 From Washington, D.C., it's theCUBE, covering ScienceLogic Symposium 2019, brought to you by ScienceLogic. Hi, I'm Stu Miniman and you're watching theCUBE's exclusive coverage of ScienceLogic Symposium 2019. Here's the Rick Carlton in Washington, D.C. Happy to welcome to the program first-time guest but a long-time customer of ScienceLogic, Carl Fosper, who's the Senior Director of Systems Integration at CUES. Thanks so much for joining us. Thanks for having me. All right, so we're here in D.C., and that's important because, first of all, you know, you're based down here and ScienceLogic is based down here. Bring us back a little bit. You've been a customer a long time to maybe even give us a little bit of the before picture if you could. Sure, so yeah, we've been a customer for 12 years now and we picked ScienceLogic for a big list of reasons. Actually wrote the RFI itself and probably 20 pages long. Lots of people came back and gave us responses. ScienceLogic was one of the shortlisted candidates that we picked out. We did a bake-off with a couple other vendors and ScienceLogic was the clear winner. All right, so Carl, let's zoom out for a second here and just give us a level set on CUES, what CUES is today. You know, I'm familiar with what CUES was back in the day and there's certain pieces that are no longer there, so give us a level set on the company and the business. Yeah, sure, so CUES is formerly known as CUES Network Systems. We're owned by Aquastar Corporation and we're a managed service provider. We have a consumer business where we provide broadband internet to folks that live really out in the countryside and can't get cable or DSL or files, things like that. We have about 1.4 million subscribers in our consumer business. We've also launched consumer services in South America, Brazil, Ecuador, Columbia, places like that, really serving underserved areas for getting them broadband. We also have an enterprise business where we sell to credit card processing, gas and oil, pipelines, fast food, places like that. So Carl, is it safe to say you use Satellites but no longer put them into space? We use Satellites, that's correct. We contract that out now. Yeah, we are the last remaining CUES company. So service providers are always fascinating to me because we talk about enterprise IT and how fast things are changing. At least for my entire career when I talk to service providers, change and growth is really just baked into the DNA. I need to move fast. When you talk about scale, it means something very different and living in that complex world. Can you just give us a little bit about what things are like in 2019 for you? Sure, yeah, the scale is always our challenge. Like I like to say, we have sales people too. And they're out there selling new products and services constantly. So we needed to be able to grow with those sales. We started out with a couple thousand devices that needed monitored in applications. Now we're up to almost 30,000 NOC systems that we monitor. Also we're keeping track of nearly two million terminals and the status of them and things like that. So yeah, scale is super important to us. Okay, so bring us inside where science logic fits into your equation. Sure, so when we put out our RFI out to industry years ago, we were trying to replace a whole bunch of different tools. And we had other vendor products and things like that. We really wanted to consolidate tools as much as possible into a single platform. Traditional ICMP, SNMP monitoring is how we originally started. Now we have lots and lots of integration with other tools, you know, APM products, different streaming media products. We're integrating more and more with streaming services now in terms of getting data into the platform. So. Yeah, Karl, I would love to get your viewpoint. You know, something that came through to me in the keynote is on the one hand here, it's like, oh well, AI office is going to replace things like, you know, some of the traditional players here. But then you see on the stage, it's like, oh okay, we're actually going to have integrations with a number of these tooling. So yes, there's overlap, but it needs to be integrated. How do you look at that? Is this the primary product? Is this a piece of the product? How does data collection between all these various tools go together? Well, that's a great question, because that's exactly what we and lots of other folks are grappling with right now. We've got data producers all over the place now. And we're really focused on the data production and high quality data back at the source into a real PubSub type of architecture, of which we believe that ScienceLogic will be both a producer and consumer of that PubSub architecture. And whether it's the one tool to rule them all or not, probably not, no one's going to be that. And we've got lots of vendors that purport to be the one tool to rule them all. But really, we're focused on ScienceLogic at this point to be really the focus, especially for our operations folks. We've got 24, seven staff. They use ScienceLogic as their main tool that they go to. That's really where we want the data to end. That's where we want as much intelligence to end as possible. So, you know, I'd be curious since you've been using the tool for a dozen years now. 12 years ago, the discussion of data was nowhere near what it was today. Can you just bring us through a little bit of that journey and you mentioned data a bunch, but how important is that? Where are you in your journey for? There was that maturity model that was put up there. The role of data today, and where do you see it going? Well, I mean, data's everything. Today, 12 years ago, we were grappling with things like naming conventions and simple firewall rules and whatnot. Those days are long, long past. Now, the data quality and the pipeline is what we're focused on right now. Because like Dave said in the keynote, garbage in, garbage out, we're really, really focused on trying to get good quality data by focusing on the source of the data, as opposed to fixing it after it's been moved into whatever platform it ends up in. So we're using proper schema management and trying to bake data governance into the actual engineered products. And if it's not governed data, then you don't get to look at it. And that's really our focus. We're an engineering company at heart. So we actually write most of our own software. So we're kind of in control of our own destiny there. And we're really focused on pushing that back because we think the benefits in the long run are going to be worth that investment to get clean data all the way back to the source. Yeah, so, Carl, one of the big shifts I've seen in the last few years, when you talked about managing and monitoring, I used to as the administrator or controller, used to be able to go and touch all of those pieces. Today, there's more and more some of those pieces. I need to manage not just the stuff that's in my environment or my hosted environment, but outside of my environment and doing public clouds. Bring us up to speed as to where does cloud fit? How do you, what's your cloud strategy? Sure, we're actually launching some of our first applications in GCP right now. So we're working with our Google partners in this particular case to integrate the data that they can collect natively in their systems, bring it back as actionable events into science logic platform, while keeping the vast majority of the data native to their platform, no need to bring back application