 Welcome, everyone, to this CUBE conversation featuring Imply. I'm your host, Lisa Martin. Today, we are excited to be joined by FJ Yang, the co-founder and CEO of Imply. FJ, thanks so much for joining us today. Lisa, thank you so much for having me. Tell me a little bit about yourself and about Imply. Yeah, absolutely. So I started Imply a couple of years ago and before trying the company, I was a technologist. So I was a software engineer and software developer, primarily specializing in distributed systems. And one of the projects I worked on ultimately became kind of the centerpiece behind Imply. Imply as a company is a database company. What we do is we provide developers a powerful tool in order to help them build various types of data and analytic applications. We're also an open source company where the company develops a popular open source project called Apache Druid. Got it. So database as a service for modern analytics applications. You're also one of the original authors of Apache Druid. Talk to me. Give me a timeline. Druid's 10-year history or so. What's the big picture? What's been the market evolution that you've seen? Yeah, absolutely. So I moved out to Silicon Valley basically to try and work at a startup because I was enamored with startups and I thought they were the coolest thing ever. So at one point I basically joined the smallest startup I could find. It was a startup called MetaMarkets, which actually doesn't exist anymore. It was ultimately acquired by Snapchat a couple years ago. But I was one of the first employees there. And what we were trying to do at the time was we were trying to build an analytics application, like a user-facing application where people could slice and dice various types of data. At the time, the data sets we were working with were like online advertising and digital advertising data sets, which were very large and complex. And we really struggled to find a database that could basically power the kind of interactive and user experience that we know we want to provide our end customers. So what ended up happening was we decided to build our own database and we were like a three or five person shop and we decided to build our own database. And that was Druid. And over time, we saw many other types of companies actually struggle with a similar set of problems, albeit with very different types of use cases and very different types of data sets. And the Druid community kind of grew and evolved from that. And in my work in engaging with the community, what I saw was a market opportunity and a market gap and that's where Imply formed. Let's double click on that. You talked about why you built Druid, the problem you were looking to solve, but talk to me about the role that Imply has. Right, so Imply is a commercial company. What we do is we build kind of an end-to-end enterprise product around Druid as the core engine. Imply provides deployment, it deploys management, it provides security, it also provides visualization and monitoring pieces around Druid as the core engine. What we aim to do at Imply is really enable developers to build various types of data applications with only the click of a few buttons and interacting with a simple set of APIs. So the goal is if you're a developer, you don't have to think about managing the database yourself. You don't have to think about the operational complexity at the database, but instead what you do is just work with APIs and build your application. So then what gives Druid its super power? What makes Druid Druid? Yeah, so Druid, the easiest way to think about it is it's like a really fast calculator and it's a very fast calculator for a whole lot of data. So when you have a whole lot of data and you want to crunch numbers very, very quickly, Druid is very good at doing that. And people always ask me this question, which is what makes Druid special? And I always struggle with it because it's never like just one thing. It's actually like layers upon layers upon layers of engineering. You start with fundamentals of how you maximally optimize the resources of any hardware. So how do you maximize storage? How do you maximize compute? And then like there's a lot of optimizations around how do you store the data? How do you like access that data in a very fast way once it's stored in order to run computations very quickly? So unfortunately there's no like silver bullet about Druid, but maybe I can summarize it in this way. Druid is it's like a search system and a data warehouse and like a time series database all mixed together. And like that architecture enables it to be very, very quickly. And unfortunately if you don't know what some of the components I'm talking about are, it's hard to describe where the secret sauce is. Sometimes you want to keep that secret sauce secret. Talk to me about the overall data space. As we see these days, every company is a data company or if it's not, it needs to be successful. Where does Druid fit in the overall data space? Give us that picture of where it fits. Yeah, absolutely. So it's pretty interesting that you see now in the public markets as well as the private markets, some of the hottest unicorns out there are actually like data companies. And I think what people are understanding out for the first time is just like how vast and complex the data space is and also how large the market is as well. So for sure, like there's many different components and pieces of in the data space and they oftentimes come together to form what's known as a data stack. So data stack is basically kind of an architecture that has various systems and each of these systems are designed to do a certain set of things very, very well. For example, a company that recently went public is a company called Confluent, which is like mostly catered towards data transport to getting data from one place to another. They're built around an open source engine called Apache Kafka. Data Bricks is another mega unicorn that's going to go public. It's like pretty soon. And they're built around an open source project called Spark which is mainly used for data processing. What we see is on the data query side. So what that means is we're a system in which people can store data and then access that