one example, a Fortune 500 company that has been very aggressively investing in cloud the past, you know, four or five years, built an entire digital team, and their entire initiative is, like a lot of people in the Fortune 500 now, is to have a direct-to-consumer type of a relationship, and one of the things that they struggled with early was how do they move quickly, support product launches that might have massive load, and have the visibility to know that they can do that and catch issues as they emerge, and they didn't have a solution that could give that visibility to them until they leveraged SignalFX. And so now, if you talk to people there they'll say that they've essentially gone from defense every time they did one of these product launches to being on offense and really understanding what it takes to successfully launch a product and they're doing way more of these, so-- >> Moving the needle on time to market. >> Moving their business forward, you know, and digital transformation just by-- >> Yeah. >> Having SignalFX as a core enabler. >> It's the cloud version of putting out fires, so to speak, when you do product launches, right? >> Yeah. >> I got to ask you guys a question. You guys are both industry veterans, obviously Facebook has a storied history. We know all the great things that happen on the infrastructure side. Karthik, you've been in VM where you've seen the movie before where VM, where it made the market, changed IT for the better, still talk about the VMwares now. Now as we see cloud taking that next transformational push, describe the wave we're on right now, because it's kind of an interesting time in tech history where the talent that's coming in is pretty amazing. The young guns coming in with opensource the way it's flourishing is pretty phenomenal. Some of the smartest computer science and/or engineering talent is really solving what was old school B2B problems that really no one really wanted to solve. I mean, it was people were buying IT. Now you're talking about building operating systems, so the computer science kind of mojo in the enterprise has upped a bit. >> Mm-hmm. >> What's this wave about, how would you describe the wave of this time in history of the tech industry? >> Do you want to... (laughs) I'll add my take but why don't you go first. >> I think the thing that I find striking is just like, you know, when people used to talk about big data, you know, a few years ago, and now that is like, that's just normal. >> Yeah. >> And like, the amount of compute and the amount of storage that people are able to, you know, bring to command at-- >> Yeah. >> On any problem, it's just incredible, and that's just going to, I think, like continue to grow, right? That's going to be an amazing thing to watch. I think, you know, what this means... It also has interesting implications for, you know, companies like SignalFX who are trying to be in the monitoring space because the mojo used to be you had to have all this complicated software to do the instrumentation. Well, the instrumentations part is easy, but now all the value that's going to come about monitoring is in what you do with all that data, how you analyze it and look for, like, you know, so the whole AI ops and all that is going to be the key of the whole monitoring problem going forward, you know, five, 10 years from now, but we already see that analytics is such a key aspect of the whole thing, so... >> Yeah, I'm very, I think we're at the beginning, still at the beginning of a massive 30 to 40 year cycle, and this hasn't happened since the PC revolution in the 1970s, right, so the smartphone comes out 2007, massively opens up the market for software-based services to several billion people who are connected all the time now, drives a massive refresh of the backend infrastructure, drives the adoption of opensource, and so we're at this magical point now where the market for software-based services is just exploding, and every enterprise, you know, is becoming a software company, and you know, I think the volume of data that we're accumulating is just growing exponentially and what you can do with AI at this point, it's just... We're just beginning to see the benefit of it, so I think this is a really, really exciting time and I think we're just at the beginning. Most of the enterprises, and even the tech companies, are just beginning to capitalize on what is in store for us in the industry. >> I find it to be intoxicating, fun, and just great people coming in. To your point about the beginning of a 40 year run, also the nature of software development is being modernized at an extremely accelerated pace, so as people in the enterprise start re-imagining how they do software, because if they're a software company they've never had product managers. I mean, so the notion of what is a product, how do you launch a product, is all kind of first generation problems and opportunities, so I think to me it's really the enablement... And this is really what I think people are looking for is who can take the burden off my shoulders, help me move faster, more gas, less brake. >> Mm-hmm. >> Go faster, drive value, and then ultimately compete, because competitive advantage with technology... What does that mean to you guys, because how do you react to that because what you essentially are doing is creating instrumentation for enabling companies to create new value faster with technology and software, in some cases at a level that they've never seen before. What do you, how do you react to that? >> Well, I think that's exactly what we do, right, I mean, every company, I think most companies realized that they had to invest in software and focus on all these new opportunities at the early part of this decade. First thing they had to do was figure out who's going to build all this software, so most of them had to go hire engineers or build digital teams. They had to decide where are they going to run, the cloud wars of, you know, the early part of this decade. Do we build a private cloud, do we use a public cloud, I think both of those things have happened and people are now comfortable with those decisions. The third leg, which is squarely in the space that we're in, which is how do you operationalize this new model, and I think people are working through that now. As they get through that in the next few years, the companies like SignalFX helping every company, operationalize it very quickly, I think that's when the true promise of this new digital era will be realized where you'll start to see all of these fantastic applications, mobile apps, web service apps, direct-to-consumer streamlined supply chains. We're just beginning to see the benefit of that, and we'll see when that happens then the volume of data that they're collecting will increase exponentially and then the promise of machine learning and AI will take an altogether nother step. >> You got to know how to automate it before you can automate it, basically. What's next, final question for you guys, what's going on with SignalFX, what are you guys going to conquer, what's the next major milestones for you guys, what are you looking to do? >> Yeah, well we're continuing to focus on driving value for our customers, so we're expanding our geographic presence, so we're doing a lot of international expansion at this point. We're hiring a lot of engineers, so if anyone is interested in a development job, reach out to us. >> What kind of engineers are you looking to hire? >> Rajesh, you want to take that, sorry. (chuckles) What kind of engineers... >> What kind of engineers you looking to hire? >> Everything. (chuckles) >> I mean, all kinds of engineers, especially distributed systems engineers, front end engineers, full stack engineers, like real tech, all the good engineers we can get. >> (chuckles) Awesome. >> A lot of product development, there's a lot of interesting things happening in this space, and so we're, you know, continuing to invest very aggressively. >> Large scale distributed systems. >> Yep. >> You've got decentralized right around the corner, so you've got a lot of stuff happening. >> Yeah. >> Yeah. >> Great job to have you coming on, thanks for coming on, Karthik. >> Great, great to be on. >> Rajesh, thank you so much. >> My pleasure. >> SignalFX here in the cloud of Google here at Next, it's theCUBE, theCUBE cloud, CUBE data, we're bringing it all to you. I'm John Furrier, thanks for watching. More coverage, stay with us, we'll be back after this short break. (techy music)

>> Announcer: Live from the Moscone Center. It's theCUBE! Covering AWS Summit San Francisco 2018. Brought to you by Amazon Web Services. (upbeat techno music) >> Hey, welcome back, everyone. We're live here in San Francisco. This is theCUBE's exclusive coverage of AWS Amazon Web Services Summit 2018 with my co-host Stu Miniman. We have two great guests. Hot startup from SingleFx, the CEO, Karthik Rau, and the CTO, Arijit Mukherji. Welcome to theCUBE. Good to see you again. >> Karthik: Yeah, great to see you again. Thanks for having us. >> So, we've been following you guys. You've been out five years. Two years in stealth, three years ago you launched on theCUBE. >> Karthik: Right here on theCUBE. >> We see you at AWS and VMware. Cloud's changed a lot. So, let's get an update. Karthik, take a minute to explain where you guys are at now company-wise, employees, traction momentum, product. Where are you guys at now? >> Karthik: Yeah, absolutely. So, SignalFx, first of all, let me tell you what we do. SignalFx is a realtime streaming operational intelligence solution. Basically, what that means is we collect monitoring data, operational data across the entire cloud environment, from the infrastructure all the way up to the applications. And we apply realtime analytics on that data to help people be a lot more proactive in their monitoring of these distributed environments. We launched the company in 2015. We come ... I'll let Arijit talk about our origins. We came out of Facebook. And we had a lot of experience building this to Facebook. In the past three years, we've been building up our company aggressively. We've now got hundreds of customers including several large Fortune 500 accounts, large web scale accounts like Acquia and HubSpot and Yelp and KAYAK. And we're over 100 employees now, about 120 employees. And yeah, doing great. >> So, Werner Vogels, the CTO, laid out on stage plus a great Matt Wood conversation about machine learning but the real thing that Werner laid out was the old way, the web server, multi-tier architecture stack kind of thing going on there to a more cloud DevOps horizontally scalable where sets of servers that could be spawned in parallel creates a new kind of operating model but also creates challenges around what to instrument. You know, as we would joke, someone left the lights on, implying EC2s been running. And all these kinds of things are going on. And you mentioned some of the Facebook kind of challenges. People were building their own scale. What have you guys learned and how does that apply today's modern infrastructure? What are some of the threshold challenges that companies are facing when they say, one, already there or I want to get there? How do you guys look at the main issues? >> Karthik: Do you want to take that? >> Yeah, so monitoring modern environments and infrastructure is actually quite a challenge. There's obviously a few things going around. One, as you mentioned, is the variety, the sheer variety of things. No longer just the three-tier architecture I have cloud services. I have containers. I have lambdas. I have my own applications. I have the cloud infrastructure itself that all needs to be monitored. And things are also becoming far more numerous. So, there's just many more of everything, right? And so, making sense of that space is becoming a big challenge. And our company was founded on the idea that monitoring is becoming an analytics problem. So, it's no longer about looking at individual servers or applications instances. It's more about making sense holistically over what's going on and being able to combine different types of data from different systems together to provide you with that high level view and that's the kind of functionality that we at SignalFx have been trying to provide. >> What are some of the data flows volumes look like. Cause I've heard multiple people talk about either Facebook or in open compute environments where there's just so much data coming in from the instrumentation that no human could actually get their arms around it. And you need to supplement it with machine learning and intelligence. I mean, is that something that you're seeing? What are some of the -- >> Yes, so actually what we see is different prospects or customers will be in different stages of a spectrum where maybe they were in a stage one where they're sort of using traditional architectures and then moving to these more modern systems. And as they get more modernized themselves, their use cases or the ways they wanted to do monitoring also gets more advanced. And so, we see the whole spectrum of it, as you mentioned. And so, understanding analytically how what we're is doing is great. But then you also want to take the human out of things as much as possible, right? >> Yeah. >> And make things more automated. And you want to look at the data and how things are behaving to learn from existing patterns to find outlines. So, that's really a very interesting challenge. And what I look at what we can do as a company going forward, like all the technological stuff that we can invest in, it's quite interesting. >> Yeah, Karthik, take us inside your customers. How does this modern monitoring, how does it change their business? How does it impact things like feedback loops and DevOps and everything that customers are having to deal in this kind of ever changing environment? >> Yeah, well I'll give you an example. There's a Fortune 500 company. They do product launches. And this is one of our customers and their product launches drive so much traffic that they do 80% of their business in the first two minutes of a product launch. And this is not at all uncommon in today's economy. And they're leveraging a lot of modern technologies, container architectures, serverless function architectures to spin up a bunch of capacity during these launches. And they were effectively flying blind most of the time. Because most of the traditional systems management monitoring solutions are not designed, A, to handle that volume. But, B, to handle the instant discovery requirements of if you're going to do 80% of your business in the first two minutes. So, the challenge is you're always playing defense. You're reacting to issues. And you're mostly flying blind. By leveraging SignalFx, they're getting realtime visibility, realtime discovery of these components as they're coming up. We're the only solution that can do that. So, literally within seconds of spinning up all of these containers, they're getting live streams into their dashboards, and live analytics, and live alerts. And what that's enabled them to do is be a lot more aggressive and effectively doing a lot more of these launches. So, that's driving their business and it's helping them drive their digital strategy forward. >> And microservices is really enabling you guys to be more relevant. Because truly the signal from the noise is where all these services reporting to? >> Karthik: Yeah. >> You talk about container madness. >> Karthik: There are two fundamental problems. So, one there's an architecture shift. And that's driving massive amounts of volume. You have physical machines that will live for three years in a data center. Divide it up into VMs, 10, 20 VMs per server. That'll maybe live for a few months. To now every process running in it's own container that might live for a few minutes. So, you have a massive exponential explosion in the number of components. But that's not the only problem. I was part of an architectural shift at VMware for a number of years. We weren't just affecting an architecture change. What's happening now is there's a cultural change and a process change that's happening as well. Because with containers, your development team can push changes directly out into a production environment. And what you're finding is you're going from sequential product development to parallel product development and a massive exponential increase in the number of code pushes. The only way you can operationalize that is you have to have realtime visibility in everything that's happening. Otherwise, the left arm doesn't know what the right arm is doing. >> John: And you need prescriptive and predictive analytics. >> Exactly. And you need predictive analytics to identify there's something unusual here. It's not a problem yet. But this is highly unusual and maybe it's your canary release. We need to do a code push. So, you want to roll it back. So, having that level of predictiveness becomes absolutely critical. >> Yeah, you mentioned realtime. We used to argue what really is realtime. And it was usually well in time to react to what the customer needs. What does realtime mean to your customers? Architecturally, is there something you do different to kind of understand what that means? >> Arijit: Yeah, so we actually fundamentally took a very different approach when we build a product. Where, typically, monitoring our metrics, monitoring was done with what we call a store and create or a batch-like architecture where you store all the data points that are coming in, then you create from it to any other use cases. While what we build at SignalFx is a fully end-to-end streaming architecture which is realtime. And what we mean by realtime is like two to three seconds between a data point coming through us and it's firing an alert or showing up in your chart. So, that's the kind of realtime. And it requires us to do lots of innovations up and down the stack. And we've built a lot of IP. We've got now patterns. And more are coming because the approach we took was quite novel. Different from-- >> John: You guys got a great management team. And looking at what you guys have done. I've been impressed with you guys. I want to just ask, Karthik, you mentioned about all these parallel processes that are going on. Totally agree. The process change, operationalizing an all new cultural way to create software manage the data. I mean, it really is the perfect storm for innovation. But also, it could literally screw people up. So, I got to ask you, who are you targeting for your customer? Who is the person that you talk to? Assuming it's kind of DevOps, so it's more like a cloud architect. Who do you target? Who do you sell to? Who's the buyer? Who uses your service? >> Karthik: Well, we see ... Every enterprise we see following a very similar journey. So, the first stage is, typically, you're just getting familiar with cloud. And you're probably just lifting and shifting enterprise workloads into the cloud. Probably experimenting with big data on the cloud. You're not yet doing microservices or containers or DevOps. And for them, we're still selling largely to classic IT. There just trying to get better visibility into their digital environment, you know, they're cloud environment. But then, what ends up happening is they very quickly get to what we call basically chaos. It's stage two. And it has a lot of parallels to shadow IT. What happened with SAS, where you have hundreds of different SAS tools is happening all over again with cloud but you've got hundreds or thousands of different operational tools. Different ways of doing monitoring, logging, security. And every team is doing it's own thing. And so, that's a big problem for enterprises who are trying to build best practices across their broader team. In that place, we're typically selling to departments because they don't have a centralize strategy yet. But what we find is the organizations at maturity have figured out that it's important to have certain centralized core services. And that doesn't mean they're forced on the end users. But they provide best practices around monitoring, logging, and such. And just make it easy for them to use those solutions. So, that's almost a new IT organization. It's platform engineering -- >> John: Is that a cloud architect? >> Platform engineering team, infrastructure engineering team, and they are effectively building best practices around the new stack not the traditional stack. >> So, you are or aren't targeting department level? Are you are? >> We sell to departments. But we also sell to the teams that are standardizing across the entire organization. >> So, cloud architects, for instance? >> Depends on the stage of the cloud journey. >> Or company. >> And the company, exactly. >> From an architectural standpoint, you talked that there's virtualization, there's containers, now serverless. How do you even figure out what to monitor in serverless? How fast is that changing? And how is that impacting your road map? >> So, serverless brings a very interesting challenge because they are very, very ephemeral. Like they're ephemeral in some sense. So, we realize there are two things. One is serverless, there's a reason why things are moving faster. It's because you want to be able to move faster. But then you also need to be able to monitor faster. It's no good monitoring serverless at five minutes later, for example. So, one of the things we invested in was how to get metrics, etc. and telemetry from these serverless environments in a very fast fashion. And that's something that we've done. The second thing we are doing that really works for this environment is afterall it's not about how many times a serverless function ran, it's about the value that it's providing the application that's running on it. And by focusing on a platform that let's you send these application metrics in great detail and then be able to monitor and analyze them, I think really amplifies the value in some sense. So, those are the two ... >> John: And talk about the ecosystems. One of the things I want to ask you guys because we've been seeing a collision between a lot of the different clouds. Clients want multicloud. Well, obviously, we're here at Amazon. They believe they should be the only cloud. But I think most customers would look at either legacy systems with some instrumentation and operational data to edge of the network, for instance. I mean, look at the edge of the network. That's just an extension of the data center depending on how you look at it. So, how do you guys view that kind of direction where customer says, "Hey, you know, I got a cloud architect. We're on Amazon. Of course, we have some old Microsoft stuff. So, we've got Azure going up there. We're kicking the tires on Google. And I got this whole IoT Edge project. SignalFx, instrument that for me. (laughs) Is that what you do? Or how do you deal with that? How would you deal with that kind of conversation? >> Well, I think most enterprises, the larger companies we see looking at multiple clouds. And they have different workloads running in different clouds, depending on the needs and what they're looking to do. So, the nice thing about a solution like SignalFx is we span all of these different architectures. And what we find is that most of the larger companies want to separate their business process solutions from their runtime architectures. Because they want to have a solution like SignalFx that it doesn't matter who you're using. If you choose to have your analytics intensive workloads in Google Cloud and your eCommerce workloads in Amazon, but you only want one system that will page someone in the middle of the night if there's a problem, then you have SignalFx to do that. And then you have your choice of runtime environments depending on what your developers need or what the business demands. We provide a lot of that glue across the different environments. >> Do you see that as the preferred architecture with most customers? Cause that makes a lot of sense. I mean, whether you're doing other data services, it kind of makes sense to separate out. Is that consistent? >> To have different applications >> Yeah. >> In different clouds? It depends. I mean, I think we see some people who are more comfortable running on a single cloud vendor and they make the decision based on what a portfolio of platforms and service features that are available. And they really like those, and they say it's easy to just go with one. But more often, we find people wanting to at least have some percentage running in a different cloud vendor. >> John: All right, final question. What's the secret sauce for the company? Tell us about the secret sauce. >> Arijit: I think-- >> We got the patents. I heard patents. You don't have to show all this exactly. But what is the secret DNA of the tech? What's the magic? >> I think it's our very unique architecture. It's entirely different from what you have. It's streaming and it focuses on scale, on timeliness, as well as on analytics capability. I think that unique combination is very special for us. And that, in a way, sort of allows us to address very, very different use cases, including this hybrid environments and what not, in a very effective way. So, it's a very, very powerful platform that can be used for many use cases. >> All right, so that was John's final question. Karthik, I've got one last one for you. What's it like being a CEO of a software company in the cloud era today compared to what it's been earlier in our career? >> Well, it's moving very, very quickly, right? I mean, technology always move very quickly. But I think compared to when I was at VMware in the mid 2000s, it just feels like every 18 months there's a new technology wave. You know, when we started our company five years ago, that was the first year that AWS eclipsed a billion dollars in sales and Dagra hadn't even launched. It launched a month after we started the company. And then serverless came. And now function architecture is all there. So, there's just so much change happening, and it's happening so quickly, it forces vendors like us to really be on the cutting edge and forward looking and making sure that you're keeping an eye out for what's coming cause the markets are moving way faster, I think, then they were 15 years ago. >> John: Well, Karthik, thanks so much. We appreciate you guys coming on, SignalFx. I'll give you the final word on the interview. Take a minute to share something with the audience that they might not know about SignalFx that they should know about. >> Well, I think what people may not realize is how realtime we can actually get. I think most people are used to doing all their monitoring and observation, and they think of realtime in the order of minutes, or if you can get stuff every 30 seconds. We really are the only realtime solution. That's why we say real realtime. We're on the order of seconds. You can build really, really sophisticated analytics and get visibility like you can't anywhere else. So, it's real, realtime. >> And that's soon to be table stakes. TheCUBE is realtime. We're live right here, on theCUBE here, in San Francisco at Amazon Web Services, AWS Summit 2018. We've been covering all the Amazon re:Invents since it started, of course. I'm John Furrier with Stu Miniman. Back with more live coverage after this short break. (upbeat techno music) (gentle instrumental music)

