Jay, welcome to our Communicopeia and Technology Conference. This might be your first Communicopeia and Technology Conference, I think. That's right. That's right. The conference itself is only two years old- That's right 'Cause we had the first version of it last. I haven't been here with this name. Yeah, exactly. Exactly. Welcome. Rohan, congratulations, on becoming CFO. How does it feel? Feels great. Yeah. You know, I've been with Confluent for three years. Yeah So a lot of continuity, but excited about what lies ahead. That's, that's great. That's good. Thank you, Gilly, for joining me as a fellow panelist. Gilly and I have worked together closely on Confluent, right from the days of the IPO. So, Jay, I think we, we first met many, many years back. You laid out the vision for real-time streaming, and now you're a public company. It's now close to $1 billion in revenue, et cetera. So just as you rightly visualized this from 2016-2017 timeframe, what is your prognostication as to what you want the company to be like in the next four to five years? What are your goals for the company in the next four to five years? Yeah, you know, the vision we hopefully talked about back in the day, you know, I think there's a lot of continuity, right? With the role we're growing into is to be the central nervous system for data. So that really everything that's happening in the company, there's a real-time stream of that that's available. Any other part of the company, any software system can plug into that, can act on that. You know, we think that's one of the most strategic platforms for data in an organization, and that's something we can build around, you know, over many, many years, right? Right now, we're just getting the full infrastructure stack to harness these streams and work with them. Over time, I think we can grow into a lot of the use cases that are emerging around that. Yeah, I think we're just in this kind of first phase of what's possible, but, you know, I think very excited about what lies ahead. Yeah. And, talk to us a little bit about the preparedness of the company to execute on your vision four to five years. What are the things that you need from a go-to-market standpoint, product motion standpoint, channels, et cetera? Yeah - to help achieve this? Yeah, there's a couple of immediate priorities. I mean, right now, the big focus for us is continue to invest in Kora, you know, our engine, which provides our Kafka service, make that something that's global scale, that's just a utility that can handle any stream anywhere in the world, you know, where it can flow anywhere across, you know, on-premises, different cloud providers, make that available. That's like the foundational layer in what's emerging around streaming. And then the next layer of infrastructure on top of that is around how you connect into all the different systems, you know, how you govern these streams of data, and then how you do real-time stream processing, how you can act on these, build applications that work with real-time data, you know, without having to do a lot of difficult, cumbersome software engineering. You know, we want to make this as easy as relational databases have become. And so those are the investments that are currently in flight. You know, each have products that are in, you know, kind of different stages of adoption. That's that next wave that we're taking out to the market. And the last, you know, priority on our side is really leaning into what's possible with a best-in-class cloud go-to-market motion. Mm-hmm. You know, as the proportion of our revenue that comes from our cloud product has grown, there's a lot more we can do to lean into consumption, to lean into product-led growth, you know, to be able to accelerate how we're taking this out to market, help our customers use it for more things more quickly, do that more efficiently. You know, those are kind of the big priorities at Confluent. Yeah. I apologize, I may have asked you this question six, seven years back, but what was the aha moment that... I know you worked at LinkedIn, you- Yeah ... had this project. What, what did you see as the thing that stimulated you to develop this real-time streaming platform? Yeah, you know, the big observation that was, you know, so weird about the world was, you know, I was working at LinkedIn. You know, LinkedIn is a very digital business, right? It runs 24/7. It has lots of different software systems. They're all generating data continuously. And yet, you know, our way of acting on that data or doing our most sophisticated data processing was to kind of store up all the data in each part of the company, you know, extract it at the end of the day, you know, put it in some central data lake, process it, and ship the results back at the end of the day. The reality was, that was just very difficult, A, to get the data that you wanted, B, to actually act on it in the time that made sense and get it back in front of customers while it still mattered. Mm-hmm. It just didn't make sense. You know, this kind of batch processing, it seemed like some weird hangover out of, like, mainframe computing, where, like, the tape drive would spin and, and so, you know, we felt like surely there's a better way, and we went and looked at all the different technology that was more about, you know, the flow of data, the, you know, the, how you would act on it in real time. And indeed, there wasn't that much. You know, there was these kind of weird niche technologies that would do different types of data movement, but the vast majority of investment had gone into data storage, databases, you know, all the data at rest. And so, you know, it seemed like there was a huge opportunity around data in motion. And so that, that, you know, that was what prompted the thinking. Then it was just a question of, well, how do you do it? You know, how can you make something that can kind of plug all of these different systems together? How can you build something that's a basis for real-time processing? And that was more a continuous learning