 From the CUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. I want to welcome to the special CUBE Conversation, I'm John Furrier, your host. We're here for a special series leading with observability, and this segment is end-to-end observability drives. Great digital experiences, we've got a great guest here, Craig Hyde, Senior Director of Product Management for Splunk. Craig, great to see you, thanks for coming on. Thanks for having me, this is great. So this series leading with observability is a super hot topic, obviously with cloud native. In the pandemic, COVID-19 has really kind of shown cloud native trend has been a tailwind for people who invested in it, who have been architecting for cloud on premises where data is a key part of that value proposition. And then there's people who haven't been doing it. So out of this trend, the word observability has become a hot segment. And for us insiders in the industry, we know observability is just kind of network management on steroids in the cloud. So it's about data and all this. But at the end of the day, there's value that's enabled from observability. So I want to talk to you about that value that's enabled in the experience of the end user, whether it's a modern application or a user inside the enterprise. Tell us what you think about this end user perspective. Sure, yeah, thanks a lot for that intro. And I would actually argue that it would, observability wouldn't even just be machine data or network data. It's more of a broader context where you can see everything that's going on inside the application and the digital user experience. From a user experience or a digital experience management perspective, I believe those, the metrics that you pull from such a thing are the most useful and ubiquitous metrics that you have invisibility in all of technology. And when done right, it can tell you what the actual end result of all this technology that you're piecing together, the end result of what's getting delivered to the user, both quantitatively and qualitatively. So my background, I actually started a company in this domain. It was called Rigor and we focused purely on looking at user experience and in digital experience. And the idea was that, this was 10 years ago, we were just thinking, look, in 10 years from now, more and more people are going to do business digitally. They're going to work more digitally. And at the same time, we saw the legacy data centers being shut down and things were moving to the cloud. So we said, look, the future is in the users and where it all comes together is on the user's desktop or on their phone. And so we set out to focus specifically on that space. Fast forward 10 years, we're now a part of Splunk and we're really excited to bolt this onto an overall observability strategy. I believe that it's becoming more and more popular, like you said, with the pandemic and COVID-19, it was already on a tear from a digital perspective. The adoption was going through the roof and people were doing more and more remote. They were buying more and more offline, but the pandemic has just pushed it through the roof. And I mean, wow, like the digital business genies out of the bottle and there's no putting it back now. But there's also other things that are driving the need for this and the importance of it. And part of it comes with the way technology is growing. It's becoming much more complex in terms of moving parts, where an app used to be run off three different tiers in a data center. Now it could be across hundreds of machines and opaque networks, opaque data centers all over the world. And the only time you often see things, how they come together is on the user's desktop. And so that's where we really think you got to start from the user experience and work back. And, you know, all the drive in computing is all about making things better, faster, cheaper. But without this context of the user, often the customer and the experience gets left out from reaping the rewards from all these gains. So that sort of like encapsulates my overall view of the space and why we got into it and why I'm so excited about it. Well, Craig, I got to ask on a personal level. I mean, you look at what happened with the pandemic. I mean, you're a pioneer, you had a vision. Folks that are on the entrepreneurial side say, hey, digital business is coming and they get it. And it slowly gets known in the real world, becomes more certain. But with the pandemic, it just happened all of a sudden so fast for everybody because everyone's impacted teachers, students, families, work, everyone's at home. So the entire user experience was impacted in the entire world. What was going through your mind when you saw all this happening and you see the winners, obviously, who people would invest in cloud native and data-driven technologies. What was your take on all this when you saw this coming? Well, the overall trend has been going on for decades, right? And so the direction of it isn't that surprising, but the magnitude and the acceleration, there's some stats out there from Forbes where the e-commerce adoption doubled within the first six months of the pandemic. So we're talking 10, 12 years of things ticking up and then within six months, a doubling of the adoption of e-commerce. And so like anybody else, you first freeze and say, what does this mean? But when people start working remote and people start ordering things from Amazon and all the other websites, it's quick to see like, aha, it no longer matters what chair somebody is sitting in when they're doing work or that they're close to a store and you have a physical storefront when you're trying to buy something. It's all about that digital experience and it needs to be ubiquitous. So it's been interesting to see the change over the past few months for sure. But again, it doesn't change the trend, it just magnified it. And I don't see it going back any time soon. Yeah, I mean, digital transformation has always been a buzzword that everyone kind of uses as a way to kind of talk about the big picture. It's actually transforming. There's also share shifts that happen in every transformation and any market shift. Obviously that's happening with cloud, cloud native edge is becoming super important. And all these, and by the way, all the applications that sit on that infrastructure which is now infrastructure as code has a data requirement that observability piece becomes super critical, not just from identifying and resolving but also for training machine learning and AI, right? So again, you have this new flywheel of observability that's really at the heart of a digital transformation. What should companies think about when they associate observability to digital transformation as they're sitting around whether they're CXOs or CSOs or solutions, architects going, okay, how does observability plug into my plans? Yeah, absolutely. I mean, my recommendation and the approach that I would take is that you want to start with the end in mind and it's