hi Jeff Rick here with the cube welcome were excited to to get out and talk to startups people that are founding companies when they come out of stealth mode we're in a great position that we get a chance to talk to him early and we're really excited to have a cute conversation with karthik rao the founder and CEO of signal effects just coming out of stealth congratulations thank you Jeff so how long you've been working behind the scenes trying to get this thing going yeah we've been at it for two years now so two years a founder and I started the company in February of 2013 so excited to finally launch and make our product available to the world all right excellent congratulations that's always a great thing we've launched a few companies on the cube so hopefully this will be another great success so talk a little bit about first off you and your journey we have a lot of entrepreneurs that watch a show and I think it's it's an interesting topic as to how do you get to the place where you basically found in launched a company yeah absolutely I started my career at a company at a cloud company before cloud really exists this is a market there's a company called loud cloud oh yeah Marc Andreessen right recent horse or two of the company and we were trying to do what the public cloud vendors are doing today before the market was really all that big and before the technologies really existed to do it well but that was my first introduction to cloud o came out of college and that's where I met my co-founder Phillip Lou as well Phil and I were both working on the monitoring products at loud cloud from there I ended up at VMware for a good run of about seven years where I ran product had always wanted to start a company and then a couple of years ago Phil and I thought the timing was right and we had a great idea and decided to go build signal effects together okay so what was kind of the genesis of the idea you know a lot of times it's a cool technology looking for a problem to solve a lot of times it's a problem that you know and if I only had one of these they would solve my problems so how did the how did that whole process work yeah it was rooted in personal experience my co-founder phil was at Facebook for several years and was responsible for building the monitoring systems at Facebook and through our personal experience and what we'd seen in the marketplace we had a fundamental belief and a vision that monitoring for modern applications is now an analytics problem modern applications are distributed they're not you know a single database running on is system you know even small companies now have hundreds of VMs running on public cloud infrastructure and so the only way to really understand what's happening across all of these distributed applications is to collect the data centrally and use analytics and so that was our fundamental insight when we started signal effects what we saw in the marketplace was that most of the monitoring technologies haven't really evolved in the past 15 or 20 years and they're still largely designed for traditional static enterprise applications where if you get an alert when an individual node is down or a static thresholds been passed that's enough but that doesn't really work for modern apps because they're so distributed right if one node out of your twenty nodes is having a problem it doesn't necessarily mean that your application is having a trough having a problem and so the only way to really draw that insight is to collect the data and do analytics on it and that's what signal okay really because that distributed nature of modern of modern apps and modern architecture yes there are three things that are fundamentally different number one modern applications are distributed in nature and so you really have to look at patterns across many systems number two they're changing for more frequently than traditional enterprise apps because they're hosted for the most part route applications so you can push changes out every day if you want to and then third they're typically operated by product organizations and not IT organizations so you have developers or DevOps organizations that are actually operating the software and those three changes are quite substantial and require a new set of products right and so the other guys are just they're still kind of in the you know fire off the pager alert something is going down it's very noisy yes when you're firing off alerts every time an individual alert goes off when you've got thousands of a DM and we all know that the trend these days is towards micro services architectures you know small componentized you know containers or VMs and so you don't have to have a very sophisticated large application to have a lot of systems it's so do you fit into other existing kind of infrastructure monitoring systems or kind of infrastructure management systems so I'm sure you know it's another tool right guys got to manage a lot of stuff how does that work yeah we are focused on the analytics part of the problem okay so we collect data from any sources so our customers are typically sending us data you know infrastructure data that they're collecting using their own agents we have agents that we can provide to collect it a lot of the developers are instrumenting their own metrics that they care about so for example they might care about latency metrics and knowing Layton sees by customer by region so they'll send us all that data and then we provide a very rich analytics solution and platform for them to monitor all of this and and in real time detect patterns and anomalies so you just said you have customers but you coming out stealth so you have some beta customers already yes we have great customers already now just beta customers right are great console customers awesome yes congratulation thank you very much they're very excited about our product and we you know they range from small startups to fairly large web companies that are sending in tens of billions of data points every day into signal effects right right and again in the interest of sharing the knowledge with all of our entrepreneurs out there you know when did they get involved in the process how much of the kind of product development definition did they did they participate in you said you've been at it for a couple years yeah we've had a lot of conviction about this space from the very beginning because we our team had solved this problem for themselves and in previous experiences but we did include we've been in beta for about six months but better to launch and so over the course of those six months we recalibrated based on feedback we got from customers but on the whole we you know are we philosophy and the approach that we took was was pretty much validated by the early customers that we engaged with okay excellent and so um I assume your venture funded we are can you can you talk about who your who your backers are yes we raised twenty eight and a half million dollars eight million dollars yeah twenty-eight point five million dollars from andreessen horowitz okay with Ben Horowitz on our board okay and Charles River ventures with a lurker on our board and how big are you now time in terms of the company well we're just getting started now right at this is 1 million all that money - well we we've got a great group of engineers or our company is you know and still in the few dozen people stage at this point ok we're planning to invest aggressively in building out our team both on R&D and on the go-to-market side this excellent once you detect patterns and anomalies what's kind of the action steps you work with with other systems to swap stuff out together because now I hear like it's these huge data centers they don't swap out this they don't swap out machines they swap out racks it's soon they'll be swapping out data centers so what are some of the prescriptive things that people are using they couldn't do before by using your yeah I'll give you a great example of that one of our early beta customers they do code pushes very aggressively you know once a week they'll push out changes into their environment and they had a signal effects console open which and we're a real-time solution so every second they're seeing updates of what was happening in their infrastructure they pushed out their code and they immediately detected a memory leak and they saw their memory usage just growing immediately after they did their code Bush and they were able to roll it back before any of their users noticed any issues and so that's an example of these days a lot of problems introduced into environments are human driven problems it's a code push it's a new user gets onboard it or a new customer gets onboard and all of a sudden there's 10x the load onto your systems and so when you have a product like signal effects where you can in real time understand everything that's happening in your environment you can quickly detect these changes and determine what the appropriate next step is and that appropriate next step will depend on your application and who you are and what you're building right so our key philosophies we get out of your way but we give you all of the insights and the tools to figure out what's happening in your arm right it's interesting that really kind of two comes from from your partners you know kind of Facebook experience right because they're pushing out new code all the time when there's no fast and break things right right exactly and then you're at VMware so you know kind of the enterprise site so what if you could speak a little bit about kind of this consumerization of IT on the enterprise side and not so much the way that the look and feel of the thing works but really taking best practices from a consumer IT companies like Facebook like Amazon that really changed the game because it used to be the big enterprise software guys had the best apps now it's it's really flipped for people like Google and Netflix and those guys have the best apps and even more importantly they drive the expectation of the behavior of an application every Enterprise is finally getting it and then are they really embracing it we're definitely seeing a growth in new application development I think you know when I spend a lot of time talking to CIOs at enterprises as well and they all understand that in order to be competitive you have to invest in applications it's not enough to just view IT as a cost center and they're all beginning to invest in application development and in some cases these are digital media teams that are separate from traditional IT and other places it's you know they're they're more closely tied together but we absolutely see a kind of growth in application development in many of these end up looking a lot like the development teams that we see here in the Bay Area you know and companies that are building staffs and consumer cloud apps yeah exciting time so you should coming out of stealth what's kind of your your next kind of milestone that you're looking forward to you have a big some announcements you got show you're gonna kind of watch out we're we're we're gonna see you make a big splash well for us it's it's steadily building our business and so we hope to you know we're launching now and we've got a lot of great customers already and hope to sign on several more and help our customers build great applications about that's our focus again congratulations two years that's a big development project Karthik thank growl the founder and CEO of signal effects just launching their company coming out of stealth we'd love to get them on the cube share the knowledge with you guys both the people that are trying to start your own company take a little inspiration as well as as the people that need the service tomorrow with the cloud with a modern application thanks a lot thank you Jeff thank you you're watching Jeff Rick cube conversation see you next time

(Upbeat music) >> Hi everyone and welcome back to the Cube's coverage of Splunk.conf 2021, virtual. We are here, live in the Splunk studios here in Silicon valley. I'm John Furrier, host of the Cube. Spiros Xanthos VP of product management of observability with Splunk is here inside the cube, Spiros, thanks for coming on. Great to see you. [Spiros Xanthos]- John, thanks for having me glad to be here. >> We love observability. Of course we love Kubernetes, but that was before observability became popular. We've been covering cube-con since it was invented even before, during the OpenStack days, a lot of open source momentum with you guys with observability and also in the customer base. So I want to thank you for coming on. Give us the update. What is the observability story its clearly in the headlines of all the stories SiliconANGLE's headline is multi-cloud observability security Splunk doubling down on all three. >> Correct. >> Big part of the story is observability. >> Correct. And you mentioned CubeCon. I was there last week as well. It seems that those observability and security are the two most common buzzwords you hear these days different from how it was when we started it. But yeah, Splank actually has made the huge investment in observability, starting with the acquisition of Victor ops three years ago, and then with Omnition and Signalfx. And last year with Plumbr synthetics company called Rigor and Flowmill and a network monitoring company. And plus a lot of organic investment we've made over the last two years to essentially build an end-to-end observability platform that brings together metrics, traces, and logs, or otherwise infrastructure monitoring, log analytics, application monitoring. Visual experience monitoring all in one platform to monitor let's say traditional legacy and modern cloud native apps. >> For the folks that know SiliconANGLE, the Cube know we've been really following this from the beginning for signal effects, remember when they started they never changed their course. they've had the right They have the right history and from spot by spot, you guys, same way open source and cloud was poo-pooed upon, people went like, oh, it's not secure, they never were. Now it's the center of all the action. [Spiros Xanthos]- Yes >> And so that's really cool. And thanks for doing that. The other thing I want to get your point on is what does end-to-end observability mean? Because there's a lot of observability companies out there right now saying, Hey, we're the solution We're the utility, we're the tool, but I haven't seen a platform. So what's your answer to that? >> Yes. So observability, in my opinion, in the context of what you're describing means two things. One is that when, when we say internal durability, it means that instead of having, let's say multiple monitoring tools that are silent, let's say one for monitoring network, one for monitoring infrastructure, a separate one for monitoring APM that do not work with each other. We bring all of these telemetry in one place we connect it and exactly because actually applications and infrastructure themselves are becoming one. You have a way to monitor all of it from one place. So that's observability. But the other thing that observability also is because these environments tend to be a lot more complex. It's not just about connecting them, right? It's also about having enough data and enough analytics to be able to make sense out of those environments and solve problems faster than you could do in the past with traditional monitoring. >> That's a great definition. I've got to then ask you one of the things coming up that came out of CoopCon was clear, is that the personnel to hire, to run this stuff, it's not everyone can get the skills gap problem. At the same time, automation is at an all time high people are automating and doing AI ops, get outs. What do you want to call this a buzz word for that basically automating the data observability into the CICB pipeline, huge trend right now. And the speed of developers is fast now. They're coding fast. They don't want to wait. >> I agree. So, and that's exactly what's happening, right? We want essentially from traditional IT where developers would develop something a little bit deployed months later by some IT professional, of course, all of this coming together, But we're not stopping that as you say, right, that the shifting left is going earlier into the pipeline. Everyone expect, essentially let's say monitoring to happen at the speed of deployment. And I guess observability again, is this not, as a requirement. Observability is this idea. Let's say that I should be able to monitor my applications in real time and, you know, get information as soon as something happens. >> With the evolution of the shift left trend. I would say for the people don't know what shift left is you put security the beginning, not bolted on at the end and developers can do it with automation, all that good stuff that they have. But how, how real is that right now in terms of it happening? Can you, can you share some vision and ideas and anecdotal data on how, how fast shift left is, or is there still bottlenecks and security groups and IT groups? >> So there are bottlenecks for sure. In my opinion, we are aware with, let's say the shift left or the dev sec ops trend, whether IT and devs maybe a few years ago. And this is both a cultural evolution that has to happen. So security teams and developers have to come closer together, understand like, say the consensus of the requirements of each other so they can work better together the way it happened with DevOps and all sorts of tooling problem, right? Like still observability or monitoring solutions are not working very well with security yet. We at Splunk of course, make this a priority. And we have the platform to integrate all the data in one place. But I don't think is generally something that we'll have achieved as well as an industry yet. And including the cultural aspects of it. >> Is that why you think end to end is important to hit that piece there so that people feel like it's all working together >> I think end to end is important for two reasons. actually one is that essentially, as you say, you hit all the pieces from the point of deployment, let's say all the way to production, but it's also because I think applications and infrastructure, FMLA infrastructure with Kubernetes, microservices are in traditional so much more complexity that you need to step function improvement in the tooling as well. Right? So that you need keep up with the complexity. So bringing everything together and applying analytics on top is the way essentially to have this step function improvement in how your monitoring solution works so that it can keep up with the complexity of the underlying infrastructure and application. >> That is a huge, huge points Spiros. I got to double down on that with you and say, let's expand that because that's the number one problem, taming the complexity without slowing down. Right? So what is the best practice for that? What do people do? Cause, I mean, I know it's evolving, it's going faster than that, but it's still getting better, but not always there, but what can people do to go faster? >> So, and I will add that it's even more complex than just what the cloud, let's say, native applications introduced because especially large enterprises have to maintain their routine, that on-prem footprint legacy applications that are still in production and then still expand. So it's additive to what they have today, right? If somebody was to start from a clean slate, let's say started with Kubernetes today, maybe yes, we have the cloud native tooling to monitor that, but that's not the reality of most, most enterprises out there. Right? So I think our goal at Splunk at least is to be able to essentially work with our customers through their digital, digital transformation and cloud journey. So to be able to support all their existing applications, but also help them bring those to the cloud and develop new applications in a cloud native fashion, let's say, and we have the tooling, I think, to support all of that, right between let's say our original data platform and our metrics and traces platform that we develop further. >> That's awesome. And then one quick question on the customer side, if I'm a customer, I want observability, I want this, I want everything you just said. How do I tell the difference between a pretender and a player, the good solution and a bad solution? What are the signals that this is the real deal, that's a fake product >> Agreed. So, I mean, everyone obviously believes that original (laughing) I'm not sure if I will. >> You don't want to name names? Here's my, my perspective on what truly is a requirement for absorb-ability right? First of all, I think we have moved past the time where let's say proprietary instrumentation and data collection was a differentiator. In fact, it actually is a problem today, if you are deploying that because it creates silos, right? If I have a proprietary instrumentation approach for my application, that data cannot be connected to my infrastructure or my logs, let's say, right. So that's why we believe open telemetry is the future. And we start there in terms of data collection. Once we standardize, let's say data collection, then the problem moves to analytics. And that's, I think where the future is, right? So observability is not just about collecting a bunch of data and that bring it back to the user. It's about making sense out of this data, right? So the name of the game is analytics and machine learning on top of the data. And of course the more data you can collect, the better it is from that perspective. And of course, then when we're talking about enterprises, scale controls, compliance all of these matter. And I think real time matters a lot as well, right? We cannot be alerting people after minutes of a problem that has happened, but within a few seconds, if we wanted to really be pro-active. >> I think one thing I like to throw out there, maybe get your reaction to it, I think maybe one other thing might be enabling the customer to code on top of it, because I think trying to own the vertical stack as well as is also risky as a vendor to sell to a company, having the ability to add programming ability on top of it. >> I completely