 From San Francisco, it's theCUBE, covering Day2IQ. Brought to you by Day2IQ. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at Day2IQ headquarters. It's a beautiful office space here right downtown. And we're talking about customers' journey to cloud data. We talk about it all the time. We hear about cloud native. Everyone's rushing in. Kubernetes is the hottest thing since Live Spread. But at the end of the day, you actually have to do it. And we're really excited to talk to the founder who's been on his own company journey as he's watching his customers' company journeys and really kind of get into it a little bit. So excited to have Toby Kanab. He's the co-founder and CTO of Day2IQ. Toby, great to see you. Thanks for having me. So before we jump into the company and where you are now, I want to go back a little bit. I mean, looking through your resume and your LinkedIn, et cetera, you're doing it kind of the classic, kind of the classic dream way for a founder. Did the Y Combinator thing? Been at this for six years. You've changed the company a little bit. So I wonder if you can just share from a founder perspective. I think you've gone through four or five rounds of funding, raised a lot of money, $200 plus million. As you sit back now, if you even get a chance and kind of reflect, what goes to your head? What, as you've gone through this thing, pretty cool. A lot of people like these. At least they think they like to be sitting in your seat. Can you share? Yeah, it's definitely been an exciting journey. And it's one that changes all the time. We've learned so many things over the years. When you start out, you create a company, a tech company. You have your idea for the product. You have the technology. You know how to do that. You know how to iterate that and build it out. But there's many things you don't know as a technical founder with an engineering background like myself. And so I always joke with the team internally, this is that I basically try to fire myself every six months. And what I mean by that is your role really changes. In the very beginning, I wrote code. And then I started managing engineers. Once we build up the team, then managed engineering managers and then did product. And nowadays, I spend a lot of time with customers to talk about our vision, where I see the industry going, where things are going, how we fit into the greater picture. So I think that's a big part of it is evolving with the company and learning the skills and evolving yourself. Right. It's just funny because you think about tech founders and there's some big ones, some big companies out there to pick on Zuckerberg just to pick on him. But when you start and kind of what your vision and your dream is and what you're coding that early passion isn't necessarily where you end up. And as you said, your role in more of a leadership position now, more of a guidance and setting strategy and communicating with the market, communicating with customers has changed. Has that been enjoyable for you? Do you kind of enjoy more the, I don't want to say elder statesmen, you're a young guy, but more kind of that leadership, thought leadership role versus getting into the weeds and writing some code? Yeah. Yeah, what always excites me is helping customers or helping people solve problems. And we do that with technology in our case, but really it's about solving the problems. And the problems are not always technical problems, right? The software that is at the core of our products, that's been running in production for many years. And in some sense what we did before we founded the company, when I worked at Airbnb and my co-founders worked at Airbnb and Twitter, we're still helping companies do those same things today. And so where we need to help the most sometimes is actually on education, right? So solving those problems, how do you train up 1,000 or 10,000 internal developers at a large organization on what are containers? What is container management, cluster management? How does cloud native work? That's often the biggest challenge for folks and how do they transform their processes internally? How do they become really a cloud native organization? And so what motivates me is helping people solve problems in whatever shape or form. Right, it's funny because it's analogous to what you guys do in that you've got an open source core but people are I think are often underestimate the degree of difficulty around all the activities beyond just the core software, whether as you said, it's training, it's implementation, it's integration, it's best practices, it's support, it's connecting all these things together and staying on top of it. So I think you're in a great position because it's not the software, that's not the hard part, that's arguably the easy part. So as you've watched people deal with this crazy acceleration of change in our industry and this rapid move to cloud native spawned by the success of the public clouds, how do you kind of stay grounded and not jump too fast at the next shiny object but still stay current but still kind of keep to your knitting in terms of the foundation of the company and delivering real value for the customers? Yeah, I know it's exactly right. A lot of times the challenges with adopting open source in the enterprise are for example around the skills, right? How do you hire a team that can manage that deployment and manage it for many years? Because once software is introduced in an enterprise it typically stays for a couple of years, right? And this gets especially challenging when you're using very popular open source projects, right? Because you're competing for those skills with literally everybody, right? A lot of folks wanna deploy these things. And then what people forget sometimes too is so a lot of the leading open source projects in the cloud native