Evan Kaplan, CEO of Influx Data, is someone who knows his way around the technology world. In a lifetime of experience that has seen him found and manage several successful companies from startups to public entities, Evan has had a great track record of success.
Influx Data is yet another success for Kaplan. Building on the open source InfluxDB, the company boasts more than 200,000 users of Influx today.
In this DevOps Chat, Evan and I discuss systems instrumentation today and what big data and velocity means for developers and everyone up the stack. It is a very different world from just 15 or 20 years ago.
As usual, the streaming audio is immediately below, followed by the transcript of our conversation.
Alan Shimel: Hey, everyone, it’s Alan Shimel, DevOps.com, and you’re listening to another DevOps Chat. Happy to be joined in this episode of DevOps Chat by, actually, someone who grew up in my neighborhood. We knew each other – well, we didn’t know each other way back when, but we’ve come from a very small area of concentrated – lot of people, you know, we know from that area, but, anyway, Evan Kaplan, CEO of InfluxData and of North Woodmere. Evan, welcome.
Evan Kaplan: [Laughs] Thanks, Alan. Thanks for having me. Look forward to _____ –
Shimel: My pleasure. So, Evan, it’s been a long, strange trip from North Woodmere. Why don’t you give our audience a little bit of your background and your journey? And then we’ll jump into –
Kaplan: Yeah. Sure. Sure. Very long for me. If those of you don’t know, that’s a suburb of New York City, right near Kennedy Airport, that we both grew up. My strange trip took me to Seattle, where I finished up college and worked as a climbing guide for years. And then worked in aerospace and started a company, the first company to develop SSL-based VPNs, called “Aventail,” back in the mid-90s. _____ through the bubble. Ran a public company called “iPass,” and, about two-and-a-half years ago, met Paul Dix, the founder of Influx. And Paul and I really connected about his vision and what he was doing, and we partnered together to start building Influx from 25 employees back then to north of 100 now. So growing fast.
Shimel: Good stuff. Good stuff. And, Evan, I’m gonna assume most of our audience has at least heard of InfluxData, but, for those who have or even those who haven’t, just to set the level-set, tell us about what InfluxData does.
Kaplan: Yeah, so InfluxData is an open-source time-series-based data platform. It’s the leading time-series platform out there. Time-series is the fastest growing market in the database, data category. And so Paul, our founder, spun up Influx back in 20 – I think it was late 2013. It received a lot of accolades on GitHub. It was a very popular project and it has grown dramatically since then, from roughly – I don’t know – 1,000 sites running it to, today, we’re approaching 200,000 sites running it.
Kaplan: And, you know, most of the large Global 2000 is running it somewhere, so.
Shimel: That’s really impressive, man. That’s something to be proud of. So, Evan, we could probably spend our whole 20 minutes talking generally about Influx DB and what’s happening, but I’m wanted to kinda dive in a little bit and talk specifically today around instrumentation in modern software installs, in the modern software factory. This whole term “modern” has become sort of a catchall buzzword, dog whistle, if you will, for when we talk about things like Agile and DevOps and serverless and microservices and all of these ways that we’re seeing software being written, managed, and deployed today. So talk to us a little bit about it, if you will, Evan. Where’s your take on this?
Kaplan: Yeah, so I think we’re – we grew up in the industry, going back a couple of decades ago, the common myth for monitoring or instrumentation of systems was you would buy a package from HP OpenView or CA or a mainframe management package, and you’d buy a monitoring-for-a-service package. They were expensive and they were largely horizontal across the stack, and you’d implement those. And what’s happened over – certainly over the last ten years, during the DevOps revolution, is that stuff has fractured and you’ve seen companies, like Splunk and Elastic and a variety of other folks, arise to handle the modern workloads in an open-source sort of fashion. And now, if you take a look a look at DevOps world, most of solutions are dominated by open-source kinds of models.
If you look at state of the art now, that people are really paying attention to, the revolution that brought you to logs and log views of stuff and the consolidation of logs has moved much closer to metrics and events. And what I mean by “metrics and events,” the instrumentation of real time of these complex systems. And they’re not just software systems that are DevOps things, so – those are obvious, but they’re also hardware systems; they’re IoT things. And so what’s happening is people wanna see these complex systems and they wanna interact with them in real time because the only way to characterize these systems is to watch them behave in real time. And “observability,” as you know, Alan, has become sort of a keyword here. And observability generally means real time and it means metrics and events; it doesn’t mean verbose logs.