agree actually, You do? In general giving more control to the users and how, what do they do with their data, let's say, right? And even allowing them to use open source, whatever is appropriate for them, right? In combination, maybe with a vendor solution when they don't want to invest themselves. >> Build their own apps, build your own experience. That's the way the world works. That's software. >> I agree. And again, Splunk from the beginning was about that, right? Like we'll have thousands of apps built ontop of our platform >> Awesome. Well, I want to talk about open source and the work you're doing with open telemetry. I think that's super important. Again, go back even five, 10 years ago. Oh my God. The cloud's not secure. Oh my God, open source has got security holes. It turns out it's actually the opposite now. So, you know finally through the people woke up. No, but it's gotten better. So take us through the open telemetry and what you guys are doing with that. >> Yes. So first of all, my belief, my personal belief is that if there is no future where infrastructure is anything about open source, right? Because people do not trust actually close our solutions in terms of security. They prefer open source at this point. So I think that's the future. And in that sense, a few years ago, I guess our belief was that all data collection instrumentations with standards based first of all, so that the users have control and second should be open source. That's why we, at Omnition the company I co-founded that was acquired by Splunk. We we're one of the main tenders of open sensors and that we brought together open sensors and OpenTracing in creating open telemetry. And now , Open telemetry is pretty much the de facto. Every vendor supports it, its the second most active project in CNCF. And I think it's the future, right? Both because it frees up the data and breaks up the silos, but also because, has support from all the vendors. It's impossible for any single vendor to keep up with all this complexity and compete with the entire industry when we all come together. So I think it's a great success it's I guess, kudos to everybody, kudos to CNCF as well, that was able to actually create and some others. >> And props to CNCF. Yeah. CNC has done an amazing job and been going to all those events all the years and all the innovations has been phenomenal. I got to ask what the silos, since you brought it up, come multiple times. And again, I think this is important just to kind of put an exclamation point on, machine learning is based upon data. Okay. If you have silos, you have the high risk of having bad machine learning. >> Yes. >> Okay. That's you agree with that? >> Completely. >> So customers, they kind of understand this, right. If you have silos that equals bad future >> Correct >> because machine learning is baked into everything now. >> And I will add to that. So silos is the one problem, and then not being able to have all the data is another problem, right? When it comes to being able to make sense out of it. So we're big believers in what we call full fidelity. So being able to connect every byte of data and do it in a way that makes sense, obviously economically for the customer, but also have, let's say high signal to noise ratio, right? By structuring the data at the source. Overt telemetry is another contributor to that. And by collecting all the data and by having an ability, let's say to connect the data together, metrics, traces, logs, events, incidents, then we can actually build a little more effective tooling on top to provide answers back to the user with high confidence. So then users can start trusting the answers as opposed to they themselves, always having to figure out what the problem is. And I think that's the future. And we're just starting. >> Spiros I want to ask you now, my final question is about culture And you know, when you have scale with the cloud and data, goodness, where you have people actually know the value of data and they incorporate into their application, you have advantages. You have competitive advantages in some cases, but developers were just coding love dev ops because it's infrastructure as code. They don't have to get into the weeds and do the under the hood, datas have that same phenomenon right now where people want access to data. But there's certain departments like security departments and IT groups holding back and slowing down the developers who are waiting days and weeks when they want it in minutes and seconds for have these kinds of things. So the trend is, well there's, first of all, there's the culture of people aren't getting along and they're hating each other or they're not liking each other. >> Yes >> There's a little conflict, always kind of been there, but now more than ever, because why wait? >> I agree. >> How can companies shorten that cycle? Make it more cohesive, still decouple the groups because you've got, you got compliance. How do you maximize the best of a good security group, a good IT group and enables as fast as possible developers. >> I agree with you, by the way, this is primarily cultural. And then of course there is a tooling gap as well. Right. But I think we have to understand, let's say as a security group, instead of developers, what are the needs of each other, right. Why we're doing the things we're doing because everybody has the right intentions to some extent, right? But the truth is there is pain. We are me and myself. Like as we develop our own solutions in a cloud native fashion, we see that right. We want to move as fast as possible, but at the same time, want to be compliant and secure, right. And we cannot compromise actually on security or compliance. I mean, that's really the wrong solution here. So I think we need to come together, understand what each other is trying to do and provide. And actually we need to build better tooling that doesn't get into the way. Today, oftentimes it's painful to have, let's say a compliance solution or a secure solution because it slows down development. I think we need to actually, again, maybe a step function improvement in the type of tooling we'll have in this space. So it doesn't get into the way Right? It does the work it provides. Let's say the security, the security team requires, it provides the guarantees there, but doesn't get in the way of developers. And today it doesn't happen like this most of the time. So we have some ways to go. >> And Garth has mentioning how you guys got some machine learning around different products is one policy kind of give some, you know, open, you know, guardrails for the developers to bounce around and do things until they, until they have to put a new policy in place. Is that an answer automated with automation? >> Big time. Automation is a big part of the answer, right? I think we need to have tooling that first of all works quickly and provides the answers we need. And we'll have to have a way to verify that the answer are in place without slowing down developers.Splunk is, I mean, out of a utility of DevSecOps in particular is around that, right? That we need to do it in a way that doesn't get in the way of, of let's say the developer and the velocity at which they're trying to move, but also at the same time, collect all the data and make sure, you know, we know what's going on in the environment. >> Is AI ops and dev sec ops and GET ops all the same thing in your mind, or is it all just labels >> It's not necessarily the same thing because I think AI ops, in my opinion applies, let's say to even more traditional environments, what are you going to automate? Let's say IT workflows in like legacy applications and infrastructure. Getops in my mind is maybe the equivalent when you're talking about like cloud native solutions, but as a concept, potentially they are very close I guess. >> Well, great stuff. Great insight. Thanks for coming on the Cube. Final point is what's your take this year of the live we're in person, but it's virtual, we're streaming out. It's kind of a hybrid media environment. Splunk's now in the media business with the studios, everything great announcements. What's your takeaway from the keynote this week? What's your, you got to share to the audience, this week's summary. >> First of all, I really hope next year, we're all going to be in one place, but still given the limitations we had I think it was a great production and thanks to everybody who was involved. So my key takeaway is that we truly actually have moved to the data age and data is at the heart of everything we do. Right? And I think Splunk has always been that as a company, but I think we ourselves really embraced that and everything we do is everything. Most of the problems we solve are data problems, whether it's security, observability, DevSecOps, et cetera. So. >> Yeah, and I would say, I would add to that by saying that my observations during the pandemic now we're coming, hopefully to the end of it, you guys have been continuing to ship code and with real, not vaporware real product, the demos were real. And then the success on the open source. Congratulations. >> Thank you. >> All right. Thanks for coming on and we appreciate it >> Thanks alot _Cube coverage here at dot com Splunk annual conference. Virtual is the Cube. We're here live at the studios here at Splunk studios for their event. I'm John Farrow with the Cube. Thanks for watching. (joyful tune)

>> From theCUBE studios in Palo Alto and Boston, Bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Recent survey data from ETR shows that enterprise tech spending is tracking with projected US GDP growth at six to 7% this year. Many markers continue to point the way to a strong recovery, including hiring trends and the loosening of frozen IT Project budgets. However skills shortages are blocking progress at some companies which bodes well for an increased reliance on external IT services. Moreover, while there's much talk about the rotation out of work from home plays and stocks such as video conferencing, VDI, and other remote worker tech, we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right. In particular, the talent gap combined with a digital mandate, means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally. Hello everyone, and welcome to this week's Wikibon CUBE's Insights powered by ETR. In this "Breaking Analysis", we welcome back Erik Porter Bradley of ETR who will share fresh data, perspectives and insights from the latest survey data. Erik, great to see you. Welcome. >> Thank you very much, Dave. Always good to see you and happy to be on the show again. >> Okay, we're going to share some macro data and then we're going to dig into some highlights from ETR's most recent March COVID survey and also the latest April data. So Erik, the first chart that we want to show, it shows CIO and IT buyer responses to expected IT spend for each quarter of 2021 versus 2020, and you can see here a steady quarterly improvement. Erik, what are the key takeaways, from your perspective? >> Sure, well, first of all, for everyone out there, this particular survey had a record-setting number of participation. We had a 1,500 IT decision makers participate and we had over half of the Fortune 500 and over a fifth of the Global 1000. So it was a really good survey. This is seventh iteration of the COVID Impact Survey specifically, and this is going to transition to an overlarge macro survey going forward so we can continue it. And you're 100% right, what we've been tracking here since March of last year was, how is spending being impacted because of COVID? Where is it shifting? And what we're seeing now finally is that there is a real re-acceleration in spend. I know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a 9% number, but what we're seeing is right now, it's at a midpoint of over six, about 6.7% and that is accelerating. So, we are still hopeful that that will continue, and really, that spending is going to be in the second half of the year. As you can see on the left part of this chart that we're looking at, it was about 1.7% versus 3% for Q1 spending year-over-year. So that is starting to accelerate through the back half. >> I think it's prudent to be cautious (indistinct) 'cause normally you'd say, okay, tech is going to grow a couple of points higher than GDP, but it's really so hard to predict this year. Okay, the next chart here that we want to show you is we asked respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that I'll call out and then I'll ask Erik to chime in. First, there's been no meaningful change of course, no surprise in tactics like remote work and holding travel, however, we're seeing very positive trends in other areas trending downward, like hiring freezes and freezing IT deployments, a downward trend in layoffs, and we also see an increase in the acceleration of new IT deployments and in hiring. Erik, what are your key takeaways? >> Well, first of all, I think it's important to point out here that we're also capturing that people believe remote work productivity is still increasing. Now, the trajectory might be coming down a little bit, but that is really key, I think, to the backdrop of what's happening here. So people have a perception that productivity of remote work is better than hybrid work and that's from the IT decision makers themselves, but what we're seeing here is that, most importantly, these organizations are citing plans to increase hiring, and that's something that I think is really important to point out. It's showing a real following, and to your point right in the beginning of the intro, we are seeing deployments stabilize versus prior survey levels, which means early on, they had no plans to launch new tech deployments, then they said, "Nope, we're going to start." and now that stalling, and I think it's exactly right, what you said, is there's an IT skills shortage. So people want to continue to do IT deployments 'cause they have to support work from home and a hybrid back return to the office, but they just don't have the skills to do so, and I think that's really probably the most important takeaway from this chart, is that stalling and to really ask why it's stalling. >> Yeah, so we're going to get into that for sure, and I think that's a really key point, is that accelerating IT deployments, it looks like it's hit a wall in the survey, but before we get deep into the skills, let's take a look at this next chart, and we're asking people here how our return to the new normal, if you will, and back to offices is going to change spending with on-prem architectures and applications. And so the first two bars, they're Cloud-friendly, if you add them up, it's 63% of the respondents, say that either they'll stay in the Cloud for the most part, or they're going to lower their on-prem spend when they go back to the office. The next three bars are on-prem friendly. If you add those up it's 29% of the respondents say their on-prem spend is going to bounce back to pre-COVID levels or actually increase, and of course, 12% of that number, by the way, say they've never altered their on-prem spend. So Erik, no surprise, but this bodes well for Cloud, but isn't it also a positive for on-prem? We've had this dual funding premise, meaning Cloud continues to grow, but neglected data center spend also gets a boost. What's your thoughts? >> Really, it's interesting. It's people are spending on all fronts. You and I were talking in the prep, it's like we're in battle and I've got naval, I've got air, I've got land, I've got to spend on Cloud and digital transformation, but I also have to spend for on-prem. The hybrid work is here and it needs to be supported. So this is spending is going to increase. When you look at this chart, you're going to see though, that roughly 36% of all respondents say that their spending is going to remain mostly on Cloud. So that is still the clear direction, digital transformation is still happening, COVID accelerated it greatly, you and I, as journalists and researchers already know this is where the puck is going, but spend has always lagged a little bit behind 'cause it just takes some time to get there. Inversely, 27% said that their on-prem spending will decrease. So when you look at those two, I still think that the trend is the friend for Cloud spending, even though, yes, they do have to continue spending on hybrid, some of it's been neglected, there are refresh cycles coming up, so, overall it just points to more and more spending right now. It really does seem to be a very strong backdrop for IT growth. >> So I want to talk a little bit about the ETR taxonomy before we bring up the next chart. We get a lot of questions about this, and of course, when you do a massive survey like you're doing, you have to have consistency for time series, so you have to really think through what the buckets look like, if you will. So this next chart takes a look at the ETR taxonomy and it breaks it down into simple-to-understand terms. So the green is the portion of spending on a vendor's tech within a category that is accelerating, and the red is the portion that is decelerating. So Erik, what are the key messages in this data? >> Well, first of all, Dave, thank you so much for pointing that out. We used to do, just what we call a Net score. It's a proprietary formula that we use to determine the overall velocity of spending. Some people found it confusing. Our data scientists decided to break this sector, break down into what you said, which is really more of a mode analysis. In that sector, how many of the vendors are increasing versus decreasing? So again, I just appreciate you bringing that up and allowing us to explain the reasoning behind our analysis there. But what we're seeing here goes back to something you and I did last year when we did our predictions, and that was that IT services and consulting was going to have a true rebound in 2021, and that's what this is showing right here. So in this chart, you're going to see that consulting and services are really continuing their recovery, 2020 had a lot of the clients and they have the biggest sector year-over-year acceleration sector wise. The other thing to point out on this, which we'll get to again later, is that the inverse analysis is true for video conferencing. We will get to that, so I'm going to leave a little bit of ammunition behind for that one, but what we're seeing here is IT consulting services being the real favorable and video conferencing having a little bit more trouble. >> Great, okay, and then let's take a look at that services piece, and this next chart really is a drill down into that space and emphasizes, Erik, what you were just talking about. And we saw this in IBM's earnings, where still more than 60% of IBM's business comes from services and the company beat earnings, in part, due to services outperforming expectations, I think it had a somewhat easier compare and some of this pent-up demand that we've been talking about bodes well for IBM and other services companies, it's not just IBM, right, Erik? >> No, it's not, but again, I'm going to point out that you and I did point out IBM in our predictions when we did in late December, so, it is nice to see. One of the reasons we don't have a more favorable rating on IBM at the moment is because they are in the process of spinning out this large unit, and so there's a little bit of a corporate action there that keeps us off on the sideline. But I would also want to point out here, Tata, Infosys and Cognizant 'cause they're seeing year-over-year acceleration in both IT consulting and outsourced IT services. So we break those down separately and those are the three names that are seeing acceleration in both of those. So again, at the Tata, Infosys and Cognizant are all looking pretty well positioned as well. >> So we've been talking a little bit about this skills shortage, and this is what's, I think, so hard for forecasters, is that in the one hand, There's a lot of pent up demand, Scott Gottlieb said it's like Woodstock coming out of the COVID, but on the other hand, if you have a talent gap, you've got to rely on external services. So there's a learning curve, there's a ramp up, it's an external company, and so it takes time to put those together. So this data that we're going to show you next, is really important in my view and ties what we were saying at the top. It asks respondents to comment on their staffing plans. The light blue is "We're increasing staff", the gray is "No change" and the magenta or whatever, whatever color that is that sort of purplish color, anyway, that color is decreasing, and the picture is very positive across the board. Full-time staff, offshoring, contract employees, outsourced professional services, all up trending upwards, and this Erik is more evidence of the services bounce back. >> Yeah, it's certainly, yes, David, and what happened is when we caught this trend, we decided to go one level deeper and say, all right, we're seeing this, but we need to know why, and that's what we always try to do here. Data will tell you what's happening, it doesn't always tell you why, and that's one of the things that ETR really tries to dig in with through the insights, interviews panels, and also going direct with these more custom survey questions. So in this instance, I think the real takeaway is that 30% of the respondents said that their outsourced and managed services are going to increase over the next three months. That's really powerful, that's a large portion of organizations in a very short time period. So we're capturing that this acceleration is happening right now and it will be happening in real time, and I don't see it slowing down. You and I are speaking about we have to increase Cloud spend, we have to increase hybrid spend, there are refresh cycles coming up, and there's just a real skills shortage. So this is a long-term setup that bodes very well for IT services and consulting. >> You know, Erik, when I came out of college, somebody told me, "Read, read, read, read as much as you can." And then they said, "Read the Wall Street Journal every day." and so I did it, and I would read the tech magazines and back then it was all paper, and what happens is you begin to connect the dots. And so the reason I bring that up is because I've now taken a bath in the ETR data for the better part of two years and I'm beginning to be able to connect the dots. The data is not always predictive, but many, many times it is. And so this next data gets into the fun stuff where we name names. A lot of times people don't like it because they're either marketing people at organizations, say, "Well, data's wrong." because that's the first thing they do, is attack the data. But you and I know, we've made some really great calls, work from home, for sure, you're talking about the services bounce back. We certainly saw the rise of CrowdStrike, Okta, Zscaler, well before people were talking about that, same thing with video conferencing. And so, anyway, this is the fun stuff and it looks at positive versus negative sentiment on companies. So first, how does ETR derive this data and how should we interpret it, and what are some of your takeaways? >> Sure, first of all, how we derive the data, are systematic survey responses that we do on a quarterly basis, and we standardize those responses to allow for time series analysis so we can do trend analysis as well. We do find that our data, because it's talking about forward-looking spending intentions, is really more predictive because we're talking about things that might be happening six months, three months in the future, not things that a lot of other competitors and research peers are looking at things that already happened, they're looking in the past, ETR really likes to look into the future and our surveys are set up to do so. So thank you for that question, It's a enjoyable lead in, but to get to the fun stuff, like you said, what we do here is we put ratings on the datasets. I do want to put the caveat out there that our spending intentions really only captures top-line revenue. It is not indicative of profit margin or any other line items, so this is only to be viewed as what we are rating the data set itself, not the company, that's not what we're in the game of doing. So I think that's very important for the marketing and the vendors out there themselves when they take a look at this. We're just talking about what we can control, which is our data. We're going to talk about a few of the names here on this highlighted vendors list. One, we're going to go back to that you and I spoke about, I guess, about six months ago, or maybe even earlier, which was the observability space. You and I were noticing that it was getting very crowded, a lot of new entrants, there was a lot of acquisition from more of the legacy or standard