Unravel is making a name in the AIOps, APM and big data markets. In this DevOps Chat, we speak with Unravel CEO Kunal Agarwal about what makes Unravel a must-have for its customers. We also take a sneak peek ahead to what the near and not so near future holds for big data, AIOps and the tech scene in general.
As usual, the streaming audio is immediately below, followed by the transcript of our conversation.
Alan Shimel: Hey, everyone, this is Alan Shimel, DevOps.com, and you’re listening to another DevOps Chat. Today’s DevOps Chat features Kunal Agarwal, CEO of Unravel or Unravel Data. Kunal, welcome to DevOps Chat.
Kunal Agarwal: Thank you, Alan. Thank you so much for having me here.
Shimel: Excellent. It’s my pleasure to have you here. So, Kunal, let’s get right to it. “Unravel” is the name of the company, correct? Not “Unravel Data.”
Agarwal: “Unravel,” yes.
Shimel: But yet Unravel is about data to a certain degree. I don’t know if everyone in our audience is familiar with Unravel. Why don’t we start there?
Agarwal: Sure. We are unraveling the mysteries of big data, Alan. That’s why the name of the company.
Agarwal: Everybody’s jumping on to big data apps, and what I mean by that is you may be doing something with machine learning, artificial intelligence, IoT, even big-scale data warehousing. And there’s just so many problems that can happen, running these applications at scale, that people just have not seen before. And this is a problem because of all these new systems and technologies that are coming about, such as Spark, Kafka, Hadoop, Cassandra, HBase, many, many more. And the complexity’s all about “Hey, how do I figure out why’s my application not performing well or if it’s failing?” And it’s usually a multi-tenant environment that these big data applications run on, which means there may be hundreds of users, thousands of applications, that you are creating every day and running them.
And figuring it all out and making sure that they run well is a black art, so there’s a lot of trial-and-error that goes on into fixing these problems, finding the root cause of these problems. And that’s what we are automating, meaning Unravel, after it’s installed in your big data cluster, will be constantly monitoring and managing everything. And if there is an issue, Unravel will be proactive about it, tell you why that issue happened, what caused it, and then even take a step further where it would resolve those issues for you so that you can spend time creating all these amazing big data applications, rather than focusing on fixing all these systems- and application-level errors.
Shimel: Mm-hmm. So, you know, I mean, there’s a lot there that we’re gonna dive into, but before we do, Kunal, just give our audience – ’cause I always find this fascinating. People wanna know “How do I become a CEO? How does one get to be the CEO?” Give us a little insight into your own personal journey on how you’ve come to be CEO at Unravel.
Agarwal: CEO is “chief everything officer,” when you first start off. You gotta sweep the floors and you gotta raise the funds and you’ve gotta grow a team. There’s so many things a CEO’s role requires to do, especially in a young company, as you’re growing it. Today, now that we’ve grown to about 75, 80 people in total, across 3 different countries – we actually have offices here in the United States, one in London, and one in India – my job is becoming more about recruiting, more about getting the team together.
And I think that’s, personally, for me, if that is taken care of, the entire company just hums and does well. Everything else is secondary for me at this time, but I do spend a lot of time doing marketing and sales and finance and product. So it really depends on the day, Alan, of what my job requires me to do.
Shimel: Amen. You know, I’ve founded or cofounded three or four companies myself and even just running MediaOps, which is DevOps.com and Security Boulevard, you are chief cook and bottle washer when you’re – you’re chief everything officer, as you said.
Shimel: You know, looking in your background, you’re a double-major graduate – electrical engineering, computer science master’s degree from Duke. I mean, you’re well positioned to be a CEO, people would say, looking at the background, but it’s – you know, you never know what’s coming out next as the CEO. If I had to ask you, Kunal, what’s kind of been the biggest surprise or biggest kind of curveball that you’ve seen in your time at Unravel that you think, “Geez, I didn’t think I’d be doing this or I didn’t think this was part of the job,” what would you say?
Agarwal: Alan, actually, going to school and getting all those double-degrees does not help you at all becoming CEO.
Shimel: You get it. I mean, you’re talking to someone who went to law school. I know all about it.
Agarwal: I wish there was a school for CEO, but you know what? The only school for being CEO and leading a company is actually leading the company because there’s so many things that you learn along the way. One thing, because we deal in enterprise software, there’s so many complexities to making a deal happen with a big company.