And so what we’re trying to do is help developers, specifically and, up the chain, architects and CTOs, build instrumentation or telemetry natively into these complex systems, so they can view these things in real time. As you can guess, the data loads with this are absurdly high. You’re talking about hundreds of millions of points a second in some of these systems and you’re talking about to visualize those in near real time, you know, control loops in near real time. And you’re talking about this sort of next generation of, lack of a better way to say it, system instrumentation and management.
Shimel: Sure. Evan, if I could stop you there for a sec, you know what I find interesting? We’re talking about building this instrumentation and to give people insight and who are we giving insight? Well, you mentioned developers and managers and architects and CTOs, et cetera, but you didn’t mention ops. And is that –
Kaplan: Yeah. Yeah.
Shimel: Isn’t that a little bit of, you know – I don’t know if it’s Freudian or if it’s just insight into what today’s world looks like for these things.
Kaplan: You know, that discrete ops, right? That discrete ops role is changing, right, into what you obviously cover, which is this DevOps. And so our view of how our – uptake of our platform into large corporations, companies like IBM and Cisco and SAP and Tesla and PayPal, has been from developer or DevOps people up. So we tend to think of “Make it super easy for developers, right? Get their time to value down to really, really minimal and then it will rise up and be taken as a complete operational system.” So we tend to think of it from developers up. Not to say the operations guys aren’t relevant – they’re super relevant – it’s just, when we enter into a large opportunity or business or that sort of stuff, it’s usually coming from a developer who figures out he can instrument his code, his service, his offering relatively quickly in Influx.
Shimel: Got it. And I’ll tell you something I – and it goes back to what you mentioned earlier – the sheer volume and the acceleration of data, right, the – so, no, it’s how much data and the speed that it comes at us. With automation and everything that that brings, it easily outstrips the ability of a human, a lot of times, to stay on top of it, so you almost need to build automation, machine learning, AI into these types of instrumentations, that you bring what the human really needs to see and touch or make a decision on to his attention ’cause so much of it is just whizzing by right now.
In the last week or two, Evan, I’ve probably had a couple of discussions – trying to think of a friend of mine who – CEO at ScienceLogic and another company called “Ravel Systems”; some friends of mine in security just launched something called “DisruptOps.” And they’re all talking about this term, “AIOps.”
Kaplan: Yeah. Yeah.
Shimel: And I’m wondering is this something relevant to what you’re talking about, with instrumentation? Where does that fit in? How do we account for just the big data nature of this?
Kaplan: Yeah, I think, first of all, absolutely. So, if you think about – so the journey. And let’s call it the end state of all of this is true autonomy, which is very rare and, in fact, almost never the case, right? But, if end goal state is true autonomy, what’s that journey look like? It looks like, first and always and most importantly – and, by the way, the stuff that we skipped a lot, when we were in the early stages of our career – it is, in fact, instrumentation, is telemetry. Telemetry always was an afterthought before whether shipping a product or a service or that sort of stuff. Now the ability to drive that cost of instrumentation down so low that the ability to see what’s going on and create the data – not just see as in “human” see but create the data – has changed dramatically.
So, if you follow that journey, you go from instrumentation; you go to observability; you go to automation in the form of “I’ve observed these sorts of things, whether it’s through AI capability or not. I built control loops because I’ve observed them, so I’ve built control loops to keep it in a relative stasis. Right? To autonomy.” All of that – the last two steps, right, even the last three steps – involves some form of machine processing of learning, whether it’s machine learning or AI or call it, but all of that gets increasingly sophisticated. But it means taking in, as you sort of articulated, you know, millions, hundreds of millions – in some case, billions – of points per second, right?
Kaplan: Think about it. Now that’s just completely – you know, what a different world, than doing server management with your CA Unicenter product 20 years ago.
Shimel: [Laughs] To say the least.
Shimel: Yeah, I hear ya. I mean, it really is. If we transported – you know, if we could do that – if we could transport someone from not that long ago, Evan, an hour or _____ _____ –
Kaplan: No. No.
Shimel: From 1997, ’98, right? I was helping an early ASP, Interliant. We were building – opening data centers, managing Lotus Notes and PeopleSoft and stuff like that.