players in the space, and that is continuing. So I think in a minute, we're going to move into that observability space, but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other. We're also going to move on a little bit into video conferencing, where we're capturing some spend deceleration, and then ultimately, we're going to get into a little bit of a storage refresh cycle and talk about that. But yeah, these are the highlighted vendors for April, we usually do this once a quarter and they do change based on the data, but they're not usually whipsawed around, the data doesn't move that quickly. >> Yeah, so you can see some of the big names in the left-hand side, some of the SAS companies that have momentum. Obviously, ServiceNow has been doing very, very well. We've talked a lot about Snowflake, Okta, CrowdStrike, Zscaler, all very positive, as well as several others. I guess I'd add some things. I mean, I think if thinking about the next decade, it's Cloud, which is not going to be like the same Cloud as the last decade, a lot of machine learning and deep learning and AI and the Cloud is extending to the edge and the data center. Data, obviously, very important, data is decentralized and distributed, so data architectures are changing. A lot of opportunities to connect across Clouds and actually create abstraction layers, and then something that we've been covering a lot is processor performance is actually accelerating relative to Moore's law. It's probably instead of doubling every two years, it's quadrupling every two years, and so that is a huge factor, especially as it relates to powering AI and AI inferencing at the edge. This is a whole new territory, custom Silicon is really becoming in vogue and so something that we're watching very, very closely. >> Yeah, I completely, agree on that and I do think that the next version of Cloud will be very different. Another thing to point out on that too, is you can't do anything that you're talking about without collecting the data and organizations are extremely serious about that now. It seems it doesn't matter what industry they're in, every company is a data company, and that also bodes well for the storage goal. We do believe that there is going to just be a huge increase in the need for storage, and yes, hopefully that'll become portable across multi-Cloud and hybrid as well. >> Now, as Erik said, the ETR data, it's really focused on that top-line spend. So if you look on the right side of that chart, you saw NetApp was kind of negative, was very negative, right? But it is a company that's in transformation now, they've lowered expectations and they've recently beat expectations, that's why the stock has been doing better, but at the macro, from a spending standpoint, it's still stout challenged. So you have big footprint companies like NetApp and Oracle is another one. Oracle's stock is at an all time high, but the spending relative to sort of previous cycles are relative to, like for instance, Snowflake, much, much smaller, not as high growth, but they're managing expectations, they're managing their transition, they're managing profitability. Zoom is another one, Zoom looking negative, but Zoom's got to use its market cap now to transform and increase its TAM. And then Splunk is another one we're going to talk about. Splunk is in transition, it acquired SignalFX, It just brought on this week, Teresa Carlson, who was the head of AWS Public Sector. She's the president and head of sales, so they've got a go-to-market challenge and they brought in Teresa Carlson to really solve that, but Splunk has been trending downward, we called that several quarters ago, Erik, and so I want to bring up the data on Splunk, and this is Splunk, Erik, in analytics, and it's not trending in the right direction. The green is accelerating spend, the red is in the bars is decelerating spend, the top blue line is spending velocity or Net score, and the yellow line is market share or pervasiveness in the dataset. Your thoughts. >> Yeah, first I want to go back. There's a great point, Dave, about our data versus a disconnect from an equity analysis perspective. I used to be an equity analyst, that is not what we do here. And the main word you said is expectations, right? Stocks will trade on how they do compare to the expectations that are set, whether that's buy-side expectations, sell-side expectations or management's guidance themselves. We have no business in tracking any of that, what we are talking about is the top-line acceleration or deceleration. So, that was a great point to make, and I do think it's an important one for all of our listeners out there. Now, to move to Splunk, yes, I've been capturing a lot of negative commentary on Splunk even before the data turns. So this has been a about a year-long, our analysis and review on this name and I'm dating myself here, but I know you and I are both rock and roll fans, so I'm going to point out a Led Zeppelin song and movie, and say that the song remains the same for Splunk. We are just seeing recent spending attentions are taking yet another step down, both from prior survey levels, from year ago levels. This, we're looking at in the analytics sector and spending intentions are decelerating across every single group, and we went to one of our other slide analysis on the ETR+ platform, and you do by customer sub-sample, in analytics, it's dropping in every single vertical. It doesn't matter which one. it's really not looking good, unfortunately, and you had mentioned this is an analytics and I do believe the next slide is an information security. >> Yeah, let's bring that up. >> And unfortunately it's not doing much better. So this is specifically Fortune 500 accounts and information security. There's deep pockets in the Fortune 500, but from what we're hearing in all the insights and interviews and panels that I personally moderate for ETR, people are upset, that they didn't like the strong tactics that Splunk has used on them in the past, they didn't like the ingestion model pricing, the inflexibility, and when alternatives came along, people are willing to look at the alternatives, and that's what we're seeing in both analytics and big data and also for their SIM and security. >> Yeah, so I think again, I pointed Teresa Carlson. She's got a big job, but she's very capable. She's going to meet with a lot of customers, she's a go-to-market pro, she's going to to have to listen hard, and I think you're going to see some changes there. Okay, so sorry, there's more bad news on Splunk. So (indistinct) bring this up is Net score for Splunk and Elastic accounts. This is for analytics, so there's 106 Elastic accounts in the dataset that also have Splunk and it's trending downward for Splunk, that's why it's green for Elastic. And Erik, the important call out from ETR here is how Splunk's performance in Elastic accounts compares with its performance overall. The ELK stack, which obviously Elastic is a big part of that, is causing pain for Splunk, as is Datadog, and you mentioned the pricing issue, well, is it pricing in your assessment or is it more fundamental? >> It's multi-level based on the commentary we get from our ITDMs teams that take the survey. So yes, you did a great job with this analysis. What we're looking at is the spending within shared accounts. So if I have Splunk already, how am I spending? I'm sorry if I have Elastic already, how am I spending on Splunk? And what you're seeing here is it's down to about a 12% Net score, whereas Splunk overall, has a 32% Net score among all of its customers. So what you're seeing there is there is definitely a drain that's happening where Elastic is draining spend from Splunk and usage from them. The reason we used Elastic here is because all observabilities, the whole sector seems to be decelerating. Splunk is decelerating the most, but Elastic is the only one that's actually showing resiliency, so that's why we decided to choose these two, but you pointed out, yes, it's also Datadog. Datadog is Cloud native. They're more dev ops-oriented. They tend to be viewed as having technological lead as compared to Splunk. So a really good point. Dynatrace also is expanding their abilities and Splunk has been making a lot of acquisitions to push their Cloud services, they are also changing their pricing model, right? They're trying to make things a little bit more flexible, moving off ingestion and moving towards consumption. So they are trying, and the new hires, I'm not going to bet against them because the one thing that Splunk has going for them is their market share in our survey, they're still very well entrenched. So they do have a lot of accounts, they have their foothold. So if they can find a way to make these changes, then they will be able to change themselves, but the one thing I got to say across the whole sector is competition is increasing, and it does appear based on commentary and data that they're starting to cannibalize themselves. It really seems pretty hard to get away from that, and you know there are startups in the observability space too that are going to be even more disruptive. >> I think I want to key on the pricing for a moment, and I've been pretty vocal about this. I think the old SAS pricing model where you essentially lock in for a year or two years or three years, pay up front, or maybe pay quarterly if you're lucky, that's a one-way street and I think it's a flawed model. I like what Snowflake's doing, I like what Datadog's doing, look at what Stripe is doing, look at what Twilio is doing, you mentioned it, it's consumption-based pricing, and if you've got a great product, put it out there and damn, the torpedoes, and I think that is a game changer. I look at, for instance, HPE with GreenLake, I look at Dell with Apex, they're trying to mimic that model and apply it to infrastructure, it's much harder with infrastructure 'cause you've got to deploy physical infrastructure, but that is a model that I think is going to change, and I think all of the traditional SAS pricing is going to come under disruption over the next better part of the decades, but anyway, let's move on. We've been covering the APM space pretty extensively, application performance management, and this chart lines up some of the big players here. Comparing Net score or spending momentum from the April 20th survey, the gray is, sorry, the gray is the April 20th survey, the blue is Jan 21 and the yellow is April 21, and not only are Elastic and Datadog doing well relative to Splunk, Erik, but everything is down from last year. So this space, as you point out, is undergoing a transformation. >> Yeah, the pressures are real and it's sort of that perfect storm where it's not only the data that's telling us that, but also the direct feedback we get from the community. Pretty much all the interviews I do, I've done a few panels specifically on this topic, for anyone who wants to dive a little bit deeper. We've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors. People are using a Datadog for certain aspects, they are using Elastic where they can 'cause it's cheaper. They're using Splunk because they have to, but because it's so expensive, they're cutting some of the things that they're putting into Splunk, which is dangerous, particularly on the security side. If I have to decide what to put in and whatnot, that's not really the right way to have security hygiene. So this space is just getting crowded, there's disruptive vendors coming from the emerging space as well, and what you're seeing here is the only bit of positivity is Elastic on a survey-over-survey basis with a slight, slight uptick. Everywhere else, year-over-year and survey-over-survey, it's showing declines, it's just hard to ignore. >> And then you've got Dynatrace who, based on the interviews you do in the (indistinct), one-on-one, or one-on-five, the private interviews that I've been invited to, Dynatrace gets very high scores for their roadmap. You've got New Relic, which has been struggling financially, but they've got a really good product and a purpose-built database just for this APM space, and then of course, you've got Cisco with AppD, which is a strong business for them, and then as you mentioned, you've got startups coming in, you got ChaosSearch, which Ed Walsh is now running, leave the data in place in AWS and really interesting model, Honeycomb is getting really disruptive, Jeremy Burton's company, Observed. So this space is it's becoming jumped ball. >> Yeah, there's a great line that came out of one of them, and that was that the lines are blurring. It used to be that you knew exactly that AppDynamics, what they were doing, it was APM only, or it was logging and monitoring only, and a lot of what I'm hearing from the ITDM experts is that the lines are blurring amongst all of these names. They all have functionality that kind of crosses over each other. And the other interesting thing is it used to be application versus infrastructure monitoring, but as you know, infrastructure is becoming code more and more and more, and as infrastructure becomes code, there's really no difference between application and infrastructure monitoring. So we're seeing a convergence and a blurring of the lines in this space, which really doesn't bode well, and a great point about New Relic, their tech gets good remarks. I just don't know if their enterprise level service and sales is up to snuff right now. As one of my experts said, a CTO of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still standalone, that there needs to be some M and A or convergence in this space. >> Okay, now we're going to call out some of the data that really has jumped out to ETR in the latest survey, and some of the names that are getting the most queries from ETR clients, many of which are investor clients. So let's start by having a look at one of the most important and prominent work from home names, Zoom. Let's look at this. Erik is the ride over for Zoom? >> Ah, I've been saying it for a little bit of a time now actually. I do believe it is, and we'll get into it, but again, pointing out, great, Dave, the reason we're presenting today Splunk, Elastic and Zoom, they are the most viewed on the ETR+ platform. Trailing behind that only slightly is F5, I decided not to bring F5 to the table today 'cause we don't have a rating on the data set. So then I went one deep, one below that and it's pure. So the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in, which is hopefully going to gain interest to your viewers as well. So to get to Zoom, yeah, I call Zoom the pandemic bull market baby. This was really just one that had a meteoric ride. You look back, January in 2020, the stock was at $60 and 10 months later, it was like 580, that's in 10 months. That's cooled down a little bit into the mid-300s, and I believe that cooling down should continue, and the reason why is because we are seeing huge deceleration in our spending intentions. They're hitting all-time lows, it's really just a very ugly dataset. More importantly than the spending intentions, for the first time, we're seeing customer growth in our survey flatten. In the past, we knew that the deceleration of spend was happening, but meanwhile, their new customer growth was accelerating, so it was kind of hard to really make any call based on that. This is the first time we're seeing flattening customer growth trajectory, and that in tandem with just dominance from Microsoft in every sector they're involved in, I don't care if it's IP telephony, productivity apps or the core video conferencing, Microsoft is just dominating. So there's really just no way to ignore this anymore. The data and the commentary state that Zoom is facing some headwinds. >> Well, plus you've pointed out to me that a lot of your private conversations with buyers says that, "Hey, we're, we're using the freebie version of Zoom, and we're not paying them." And that combined with Teams, I mean, it's... I think, look, Zoom, they've got to figure out how to use their elevated market cap to transform and expand their TAM, but let's move on. Here's the data on Pure Storage and we've highlighted a number of times this company is showing elevated spending intentions. Pure announced it's earnings in May, IBM just announced storage, it was way down actually. So still, Pure, more positive, but I'll on that comment in a moment, but what does this data tell you, Erik? >> Yeah, right now we started seeing this data last survey in January, and that was the first time we really went positive on the data set itself, and it's just really continuing. So we're seeing the strongest year-over-year acceleration in the entire survey, which is a really good spot to be. Pure is also a leading position among its sector peers, and the other thing that was pretty interesting from the data set is among all storage players, Pure has the highest positive public Cloud correlation. So what we can do is we can see which respondents are accelerating their public Cloud spend and then cross-reference that with their storage spend and Pure is best positioned. So as you and I both know, digital transformation Cloud spending is increasing, you need to be aligned with that. And among all storage sector peers, Pure is best positioned in all of those, in spending intentions and adoptions and also public Cloud correlation. So yet again, to start another really strong dataset, and I have an anecdote about why this might be happening, because when I saw the data, I started asking in my interviews, what's going on here? And there was one particular person, he was a director of Cloud operations for a very large public tech company. Now, they have hybrid, but their data center is in colo, So they don't own and build their own physical building. He pointed out that during COVID, his company wanted to increase storage, but he couldn't get into his colo center due to COVID restrictions. They weren't allowed. You had 250,000 square feet, right, but you're only allowed to have six people in there. So it's pretty hard to get to your rack and get work done. He said he would buy storage, but then the colo would say, "Hey, you got to get it out of here. It's not even allowed to sit here. We don't want it in our facility." So he has all this pent up demand. In tandem with pent up demand, we have a refresh cycle. The SSD depreciation cycle is ending. SSDs are moving on and we're starting to see a new technology in that space, NVMe sorry, technology increasing in that space. So we have pent up demand and we have new technology and that's really leading to a refresh cycle, and this particular ITDM that I spoke to and many of his peers think this has a long tailwind that storage could be a good sector for some time to come. >> That's really interesting, thank you for that extra metadata. And I want to do a little deeper dive on storage. So here's a look at storage in the industry in context and some of the competitive. I mean, it's been a tough market for the reasons that we've highlighted, Cloud has been eating away that flash headroom. It used to be you'd buy storage to get more spindles and more performance and we're sort of forced to buy more, flash, gave more headroom, but it's interesting what you're saying about the depreciation cycle. So that's good news. So ETR combines, just for people's benefit here, combines primary and secondary storage into a single category. So you have companies like Pure and NetApp, which are really pure play primary storage companies, largely in the sector, along with Veeam, Cohesity and Rubrik, which are kind of secondary data or data protection. So my quick thoughts here that Pure is elevated and remains what I call the one-eyed man in the land of the blind, but that's positive tailwinds there, so that's good news. Rubrik is very elevated but down, it's big competitor, Cohesity is way off its highs, and I have to say to me, Veeam is like the Steady Eddy consistent player here. They just really continue to do well in the data protection business, and the highs are steady, the lows are steady. Dell is also notable, they've been struggling in storage. Their ISG business, which comprises servers and storage, it's been softer in COVID, and during even this new product rollout, so it's notable with this new mid range they have in particular, the uptick in Dell, this survey, because Dell is so large, a small uptick can be very good for Dell. HPE has a big announcement next month in storage, so that might improve based on a product cycle. Of course, the Nimble brand continues to do well, IBM, as I said, just announced a very soft quarter, down double digits again, and they're in a product cycle shift. And NetApp, it looks bad in the ETR data from a spending momentum standpoint, but their management team is transforming the company into a Cloud play, which Erik is why it was interesting that Pure has the greatest momentum in Cloud accounts, so that is sort of striking to me. I would have thought it would be NetApp, so that's something that we want to pay attention to, but I do like a lot of what NetApp is doing, and other than Pure, they're the only big kind of pure play in primary storage. So long-winded, intro there, Erik, but anything you'd add? >> No, actually I appreciate it as long-winded. I'm going to be honest with you, storage is not my best sector as far as a researcher and analyst goes, but I actually think that a lot of what you said is spot on. We do capture a lot of large organizations spend, we don't capture much mid and small, so I think when you're talking about these large, large players like NetApp not looking so good, all I would state is that we are capturing really big organization spending attention, so these are names that should be doing better to be quite honest, in those accounts, and at least according to our data, we're not seeing it in. It's longterm depression, as you can see, NetApp now has a negative spending velocity in this analysis. So, I can go dig around a little bit more, but right now the names that I'm hearing are Pure, Cohesity. I'm hearing a little bit about Hitachi trying to reinvent themselves in the space, but I'll take a wait-and-see approach on that one, but pure Cohesity are the ones I'm hearing a lot from our community. >> So storage is transforming to Cloud as a service. You've seen things like Apex in GreenLake from Dell and HPE and container storage. A little, so not really a lot of people paying attention to it, but Pure bought a company called Portworx which really specializes in container storage, and there's many startups there, they're trying to really change the way. David Flynn, has a startup in that space, he's the guy who started Fusion-io. So a lot of transformations happening here. Okay, I know it's been a long segment, we have to summarize, and let me go through a summary and then I'll give you the last word, Erik. So tech spending appears to be tracking US GDP at 6 to 7%. This talent shortage could be a blocker to accelerating IT deployments, so that's kind of good news actually for services companies. Digital transformation, it remains a priority, and that bodes, well, not only for services, but automation. UiPath went public this week, we profiled that extensively, that went public last Wednesday. Organizations that sit at the top face some tough decisions on how to allocate resources. They're running the business, growing the business, transforming the business, and we're seeing a bifurcation of spending and some residual effects on vendors, and that remains a theme that we're watching. Erik, your final thoughts. >> Yeah, I'm going to go back quickly to just the overall macro spending, 'cause there's one thing I think is interesting to point out and we're seeing a real acceleration among mid and small. So it seems like early on in the COVID recovery or COVID spending, it was the deep pockets that moved first, right? Fortune 500 knew they had to support remote work, they started spending first. Around that in the Fortune 500, we're only seeing about 5% spend, but when you get into mid and small organizations, that's creeping up to eight, nine. So I just think it's important to point out that they're playing catch up right now. I also would point out that this is heavily skewed to North America spending. We're seeing laggards in EMEA, they just don't seem to be spending as much. They're in a very different place in their recovery, and I do think that it's important to point that out. Lastly, I also want to mention, I know you do such a great job on following a lot of the disruptive vendors that you just pointed out, with Pure doing container storage, we also have another bi-annual survey that we do called Emerging Technology, and that's for the private names. That's going to be launching in May, for everyone out there who's interested in not only the disruptive vendors, but also private equity players. Keep an eye out for that. We do that twice a year and that's growing in its respondents as well. And then lastly, one comment, because you mentioned the UiPath IPO, it was really hard for us to sit on the sidelines and not put some sort of rating on their dataset, but ultimately, the data was muted, unfortunately, and when you're seeing this kind of hype into an IPO like we saw with Snowflake, the data was resoundingly strong. We had no choice, but to listen to what the data said for Snowflake, despite the hype. We didn't see that for UiPath and we wanted to, and I'm not making a large call there, but I do think it's interesting to juxtapose the two, that when snowflake was heading to its IPO, the data was resoundingly positive, and for UiPath, we just didn't see that. >> Thank you for that, and Erik, thanks for coming on today. It's really a pleasure to have you, and so really appreciate the collaboration and look forward to doing more of these. >> Yeah, we enjoy the partnership greatly, Dave. We're very happy to have you on the ETR family and looking forward to doing a lot, lot more with you in the future. >> Ditto. Okay, that's it for today. Remember, these episodes are all available as podcasts wherever you listen. All you have to do is search "Breaking Analysis" podcast, and please subscribe to the series. Check out ETR website it's etr.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me, david.vellante@siliconangle.com, you can DM me on Twitter @dvellante or comment on our LinkedIn posts. I could see you in Clubhouse. This is Dave Vellante for Erik Porter Bradley for the CUBE Insights powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (bright music)