of, like, looking at what was out there in the computer science literature, looking at what had been done in previous systems, and trying to put together a model that could be successful. And that was what led to the genesis of Kafka, which is an open-source technology that actually predates Confluent and is the basis for our offering. And that kind of sparked a whole revolution in this idea of thinking about data not as something that's just static and stored, but as, you know, something that's a real-time stream. That kind of every activity in a business can be thought of as that kind of real-time stream, generating data all the time, and that businesses can react to that and use it, you know, as it occurs. ... Rohan, shifting to you. I didn't say shifting gears. We have—I've sworn that I will not use clichéd expression, shifting gears, segueing, double drilling, so double—I'm sorry, double click and drilling. We're not going to use these. So, Rohan, turning our attention to you- Mm-hmm. Tell our investor base a little bit about your background. How did you get to be CFO of Confluent? Yeah. So, well, I started my career in more on the, on your side, on the financial services side, and made a pivot into operating finance roles about 15 years back. And in the last 15 years, this is stop number three, and probably the most consequential and meaningful stop for me. And, you know, in the last three years at Confluent, I've joined the company before the IPO and built our FP&A investor relation and treasury teams ground up. And, you know, I was doing a lot of our financial strategy in close partnership with Stefan and Jay over the last couple of years. So this feels like a fairly natural transition to me. Mm-hmm. And as I think ahead, what- It's a natural transition from making money and the pressure of making money every day. What was it like? I mean, so, was it like a... You said, you said it's a natural transition, but- Right. I find it hard to... I mean, it must have been something unnerving along the way, right? "Oh, I don't have to make money every single day, and, so now just do the long-term thing." I mean, what was that switch like? From being on the buy side- Yeah ... or rather to, yeah. Yeah. I mean, like, to tell you the truth, it was 2008 when I was graduating from business school, and probably not the best time- Yeah to get into a job in Wall Street. So, you know, it just kind of made sense- Yeah -to take an operating role. I liked finance, I liked business, and took an operating role, and yeah, that's how I ended up here. That's great. That's great. What are your objectives, or what are Jay's objectives for the CFO of the company? Just as he has this audacious goal for the next five years, as the CFO, how are you supporting the company? Yeah, I think when I, when I take a step back, what's really important when I think probably three things that matter. The first is, you know, driving durable growth. The second one is driving durable growth in an efficient manner. Mm-hmm. You essentially do the second one if you have a real sharp focus on unit economics- Mm-hmm and how you approach your day-to-day. Mm. So, those are our probably the three pillars that how we think about resource allocation— Mm-hmm which is ultimately, you know, thinking about not just the next 12 months, but how do you sustain growth and put in money in the right places over the next three years, to Jay's points earlier, so. Outside of Steffan, who is your role model CFO? Well, I, I'm very fortunate in my 15-year career to work under, I'd say, three very high-quality CFOs. Started at Symantec Corporation, a person named James Beer, who ended up being the CFO at Atlassian. He's kind of a mentor to me. My prior boss, Kathy Bonanno, she's currently the CFO at, Google Cloud, and Steffan. So three- Great ... very probably accomplished people- Yeah that you get to work with and learn from. That's great. That's great. I'm good to hear that. Jay, real-time architectures have been typically hard to develop at scale at an economic price point. So help us understand the unlock that you bring to this market. Yeah. Yeah, you know, that, that's been exactly the dilemma. You know, on one hand, it makes total sense. Nobody ever said they wanted their data, like, slower- Late, yeah ... or, you know, at the end of the day, you know, everybody wants it fast. Yeah. That's the natural pace of business. That's the way application software works. How can you make that kind of easy enough to make it the default? Mm-hmm. And, you know, if you were to go back 10 years ago, people would have told you, like, "Oh, yeah, there's all these fundamental difficulties," right? "You can't get kind of transactional correctness. You can't get the kind of, you know, throughput and cost efficiency. You know, it's too fragile and difficult." I think all those fundamental difficulties have been checked off now. You know, I think a lot of it now is that last mile of really turning this into a cloud service, where you can just kind of consume it on demand, not something you have to, you know, operate and hire people to manage. Mm-hmm. And then really building the tools to build these kind of real-time applications and make that as easy as possible. So like the, you know, investments by us on the stream processing side are really, you know, bringing the same kind of SQL-based tools that people have had, you know, with databases for decades. You know, bringing that into the world of continuous real-time data. Mm-hmm. That, you know, that's the kind of thing that allows organizations to take the skills they already have and apply it to the new thing that's possible. Mm-hmm. And indeed, as that becomes true, this whole area of batch data processing, of bulk data, all that goes away, and it kind of moves into the real-time world, and that's the exciting thing that's happening in our space. Yeah. And then, Jay, I just going off of that a little bit and kind of get entering the conversation here. I'm curious to get your thoughts on, you know, Kafka and that conversion to Confluent, right? So