space came out of big software companies, right? Kubernetes came from Google, Kafka came from LinkedIn, Cassandra from Facebook and when those companies deploy these systems internally they have a lot of other supporting infrastructure around it, right? And a lot of that is centered around day two operations, right? How do you monitor these things? How do you do log management? How do you do change management? How do you upgrade these things, keep current? So all of that supporting infrastructure is what an enterprise also needs to develop in order to adopt open source software and that's a big part of what we do, right? So I'd love to get your perspective. So you said you were at Airbnb you're at founders, we're at Twitter. Often people I think enterprises fall into the trap of we wanna be like the hyperscale guys, we wanna be like Google or we wanna be like Twitter but they're not. But I'm sure there's a lot of lessons that you learned in watching the hyper growth of Airbnb and Twitter. What are some of those ones that you can bring and help enterprises with? What are some of the things that they should be aware of as not necessarily maybe their sales don't ramp like those other companies but their operations and some of these new cloud native things do? Right, right. Yeah, so it's actually, when we started the company the key or one of the drivers was that we looked at the problems that we solved at Airbnb and Twitter and we realized that those problems are not specific to those two companies or Silicon Valley tech companies. We realized that most enterprises in the future will be facing those problems and a core one is really about agility and innovation. Mark Andreessen, one of our early investors said, software's eating the world. He wrote that op many years ago. And so really what that means is that most enterprises and most companies on the planet will transform into a software company with all that entails, right? With the agility that software brings and if they don't do that, their competitors will transform into a software company and disrupt them. So they need to become software companies and so a lot of the existing processes that these companies have around IT don't work in that kind of environment, right? You just can't have a situation where a developer wants to deploy a new application that brings a lot of differentiation for the business but the first thing they need to do in order to deploy that is file a ticket with IT and then someone will get to it in three months, right? That is a lot of waste of time and that's when people surpass you. So that was one of the key things we saw at Airbnb and Twitter, right? They were also in that old school IT approach where it took many months to deploy something and deploying some of the software we worked with got that time down to even minutes, right? So it's empowering developers, right? And giving them the tools to make them agile so they can be innovative and bring the business forward. Right. The other big issue that enterprises have that you probably didn't have in some of those kind of native startups is the complexity and the legacy, right? So you've got all this old stuff that may or may not make any sense to redeploy. You've got stuff running in data centers, stuff running on public clouds. Everybody wants to get the hybrid cloud to have a single point of view. So it's a very different challenge when you're in the enterprises. What are you seeing? How are you helping them kind of navigate through that? Yeah, yeah. So one of the first things we did actually, so most of our products are sort of open core products. They have a lot of open source at the center, but then we add enterprise components around that. Typically the first thing that shows up is around security, right? Putting the right access controls in place, and making sure traffic is encrypted. So that's one of the first things. And then often the companies we work with are in a regulated environment, right? Banks, healthcare companies. So we help them meet those requirements as well. And oftentimes that means adding features around the open source products to get them to that. Right. So like you said, the world has changed even in the six or seven years you've been at this. Containers, depending who you talk to, we're around not quite so hot, Docker's hot, Kubernetes is hot. But one of the big changes that's coming now, looking forward, is IoT and Edge. So you just mentioned security, from a security point of view, now your attack surface has increased dramatically. We've done some work with Forescout and their secret sauce is they just put a sniffer on your network and find the hundreds and hundreds of devices that you don't even know are on your network. So as you look forward to kind of the opportunity and the challenges of IoT, supported by 5G, what's that do for your business? Where do you see opportunities? How are you guys gonna address that? Yeah, so I think IoT is really one of those big mega trends that's gonna transform a lot of things and create all kinds of new business models. And really what IoT is for me at the core is it's all around data, right? You have all these devices producing data, whether those are sensors in a factory in a production line or those are cars on the road that send telemetry data in real time. IoT has been a big opportunity for us. We work with multiple customers that are in the space. And one fundamental problem with it is that with IoT, a lot of the data that organizations need to process are now all of a sudden generated at the edge of the network. This wasn't the case many years ago for enterprises. Most of the data was generated at HQ or in some internal system, not at the edge of the network. And what always happens is when with large volume data is compute generally moves where the data is and not the other way