(upbeat music) >> Announcer: From the keeps studios in Palo Alto in Boston, connecting with that leaders all around the world. This is theCube conversation. >> Welcome to theCube conversation here in Palo Alto, California. I'm John Furrier, host of theCube. With a special content series called, Leading with observability, and this topic is, Keeping watch over microservices and containers. With great guests, Patrick Lin, VP of Product Management for the observability product at Splunk. Patrick, great to see you. Thanks for coming on remotely. We're still in the pandemic, but thanks for coming on. >> Yeah, John, great to see you as well. Thanks for having me. >> So, leading with observability is a big theme of our content series. Managing end to end and user experience is a great topic around how data can be used for user experience. But now underneath that layer, you have this whole craziness of the rise of the container generation, where containers are actually going mainstream. And Gardner will forecast anywhere from 30 to 40 percent of enterprises still yet, haven't really adopted at full scale and you've got to keep watch over these. So, what is the topic about keeping watch over microservice and containers, because, yeah, we know they're being deployed. Is it just watching them for watching sake or is there a specific reason? What's the theme here? Why this topic? >> Yeah, well, I think containers are part of the entire kind of stack of technology that's being deployed in order to develop and ship software more quickly. And, the fundamental reasons for that haven't changed but they've been greatly accelerated by the impact of the pandemic. And so I think for the past few years we've been talking about how software's eating the world, how it's become more and more important that company go through the transformation to be more digital. And I think now that is so patently obvious to everybody. When your only way of accessing your customer and for the customer to access your services is through a digital media. The ability for your IT and DevOps teams to be able to deliver against those requirements, to deliver that flawless customer experience, to sort of keep pace with it the digital transformation and the cloud initiatives. All of that is kind of coming as one big wave. And so, we see a lot of organizations migrating workloads to the cloud, refactoring applications, building new applications natively. And so, when they do that oftentimes the infrastructure of choice is containers. Because it's the thing that keeps up with the pace of the development. It's a much more efficient use of underlying resources. So it's all kind of part of the overall movement that we see. >> What is the main driver for this use case microservices and where's the progress bar in your mind of the adoption and deployment of microservices, and what is the critical things that are there you guys are looking at that are important to monitor and observe and keep track of? Is it the status of the microservices? Is it the fact that they're being turned on and off, the state, non-state, I mean take us through some of the main drivers for why you guys are keeping an eye on the microservices component? >> Sure, well, I think that if we take a step back the reason that people have moved towards microservices and containers fundamentally has to do with the desire to be able to, number one, develop and ship more quickly. And so if you can parallelize the development have API is the interface between these services rather than having sort of one monolithic code base, you can evolve more quickly. And on top of that, the goal is to be able to deliver software that is able to scale as needed. And so, that is a part of the equation as well. So when you sort of look at at this the desire to be able to iterate on your software and services more quickly, to be able to scale infinitely, staying up and so on. That's all like a great reason to do it, but what happens along those lines, what comes with it is a few kind of additional layers of complexity because now rather than have, let's say an end to your app that you're watching over on some hosts that you could reboot when there's a problem. Now you have 10's, maybe 100's of services running on top of maybe 100,000's, maybe 10,000's of containers. And so the complexity of that environment has grown quite quickly. And the fact that those containers may go away as you are scale the service up and down to meet demand also adds to that complexity. And so from an observability perspective, what you need to be able to do is a few things. One is you need to actually be tracking this in enough detail and at a high enough resolution in realtime. So that you know when things are coming in and out. And that's been one of the more critical things that we've built towards a Splunk, is that ability to watch over it in realtime. But more important, or just as important in that is, understanding the dependencies and the relationships between these different services. And so, that's one of the main things that we worked on here is to make sure that you can understand the dependency so that when there's an issue you have a shot at actually figuring out where the problem is coming from. Because of the fact that there's so many different services and so many things that could be affecting the overall user experience when something goes wrong. >> I think that's one of the most exciting areas right now, on observability is this whole microservices container equation, because a lot of actions being done there, there's a lot of complexity but the upside, if you do it right, it's significant. I think people generally are bought into that concept, Patrick, but I want to get your thoughts. I get this question a lot from executives and leaders whether it's a cloud architect or a CXO. And the question is, what should I consider? What do I need to consider when deploying an observability solution? >> Yeah, that's a great question. Cause I think they're obviously a lot of considerations here. So, I think one of the main ones, and this is something that I think is a pattern that we are pretty familiar with in the this sort of monitoring and management tool world. Is that, over time most enterprises have gotten themselves a very large number of tools. One for each part of their infrastructure or their application stack and so on. And so, what you end up with is sprawl in the monitoring toolset that you have. Which creates not just sort of a certain amount of overhead in terms of the cost, but also complexity that gets in the way of actually figuring out where the problem is. I've been looking at some of the toolsets that some of our customers have pulled together and they have the ability to get information about everything but it's not kind of woven together in a useful way. And it sort of gets in the way actually, having so many tools when you are actually in the heat of the moment trying to figure something out. It sort of hearkens back to the time when you have an outage, you have a con call with like a cast of 1000's on it trying to figure out what's going on. And each person comes to that with their own tool, with their own view, without anything that ties that to what the others are seeing. And so, that need to be able to provide sort of an integrated toolset, with a consistent interface across infrastructure, across the application, across what the user experience is and across the different data types. The metrics, the traces, the logs. Fundamentally I think that ability to kind of easily correlate the data across it and get to the right insight. We think that's a super important thing. >> Yeah, and I think what that points out, I mean, I always say, don't be a fool with a tool. And if you have too many tools, you have a tool shed, and there are too many tools everywhere. And that's kind of a trend, and tools are great when you need tools. To do things. But when you have too many, when you have a data model where essentially what you're saying is, a platform is the trend, because weaving stuff together you need to have a data control plane, you need to have data visualization. You need to have these things for understanding the success there. So, really it's a platform, but platforms also have tools as well. So tools or features of a platform if I get what you're saying, right? Is that correct? Yeah, so I think that there's one part of this which is, you need to be able to, if I start from the user point of view, what you want is a consistent and coherent set of workflows for the people who are trying to actually do the work. You don't want them to have to deal with the impedance mismatches across different tools that exist based on, whatever, even the language that they use but how they bring the data in and how it's being processed. You go down one layer from that. You sort of want to make sure that what they're working with is actually consistent as well. And that's the sort of capabilities that you're looking at whether you're whatever, trying to chart something to be able to look at the details, or go from a view of logs to the related traces. You sort of want to make sure that the information that's being served up there is consistent. And that in turn relies on data coming in, in a way that is sort of processed to be correlated well. So that if you say, Hey, I'm I'm looking at a particular service. I want to understand what infrastructure is sitting on or I'm looking at a log and I see that it relates to a particular service. And I want to look at traces for that service. Those things need to be kind of related from the data on in and that needs to be exposed to the user so that they can navigate it properly and make use of it. Whether that's during kind of, or time during an incident or peace time. >> Yeah, I love that wartime conciliary versus peace time. I saw blog posts from a VC, I think said, don't be a Tom Hagen, which is the guy in The Godfather when the famous lines said, you're not a wartime conciliary. Which means things are uncertain in these times and you've got to get them to be certain. This is a mindset, this is part of the pandemic we're living in. Great point, I love that. Maybe we could follow up on that at the end, but I want to get some of these topics. I want to get your reactions to. So, I want you to react to the following, Patrick. it's an issue in a topic, and there it is, missing data results in limited analytics and misguided troubleshooting. What's your reaction to that? What's your take on that? What's the Splunk's take on that? >> Yeah, I mean, I think Splunk has sort of been a proponent of that view for a very long time. I think that whether that's from the log data or from, let's say, the metric data that we capture at high resolution or from tracing. The goal here is to have the data that you need in order to actually properly diagnose what's going on. And I think that older approaches, especially on the application side, tend to sample data right at the source and provide hopefully useful samples of it for when you have that problem. That doesn't work very well in the microservice world because you need to actually be able to see the entirety of a transaction, to a full trace across many services before you could possibly make a decision as to what's useful to keep. And so, the approach that I think we believe is the right one, is to be able to capture at full fidelity all of those bits of information, partly because of what I just said, you want to be able to find the right sample, but also because it's important to be able to tie it to something that may be being pulled in by different system. So, an example of that might be, in a case where you are trying to do real user monitoring alongside of APM, and you want to see the end to end trace from what the user sees all the way through to all the backend services. And so, what's typical in this world today is that, that information is being captured by two different systems independent sampling decisions. And therefore the ability to draw a straight line from what the end user sees all the way to what is effecting it on the backend is pretty hard. Where it gets really expensive. And I think the approach that we've taken is to make it so that that's easy and cost-effective. And it's tremendously helpful then to tie it back to kind of what we were talking about at the outset here where you were trying to provide services that make sense and are easy access and so on to your end user. to be able to have that end to end view because you're not missing data. It's tremendously valuable. >> You know what I love about Splunk is, cause I'm a data geek going back when it wasn't fashionable back in the 80's. And Splunk has always been about ingesting all the data. So they bring all the data, we'll take it all. Now from at the beginning it was pretty straightforward, complex but still it had a great utility. But even now, today, it's the same thing you just mentioned, ingest all the data because there's now benefits. And I want to just ask you a quick question on this, distributed computing trend, because I mean everyone's pretty much in agreement that's in computer science or in the industry and in technology says, okay, cloud is a distributed computing with the edge. It's essentially distributed computing in a new way, new architecture with new great benefits, new things, but science is still kicking apply some science there. You mentioned distributed tracing because at the end of the day that's also a new major thing that you guys are focused on and it's not so much about, it's also good get me all the data but distributed tracing is a lot harder than understanding that because of the environment and it's changing so fast. What's your take on it? >> Yeah, well fundamentally I think this goes back to, ironically one of the principles in observability. Which is that oftentimes you need participation from the developers in sort of making sure that you have the right visibility. And it has to do with the fact that there are many services that are being kind of strong together as it were to be able to deliver on some end user transaction or some experience. And so, the fact that you have many services that are part of this, means that you need to make sure that each of those components is actually kind of providing some view into what it's doing. And distribute tracing is about taking that and kind of weaving it together so that you get that coherent view of the business workflow within the overall kind of web of services that make up your application. >> So the next topic, I want to get into, we've got limited time, but I'm going to squeeze through, but I'm going to read it to you real quick. Slow alerts and insights are difficult to scale. If they're difficult to scale it holds back the meantime between resolving. And so, it's difficult to detect in cloud. It was easier maybe on premise, but with cloud this is another complexity thing. How are you seeing the inability to scale quickly across the environments for to manage the performance issues and delays that are coming out of not having that kind of in slow insights or managing that? What's your reaction to that? >> Yeah, well, I think there are a lot of tools out there that we'll take in events or where issues from cloud environments. But they're not designed from the very beginning to be able to handle the sort of scale of what you're looking at. So, I mentioned, it's not uncommon for a company to have 10's or maybe even 100's of services and 1000's of containers or hosts. And so, the sort of sheer amount of data you have to be looking at on an ongoing basis. And the fact that things can change very quickly. Containers can pop in and go away within seconds. And so, the ability to track that in realtime implies that you need to have an architectural approach that is built for that from the very beginning. It's hard to retrofit a system to be able to handle orders to magnitude more complexity and change in pace of change. You need to start from the very beginning. And the belief we have is that you need some form of a realtime streaming architecture. Something that's capable of providing that realtime detection and alerting across a very wide range of things in order to handle the scale and the ephemeral nature of cloud environments. >> Let me ask a question then, because I heard some people say, well, it doesn't matter. 10, 15 minutes to log in to an event is good enough. What would you react to that? (chuckles) What a great example of where it's not good enough? I mean, is it minutes is it's seconds, what are we talking about here? What's the good enough bar right now? >> Yeah, I mean, I think any anybody who has tried to deliver an experience digitally to an end user, if you think you can wait minutes to solve a problem you clearly haven't been paying enough attention. And I think that, I think it almost goes without saying, that the faster you know that you have a problem, the better off you are. And so, when you think about what are the objectives that you have for your service levels or your performance or availability. I think you run out of minutes pretty quickly, if you get to anything like say, three nines So, waiting 15 minutes, maybe would have been acceptable before people were really trying to use your service at scale. But definitely not any more. >> And the latest app requires it. It's super important. I brought that up and tongue in cheek kind of tee that up for you because these streaming analytics, streaming engines are super valuable, and knowing when to use realtime and not also matters. This is where the platforms come in. >> Yes, absolutely. The platform is the thing that enables that. And I think you have to sort of build it from the very beginning with that streaming approach with the ability to do analytics against the streams coming in, in order for you to deliver on this sort of promise of alerts and insights at scale and in realtime. >> All right, final point. I'll give you the last word here. Give a plug for the Splunk observability suite. What is it? Why is it important? Why should people buy it? Why should people adopt it? Why should they upgrade to it? Give the perspective, give the plug. >> Yeah, sure. I appreciate the opportunity. So, I think as we've been out there speaking to customers right over the last year as part of Splunk and before that, I think they've spoken to us a lot about the need for better visibility into their environments. Which are increasingly complex and where they're trying to deliver on the best possible user experience. And to sort of add to that, where they're trying to actually consolidate the tools. We spoke about the sprawl at the beginning. And so, with what we're putting together here with the Splunk observability suite. I'd say we have the industry's most comprehensive and powerful combination of solutions that will help both sort of IT and DevOps teams tackle these new challenges for monitoring and observability that other tools simply can't address. So you're able to eliminate the management complexity by having a single consistent user experience across the metrics and logs and traces, so that you can have seamless monitoring and troubleshooting and investigation. You can create a better user experiences by having that true end to end visibility, all the way from the front end to the backend services, so that you can actually see what kind of impact you're having on users and figure it out within seconds. I think we're also able to help increase developer productivity. As these high performance tools that help the DevOps teams get to a better quality code faster, because they can get immediate feedback on how their coachings are doing with each we would see each release and they're able to operate more efficiently. So, I think there's a very large number of benefits from this approach of providing a single unified toolset that relies on a source of data that's consistent across it but then has the sort of particular tools that different users need for what they care about. Whether you're the front end developer, needing to understand the user experience, whether you're backend service owner wanting to see how your service relates to others, whether you're owning the infrastructure, and needs to see, is it actually providing what the services are running on it need. >> Well, Patrick, great to see you. And I just want to say, congratulations has been following your work, going back in the industry specifically with SignalFx, you guys were really early and seeing the value of observability before it was a category. And so how has more often so relevant as you guys had saw it. So, congratulations and keep up the great work. We'll keep a competition's open. Thanks for coming on. >> Great, thanks so much, John. Great talking to you. >> All right, this is theCube, Leading with observability, it's a series, check it out. We have a multiple talk tracks. Check out the Splunk's a series, Leading with observability. I'm John Furrier with theCube. Thanks for watching. (upbeat music)

