>> live from Boston, Massachusetts. It's the queue covering your red. Have some twenty nineteen brought to you by bread hat. >> Well, good afternoon. Where you might be watching us here on the Cube. We are live in Boston. Is we wrap up our coverage headed toward the homestretch? You might say of Red had Summit twenty nineteen. Want was to Mittleman. I'm John Walls. And thank you for joining us here. We're now joined by Ah, Louise, right. Bower, who was the vice president and chief technical strategists and head of innovation lab at Dinah Trees. And always good to see you today. Thanks for being with us. Hello. Thanks for having me s O software intelligence that that's your your primary focus. You've got headquarters here in the Boston area back in Austria. Tell a little bit about it. You would, Dina Trace. And I guess first off, what this news this week has met to you in terms of the release is and then maybe what you're doing in general. You know what Dina Trace is all about? >> Yes. Oh, that phrase has been around for, like quite a time. Started out as an a P M. Company. like fourteen years ago have been reinventing ourselves over and over again on DH. So we move from the traditional monitoring approach. So the innovation we had in the very beginning when we launched the first product was really would be practical, pure passer. The ability trace and went that way a lot about facing racing, like becoming super cool for micro services. So it would be like the first teacher we could be burying, doing, tracing before it was cool, like forty, fifty years ago. And then I was were involving the product more, more Skilling into bigger and bigger environments. So what's bigger and bigger mean? I remember in the beginning when we were working on environments who we're talking about, like one hundred host has a big environment like five hundred told that that's a big environment today, we say, for even one hundred thousand toast. Okay, it's a big environment, but they can't get even bigger then. The massive change was really for us five years ago, where way implemented our entire product offering, built the new Dina trays, Mr Focus, that we realize that okay, it's data and between people date and having them analyzed data is nice, but it's only getting you so far. So the more complex the replication get, more data you get to analyze. And it's just more exponentially scaling how many people you would need to deal with this. And that's why five years ago, we started to incorporate a I into our new court platform, then for automatic problem analysis. That's also where we're not just BPM. That's just what we call like the Dogg Tools data on glass tools to show a lot of data. Do some analysis on top of it. But it don't help you, too, really resolve a problem. So we used build in the eye, and that automatic would cause analysis again. Next teacher doing Aye, aye ops, affordable school like five years ago. Andi. The latest evolution. We also so again, and not a change in the way people are using monitoring tools. Um, we've invested a lot into building out in AP eyes don't see monitoring tools like be the Martin still here and the application over there, but having them monitoring who being highly integrated into the fabric fire eyes. So we have, As of today, eighty percent of our customers are using the product also via reprise, but tying them into operational automation. What we heard even today in the keynote here about a ABS and Howie iop starts to control and manage that form. More is becoming the intelligence or the back plane behind a modern native stack. >> So we have Chris right on. Who was in the keynote this morning? Came on our program this morning, too, when we talked about just the rippling effects of distributed architectures. I look at my applications there, you know, going to micro service architectures. You look at where's customers data? Well, lots of stuff all over the clouds and sass, and that has a ripple. Effect it to your space. You know, I hear observe, ability monitoring, you know, hack even bring up, like, you know, the civilised world. It becomes a whole separate meeting. So Donna Trace has been going through a transformation. You know, give >> us a >> point check ins to you know, where your customers are, how you're helping them move through this modernization and, you know, move to distributed architectures where that fits in >> so that their customers we focus on mostly are like Fortune five hundred customers who we work with. And obviously they have everything that exists on the planet. When we talk about self for like even from the mainframe to cloud native to serve less, as you mentioned here. And they were in this transition process right now, like modernizing their applications, which, as a necessity, we all want to move fast. There we want therefore flexible architects is we want to build more enough innovative products but at the same time to realize that this is also a message business risk behind following this approach. Think about you in the role of the CEO and say where we're going to modernize our architecture. We're going to rebuild everything we platform and so forth. You can if you succeed. Everybody would say you had. Yes, you did what you had to do. I mean, sorry if you failed, you failed. It's s so for them, it's a It's a big risk to move down that route and retired to