specific data, unless we're actually going to do something with it or if we need to cross correlate it with other information. The data sources live in our data centers, not in GCP, so we need to combine it with information we know about our on-prem equipment plus the applications running there. So that's the data we'll bring back to cross correlate. Yeah, how do you decide what lives where and where does science logic fit in in the whole discussion? Yeah, it's a good question. What lives where? We kind of go back to license models and cost models. We're pretty good sticklers about focus on doing proper upfront analysis to make sure we don't end up with some six or seven figure bill at the end of the year from a cloud provider. We also tend to do a lot of stuff on-prem because a lot of our systems have to run in one of our data centers. If you ever driven past our building, you'll see these large, large dishes and antennas outside, a lot of our equipment has to be within milliseconds or microseconds even of those dishes. So we actually have a large data center presence kind of scattered around the country and around the world. So we have the compute resources to do it ourselves. Yeah, and even I would think edge computing something that plays into what you're doing. What do you see as some of the main challenges is the kind of footprint for what you're doing and things spread out more? Yeah, keeping let's say pet projects and shadow IT projects, keeping them in check is a really big focus right now. Because, and also with DevOps, sort of the, I'll do everything, I'm going to be my own IT department philosophy is a new challenge that we're facing. So integrating with what the DevOps guys are building into our overall monitoring strategy, that's been a new challenge that has really creeped up or the last, let's say six months or a year. Okay, is there an intersection between your use of the science logic in the DevOps team yet? Not a big one yet. I think we're still learning DevOps at this point. I consider it a lifestyle change, not really a thing that you go get. So I think we're still kind of early adoption for DevOps and really only Greenfield projects at this point in time. Okay, how about the term of the show is AIOps. So what's your act in AIOps? Where do things like machine learning and automation fit into your environment? Yeah, we actually have quite a few use cases where we really think that machine learning is going to help us a lot. Cross-correlation is a big area for us. We have lots of information, but figuring it out, feeding like the APMs and our Cisco ACI software-defined networking and those bits of information all into one product, we've been challenging science logic on this for quite a while. It's like, okay, you guys know about everything now. Tell us something that we didn't know before. And that's kind of where we're at and seeing the announcements from this morning was really encouraging that we're finally see the horizon at this point. Yeah, if you can, it's found out that a little bit. How has science logic been doing on the roadmap? What helps between science logic and your vendor ecosystem out there? What more could they be doing to make your life easier? Yeah, that's a good question. So if you would ask me that a year ago, I probably wouldn't have been as encouraged as I am today. It was a challenge and they're an engineering company. We're an engineering company. Sometimes you have to focus on foundation and it's not cool, it's not sexy, it's not shiny, but you have to do it. And I think they've been focused a lot on their foundational aspects of the product which will actually enable doing things like machine learning. There's no point doing machine learning if you have bad data or if you have a platform that doesn't support very, very fast queries. And the GraphQL database, we think that we're gonna use that extensively and through the API and not even through the UI. So I think foundation is important. I think they've focused on it for the last couple of years. I think we're finally gonna start to see the benefits of it. Both single factor sort of machine learning, anomaly detection, but we really wanna see it on the cross domain. I wanna be able to see in science logic, impacts impacted by in our full stack environment. I'd expect you probably had some visibility into what was coming out in the big Ben release. Is there anything that jumped out at you or that you're ready to use day one? So automations for sure. We'll use that definitely day one. The way they've gone through and really made it a lot easier to use. You don't have to be a Python developer anymore to actually get a lot of benefits out of the product. So I can turn that over to some of our junior engineers to actually handle those things. And we get a lot more sophisticated with them now. Primarily we used to focus on, let's send an email type of thing. Now we can actually execute back end actions without having to have a programmer to do it. So that right away we're gonna use out of the box. Okay, and in that forward looking piece without breaking any visibility you have into their roadmap, what would you like to see more? I'd like to see more getting performance data into their real scalable, laterally scalable backend. And that's certainly an area that I'd love to see as much progress as fast as possible on. Also the PubSub, subscribing to streams coming out of our Kafka cluster, we want that to be in the product as soon as possible because we really believe that that's where the majority of our data of the future is gonna come from. Also new applications, they come and go. Docker containers spin up, spin down. So the state of something is no longer fixed and we need to be able to integrate with Kubernetes and our OpenShift platform to be able to know, well, what should be running right now? Those are the things that are on our roadmap that we need out of the product as soon as possible. Yeah, so it definitely came to me that ScienceLogic's listening, are they moving fast enough for you? No, no one's ever moving fast enough. So yeah, they're moving, so that's good. But yeah, I mean, I could use it today if they had it. All right, Carl, last thing, you've been to a few of the ScienceLogic events in the past, give us, you've been to other industry shows. What's special about this show? What brings you and your team to ScienceLogic symposiums? Well, one of the things that ScienceLogic does a really good job is they bring a lot of resources here and actual resources that actually know stuff. It's not just a bunch of salespeople telling me, oh, that shiny new object's going to be in the platform at some indeterminate time in the future. It's the actual engineers, people writing code, product managers, things like that. So having access directly to the people who actually do the platform updates and changes is super valuable. The new center where we can touch and feel the kick of tires on new things has been excellent this year. So I think that's probably the thing, is just quick access to all the resources. We have a bit of advantage. We're only 45 minutes up the road. We can come down here as need be to visit their headquarters, but having everyone here at one time is great. All right, well, Carl Fosberg, really appreciate you sharing your history and experience and future direction as to where things are going on your end. All right, I'm Stu Miniman. We'll be back with lots more coverage here from ScienceLogic 2019. Thanks for watching theCUBE.