>> From theCube Studios in Palo Alto in Boston, bringing you data driven insights from theCube and ETR, this is breaking analysis with Dave Vellante. >> As we reported in our last episode tech spending overall continues to be significantly muted relative to 2019. Now, our forecast continues to project a 4 to 5% decline in 2020 spending, and a tepid 2% increase in 2021. This is based on the latest data from ETR surveys of CIOs and other it buyers. Nonetheless, there continues to be some sectors and vendor bright spots in what is generally an overall challenging market. Hello everyone, and welcome to this week's Wikibon Cube Insights powered by ETR. My name is Dave Vellante, and in this breaking analysis, we welcome back Erik Bradley from ETR to provide added color from my solo flight from last time. Erik always a pleasure to see you, thanks so much for coming back in theCube. >> I always enjoy it. Happy Friday Dave, We're almost through. >> Happy Friday. They just blend together. Guys, if you would bring up the first slide, I just want to summarize the situation. This is from ETR's latest findings, I just extracted some. And I want to go down very quickly, Erik, and then get your take. As I said, technology buyers expect the downturn for 2020, but this quarter, coming into fourth quarter, minus 3.2% was ETR's forecast, that's year to year spending decline and a 2% uptick in 2021. Now, Erik this is slightly, what I call it slightly less bad, relative to last quarter. So sequentially it's less bad. >> Yeah, there's a couple of things to break down there. So first to begin with, beginning of the year, when we launched not only our spending attention surveys, we did a simultaneous COVID impact survey, and that's where we caught originally a 5% decline was expected. So although negative 3.2 was probably the worst quarter over quarter lapse we've seen, as a matter of fact it is the lowest drop we've had theory, going into 2021, the IT people that we've actually surveyed are actually expecting a 2% increase. So there is a reason for optimism, but if we're looking at the current data set, there is no doubt the picture remains a little bit bleak. We can go into different sectors and vendors where they are impacted, but I think maybe if you're willing, I think it might be worth just sort of breaking down the demographics of the survey a little bit and how we got to that 3.2% survey over survey decline. >> Yeah, and we have a chart on that. But before we get that, I just wanted to lay out some of the other key points of your analysis. The other one, which is we talked about this in the last episode, we call it a slow thawing. Hiring an IT project freezes are thawing, with fewer companies expecting layoffs. So that gives us some bright spots, but there are definitely a widening bifurcation between vendors gaining share and those who are donating share. And then, you know, again, relative to last quarter survey we're seeing government and education and fortune 100, you guys are showing the deepest cuts from the last survey. Where's IT Telco, retail and retail consumer are showing a little bit more stability. And then of course you talked about the work from home which we've covered doubling from pre pandemic. Pretty interesting findings from your COVID survey. >> Yeah, it's a fantastic, and this is the fourth iteration of this survey that we've done now. So we've been able to track it very quickly, launched it in the field when we realized the true impact of what was happening in early March. This is our fourth version, and we've been able to track it overall. Yes, without a doubt government, education are being the biggest impact, the biggest declines without a doubt. Now, clearly the caveat to that is if there's any sort of government policy maybe those could actually help a little bit, but for right now, those are getting hit the most. Retail consumer is fairing much, much better, and the IT companies, as generally, we're seeing in the market as well, they can, you know, are still spending money and still moving. But the reason for optimism actually comes from multiple metrics. And I will say, we have caught a bottom on all of the negative metrics at this point. Now, who knows what will happen the next time we do it, right? The world is always fluid. But based on this, this is our fourth iteration of this survey, whether it be IT projects being frozen, whether it be layoffs, whether it be just overall expected budget increase, everything looks like it is already bottomed and there is some optimism going into 2021. Of course, the January survey that we launched will be able to corroborate that hopefully, and we'll have much more granularity into those findings at that time. >> Great. Okay, now let's get into the demographics that you referenced for. This next slide shows those. The record number of respondents Erik, congratulations on that. And so take us through the makeup of the survey respondents guys, if you bring up this next slide. >> Yeah. So for the October 20, what we're really doing here is we're asking the it decision makers to update the survey responses they gave us in July. We're basically saying, okay, you thought you were going to spend this in the back half, what did you actually do? And in this particular survey we had 1,438 qualified IT decision makers get involved. That's 60% of the fortune 100 is represented, almost a quarter of the global 1000, and we had about 35% of the fortune 500. The industry breakdown is all across the board, whether it's financials/insurance, IT/Telco, we have industrials/manufacturing, we have energy/utilities, we have government. So it's really a great cross section. Now, geographically, that tends to be about 80% North America. We are heavily concentrated in that area, but we also have a 12% EMEA, 5% APAC and remainder is Latin AmErika. If there were any visibility concerns at all would probably be in China. It's just not that easy to get qualified IT decision makers from China to respond to us. But that's an area we are working on going forward, but overall a huge survey response, certainly meaningful end, and we're very happy with the data that we collected this time. >> Okay, thank you for that. Now, I want to go into the next graphic here, and I want to look at how net score has changed over time. And I want to remind people that, so this slide basically goes back to 2016, and shows some ebbs and flows and then some real strength coming in, 'cause you see 17 and 18, and you may forget going into Q4'19 and into 2020, the ETR data was telling us, hey, things are going to slow down a little bit. It's hard to remember that. And so, and the thinking back then was okay, last couple of years, people have spent a lot on digital transformation, and would a lot of experimentation, they were hanging on to their legacy stuff, and with all that technical debt and they were experimenting with a lot of the new technologies. And what we saw coming into Q4 2019 was people beginning to unplug some of that and making bets basically, unplugging some of the legacy stuff. Oh, and by the way, maybe saying hey, the new stuff that we tried didn't work, we're going to do less experimentation. So we saw a somewhat depressed next score, and you can see that in here coming into 2020, and then of course COVID hit and you can see the bottom fell out. But wow what a drop, I mean, that says it all, a lot different than what we're seeing in the stock market. >> Yeah, first of all, just a great recap on what we caught last year. Really well done. So at that time there was concurrent spending. There was a lot of proof of concepts being done. People weren't exactly sure how to transition off, how fast they were going to get into the cloud, how fast they could make that digital transformation. And they were kicking the tires on everything, and there was a ton of spend. It was the golden era of IT spending at the time. But we did catch that some of that was coming down. So what we will see now is obviously that spending was going to cool off either way, but now with the global pandemic impact hitting what we've caught, of course, is the biggest survey over survey decline. 3.2% was matched at one other point in our survey's history, but that was at very elevated spendings, so that drop was not as meaningful. When we're seeing from a more baseline that drop right now is extremely seasonal, and extremely meaningful, my apologies. Now, I do want to make a quick caveat that usually the October survey catches some seasonality, because a lot of people have expected spend in the back half that doesn't always materialize. But make no mistake, this is way beyond our normal seasonality. This trough is a real metric. >> Yeah, and when I talk to buyers and I talk to even salespeople, for if you want the truth, you'll talk to salespeople, if you can get the truth out of them, which you usually can. Sales and engineering, that's really if we want to know what's happening in companies, but they will tell you that their visibility, same with the buyers, they're saying, look, I think I'm going to spend and I think I'm going to get approval on it, but the normal buying signals, you kind of have to take with a grain of salt because it's, the buyers don't know the sellers don't really know. I mean, they think they've got reasonable visibility but things change so fast as we know. So you have to be really, really careful. All right, let's drill in to some of the sectors, and that's really the next two slides, guys, if you bring up the first of the next two. So this shows the change from July to October. So the last survey to this survey, 2020, and the green bars of July, yellow bars are October. And you can see right away, jumps out at you, container orchestration and ML and AI, and we've got some other data on this jump right off the charts. They're still elevated levels, so that's a real positive. You can see AI actually, maybe waning a bit, and I think that's probably, Erik, is a lot of it is just, you don't even see it, it's just embedded. But take us through this first chart and then we'll dig into some of these sectors. What are you seeing? >> Yeah, certainly. So from a sector breakdown point of view, that lesson, none of them were spared, let's be honest, right? There's a slow down in spending. But containers and containerization were by far the most stable. So clearly this is a priority. People are recognizing that they need to go that route. Nobody wants to be tied to any particular cloud provider. So container and containers are moving the best, they are looking about as stable as they can be. When we drill down a little bit further in there, we're seeing Kubernetes of course, Microsoft and AWS really supporting in that sector. Now, when you talk about the ones that had the biggest survey over survey declines, we are looking at ML/AI, but like you said, still elevated spend. So even though there was a big survey over survey decline, the overall spending intentions are healthy. Nobody is getting away from it. Also to corroborate that in the COVID impact study, we asked people, given the current situation where their priorities are, and unfortunately in that area ML/AI and the RPA we're actually not positioned as well. So it actually corroborates the COVID impact survey, corroborates what we're seeing here in our larger intentions. Now, when you look at ML/AI, Microsoft is still very well suited in that area. Virtualization was another big area that dropped, which was interesting because I think the immediate COVID impact and the work from home, we saw a little spike there. I think we definitely saw companies like Citrix, right? F5 and Nutanix and AWS workspaces. They all had a really good impact, positive, when we first hit, but virtualization is dropping quite a bit there. And again, no surprise, Microsoft is well positioned as well. And then lastly, enterprise content management also had a big, big drop-off, and there you're looking at Adobe Box, Open Text, those are the type of companies that seem to be having the biggest survey over survey decline and ECM. >> Yeah. And I just want to make a comment on this first of the two slides. Is you see security, it's okay, there's a little bit of decline, but there's the story of the haves and the have nots. If you're an end point security, you're in cloud security, you're in identity access management, there's some real tailwinds for you right now. You're seeing that with Octa, CrowdStrike and Zscaler, SailPoint, you know, had a really good quarter. So that's the story of kind of the, a mixed bag. If you go to the next slide, guys, what jumps out here on the second sector breakdown, and Erik you alluded to this as RPA, very elevated, although down, somewhat still, again, very elevated and cloud computing. I mean, that's all everybody wants to talk about. This is a large market that continues to grow very, very fast. >> Yeah. It's a A2 cloud, right? I mean, even the cloud, we're kind of shocked and we saw that too. But, you know, again, it's still a healthy survey at 4Cloud. Spending is still there, but what we are seeing is a pretty big survey over-serving decline that is probably, if you had to translate that, it's going to show slower growth. Still double digit growth, but slower than we expected. And interestingly in the cloud, again, Microsoft is very steady, GCP steady. We saw AWS soften a little bit, and that's something that I think we need to keep an eye on there, we are seeing some softening trends. IBM and Oracle, unfortunately, no matter how hard they push, it doesn't really seem to be making a dent, at least with our it decision makers that respond to the survey. But one thing that was interesting was VMware on AWS actually looked much, much better than VMware alone. So on the cloud side, those are pretty interesting takeaways. >> Yeah, we talked about that a couple of episodes back as the, well, couple of things to pick up on your comments. You mentioned IBM and Oracle, they're just so large, they're growing businesses are not growing fast enough and they're not large enough to offset the decline and their declining businesses. Yet they're huge, they have, they throw off a lot of cash and so maybe their stock's not going through the roof, but they're pretty stable companies from that regard. I wonder, maybe AWS is starting to hit some of those, the law of large numbers. I mean, it's still growing very, very rapidly for a 45 plus billion dollar organization, still growing well into the double digits, so it just gets harder. And then, but the other thing I wanted to pick up on is you mentioned VMware cloud on AWS, we're seeing those hybrid solutions really start to pick up the multi-cloud solutions, which I was a real skeptic a couple of years ago 'cause it wasn't really real, now becoming real. And I think when you talk to, you know this well from your Ven discussions, people are looking at options for cloud. They want multiple clouds, the right horse for the right course, they want to reduce their risk, they want to ensure exit strategies and some clouds are just better at some things than others. >> Yeah, completely agree. And as you know, I do interview a lot of these IT decision makers that we survey to get a little more granularity and to dig into the details, and you and I just, great example. We did a session on Data Warehousing as a Service, we're at Snowflake. And the main reason that people love them is 'cause they have cloud portability. They can move across multiple clouds. Nobody wants to be tied to one cloud provider, they need to be agnostic. And if you look at, you know, something like Microsoft, right? Their Software Suite is fantastic. So most people are going to be aligned for them. They provide great active directory, the enterprise applications are absolutely incredible. But if you're looking to do straight ML/AI or straight data warehousing, maybe AWS Redshift, maybe Google Big Query might be a better fit for you. There's no reason to be tied into one. So what we're seeing more and more is those vendors that offer cloud portability or hybrid availability to do some on-prem for security, some cloud, they're really taking a step up in our recent surveys. Another comment you made Dave, if I can just backtrack to it is, you kind of mentioned how some of the vendors are taking more and more share. We are continuing to see this theme of a widening bifurcation, where although the overall spend that pie is shrinking, the leading vendors are taking much bigger slices from that pie. And that is continuing across the entire year. >> Yeah, definitely a time of disruption. So thank you for bringing that up. Okay, the next graphic I want to show you is actually a motion graphic, and what we're showing here is one of our favorite views. On the vertical axis you've got net score, remember, net score, essentially ETR, every quarter like clockwork asks customers are you spending more you're spending less, it's more granular than that, but essentially they subtract the red from the green and that leaves you with net score. So the higher the net score the better on the vertical axis, on the on the horizontal is axis is market share, its presence, its pervasiveness in the dataset. So you want to be up into the right, of course, like all these charts and XY's. And what we're showing here is, we go back to October, 2018. Remember this is the October survey and you can see the movement and what's happening. And a couple of points here really is one is container orchestration and container platforms, cloud, RPA, ML, they all stand out. And now we, you can see the the context of their "market share" as well, and you see that bunching, you see some of the Legacy stuff, the more mature markets like storage and PC tablets and laptops. They don't have a huge next or outsourcing, not a big net score, but they're there and they're kind of bunched up, down in the middle. But you can also see how they've slowly got depressed over time, even the elevated ones. Nobody in the recent survey is over a 60% net net score. I think you guys said that the overall net score was the lowest in history. So this is just a good way to visualize the various sectors and how spending, momentum and share is shifting. >> Yeah, that's a very good point, and you are right. The overall survey net score is actually 25.3% and it is the lowest ever we've captured. So that actually is translating into what we expect to be single digit declines in overall growth in IT budgets, which again is in line with what we've been saying. We caught early on about negative 5 1/2, that is improved now it's in this quarter to about negative 3 1/2, but if you look at the mid point here, we're very clearly in mid single digit declines, and the entire area is being impacted. Now, there are certainly some areas that are more important than others, there's no doubt about it. But yeah, outsourcing is one you mentioned, absolutely getting decimated. Nobody really has the money right now to be doing IT outsourcing, that's just not a priority. The priority is remote connectivity, remote security, how do I get identity access and governance to make sure that my employees are doing what they're supposed to be doing, even though they're not on my network anymore. All of those things are continuing. And as you saw on the COVID-19 Impact Survey, they're not going away. You had mentioned on a solo session you did, I think a week ago, where you have cited our data saying that permanent workforce is going to double from where it was in pre-pandemic levels. So that means a lot of the people that slapped a bandaid on their networking to get their employees to work from home, that bandaid solution is not going to work. They need to find one that's permanent now. So the areas of spend, although it is declining, there are very clear delineations of where that spend is going. >> Yeah, I want to just pick up on something you said about the work from home doubling, 'cause I've shared that data with some folks and had some discussions. We're talking about people that work from home, not come in a couple of times a week, this is the work from home component. And so I think the hybrid is going to increase as well, but the hardcore work from home, I think it was mid-teens, 16% or something doubling in the post pandemic was the expectation. And again, I just wanted to sort of clarify that I think your data there is quite good. How about some of the vendors? I think, now that's Snowflakes public, you guys may be doing some forecasts there. Let's start there. >> Sure, yeah. So it's fun to talk about the high level, right? And talk about the sector breakdown and where we're seeing things, but at the end of the day, people just love to talk about the individual vendors. So there's a few things that were interesting, yeah. We were able to finally come out with a real viewpoint on Snowflake now that they're out in public, and we kind of launched with a positive to neutral viewpoint. I don't think there's going to be anything here that shocks you. We're absolutely outstanding expansion rates. All the commentary we get from our CIOs are just incredible, the market share gains are about as high as you're going to see in the survey, they are extremely well positioned to continue executing, and this is not in the data set, but we also know that that management team is fantastic. I would think that they had set themselves up coming out as a public company not to completely disappoint. And everything in our data set shows absolutely no reason why they would disappoint. >> Well, and so you may be wondering folks, like, well, wait a minute, with all that great news, I mean, how could they be positive to neutral. Maybe it maybe neutral, the reason is because they have a 66, roughly $66 billion valuation. And what ETR is doing is they're taking that into consideration as well relative to, so they're looking at the street forecast, the consensus forecast and saying, okay, how does the data line up to that? And so a lot of people are asking the question, can Snowflake live up to its valuation. I don't think there's any lack of total available market here. I mean, it's very, very large, the data market, it's enormous. And as, just a plug for an event that we're doing on November 17th, it starts, we're doing a global event, and we're going to be looking at this issue very closely, interviewing customers and partners and executives and, you know, you can judge for yourself if you think the vision, they're putting out this vision of a data cloud. You see this, if this vision, you think is going to have a big enough term that they can grow into, and as Erik said, great management team, will they be able to execute? Decide for yourself, but very exciting IPO obviously that we've tracked quite closely. Elastic is another one that you guys have followed quite closely. I know you've got some data there that you want to share as well. >> Yeah, I certainly do. The APM spaces is really interesting. One last quick point on Snowflake. We don't have regression forecasts on them, because they haven't been out public long enough for us to be able to do that sort of back-testing. So without that data science behind us, we will never really go with a full positive. So to your point that saying positive to neutral is not negative or neutral stance whatsoever, it's just without that regression support behind our data, that's what we just tend to do. Because at the end of the day, we're a data science company, so.. >> Yeah. You need some some history there to really make those calls. But yeah, let's talk about Elastic. >> Yeah, sure, you got it. So recently I hosted a panel on the APM and monitoring space. It was incredibly enlightening. It's a very crowded space that our CIOs told us is right for disruption. And it ended up being a little bit of an avalanche in our data, because it wasn't just Elastic, but it was also Splunk and Dynatrace that we ended up putting ratings on. Now, Elastic as we know is an open source model, a freemium to pay type of model. And we normally try to stay away from open source models, 'cause it's kind of hard to predict how that converts to revenue, but the data was so strong that again, we came out with a positive to neutral rating on Elastic. It was based on just elevated spend levels across, there was almost no negativity, we weren't seeing any decrease or replacement indications, really solid positioning in the fortune 500 accounts, which I was a bit surprised about. And the other thing here is that Elastic tends to be really expanding in the information security. This is no longer just about monitoring and logging, they are becoming a very relevant infosec play and they are breathing down the necks of Splunk. They can do the same thing and they can do it much cheaper. The caveat being, you need to have the IT and the human skillset to run Elastic. So it really comes down to, are you sophisticated enough with the human capital management to run it? But everything we saw here just incredibly improved competitive positioning, they actually had the number one net score in all of information security in any vendor that had over 50 citations. It was just too hard to ignore, we had to come out with a positive neutral. >> That's super interesting Erik, and of course, yeah, we covered that space recently. Everybody wants a piece of Splunk and have for a number of years, but, you know, you see in Datadog come after it, then you see some startups getting into the space. Jeremy Burton launched his company, Observe, Honeycomb is in that, they kind of coined the term observability. Kakao Search is another one. Ed Wall's joined that company, and so you see a lot of folks really going after that space, why not? I mean, it's such a successful company. The pickup of SignalFX filling some holes, we talked about that on the Ven, and it's a very interesting space, and one I think has some somewhat depressed levels from a net score standpoint but as some of your Ven observers said, this market is here to stay and it becoming much more important as part of digital transformation, as part of a dashboard of digital transformation. >> Yeah. Coining that term observability really just hit it on the nail on the head. When we just talked about monitoring an application, that's not what it's about anymore, right? You need to have observability in multi hybrid cloud environments, whether it's your infrastructure or people actually writing code for your application. And so that single pane of glass, end-to-end is the holy grail of monitoring, and that's what these guys are pushing for. The New Relics, the Datadog's, the Elastics, they're getting there more quickly than Splunk and Dynatrace or AppDynamics from Cisco are. That's what the people are telling us, the ones I speak to, the CIOs that use it in the field. They're getting there more quickly and they're doing it more cheaply. Now, this is not to say Splunk is not a great company, we know it is. And also Splunk has more API integration into any ecosystem you want. They're not getting pulled or ripped out anytime soon, we're not saying that. But when we look at our data, we had no choice but to come out with a neutral to negative. They are deteriorating and their spending intentions, their customer growth is completely stalling, we're not seeing any more increased perversion in our dataset or among customers. There just wasn't really anything we could really do. Looking at the data set and that's what we do, we had no choice. There's a lot of skepticism heading into the back half of this year and next year, there's so much competition coming after them, and some of these people are just giving it away for free. It's pretty hard to compete with free. >> Yeah, free is very powerful. All right, speaking of skepticism, Rackspace had their IPO, what do you see in there? >> Oh man, I'm not really sure how to start there. But listen, I don't want to beat a company while it's down, but their net scores are actually negative. I think at the negative 20% range, if I could possibly recall that. But listen, Rackspace, when they were private, let's give them some credit, right? They decided to go out and buy a bunch of different managed service providers, they tried to align themselves with AWS, with Oracle. So they've got this whole bundle thing right now that isn't just straight cloud computing anymore. We'll see if that plays out. But clearly we saw that the IPO was not a very special IPO. In this environment the valuations in the technology stocks being very elevated, having a negative IPO was very telling. But sticking straight to the data, basically we're seeing negativity across several years, it's the worst position vendor in cloud computing that we even cover. We just had to take a look at it right now, and just be honest and say according to the data, this is a very negative data set, there just isn't much we can do about it. Wish them the best, I hope their MSP revenue starts kicking in, and hopefully it'll change. But for right now the snapshot of our data was quite dire. >> Okay, Erik, Well, thanks so much. So let's update folks, so the ETR is exiting, it's quiet, period, which I love, because that means I can have the data and share with you. So we'll be updating our cloud scenarios, security, automation, our infrastructure, and many other segments as well. Certainly the data piece, we've been tracking snowflake very closely. And of course, Erik, you guys are already gearing up for your January survey. So, you know... >> It never ends Dave. And I've... >> Well, I got a really... I've got a sizzle panel that I'm doing next week as well, where we got four sizzles talking about security threats and priorities for 2021. So as soon as I wrap that, you'll be the first one I get my summary to. >> Oh, those are great. I mean, there's such deep dives with practitioners, and it's just an open discussion. So Erik Bradley, thanks so much for coming back in theCube. >> Have a great weekend Dave. >> Yeah, you too. And thank you for watching everybody this episode of Cube Insights powered by ETR. Go to etr.plus, that's where all the survey action is. I publish every week on wikibon.com and siliconangle.com. All these episodes are available on podcast. Wherever you watch, you can DM me, I'm @DVelllante. I post on LinkedIn, you can comment there or email me @david.vellanteat, @siliconangle.com. This is Dave Vellante for Erik Bradley. Thanks for watching everybody, we'll see you next time. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Application performance management AKA APM, you know it's been around since the days of the mainframe. Now, as systems' architectures became more complex, the technology evolved to accommodate client-server, web-tier architectures, mobile and now of course, cloud-based systems. A spate of vendors have emerged to solve the sticky problems associated with ensuring consistent and predictable user experiences. The market has grown, I mean it's decent size, it's about $5 billion globally. It's growing at a consistent 10% CAGR. It's got a variety of established companies and new entrants that are attacking this space. Hi everyone, welcome to this week's Wikibon Cube Insights powered by ETR. My name is Dave Vellante and today, we welcome back ETR's Erik Bradley, who was the chief engagement strategist at Aptiviti which is the holding company of our data partner, ETR. Erik, my friend, great to see you. Thanks so much for coming on and spending some time with us. >> Oh, always enjoy it Dave. Great to see you too and I'm just glad I got some fresh material for ya. >> As always, you have fresh data. Now, Erik just recently hosted an ETR VENN session and on this particular topic, APM. Now VENNs are an open round table, they're exclusively available to ETR's clients and what we do is we sometimes come in theCUBE and we summarize those sessions in our Breaking Analysis. Now Erik, yo let's start with a summary slide here, guys, if you could bring that up, we just want to make a couple of points and... So as I said Erik, I mean this started back, you know in the System/390 days. Now, distributed systems and cloud of course create a lot more complexity, you got data that's really fragmented. You got user data, you got application data, you have infrastructure data and it gets complicated and you've got guys in lab coats having to come in and diagnose these stuff, lot of tribal knowledge. What are you seeing in the space? >> Well yeah, you know to start back, you know it's funny when the panel I hosted, one of the guys even brought up Tivoli, how long ago that was right? Then of course you get, you know you have the solar winds and you had people like that trying to just kind of monitor your network. You know what we've heard a lot about now is infrastructure has really become code-based. So when that happens, you really start wondering to yourself the lines are blurring between infrastructure and application because at the end of the day, what you're really monitoring is code. So it has gotten incredibly complex, you have OnPrem, you have hybrid, you have multi-cloud approach so it has gotten extremely complex and there's also now a third wave of next-gen vendors getting involved in the mix as well. As you're aware, New Relic and Datadog, obviously, Splunk has been in logging and monitoring for a long time. You also had some of the traditional players throw their hat in the ring through acquisition, that you know AppDynamics gobbled up by Cisco and obviously Splunk trying to continue to reinvent themselves a little bit by SignalFx. So it is a very crowded, complex space, it is a complicated problem but it's also a problem that needs to be solved. You know, we were looking at, you said in your intro about, it's only about a $5 billion market right now but there's been a lot of data out there from industry analysts saying that that's going to grow quite handsomely over the next five years and it could get up to 13, 14, 15 billion. And when I asked my panel about that, I had one gentleman say without a doubt, they see the next 10 years that spending in this space will continue. And when you pry and ask why, they simply state that digital transformation is not going to stop, it's marching forward, whether anyone likes it or not and as it does, monitoring is going to be critical, it's only going to increase and increase and increase. So right now, to your point, it's a small market but it's a growing market and there's a lot of entrance in there and their whole goal is to reduce this complexity that you're talking about. >> Now, one of the things we heard from the panel, guys if you bring up that same slide again, you know the third point on that slide was what's closely tied to digital transformation. You heard a number of individuals say, "Look, your digital business is critical, it's all about monitoring your applications and your data and your infrastructure. And we heard a lot that they wanted a, a single pane of glass and you made a number of points about the market. What are your thoughts on both the digital transformation, maybe the COVID acceleration of that mandate and that notion of a single pane of glass, is that aspirational or is it, in your view, something that is actually technically feasible? >> Not only is it technically feasible, it has to happen. It's going to be demanded by the large enterprise, they can't continue to monitor hundreds and hundreds of applications. They need something that not only can give them observability through their entire stack, but they need to be able to view it in one way, there's enough fatigue in monitoring and logging. And actually it goes even further than one pane of glass, they're demanding that these systems can now actually employ machine learning algorithms to be proactive. It's not enough to just say, "Okay, I observed this," you have to let me know that this may happen in the future and what to do about it. So not only is it feasible, it's something that is being demanded by the end-user market and the players that survive are the ones that already have that in their roadmap. >> Now, as we always like to do in these sessions, we're going to bring up some ETR data and we like to position the companies. So what we do is, we're going to bring up some of the pure players, pure-play companies and you can see them on this slide. But Erik, and when we talk about companies in this space, they are well over a dozen. It's just again for reference, you know it's Cisco with AppD, you mentioned that before Dynatrace is one of the leaders, New Relic has been around for awhile and is doing well, Splunk, Datadog. Now of course, and we're not showing them here, AWS, Microsoft and Google cause they just sort of, they pollute the chart. But so I want to start with the guys that are on this view and maybe talk about a few. Elastic came up a lot, certainly AppD came up a little, Dynatrace was obviously mentioned, especially in large organizations. Lot of conversations about New Relic. So let's go through them. Where do you want to start here? >> Yeah there's a lot to go through and we did spend the majority of the panel talking about the individual players, the differences between them and also what we thought their longer term prospects were but yeah, we'll go through each one. I think maybe to start with, let's go back in time a little bit, right? Cisco is a wonderful acquirer, they do a great job at M&A. A lot of companies will acquire something and let it die on the vine. Cisco has proven recently that they are reinventing themselves as a full platform play, whether that be through, you know, kind of, their networking reach or whether it be through the security. And AppDynamics is one of those that actually kind of gives you a little bit of both with being able to monitor. It is a great play for people that are already involved with Cisco. Now, I don't think you're going to see too many people that are non-Cisco customers run out and buy it. There you're going to see some of them, maybe the pure plays or one of my guests called the third wave of vendors. And that third wave is really about a Datadog and a New Relic. Let's talk about Datadog first. >> Yeah let's bring that back up guys, if you would. Now let me just, sorry to interrupt you Erik (indistinct) The vertical axis here is net score, that's the ETR's primary metric, and that's an indication of spending velocity, the higher, the better. And on the horizontal axis is market share. Now we're showing the July data, the October data is in the field, you know once ETR releases that to its clients, then we'll share that with you. But the first thing that jumps out at me is other than Elastic Erik, I mean, I'm not blown away by the spending momentum in this space but let's talk about that and then some of your thoughts on the specific vendors. >> Yeah, you know I'll go back because you asked a little bit about the digital transformation, I don't think I answered it fully. So to your comment about maybe not being impressed with the spend, I think this is one where the spend is going to come, kind of as a laggard because you're not going to rush out and go buy the software to monitor until you've built out the, what needs to be monitored. So as we're seeing this increase in the digital transformation, and I think you and I had a conversation in the past, but when COVID first hit and I did a series of panels, we had one person say that this virus is going to increase digital transformation by five to 10 years. Now that was an amazing statement. Basically, if you were on the fence, if you didn't, if you weren't already heading down to digital transformation, you needed to play catch up quickly. So now that you are doing that right, now that you're moving from OnPrem to a multicloud or a hybrid cloud environment, you have to get observability, you have to get monitoring into it. So now these players start to play catch up and this is where you're going to see the proof of concepts and you're going to see people trying to decide which direction they're going to take their company. Now back to the actual vendors. I believe that there is some differentiation, right? So we'll just take, for instance, Splunk. Splunk is obviously probably the biggest boy on the block when it comes to just straight up logging and monitoring. They've leveraged that big boy position to really, you know, add some costs, kind of intimidate their customers they've been compared in the past of the type of things that Oracle used to do from their cost perspective. And that's opened up some new competition, Datadog is one of those. According to my panel, Datadog is viewed more for logging and monitoring than it is truly full end-to-end observability throughout your entire network and application system. So that is one of the areas that's there. Now, to stay on those two names for a quick second, Splunk obviously has some holes in what they're trying to offer, they went out and tried to buy SignalFx to fill one of those holes. Now according to my panel again, did a great job filling that hole, problem is if you have a boat with three holes, you can't put your fingers everywhere. So they think, hey listen, Splunk scrape, they're going to keep the company they have and I know that we can talk a little bit more about valuations and the equity side later, but I think it's very clear that their sales and revenue are trending flat to down, whereas some of these other names still have great acceleration in their sales. So Splunk and Datadog both are really facing pressure from Elastic or generally just open-source. >> I was struck by the panel and how much emphasis they, how much complaining they did about Splunk pricing. Generally, I feel like hey, if your price is too high is the biggest objection, that's actually not a bad thing for a company but the way they kept hitting on it and said, "Hey, we're actively looking for alternatives" and Datadog was one of those and given the momentum that Datadog has, I don't think that that's necessarily a positive. But you know Splunk has a lot of loyal customers but you know to your point if you go back to the slide, Elastic came up very, very strong and they are head and shoulders from a spending momentum above the rest of the crowd here. >> Right. And you know, so you're right. If the only problem with a vendor or a technology is cost, usually you live with it because that means it's giving you what you need. So okay, it's expensive but it's also the best in breed and that's where Splunk has been for a very long time. And I think they're resting on their laurels knowing that. Enter Elastic and you say to these guys, the panel, I asked them, well okay, you can make Elastic work but is it truly a viable alternative from a technology standpoint? And the answer to that was not only is it viable, it's half the price. So if you can bring something in that can do the job the same and it's half the cost, it's really difficult not to at least try. And I had one of the other gentlemen who was a Datadog customer said, "Listen, we love Datadog, we were a huge customer and then I started getting enormous bills and I just switched over to open-source, I switched to Elastic, I switched to Kibana, I switched to Kafka and I can do this search myself. Now the difference is not every enterprise has the human skillset to do so and I'm not saying Splunk's going to turn around to disappear tomorrow, not even close. Because there is a difference in spending that money with the vendor or spending that money developing the human skillset to use open-source. But the bigger backdrop here is there are more alternatives than there used to be, there's more competition and the space is getting very crowded. >> Yeah, comment on open-source. I mean open-source is free like a puppy. But the thing about that, and we had one of the panelists was a very senior consultant, exclusively work with very large companies, he told a story about one of the companies years ago, he came in to solve a problem. The problem was they had 70% availability and then they had no visibility on their infrastructure and there's really no great, no good monitor, they get them up to whatever, five nines or two, three nines or wherever they got them to, but dramatic improvement. And so, but he said, "Look it, I work with companies with billions of dollars, $3 billion IT budgets so they don't rely on open-source for this stuff, they're happy to spend." But there's a huge market, particularly in the mid size where we heard that New Relic plays in a big way, it might be more receptive to open-source. >> Couple of great points there Dave, honestly. I'm going to jump over to the use case that was given by that person who was in a healthcare role. And essentially the part I didn't write into my summary was that his CEO was two days away from shutting down the entire business because he was so frustrated that he had no observability and Dynatrace was the one that was able to step in and fix that. And this gentleman did say that the majority of the companies that he does work with which are all in the Fortune 100, Dynatrace has a stranglehold in that spot. So that's really interesting to note. Now on the flip side, when pushed a little bit more later in the panel, he said, "Dynatrace is sort of resting on its laurels from a product roadmap standpoint and that's going to open up the possibility of a New Relic getting in," a transition to New Relic as you mentioned on their small to medium sized business. They recently launched a new pricing strategy which is basically a free version to get you involved to kind of get their hooks into you and see if you can work it out. And basically what they're trying to do there I think is, you know, make up for their lack of marketing. As you saw the panel that we spoke about said, "New Relic's technology is fantastic." They have the ability to provide a single pane of glass which is the Holy Grail in this space and they have the ability to provide machine learning and proactive type of ability which again are the two things that all of the end-users are asking for. The problem is that most people might not be aware of it because New Relic doesn't have as flashy a marketing department, they don't have the dollars as much as the others to go out there and compete with the Splunk and Dynatrace and Cisco. But from a roadmap perspective, it was almost unanimous that our panel agreed, New Relic is by far, one of the leaders from a functionality standpoint. >> Yeah, if you guys bring that slide up one more time, the X Y. I mean, I look at where New Relic is and I'm like wow, I'm surprised. I mean this company, I mean they were the hot company for awhile and I think still have the capability. You're talking about the technology. NRDB, New Relic database is like, it kicks ass. In fact, you know Erik, somebody brought up in the panel that they thought that snowflake could compete in this market because essentially Snowflake's positioning is this data cloud. But you know, here's New Relic, they have a purpose-built database specifically for monitoring an APM so you would think that with that technology, they could really make some moves. And then I just want to bring in two other companies to the mix here. Honeycomb who I think even their founder and former CEO now CTO, she coined the term I believe, observability. And there's another company that is run by Jeremy Burton, company's called Observe, okay (indistinct) and it's funded by the Silicon Valley Mafia. So that's going to be an interesting one to watch, they're coming out, well they're out of stealth but they're doing a launch on October 7th. So I think those are two companies that could disrupt this space and I would expect to see, as you said, it's a latent momentum in net score from a dataset standpoint because people are trying to plug the holes cause of COVID, you know security, work from home, that pivot and now it's really on to digital transformation and that's where APM really comes in. >> It really does and again, it comes back to that comment someone made a long time ago that everything's becoming code as software eats the world and everything becomes code, you need the ability to kind of monitor that code, enter Honeycomb. And as you know, we have two different studies at ETR, one of them is for emerging technology. Honeycomb is in our emerging technology study that's more of a private series B to series E round stage whereas our main study is for companies that are pre IPO or already public. But Honeycomb is a little bit different in my opinion, that they're focused very much so on the developers or the software engineers. They're a very microservices oriented type of product whereas some of the other ones may have started as an infrastructure monitoring and then kind of work their way backward into application. But Honeycomb certainly needs to be observed and it's funny when you talk about that, the one thing I think is, "Oh great, more players." The crowded space gets even more crowded. And I think well you know, kind of foreshadowing something you and I will be speaking about in a little bit but there's a lot of players in this space and there's a lot of other possible interest in there. You mentioned Snowflake. It actually wasn't brought up from our panelists, it was a question that came from one of my clients that said, "Hey, I'm curious, can snowflake play in this space?" And the panel thought about it for a second and said, "There's absolutely no reason why they can't, they most certainly can." And we all know the cash they have so I mean the easiest way to play in that would maybe be to buy some of the technology, integrate it in and yeah, they have that portability. And if I can real quickly, they've just, one of the things that came out that was so important about this, we haven't spoken about the vendors is, is the public cloud. The public cloud offers this. They offer monitoring, they'll give it to you for free. If I'm going to run Kubernetes at Google, I'm going to get the monitoring for free which is super nice, right? But if I have an enterprise that has multicloud or hybrid cloud, and I'm working outside of that public cloud silo, it doesn't work. This is the exact conversation you and I had about Snowflake. AWS Redshift's fantastic but it doesn't work outside of AWS. So if every one of our enterprises continues on the digital transformation, they need portability. They have to be able to go across any architecture structure and that's why these independent providers are really starting to gain steam when you would think they could never compete with the public cloud. >> Yeah man, that's a great point. And we've talked about this in the context of Snowflake that who are you going to trust with your multi-cloud strategy? Are you going to trust AWS? Are you going to trust Google? Yeah, okay, they got Anthos but we kind of know why they're taking that posture. Microsoft, look, I'm probably going to partner with somebody who can, who's maybe I have a relationship with them with my OnPrem and that is really sort of agnostic to the various clouds so I'm glad you brought that up. And you know the point you're making about Honeycomb is a good one and I'll add that, again, it gets more complex with microservices and containers, that's spinning them up, spinning them down. Sometimes these, first of all, these microservices, sometimes aren't that micro and second of all, you're sometimes talking about hundreds of thousands of containers so it's a really increasingly complex environment. All right. What I want to do is-- >> You didn't even touch on serverless, we'll do that some other day. >> Oh, yeah, I mean absolutely. A hundred percent, right. So, now let's take a look at some of the valuations, guys if you bring that up for me. So I put this little chart together and it's