Agarwal: And I think that was one of the things that took me a while to grasp and make sure that we’re doing it right. And I mean all the boring stuff, like the contractual paperwork, all the legalities, going through info-sec requirements. All the operational stuff around making a deal happen. You know, when you’re in college and you’re starting to think about a startup, you think about the cool products you’ll make. You’ll think about the impact your product will have in the market and how so easily everybody will flock and come to you and get your product. [Chuckles] But it’s not that simple.
Shimel: I wish it was.
Agarwal: and it takes 9 months to 12 months to close some of these deals, so I think, more than the “surprise” factor, preparing yourself and making sure that you can drive efficiencies in all these different processes. I think that’s been one of the key challenges for us.
Shimel: Absolutely. Absolutely. So, Kunal, if I’m a Silicon Valley VC, I’m looking at a company like Unravel, man, you’re playing in all the right places. Big data is certainly such a huge – I think, when we talk about things like machine learning and AIOps, really, they’re almost the children of big data – you couldn’t have those things without the big data, first of all – so you’re in the right place, obviously. The right time. This is a very happening kind of space. What is Unravel doing that is so – what’s the special sauce? What’s unique about it?
Agarwal: Yeah, Alan. So, when we created this company – and by “we,” I mean myself and my cofounder, Dr. Shivnath Babu, who’s actually a professor of computer science at Duke University – we both met at Duke – and the problem we were trying to resolve is one of the most fundamental but also probably the most complex and challenging problem to solve, which is “How does one ensure that all of these big data technologies actually work and deliver on their promise?” And, you know, our approach to the problem was always “Hey, there will be a shortage of experts in the market. Even if there are experts, these problems will be requiring a machine to solve them on time and efficiently.”
So we took the long path of actually creating not just a monitoring tool but a AIOps tool, if you may, which can decipher the problem, connect the dots, and resolve it for you. So it actually took us about three-and-a-half years to build this product or at least the first version of this product. And we moved west to start working with some companies – I lived in New York; he was in North Carolina – and we gave up our cushy jobs, moved to Silicon Valley, because you wanted to work with companies who were using big data tech at scale.
And some of these companies, we still have advisors in, from Zynga, Rocket Fuel, Twitter, LinkedIn, and they really helped us understand the day-to-day issues that come up when you’re running these technologies at scale. And we took a lot of those learnings into our first product. So we were always about solving a core need that teams in big data tech would definitely need. And, you know, they always say, “Hindsight is 20/20,” right?
Agarwal: But we always put a big bet that, “Hey, these technologies – Hadoop, Spark, Kafka, et cetera – will become mainstream and companies will start using this a lot more.” So what LinkedIn was doing five years ago, the biggest banks would start doing it now, and that’s where we are in the journey today.
Shimel: Absolutely. And, you know, it’s funny, full circle, talking about that journey and what you’re doing. That’s how you become a CEO. It’s about being willing to take the risk, giving up those cushy jobs. Right? To do something that you really believe in. And I think that’s what separates CEOs from people who kinda look and say, “Boy, I’d like to do that,” but they don’t actually do it.
Let’s talk about the market. We’re coming to the end of the year. You’re participating in our Predict 2019 panel, which will be December 18th event. You’re gonna be on the AIOps panel, looking ahead. Why don’t you give us a sneak peek? What’s kind of your take on what 2019 holds for AIOps and big data and so forth, Kunal?
Agarwal: Well, yeah, Alan. So AIOps is an evolution of IT Ops or APM or DevOps, where you’re using machine learning to, again, understand all of the data – and this could be monitoring data, instrumentation data – that all these systems and applications are generating to draw insights from.
Now, funnily enough, this is what our customers do with their data set, right? So with some of the biggest banks, some of the biggest insurance companies, some of the biggest health care companies, all Fortune 500. And our methodology of going about monitoring for big data stack was always the same, which is “Why aren’t we using big data technology on our data set, which is machine-generated data, to glean those insights and drive some prediction and actually make things simpler and more accurate?”