all about how you set your goals when you're setting out and getting into digital transformation. And the late Steve Jobs to borrow one of his quotes, he said that you have to start with the customer experience in mind and work backwards to the technology. And so I think that applies when you get into an observability strategy. So without understanding what the actual user experience is, you don't have a good enough yardstick to go out there and start working towards. So availability on a server or CPU time or transaction time in a database, like those are all great but without the context of the goal you're actually going after, it's kind of useless. So like I said, it's not uptime, it's not server time, it's not any of that stuff. And it's user experience and these things are different. So they're like visual metrics, right? So what a user sees because all kinds of things are going on the background but if it can see that the person can see and their experience is that they're getting some kind of response from the machine then that's how you measure where the endpoint is and what the overall goal is. And so like to keep kind of going on with that it's like you start with the end in mind you use that end to set your goals you use that domain and that visibility to troubleshoot faster. So when the calls start rolling in then they say, hey, I'm stuck at home and I'm on a slow internet connection I can't get on the app and core IT is taking a phone call you can quickly look and instrument that user and see exactly what they're seeing. So when you do troubleshooting you're looking at the data from their perspective and then working backwards to the technology. That's super interesting. I want to get your thoughts on that. So just to double down on that because I think this highlights the trend that we were just talking about but I'll break it down into three areas that I see happening in the marketplace. Number one, availability and performance that's on everyone's mind. You just hit that, right? Number two, integrations. There's more integrations going on within platforms or tools or systems whether it's an API over here people are working together digitally, right? And you're seeing e-commerce. And third is the user patterns and the expectations are changing. So when you unpack those kind of like trends there's features of observability underneath each. Could you talk about that? Because I think that seems to be the common pattern that I'm seeing. Okay, high availability, okay, check. Everyone has to have that, almost table stakes but it's hard when you're scaling, right? And then integrations, all kinds of APIs being slinged around. You've got microservices, you've got Kubernetes people are integrating data flows, control planes, whatever. And then finally users. They want new things. New patterns emerge, which is new data. Yeah, absolutely. And to just kind of talk about that it reminds me of like a Maslow's hierarchy of needs of visibility, right? Like, okay, the machine is on, check. That's like you said it's table stakes make sure it's up and running, that's great. Then you want to see sort of the applications that are running on the machine how they're talking to each other or other components that you're making APIs calls to are they timing out, are they breaking things? And so you get that visibility of like, okay, they're on what's going on on top of those machines or inside of them or in the containers or the virtual machines or whatever segment of computing that you're looking at. And then that cherry on top, the highest point is like how is that stack of technology serving your customer? How's it serving the user and what's the experience? So those are sort of the three levels that we kind of look at when we're thinking of user experience. And so it's a different way to look at it but it's sort of the way that kind of we see the world is that three-tier, that three-layer cake. And you need all the layers. It's super relevant. And again, you can, it's better together but you can mix and match and have product in there. So I want to get into the Splunk solution. You guys have the digital experience monitoring solution. Can you explain what that is and how that fits into all this? And what's in it for the customers? What's the benefit? Right, sure. So with the digital experience monitoring and the platform that we have we're giving people the ability to basically do what I was talking about where it enables you to take a look at what the user's experience are and pull metrics and then correlate them from the user all the way through the technical journey to the backend through the different tiers of the application and so on. So that technology is called real user monitoring where we instrument the users. And then we also layer in synthetic monitoring which is the sort of robot users that are always on for when you're in lower level environments and you want to see what experience is going to look like when you push out new software or when nobody's on the application, did something break. So we couple those two together and then we feed that into our overall observability platform that's fed with machine data. We have all the metrics from all the components that you're looking at in that single pane of glass and the idea is that we're also bringing you not only just the metrics and the events from logs and all the happenings but we're also trying to help tease out some of these problems for you. So many problems that happen in technology have happened before and we've got a catalog with our optimization platform of 300 plus things that go wrong when web pages or web applications or API calls start acting funky. And so we can provide based on our intelligence built into the platform basically run books for people to fix things faster and build those playbooks into the release process so you don't break the applications to begin with and you can set flags to where people understand what the performance is before and it's being delivered to the customer. And if there are problems, let's fix them before you break the experience and lose the trust for the user. So again, it's the metrics from the stats that are coming across the wire of everything all the way to the users. It's the events from the logs that are coming in so you can see context and then it's that user experience. It's a trace level data from where you can double click into each of the tiers and say like, what's going on in here? What's going on in the browser? What's going on in the application? What's going on in the backend? And so you can sort of pull all that together in a single pane of glass and find problems faster fix them faster and prevent users from having problems to begin with and to do this properly, you really need it all under one roof and so that's why we're so excited to bring this all together. You know, I've been saying on theCUBE since for 10 years now we've been 11 years on 11th of the year doing theCUBE but digitally can measure everything. So why not? There