always instructive. Now I like to, simple guy Erik so I like to... So you see, the company, I take a trailing 12-month revenue and then the market cap as of 9/25. And then just a simple revenue multiple, just to get a sense, it's not a hardcore valuation model but it's interesting and there usually is a correlation to the growth rate, I just pulled that off the latest quarterly growth rate. I mean, look at Datadog. I mean that's like Snowflake pre IPO valuations. I mean you're really, right around there with smaller revenue, smaller growth rate, Snowflakes up in the whatever 120% range but well eye-popping. You know the same valuation as Splunk, I mean that's just amazing. What do you make of this data? >> Well, you know I was an equity analyst for almost 15 years on the Wall Street side. So the, my first caveat is a trailing revenue to the multiple is not always the same because people are looking at what the forward expected revenue will be but I actually do see the correlation here. And when you brought this up, my eyes popped open. I do not understand why Datadog has a 27 billion market cap on a trailing 350 million in revenue. I just don't know if their forward looking growth really warrants that and at the same time, then you look at a Splunk, right? I mean they have two and a half billion in revenue but their growth rate's down and truthfully, when I see a -5% growth rate, I don't know why you weren't at 12% sales either. I would argue that there's quite a few names on here that could be in for a reckoning, ETR actually as far back as a year ago caught this in our data and said, "Hey, there's some inflection points here and I think investors need to pay attention to them." And since we came out with the July report, a lot of these names we're talking about, despite insane valuations in the equity markets are flat to down. And, you know I do think that, hey if they stay stagnant and their technology is right but it's a crowded space, I think we're really leading to the point where as one of my panelists said, this industry is ripe for consolidation. These players are not all going to be here in 12 months, it's that simple. >> Yeah and by the way, thank you for mentioning that as a former equity analyst, you were right (indistinct) 12 months, it's kind of the rear-view mirror. But I'll tell you, two reasons why I do that. One is, I put the growth rate in there so you can pick your own growth rate and your own forward revenue. The other is it's really easy for me to get TTM off a Yahoo as opposed to >> Right exactly. >> And so truth be told. But, guys bring that back up one more time cause I want to make a point about New Relic. I mean I think they are potentially right for an M&A because they got great technology. Now remember Elliot Management is in there and when Elliot's is in there, stuff's going to happen. They're going to start cleaning house, they're going to really create changes, they don't just get in in a big way and sit back and watch, they are extremely active. And the New Relic, leader in this space, great technology, great heritage. So either they got to clean up and get that valuation back up maybe as you pointed out, little bit better marketing posture, et cetera or they get taken out. >> Yeah and let's think about the two things that coincide, right? You have one of the world's best activist funds get involved in Elliot Management. And as you said, they don't get involved to just sort of watch or observe as we're talking about here today, they are very active in trying to get some sort of a, you know, corporate action done. And at the same time, all of a sudden New Relic comes out with a new pricing model. They're trying to create a moat around the small to medium business, right? They're trying to grow their footprint. Now the great thing about getting involved in small to medium businesses, it starts off for free but you grow with them. So I don't think those two are a coincidence, let me just put it that way. I think that they're coming in, they're trying to entrench themselves in a new market and set themselves up for future growth and I truly believe that based on the product roadmap and the feedback we were getting from the end-users in my panel, New Relic has the ability to look across all architecture, it has the ability to provide a single pane of glass and it has the ability to incorporate machine learning for proactive response. Their roadmap is fantastic, they have an active manager inside as an investor, I don't think they're going to be around for much, much longer. And obviously that you look around and you wonder who the acquirers will be and it might be one of the major cloud players. >> Yeah that would be interesting. I mean it gives them a play in a multicloud world and either they're going to just use that for their own advantage or they will actually see that as an opportunity, we'll be itching to watch. Alright, anything we didn't cover that you want to touch on or give us your final thoughts, please Erik. >> You know I would also just sort of mention a little bit about Splunk. This is a company that has a tremendous amount of revenue, a tremendous installed customer base but many, many times we've seen it before and Oracle is the greatest example. They kind of forget about their customers and they don't treat them properly. And I can't tell you how many people I have mentioned to me said, "Hey when this all went down in the viral pandemic and I went to Splunk and I asked for a little bit of pricing flexibility, I asked for this, I asked for that and they just wouldn't give it to me." And I wrote an article once called (indistinct) never forget similar to an elephant. And when they come out the other side, they're going to find a way to replace them. And today I also wrote an article that it was our 200th interview and I entitled it, The Splunk Funk. And basically it's about all the alternatives that are now out there, not just open source, but other vendors, even the vulnerability management players like a Rapid7, like a Tenable are getting into this space now. Fortinet, which one guy called "Fortaeverything" is a company that's really expanding. So I would just really kind of caution some of those vendors out there that don't rest on your laurels, don't take your customers for granted because sooner or later, they're going to be in a position to bite the back. >> Well I'll say this about Splunk, I've been following the company since the early part of last decade and I've done a lot of Cube interviews at their shows. They do have a passionate, passionate customer base, they got the experts that run around with that crazy hat and I've seen Splunk killers emerge for the last decade and so... But I think your point is right. I mean they've, the SignalFx acquisition was something that, it was a hole to fill and it gets them into a subscription-based model, they're going through that transition now. But I think they have some real gravity with their customer base. So, all right, let me summarize. For years, the application monitoring and management, it's really relied on alerts, logs, traces and even what I call tribal knowledge. In that world of pre-distributed systems, that was fine, like I said a trace can tell you what was going on. But things have begotten much more complicated architecturally with cloud and mobile and they're really changing fast now. Erik mentioned serverless, we talked about containers. So, today it's much harder to understand the customer experience because it's difficult to get a full picture of the data. And what I mean by that is that the user data, the application data, the infrastructure data, they're all fragmented and the Holy Grail solution really takes all this disparate data, it ingests it, it transforms it. Connects the dots if you will, across clouds, Onprem and then it shapes it, brings in machine intelligence, really creating an organic systems view that can proactively tell you that there's a problem coming. And finally, nearly absolute Nirvana is doing this in a way that non-technical people are going to be able to understand the true user experience. You know in theory, this is going to allow organizations to remediate in 110th the time with much, much lower costs and that's going to be critical in this world of digital transformation. So thank you Erik, really appreciate you coming on today. >> Always enjoy it Dave, it's always great talking to you and hopefully we'll do it again soon. >> All right, I can't wait. And thank you everybody for watching this episode of theCUBE Insights powered by ETR. Remember these episodes, they're all available on podcasts. We publish weekly on wikibon.com and siliconangle.com so you got to check that out. And don't forget, go to etr.plus for all the survey action. Would appreciate if you kindly comment on my LinkedIn post or tweet me @dvellante or email at david.vellante@siliconangle.com This is Dave Vellante. Thanks so much to Erik Bradley, be well and we'll see you next time. (bouncy music)

>> From The Cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> The isolation economy has created substantial momentum for certain cybersecurity companies, notably, as of the big stock market sell off on June 11th, relative to our last cyber report, which we did in February, the S and P 500, and the NASDAQ are off 11% and 3% respectively. But the valuations of three companies that we cited as four-star firms in our February cyber report are up significantly. In particular, Okta's valuation is up 34% since our last look in February. CrowdStrike, almost 50%, and Zscaler over 60%. Yet several other companies that were named as four-star players have really either tracked the S and P or even performed more poorly, despite still showing decent strength and spending momentum based on survey data from ETR. Welcome, everybody, to this week's Wickibon Cube Insights powered by ETR. My name is Dave Vellante and in this breaking analysis, we want to update you on our cybersecurity outlook and try to answer several questions, such as what has changed in the cybersecurity landscape. since our last report. Much has, as you know, Has the isolation economy created a permanent shift in security spend, or are these upticks just anomalies? What can we learn from the ETR spending data, and is the divergence and valuations amongst security leaders justified? Let's start by taking a look at what has changed since our last cyber report. Now, we produce this just ahead of the RSA conference in February, and one of the last physical conferences. So there's some big changes going on in the market. We really want to understand, are they systematic? In other words, are there fundamental changes to the system and its underlying principles, and by many accounts, the answer appears to be yes. Recently I listened in to a number of CSOs. of it was a call with ETR's Eric Bradley. And we heard the executives echo some of the themes that we've been discussing previously. It was notion of the work-from-home pivot, creating a focus on things like zero trust networks, changes in identity and access management, and way more focus on cloud, and of course, as a service, really reducing reliance on traditional firewalls and appliances that would reside in organizations' data centers. You know, we've gone from a world where digital transformation was an important strategic initiative to one where if you weren't digital, you largely couldn't transact business. Now, people are, the question they have is that is the longterm viability of VPNs makes sense? And even things like SD-WAN are being called into question, as corporate offices are empty and the internet is becoming the new private network. Now, one thing that hasn't changed is there are still a lot of technologies in this space. And that seems to be continuing as buyers need solutions to problems quickly to plug holes, and on balance IT budgets, they are contracting, so most companies still have to justify security spending based on the amount of risk reduction versus the cost. Of course, it's easier to justify for securing remote workers. So what I want to do now is take a pause and let's look back at some of the ETR data that we shared back in February. Now remember, this data is from the January ETR survey, ETR surveys organizations once every quarter. And if you recall, we keyed on two key metrics, some of our favorite metrics. Net Score, which is a measure of spending momentum, and Market Share, which measures pervasive per, sorry, pervasiveness in the dataset. Now, as you might recall, the left most chart here shows the cyber players and we sorted them by Net Score. The right hand side, that sorts those companies on Shared N, which measures the number of mentions of that company within the cybersecurity sector. Now, at the time, we named several four-star companies, actually we started this last year when we initiated coverage in the security space. These four-star security firms, really based on their rankings within both of those metrics, Net Score and Shared N. So you could see the four stars, Microsoft, Splunk, Palo Alto Networks, Proofpoint, Okta, CrowdStrike, and we added Zscaler as new, and then CyberArk. And we gave Cisco and Fortinet two stars, as they were kind of on the cusp. Now let's look at some of these companies from the April survey that ETR did. So this chart shows a subset of the vendors that we showed before. Now remember, this survey was taken at the height of the lockdown, from kind of early part of March to the early part of April. Budgets were under immense pressure. Nonetheless, look at Microsoft, Cisco, Palo Alto, Fortinet, and Zscaler all held up pretty evenly. CrowdStrike also held steadily and maintain a very high level. Okta dipped somewhat, but from a pretty high level as well. Only Proofpoint is one of the ones that showed decline notably from 48% to a 40% Net Score relative to the chart I showed earlier. Now, SailPoint didn't make the four-star cut because it doesn't have the presence in the dataset, but it's Net Score is solid, and the Shared N jumped from 66 last survey to 88 in the latest checkpoint. So this identity and access management player, it seems to be one to watch. We'll come back to that in future episodes. Now let's plot some of these players in context, you know, using this two-dimensional axis that we often show. This chart shows that that view that we like to share. It plots Net Score, or spending velocity, on the Y axis, and then market share on the X axis. Remember, our market share is calculated by dividing the number of mentions for a company by the total number of mentions within that sector. So it's not like true IDC market share, it's market share within the survey. So you can see here a continued theme of Microsoft momentum, very high Net Score, or high Net Score and big presence. We plotted IBM and Dell EMC, which is really the legacy RSA business, just for context. And these are two companies with strong security brands, but as you can see, they're really not the giants that they used to be in cybersecurity software. So a couple of points on this graphic. CrowdStrike really jumps out as the momentum play on this chart. And that's really no surprise given its focus on endpoint security and the pivot to work-from-home. Okta has a focus on cloud-based identity management and they continue to show very strong. And CyberArk, with a focus on privileged access is also very important in this remote worker environment. We'll talk about that some more later. And you can see Zscaler, quite strong and steady from the last survey, but that company saw some of the biggest action in the stock market, which we're going to try to explain in a moment. Proofpoint, we talked about a deceleration in Net Score, but they're right in the mix as is Fortinet. Now finally, Palo Alto, you know, they remain strong. And Cisco, like many of its businesses, very credible with a Net Score that's decent and a large market presence as always. Now, as we've reported, security is one of the brightest spots in that Cisco portfolio. So the big takeaway from the ETR data is that despite the pandemic, cybersecurity software has held up very well from a spending standpoint. But now let's look a little bit deeper into what's happening in the stock market with these firms. And first as we know, there's a clear disconnect between what's happening in financial markets and the fundamentals of the economy. You know, Wall Street versus Main Street is kind of that narrative. And within the security sector, there's also a dissonance between companies, and we want to discuss that next. Here's an updated chart that we showed in February from our last cybersecurity episode. It compares the performance of the S and P 500 and the NASDAQ as of February 19th, with the performance of four-star cyber players from that date to Thursday, June 11th, the day that saw an 1800 point drop in the Dow. So some of the steam has been let out of the market, but the story really isn't going to change that much. First, the S and P is off 11% since that time, but the NAS is only off of 3%, tech heavy. But look at the deltas of our four-star companies. Let me start with Splunk. I didn't show Splunk earlier on the charts, but the value metrics of Splunk, they really haven't moved much since our February report. Splunk's Net Score was down somewhat in the sector, but remember, Splunk does more than just security. It's really becoming a critical big data player in analytics. I think people maybe don't like the tepid 2% revenue growth that Splunk showed, but remember Splunk is transitioning to an ARR model, an annual recurring revenue model, and that's going to take some time. It acquired SignalFx late last year to give it a stronger SaaS play in monitoring, and of course the analytics. I like Splunk, just like Adobe and Tableau had to make a similar transition, and ultimately they powered through it because they're great companies with really loyal customers, and I think that really does apply to Splunk. Let's take a look now at Palo Alto Networks and Fortinet. Now, you might remember in our last security update, we spent a fair amount of time explaining the valuation divergence between Palo Alto and Fortinet due to some of the cloud challenges that Palo Alto was facing, even some of the sales motions. So we said Fortinet at the time had done a better job transitioning to a cloud, but Palo Alto really had a good quarter. It beat earnings revenue, and it gave guidance, and the stock moved up very nicely. But then it ran into resistance, and you can see it's a tracking about what the S and P 500 over this period of time. And you can see the revenue multiples show the valuations divergence between those two companies. It's even more stark. So you've got Fortinet's kind of holding firm, and Palo Alto, dipping a little bit. Now, let me make some comments here. I mean, I like Palo Alto Networks. Not only are they solid in the ETR dataset, despite the COVID pandemic, but anecdotal evidence in discussions with IT leaders suggests that organizations want to do business with Palo Alto. They're really considered a thought leader in the space. And I personally, I think they're going to do very well this decade. So now maybe there's some technical aspects going on with the stock. I'm not really qualified to address that. But they clearly saw some resistance despite bouncing on the strong quarter. Just couldn't hold. Now, let me skip over the green box, and I want to quickly comment on the last two here. I'm going to start with CyberArk. They are underperforming, this group, even though you would think with the focus on privileged access security, they'd do well in this environment. And they beat last quarter, but they suspended guidance, and they cited exposure to some hard hit industries on their earnings call. And as well, it just is interesting, the company is aggressively hiring. And so that increased op ex substantially. The thing in management is confidence, you know, what do they know that the street doesn't know? And they're just being cautious, you know, but they are taking a valuation hit as a result. We'll see how that plays out. Now, Proofpoint has also taken a valuation hit in our period of analysis back from February to now, despite beating estimates last quarter. You know, maybe not as strong as a work-from-home play, but again, a beat in this environment is definitely a positive. Now I want to come back to the three key companies highlighted in the green, Okta, CrowdStrike and Zscaler. Zscaler, remember, we added new in February to our four-star list, which we initiated last year. The valuation of these three companies has soared since the pandemic, and they've reported tailwinds as a result of the new reality. Okta with its identity management focus, CrowdStrike with endpoint, and Zscaler with its security cloud, are all seeing momentum. And it makes sense that these three are very focused and they're aligned with our remote worker economy, and of course, a shift to the cloud. As well, they all beat earnings and management had a pretty sanguine outlook going forward. But I want to call your attention to the revenue multiples of these three companies and take a look and compare them to their peers. You know, are these justified? Well, as I said before, there's really a difference between the stock market and what's happening in the real world today. So I would say, you know, I want to see these companies continue to outperform their estimates, and their strong guidance. And frankly, at these revenue multiples, I'd expect, you know, even higher growth rates of, especially from Okta and Zscaler. So we'll see. The point is, the market's exuberance, it's really based on future expectations. And I do think there was a bit of, you know, FOMO, fear of missing out, at play here with investors hopping on the bandwagon. Remember, look, the data from ETR shows that these companies are pretty strong, and of course, much of the stock action is based on performance relative to earnings estimates. So we'll see if this can continue. I mean, to me, it does feel a little frothy even after that recent sell off. All right, let's wrap up. So the disconnect between financial markets and the real world economy, it creates uncertainty in the market. So you got to be cautious, really, if, especially if you're chasing momentum. I just want to say, I know a lot of young investors who reach out to me and they comment to me in these segments. And look, I'm not qualified to tell you where to invest. I just report on the fundamentals and I try to tie in financial trends, and market trends, of course, But you got to do your own research, you know, be patient, do your dollar cost averaging thing. You got a long life to live. Now, the after COVID AC economy and the remote work-from-home momentum will not be a rising tide that's going to lift all ships in this segment. But there's no doubt that CSOs are rethinking cyber. We've said for years that protecting the perimeter was going to change as the main focus. And it has to a degree. But I'll tell ya, I think the mindset has changed more in the last 90 days than in the previous three years. The scourge of VPNs, and even the efficacy of SD-WAN are being called into question as security technologies that exploit the internet and cloud appear to be very sensible to CSOs and have momentum. You know, we're also seeing more collaboration between organizational boundaries, and even many CIOs are becoming much more involved in security as their line of business tends. And even some CSOs reporting it to CIO's. As we've said many times, cyber has become and will continue to be a board level agenda item and topic. On near term, we really don't see the fragmentation of the products that we've talked about for years changing. If anything, the shiny new security tools, you know, might even increase granularity in the marketplaces organizations, they can't just unplug their legacy infrastructure as much as they they'd like to. But longer term, there will be more consolidation in this market, as the whales are going to buy companies to fill holes in their lines. I mean, look at VMware, there's a good example of a company we really haven't talked about trying to elbow its way into the security space. And the cloud, as well, was going to attack some of the problems of complexity, which in part stems from too many tools, and that will foster some of this collaboration expectation. Okay, well, that's it for this week. Remember, these episodes are all available as podcasts. So please subscribe. I publish weekly on wikibon.com and siliconangle.com. So check that out and please do comment on my LinkedIn posts. You can email me as well, at david.vellante@siliconangle.com. This is Dave Vellante for The Cube Insights powered by ETR. Thanks for watching, everyone. We'll see you next time. (mellow digital music)