Shimel: If I could take a NOC guy from there and show him today’s velocity and the fact that we actually do have telemetry and we actually are building tools that allow us to manage at that level, it would be like taking someone from colonial times and showing them highways with cars. I mean, the supports –
Kaplan: But, Alan, these solutions are still sitting out there. They’re a huge _____ _____ _____ –
Shimel: No – well, that’s the scary thing. Yeah, I mentioned my friend, ScienceLogic, my friend Dave Link. He was saying that’s his – you know, and they’ve done very, very well, and his thing was just replacing the Tivolis and the Unicenters and that generation, if you will, of network management, of systems management.
Kaplan: If I could tell you what, back in the late ’90s, is I was building an ASP also, based on the Aventail, the SSL VPNs, what we were standing on Netcool, back in the day, and things like that, _____ _____ –
Shimel: And Netcool was a new program, right?
Shimel: Before IBM bought it, right? It was one of the cool, new kids at the time.
Kaplan: Exactly. If I could tell you what somebody could take with Influx today and do, relative to what Netcool did back in the day, and the difference, right – first of all, they could do it for nothing. Forget paid licenses. And the amount of scale, the amount of data collection, and the ease of sort of visualization has dramatically – and all those _____ _____ –
Shimel: It’s phenomenal. Phenomenal, man. It’s just crazy. So, Evan, let’s bring this back to Influx. How does all of this now manifest itself with your business today?
Kaplan: Yeah. So that’s right. So we look at our businesses in two segments, right? We look at it as the instrumentation of and the collection of data around software, being the platform of record for this next generation of cloud software. And I don’t have to describe to you is is that, if, in the old days, it was an X Windows terminal talking to a Unix server app, today, there is no “there” there. There’s a virtual network; there’s VMs, containers, and microservices that are growing at exponential rate. The whole infrastructure is ephemeral, so the monitoring and the instrumentation workload has dramatically changed. So one side of our business is really focused on that, that opportunity, and we’d call that the “DevOps chain” of the business, right? Because it comes up into organizations through DevOps folks who bring in Influx and instrument.
The other side is IoT, which is completely different. So, while DevOps is largely metrics-driven, IoT is largely event _____ driven. And that’s the instrumentation of the physical world, right? So our watches, our clothing, our health care, our cars, our homes, are all being instrumented with sensors. The amount of data pouring in through that is driving whole other systems into business. And, while most of our time was spent on the management of software systems back, now it’s this deep, deep integration of software and hardware. Deep. And so our business breaks out into those two categories, largely.
Shimel: Got it. Got it. Evan, anything – any new news coming out of Influx you wanna share with our audience or –
Kaplan: You know, where – we have – you know, we’re almost constantly releasing stuff into the community on a regular basis, so no news that I’d share at this moment. We can touch base in three or four weeks. We have our big InfluxDays event. We’ll make some announcements there, but you can look for us to build increasingly sophisticated solutions that allow people to do their machine-learning workloads, their application tracing, their log and metrics, their asset management, on top of Influx. What we want is people to use us as the platform of record for all of these metrics and events pouring in.
Shimel: You know, we live in interesting times and I can’t wait to see where this all goes as well, so we’re about out of time, though, here. As I mentioned to you, the 15, 20 minutes goes so quickly.
Kaplan: Yeah, it really does.
Shimel: And you barely ever – you know, it is what it is. But, Evan, thanks for joining with us. Evan Kaplan, CEO, InfluxData. You know, so, in three, four weeks, you have your event coming up; maybe we’ll have you back. We can talk a little more about that. We’re doing a big – I didn’t even announce it yet, but I’m gonna announce it right here on the show with you – we’re gonna be doing a big virtual event, December 18th, called “Predict 2019,” and we’re gonna –
Shimel: Yeah. We’re gonna be opening up the crystal ball and asking a lot of pundits and experts and so-called experts what their predictions are for what 2019 has in store. Maybe we can have you back then and we can do some crystal-ball gazing.
Kaplan: That’d be great, Alan. Always a pleasure. Always a pleasure.
Shimel: All right, my friend.
Kaplan: Have a great day. All right.
Shimel: Well, Evan Kaplan of North Woodmere and InfluxData, thanks for being our guest on this episode of DevOps Chat. This is Alan Shimel for DevOps.com, and you’ve just listened to another DevOps Chat. Have a great day.
Kaplan: Take care.