should be no debate if done properly. So that brings up this whole concept that you guys are talking about full fidelity. Can you just take a minute to explain what that is? What is full fidelity mean? Sure, you know, full fidelity really comes down to a lot about these traces. So when we talk about metrics, logs and traces it's all about getting all the activity that goes on in an application and looking at it. So when, you know, when you or I interact with our, you know, our company's app online and there's problems that the person who's going to fix this problem they can actually see specifically me. They can look at my experience and look at what did it look like in my browser? You know, what were all the services that I was interacting with and what was it, what was going on in the application? What code was being called? What services were being called? And look at specifically me as opposed to an aggregate of all the domains all put together. And it really is important from a troubleshooting standpoint it's really important from understanding the actuals because without the full fidelity and capturing all of the data you're kind of going, you know, you're taking guesses and it eliminates a lot of the guesswork. And so that's something that's special with our platform is that ability to have the full fidelity. When does the client, a customer not have full fidelity? I might think I have someone sold me a product. What's the tell sign that I don't have full fidelity? Oh yeah, well with observability there's a lot of tricks in the game. And so you see a lot of summary data that looks like, hey, this is that one call but usually it's knitted together from a bunch of different calls so that it's summary data just from because this stuff takes up a lot of storage and there's a lot of problems with scale. And so when you might see something that looks like it's this call, it's actually like in general when a call like this happens this is what it looks like. And so you've got to say like, is this the exact call? And it makes a big difference from a troubleshooting perspective and it's really hard to implement. And that's something that Splunk's very good at, right? Is data at scale. It's the 800 pound gorilla in machine collecting and slicing part machine data. So like you have to have something of that scale in order to ingest all this information. It's a hard problem for sure. Yeah, totally. And I appreciate that. Well, I got you here, you're an expert. I got to ask you about open telemetry. We've heard that term kicked around. What does that mean? Is it an open source thing? Is it an open framework? What is open telemetry and what does it mean for your customers of the marketplace? Yeah, I think of open telemetry as finally creating a standard for Howard collecting data from applications across APM. In the past it's been one Z2Z here and there each company coming up with them, hit themselves. And there were never any standards of how to look at transactions across data across applications and across tiers. And so open telemetry is the attempt and it's a consortium. So there's many people involved in pushing this together. But think of like a W3C, which creates the standards for how websites operate and without it, the web wouldn't be what it is today. And now open telemetry is coming behind and doing that same thing from an observability standpoint. So you're not just totally locked into one vendor in the way that they do it and you're a whole held hostage to only looking at that visibility. We're trying to set the standards to lower the barrier of entry into getting to application performance monitoring, network performance monitoring and just getting that telemetry where there's standards across the board. And so it's an open source project. We commit to it. And it's a really important project for observability in general. So does that speak to like the whole more data you have, the less blind spots you might have? Is that the same concept? Is that some of the thinking behind this? It enables you to get more data faster. Now, if you think about, if there are no standards and there are no rules of the road and everybody can get on the road and they can decide if they wanna drive in the left lane or the right lane today, it makes getting places a lot harder. And the same is true with open telemetry. Without the standards of what, the naming conventions, where you instrument, how you instrument it, it becomes very hard to put some things in a single pane of glass because they just look differently everywhere. And so that's the idea behind it. Well Craig, great to have you on. You're super smart on this and bleeding with observability. It's a hot topic. It's super cool and relevant right now with digital transformation as companies are looking to re-architect and change how they're going to flip the script on software development, modern applications, modern infrastructure, Edge. All this is on top of mind of everyone's thing on their plants. And we certainly wanna have you back in some of our conversations that we have around this on our editorial side as well, when we have these clubhouses and we're gonna start doing a lot of those. Definitely wanna bring you in. I'll give you a final word here. Tell us what you're most excited about. Put the commercial for Splunk. Why Splunk? Why are you guys are excited? Take a minute to get the plug in. It's so easy. Splunk has the base to make this possible. Splunk is like I said, it's 800 pound gorilla in machine data and taking in data at scale. And when you start going off into the observability abyss, the really, really hard part about it is having the scale to not only go broad in the levels of technology that you can collect but also go deep. And that depth when we talked about that full fidelity, it's really important when you get down to brass tacks and you start implementing changes and troubleshooting things and turning that data that you have into doing so understanding what you can do with it. And Splunk is fully committed to going not only broad to get everything under one roof but also deep so that you can make all of the information that you collect actionable and useful. And it's something that I haven't seen anybody even attempt and I'm really excited to be a part of building towards that vision. Well, I've been covering Splunk for many, many years, 10 years plus I think since it's been founded and really the growth and the vision and the mission still is the same. Leveraging data, making use of it, unlocking the power of data as it evolves and there's more of it. And it gets more complicated when data's involved in the user experience end to end from cybersecurity to user flows and new expectations. So congratulations, great product. Thanks for coming on and sharing. Thanks again for having us. Okay, this is John Furrier on theCUBE. Leading with Observability is the theme of this series and this topic was end to end observability to enable great digital experiences. Thanks for watching.