take that risk out of the process as much as possible. Really Starting, obviously was monitoring their traditional sex, as they have to today, but really supporting that along that entire journey to a cloud native architecture er, starting with what we referred to as our support for monoliths to micro service architecture's. So Theodore is basically you don't want to rip apart the replication and figure out how it's going to work in my purse services world. But we have to technology that's called smart scape smart. Skip Moelis bills a real time, all of your entire data center and old applications running into it. And it was virtually that sect. You're marvelous, you came. How would they look like in a microt services architecture without catching any codes and then making it work? So once you've done this once, you've decided to move there the next step? Obviously, yes, you could have rebuilt that application. Usually we see applications with micro services architectures being significantly Mohr complex or more distributed by the sign that a traditional that you might have Web server application, Teo Database Server. Now you might be talking about maybe two hundred micro services or more so twenty times ranges. Writer on this under under lower bound here, which means that your traditional operational approach up okay, it's either the database of observer. The application server doesn't work anymore. on top of this. You did all of this to deploy fast. Like for like, bi weekly releases, even maybe daily off, like a smaller granularity. So you were reading a lot of entropy to that system and you have to analyze way more data. Did he ever had to do before? And this is where we kind of getting to the level where theoretically humans could do it. But it would just take us too long where the Holy I ops capability come in where we let let the machines that a monitoring tto take care of it at that level. So we helping them to operation US thieves processes and then really supporting them along the whole journey, where every customer we talked like this vision. But we're also here today in the keynote of an autonomous cloud and with carbonated, we already made a great step in this direction, looking at the interest, actually, like today say, I need five replicas off this container. I don't know, given that it's does it open shift and specifically here, it's going to happen. But if we move to the application layer is a lot, that has to be done and it has to make it easier for people to do. And that's where we tied into the entire customers. Ecosystem toe, automate like their cloud environment and have actually built a practice around which we call autonomous cloud management that we have been working with with customers on to enable them to achieve this over time. But it's going to be a lot maturity there. >> Yes, I mean, so what it talked about that you know, a CIA autonomous cloud management. What exactly you know, is that and how are you bringing that to your customer >> base? Autonomous Cloud Management resulted out off two different areas. The first one was when we were implement re implementing our platform. What I mentioned before, one step for us was to move to the SAS platform, and we looked at all the operation practices that were around back then, you know, we don't want to tell the doc I really don't want to do it. Like having people twenty four seven look at dashboards, then goingto a wicky, then reading a description of how to fix the problem. If you're the engineer, that why why do we do this this way? Must make any sense. So we developed our own practice, which we referred to as no wops. I know it doesn't mean that you're not doing operations. That would be pretty crazy, but not doing this traditional Naga type of operation, sitting there staring at a screen twenty four seven and then mentally executing any operation. So we had our own practice that we've built around it and, quite frankly, which has spilled it because we needed it for ourselves, and then we kept talking to customers and partisan, he says. Really cool what you did there like, Oh, how did you do this? What's like yourself? Respect behind this and what does the practices? What do your process? What's the culture change? So we were engaging with some customers, and then we were seeing that some of our customers back then, even we're doing bits and pieces off. This isthe well because there's a lot of practice and a lot of knowledge around. How did the autonomous count management and at the same time that we talked about the other customers who not yet on a charity who definitely want to get there? But I'm not quite sure how to do it, and I don't want to figure it out themselves. So we thought, Okay, let's take all of these best practices that we have and build more or less a methodology around it. How to make this actually works like how to do this. We really broke it down into, like, individual sprints to distance sprint one that distance sprint to to really have the results within three months, six months, twelve months. Whatever the cases that you want to run on. And then we realised talking to customers. This by itself isn't still enough. So that's why we started to open up this to an entire ecosystem. So WeII brought ecosystem partners along, like working closely with read a lot of our companies, but also system integrators who