data very, very quickly. And there's other systems that do that but where our bread and butter is, is when you're building some sort of application, when you have end users that are clicking buttons in order to get access to data, we're a platform that enables the best end user experience. We return queries very, very quickly with a consistent SLA. We immediately visualize data as soon as it's made available and then we can support many, many, many concurrent end users trying to access the system at the same time. So real-time, one of the things I think that we learned during the pandemic, one of the many things is that access to real-time data, it's no longer a nice to have. It is table stakes for, as I said, every company these days as a data company. So with how you describe it, how should people think of Druid versus a data warehouse? Yeah, so that's a great question. And obviously data warehouses have been around since the 70s in the B2B space. They're among the largest players that kind of exist in enterprise software. So it's only natural that when you come up with sort of a new analytics database that people compare with what they already know, which is data warehouse. So a lot of how we think about why we're different than data warehouse goes back to how I answered the previous question and that we're focused right now really on powering different types of data applications. Data applications are UIs in which people are really accessing and getting insights from data by clicking buttons versus writing like more complexity queries. And when you click buttons and you get access to data, what you want in terms of an end user experience is you want answers to questions to come back almost immediately. So you don't want to like click a button and then see a spinning dial that goes on for minutes and minutes before and as it comes back, you basically want results to come back immediately. You want that experience no matter what types of queries that you're issuing or how many people are issuing those queries. If you have thousands, if not tens of thousands of people that are trying to access data at the exact same time, you want to give them a consistent user experience like Google, which is one of my favorite products. So like there's millions of people that use Google and ask questions and they get their answers back immediately. So we try to provide that same experience but instead of a generic search engine, what we're doing is we're providing a system that basically answers questions on data and users get a very interactive and fast experience when asking questions. And that's something that I think is very different than what data warehouses are primarily specialized in. Data warehouses are really designed to be systems in which people write very large complex SQL queries. They might take minutes or hours sometimes to run, but like the experience of using a data warehouse to power an application is not a great one. So I'm just curious, FJ, in the last couple of years with as I mentioned before the access to real-time data no longer a nice to have, but it's something business critical for so many industries. Did you see any industries in particular in the recent years that were really prime candidates for what Giro would can deliver? Yeah, that's a great question. And you can imagine that the industries that really heavily rely on like fast decision-making are the ones that are earliest to adopt technologies like this. So in the security space and the observability space as well as like working with networking and various forms of backend kind of metrics data, this system has been very popular and it's been popular because people need to triage instance as they occur, they need to resolve problems and they also need immediate visibility as well as very fast queries on data. Another space is online advertising. Online advertising nowadays is almost entirely problematic and digital. So response times are critical in order to make decisions and that's where Giro was actually born. He was born for advertising before it kind of went everywhere else. We're seeing it more in fraud protection, fraud prevention as well as fraud diagnostics nowadays. We're seeing it in retail as well, which is pretty interesting. And the goal of course is I believe every industry and every vertical needs the capabilities that we provide. So hopefully we see a whole lot more use cases in the near future. Right, it's absolutely horizontal these days. So 10-year history, you've got a community of thousands. What's the future of Giro? What do you see when you open the crystal ball and look down the 12 months, 18 months road? Yeah, so I think as a technologist, like your goal as a technologist, at least for me, is to try and create technology that has as much applicability as possible and solves problems for as many people as possible. But that's always the way I think about it. So I want to do good engineering and I want to build like good systems. And I think what the hallmark of a really good system is you can solve all different types of problems and condense all these different problems actually into the same set of models and the same set of principles. And I think that makes me most excited about Girod is the many, many different industries that it's found value and the many different use cases it's found value. So if I were to give like 30,000 foot roadmap, that's what we're trying to do with the next generation of Girod. We're actually doing a pretty major engine upgrade right now in a pretty major overall in our system. And the goal of that is to take all the learnings that we've had over the last decade and to create something new that can solve an expanded set of problems that we've heard from the community and from other places as well. Excellent, FJ, exciting work that you've done the last 10 years, congratulations on that. Looking forward to the roadmap that you talked about. Thanks for sharing what Girod is, the imply connection and all the different use cases where it applies. We appreciate your insights. Appreciate you having me on the show. Thank you very much. My pleasure. For FJ Yang, I'm Lisa Martin. You're watching this CUBE Conversation, the leader in live tech enterprise coverage.