around. So for many of these deployments, it's not efficient to move all that data from those IoT devices to a central cloud location or data center location. So those companies need to find ways to process data at the edge. That's a big part of what we're helping them with. It's automating real time data services and machine learning services at the edge where the edge can be factories all around the world. It could be cruise ships. It could be other types of locations where we're working with customers. And so essentially what we're doing is we're bringing the automation that people are used to from the public cloud to the edge. So with the click of a button or a single command, you can install a database or a machine learning system or a message queue at all of those edge locations. And then it's not just that stuff is being deployed at the edge. I think the standard type of infrastructure mix for most enterprises is a hybrid one. I think most organizations will run a mix of edge, their data centers and typically multiple public cloud providers. And so they really need a platform where they can manage applications across all of those environments. And well, that's a big value that our products bring. Yeah. I was going to talk the other day with a senior exec probably from Intel and they thought that it's going to level out a probably 50-50 kind of cloud-based versus on-prem. And that's just going to be the way it is because there's just some workloads you just can't move. So exciting stuff. So what, as you, I can't believe we're coming to the end of 2019, which is amazing to me. As you look forward to 2020 and beyond, what are some of your top priorities? Yeah, so one of my top priorities is really around machine learning. I think machine learning is one of these things that it's really a general purpose tool. It's like a hammer. You can solve a lot of problems with it. And besides doing infrastructure and large-scale infrastructure, machine learning has always been sort of my second baby. Did a lot of work there in grad school and at Airbnb. And so we're seeing more and more customers adopt machine learning to do all kinds of interesting problems like predictive maintenance in a factory where every minute of downtime costs a lot of money. But machine learning is such a new space that a lot of the best practices that we know from software engineering and from running software in the production, those same things don't always exist in machine learning. And so what I'm looking at is what can we take from what we learned running production software, what can we take and move over to machine learning to help people run these models in production? And where can we deploy machine learning in our products too internally to make them smarter and automate them even more? That's interesting because the machine learning and AI, there's kind of the tools and stuff and then there's the application of the tools. And we're seeing a lot of activity around people using ML in a specific application to drive better performance. As you just said, you could do it internally. Do you see an open source play in machine learning and AI? Do you see kind of open source algorithms? Do you see a lot of kind of open source ecosystem develop around some of the stuff? So just like I don't have to have my own data scientist necessarily, I won't necessarily have to have my own algorithms. How do you see that kind of open source meets AI and ML evolving? Yeah, it's a space I think about a lot. And what's really great I think is that we're seeing a lot of the open source, best practices that we know from software actually move over to machine learning. I think it's interesting, right? Deep learning is all the rage right now. Everybody wants to do deep learning, deep neural networks. The theory behind deep networks is actually pretty old. It's from the 70s and 80s. But for a long time, we didn't have enough compute power to really use deep learning in a meaningful way. We do have that now, but it's still expensive. So to get cutting edge results on image recognition or other types of ML problems, you need to spend a lot of money on infrastructure. It's tens of thousands or hundreds of thousands of dollars to train a model. So it's not accessible to everyone. But the great news is that much like in software engineering, we can use these open source libraries and combine them together and build upon them. There is, you know, we have that same kind of composability in machine learning using techniques like transfer learning. And so you can actually already see some, you know, open community hubs spinning up where people publish models that you can just take, they're pre-trained. You can take them and, you know, just adjust them to your particular use case. So I think a lot of that is translating over. And even though it's expensive today, it's not gonna be expensive tomorrow, right? I mean, I think if you look through the world in a lens with, you know, the price of compute stored networking asymptotically approaching zero and the not too distant future and think about how you attack problems that way, that's a very different approach. And sure enough, I mean, some might argue that Moore's Law is done, but kind of the relentless march of Moore's Law types of performance increases is not done. It's not necessarily just doubling up of transistors anymore. So I think there's huge opportunity to apply these things to different places. Yeah, yeah, absolutely. Gonna be an exciting future. Absolutely. Toby, congrats on all your success. It's a really fun, fun success story. We continue to like watching the ride and thanks for spending a few minutes with us. Thank you very much. East Toby, I'm Jeff. You're watching theCUBE. We're at day two IQ headquarters of downtown San Francisco. Thanks for watching. We'll catch you next time.