The Exchanges

Q What made you sort of decide to open source it?

A Yeah, that's a great question. So the first reason was that we saw it as a foundational technology. So it is the heart and soul of, uh, data in a company. And in order for it to become, you know, successful out in the world, which was, you know, one of our ambitions, uh, it needed to be open source because developers like foundational technologies to be open source that they can take, tweak, Contribute to and so on. So that was one of the reasons. The second reason is open source just makes product much more, you know, easier to use, better because a whole community of developers contribute to it. So that was, um, sort of the third reason and, uh, second reason. And the third one was, you know, we just believed that nothing else existed like Apache Kafka and for the rest of the companies to benefit from this, it needed To be open source. So those were some of the reasons that sort of came to mind and we open sourced it under the Apache software foundation. So it was, you know, independently governed that, uh, I think played a role in its overall success.

AI assessment note: “The first reason was that we saw it as a foundational technology.”

Q Was it an easy decision to go start an actual company once Kafka started to take off? Or was that more of a winding journey?

A The decision was, it sort of looked like this, you know, I was in a meeting with my co-founder and we were helping a fortune 500 company with some of their Kafka problems in production. And while my co-founders asked, you know, answering some question, I just sort of was Thinking, hey, you know, if there was a company around Kafka, which there will be, it will, you know, be really sad that the Kafka co-founders or co-creators did not create it. And so that's when I pitched this idea to my co-founders is that, hey, let's reduce our regrets and try to do this. And, uh, that was a pretty easy decision because, you know, we had the context around the product to be, the product was Adopted very, very broadly. And so you, we were starting from a very strong position.

AI assessment note: “that was a pretty easy decision because, you know, we had the context”

Q Something we didn't talk about very much yet is the co-founders in the business. What was sort of the early dynamic there? Was it, were you all very close and it made sense to start a company? And then how did you figure out who's going to do what and who's going to get what title and those types of things?

A Yeah. You know, it, for the success of an open source company, all the creators should be part of it. And when that doesn't happen, you have two companies and they're competing on things they shouldn't be competing on, which mostly comes down to pricing and, and it's just a race to the bottom. So it made sense for all three of us to work together and all three of us to kind of come together and start Confluence. So that was actually an easy decision because we worked very closely. On creating the product in the first place. So there was a lot of experience working together. That, you know, was one of the most important decisions that the founding team took. And the second one was pretty organic. Each one of us had our strengths and, uh, you know, each one of us, uh, were playing a specific role at LinkedIn in the Apache Kafka team. So the roles that we took in the company were just an extension. Of the roles we had at LinkedIn. So it was a very easy and organic decision, but a crucial one that we took.

AI assessment note: “the roles that we took in the company were just an extension”

Q Who was the actual buyer in the very early days? Like who ultimately was the person that could sign and, you know, pay for the product?

A So in the early days, um, and this is probably even true today, is they were very much sort of line of business buyers, mostly because Kafka powered specific but diverse use cases inside a company. So the challenge at Confluent was less sort of convincing each use case that they needed Confluent. It was more engaging with different line of business buyers, You know, bringing them together on a central platform and convincing and, you know, sort of helping them scale multiple use cases or gradually over a period of time inside the companies. It was, you know, the reps sort of spent a lot more time on this sort of land and expand motion and bringing different buyers together over a period of time until Kafka or Confluent became sort of the de facto System for all the real time applications in a company.

AI assessment note: “they were very much sort of line of business buyers”

Q you go to take the thing that worked the first time and apply it in a new context, it doesn't necessarily work. But because a company was so successful, it tends to lead to people having a strong point of view on things. And so do you think about that? Like, what can I take and what do I have to just go unlearn or relearn or start from scratch?

A Yeah, I'm very much so. Um, and the reason is because A, it's not only a completely different market and a completely different buyer, but it's also, you know, it's different in the sense that now I'm not building an open source based company and it's a traditional sort of closed source based SaaS product. So the world is a completely different. So going into it, I was very aware that it's going to be a different zero to one. It's probably going to be a different approach Scaling the business. So really needed to be aware of what needs to be replicated versus what needs to be different. So there are a couple of things that I've kept the same, which is, you know, Oslo strategy is very deeply rooted in a customer-centric approach, really is in our DNA. Everyone from engineering to sales engages with our customers directly. Second is long-term vision. So while the immediate market dynamics are essential, I've always believed Just like Confluent did in the power of a long-term vision. Building a platform company. So that's, you know, the most exciting things that is common between Confluent and Ocelor is that both are conceptualized as a platform company. So it allows us to offer like a suite of interconnected solutions and addressing a really large market versus focusing on one aspect of the problem. And, um, prioritizing culture from day one, and this is, you know, something I le…

AI assessment note: “really needed to be aware of what needs to be replicated versus what needs to be different.”

Q So I wanted to wrap up where we often do, which is ask you in your role as a company builder, who are the folks that have had the biggest impact on you? And were there any sort of specific, tangible things that they taught you or insights that you gleaned from them?

A I don't think that anyone specific comes to mind, but what I've, um, done pretty frequently at Confluent is realize that I was a first time founder and just didn't know much about anything on the business side, and I was very proactive in reaching out to leaders at different companies to just learn how they had solved the problem and the problems that they ran into and married that by reading every book there is on that topic and sort of triangulate those two inputs to formulate my own Perspective on the problem and my more, um, you know, own approach to the problem. So sort of, I wouldn't call it mentorship, but I would call it a lot of proactive research that has influenced my thinking around company building. The second thing is just having done it before now, in hindsight, you just learned a lot of lessons from the success of the company, as well as some of the challenges that I experienced Along the way. And, you know, leaders that I now see in the space who have done something special repeatedly, uh, and, you know, reading about them or having a chance to talk to them, that has been, you know, those are, I would say are the three factors that have influenced my thinking as a leader.

AI assessment note: “I don't think that anyone specific comes to mind, but what I've, um, done”