can help us. We speak of projects because we as a company, our software companies were not a services are consulting company, and we do support customer that some of those engagement. But if you think of like a really Fortune five hundred company that's a multi approaches, it will keep hundreds of people busy. So to recap like built in methodology, we built ecosystem to deliver on that promise at scale. And now the last step was we were doing this. We also built like a reference architecture for it, and I was just in an eternal ideas. So how do we, like structure this building reference architecture and then realized Okay, It's kind of like super helpful for customers. So that is why we don't decided to open source this reference architecture this fabric as well, too, like the tires after community, so they can also use it. So technically, stability is three pieces. It's the methodology, it's the ecosystem. And it's like the reference architecture that you can work with to help you, Chief. Go. >> All right, um, tell us how your a I fit into this. I've heard some analyst firms are saying, you know, some of the next generation of your space could be a I ops. Do you consider yourselves moving in that direction, or do you have some counter view on that? >> I think today a lot of things ar e I upset my now b a i ops, and it's a very undefined goal. This mentioned earlier. We decided to have aye aye based algorithms as powerful platform five years ago and nobody back then was talking about the layoffs. Funny story. Some of our competitors even told us you can't use the eye for monitoring just like totally stupid that there are other companies that they were doing it. But again, so the whole industry is learning here. I think it's really about data analysis. If you look at, if you scare the bigger and bigger environment, you really have to look at the process off what human operations people are doing on. There's obviously some hard decisions that you have to take their have. You have to work with teams to resolve our problems. But the biggest portion is really data analysis interpretation, right and a lot of this can be put into, and a I component that doesn't What's the Dyna trees, eh? I does it more. This is like your saree in codes, so to speak, which is able to find what's broken in the education, what was related to an issue in the application and being able to automatically find the root cause. Very importantly, we're kind of like opinionated and how in a I for operational practices should be working because one thing you don't want it serious you want? Don't want it happening. Iop system tell you well, you should. We start this service because some neural and that were told to do so. That's a building, a lot of confidence. That's why our approach is really tio follow. Like what we call a deterministic a pia a sari. And hey, I did it able to explain back to the user White came to a certain conclusion. So why should their we sort this herb is west of the rollback, this deployment or why that's the I b. Believe that if I fixed this problem, then like the bigger problem will be solved. So that's our approach, Teo T. I actually started like, roughly four years ago, five years ago, even a bit more than that on you. And I think that have a lot of experience, really rolling it out its scale and seeing it will help people because the next the ultimate next question, without always Scott Wass. If you wanna know what the problem is, why don't you fix it? And that's exactly the conversation you want to have, maybe just briefly at here, because it usually comes up okay, f a I and isat replacing people's jobs? I don't think so. We also heard it in the keynote today from Chris. It's augmenting our capabilities. There's hard decisions that you have to take, but just going through tons and tons of data. It's not going to your isn't very often when we talked at operations team or almost every time. First of all, you can't hire enough people anyways to get all the old done that's on your plate. Secondly, um, just by the amount of data and the time that I had to react. It's just long with a human understanding scenario way. Do this demo on self healing, often application. Where were deployed, something broken into production and have it being rolled back and we can do fifty one seconds. No human can do it that fast. That's just what pure, softer automation can do for you. So I think that then you can focus on other areas and more important, new project with us people in on the off space. What's what the three projects that you want to work and you never have time to work out and usually come up with the list. Yet this is what we give you back that time to work on exactly the things that move your business forwards. You >> said fifty one seconds. You've never seen Stew in action. You still have a lot of confidence. >> Well, we we love the machine, enhance human intelligence. You're definitely We could all use those machines to help us all get away from the drudgery and be able to do more. >> Always safe travels. Thanks for being with us. Headed back to Austria. Say, hide all your folks back in Austria, right There always is on his way home on his way to the airport. Thank you for being with us here on the Cube. Thanks. Appreciate the time our coverage continues here. Red hat some twenty nineteen. You're watching the cube?