So AIOps is also one of the use cases of artificial intelligence and machine learning. Now, just as I’ve said the industry’s evolving, what we predict to see is the evolution of AIOps, so Unravel, for example, is already on one of the end-of-the-spectrum places, where we actually make decision on behalf of the operations and the development teams, whether it is re-evaluating an application, whether it is making sure that your SLAs for your applications are being met, by moving low-priority jobs to a sub-optimized queue and high-priority jobs to a priority queue or killing bad applications in the cluster, all on your behalf, to make it more proactive.
But what I predict is there’s gonna be an evolution in just the incumbent APM companies as well, where it’s not just about giving people a monitoring screen or even a native monitoring screen and saying, “Hey, check out all the information we know about an application,” but then I leave it up to you to decide and understand what’s amiss, what’s wrong, and how do you fix it. But I feel we’re already now in the correlation and the insights phase, which is now I can start to connect the dots, with these new software technologies that are out there. But, in 2019, we’ll start to also see some of these companies take the leap to be able to do automated root cause analysis, as well as guided remedies or automatic solutions, to get out of these problems as well.
This industry’s evolving at a very fast pace, so I’m super excited. And it’s all about making sure that some of the biggest applications in the world, which millions of people actually rely on, all work like a well-oiled machine. And it’s all about guaranteeing reliability, which, at the end of the day, improves confidence of the customers using those products, which further fuels more innovation and more growth. So it’s an amazing cycle and AIOps is gonna be a very, very key part of it.
Shimel: No doubt about it. No doubt about it. You know, in many ways, I look back on 30 years in IT and it really is a golden time to be alive, if you’re a developer or ops person or just in IT in general. Right? There’s the possibilities and the tools, and what’s in front of you is so exhilarating, right? I mean, I think back, if I was 20 years old now, 22 years old, coming into the –
Agarwal: So you mean like 10 years ago, Alan?
Shimel: Yeah, something like that. About 10.
Shimel: About 10. And, I mean, just unlimited opportunity. I mean, that’s kinda almost the way I look at it. And, of course, it’s companies like Unravel, right, that are kinda letting these opportunities take shape and packaging them for people to run with. So great stuff. Specifically, Unravel – we only have a couple moments here left, but – so, Kunal, anything on the horizon with Unravel you wanna share with our audience?
Agarwal: Yeah, Alan, absolutely. So, even in big data technologies, there’s so many to cover. We started our life off supporting Spark, Kafka, the entire Hadoop stack, some NoSQL systems, like HBase and Cassandra, and some newer NPP platforms. In 2019, we’re gonna further expand some of those technologies and systems that we cover, especially as we start to see more and more big data workloads move to the cloud.
We’re also seeing a lot of our companies demanding a hybrid real estate management from the cliched “one pane of glass,” that “Can I see everything that’s in my big data environment, whether that’s on premise and on the cloud, together?” So Unravel is launching that support as well.
And, last but not least, we are also creating a solution, from our product, which is directed towards helping companies move from their on-premise environments to cloud environment. So actually using Unravel as a migration project management tool, to help you assess, migrate, and then validate your migration. And, on the assess side, figuring out “What system should I get? Which instance type is best for my workloads? How can I keep under cost? What is the cost of running my big data workloads on the cloud?” to then validating and making sure that your SLAs are met, that you’re under cost and under time, and everything is flowing like a well-oiled machine.
The way we look at it, Alan, again, the way to look at our road map and anything on the horizon is we are here to help our customers in the entire journey of big data tech, so, whatever they’re deploying today and they wanna use tomorrow, Unravel will be supporting that. And this is a natural evolution where companies are going from trying something out to getting something in production, first starting on premise and then going to the cloud, and those are all the shifts and transition points that we wanna hold the hands of the customers that we work with.
Shimel: Excellent. Excellent. Well, Kunal, thanks for spending a few moments with us. As I told you, the time goes quickly.
Shimel: We’re way over time, but it’s okay. Hopefully, people have stayed on till now to listen. I wanna wish you lots of continued success with Unravel. We’ll be checking in on it. Again, a reminder: Kunal will be on our December 18th Predict 2019 virtual event and Kunal will be participating in the CEO panel, taking a look ahead at AIOps in 2019. So join us. You can register for that at predict2019.com.
Agarwal: Thanks, Alan.
Shimel: Thank you. Hello to all of our friends at Unravel. And that’s gonna make a wrap on this episode of DevOps Chat, everyone. This is Alan Shimel for DevOps Chat, DevOps.com. Have a great day.