how do you first engage those customers that have elaborated Kafka use cases internally or on premise? And how do... What, what's the technical process in actually getting them to be a Confluent customer? Yeah, yeah, it's a good question. So, you know, when we thought about the company, the open source played a really important role because we didn't want to be kind of going around and starting at the top of the organization and trying to pitch some big vision around data streaming as the new paradigm, and then trying to connect, hopefully, eventually to some use case. You want something that's like a much more immediate fix to a problem that people have. And open source enables that, right? We basically have something that is cloud-native and a complete stack around data in motion, and it's available in all these environments, and we offer this as a service anybody can consume. Mm-hmm. So we can come into an organization that is already using open source Kafka, and there's, you know, hundreds of thousands of those, or one that's thinking about a new project that will use Kafka. And we have a proposition where we have something that really has a more complete feature set, that has a better TCO proposition, where instead of hiring a team to run it, instead of buying a bunch of upfront cloud infrastructure to run it on, you can get a service that's more cost-effective and does more. And that's a really immediate value. And so for that first project, we're already better. And then as this spreads across the organization, it gets better still, right? This is something where the streams of data that are brought in for the, you know, the first use case are often usable by the next use case that's trying to get that same data, and it's very likely that this is the only place you can get that real-time data in the organization. And that, that next use case will bring its own streams, which will attract more use cases, which in turn bring their own streams. So that, that's kind of the process of spinning up this, you know, central nervous system around data streaming in an organization. It's not a big bang, it's something that happens step by step as kind of real use cases make it out to production. Thank you. And then, Rohan, for you, a little bit on that is, you know, as you just mentioned, thinking through these applications, and it does have very real-time value and quick time to value. One of the things I'm curious to get with your thoughts on what you're seeing today in kind of the macro environment. I know that Confluent specifically has been talking about seeing smaller commitments over the last 12 months, and customers getting increasingly comfortable and exceeding those commitments. And how does that, how is that trending today, and how does that also marry with, like, the longer-term vision of the company and the overall vision you guys have? Yeah. Talking about the macro environment, I mean, you know, over the last 12 months, we're seeing a couple of dynamics that were playing out. The first is, we've been seeing this elongation of deal cycles, which was primarily driven by, I'd say, more scrutiny and the CFOs getting more involved in deal cycle. And how that's panned out is, you know, when you look at the last couple of quarters, we've shared some commentary in our earnings calls around, customers are committing to lower duration deals. The upfront commitment, specifically for cloud deals, is lower. Mm-hmm. Which is not necessarily a bad thing for us, but it shows up in our RPO numbers. But purely from a cloud consumption standpoint, what we are seeing is our customers are consuming more than they are committing. And like I said, it's not a bad thing for us because, we're doing the right thing for the customer, and when you couple that with our net retention rates of the cloud business of 140%, plus good retention of customers, you know, we feel pretty good with respect to the broad setup as we, you know, as we sit here today. Mm-hmm. And then, I mean, I see that you just mentioned the cloud and the rate of growth that you've been seeing, and we started seeing a little bit of a pace of deceleration there. And what do you think, or what are you even evaluating internally and externally to give you conviction that that might re-accelerate or, even, pockets of unlock that you might see in calendar 2024? Well, our last reported quarter, we grew our cloud business 78%. We're very pleased with the results, and we're- He's saying, why not triple digit? Why not triple digit? That's true. He's really excited about the cloud opportunity. Well, yeah, you know, 78% growth and, candidly, at a run rate of $330 million plus. Yeah. So broad brush, the business, you know, did very well. Mm-hmm. As we look ahead, you know, of course, I'm not gonna guide for rest of the year or 2024, but if you look at the cloud business, and Jay touched on it a little bit, our data streaming platform and how we think about monetizing the entire data streaming platform will be very critical. Mm-hmm. Of course, it starts with the Kafka side of it, but then there is connect- Right ... the governance, the stream processing, and our ability to share data across. And I think these are probably, I'd put it in the category of the long-term drivers of the cloud business. And so, you know, those are probably the areas- You don't have those capabilities in the Confluent Cloud that you think in the future that might lead to more primary driver of growth? Sorry? You said those will be the drivers because you don't have those features available robustly. Yeah. Yeah. Each is in a different state of maturity. Uh-huh. You know, I would say with this kind of cloud infrastructure, there's a bit of an arc, you know, of kind of getting it to full maturity, getting it across all the cloud providers, getting it to work with all the networking types. And as you see that, you see unlock. And so we saw the same kind of, you know, S- curve with Kafka offering. You know, we've seen very good adoption of some of these same, you know, features and functionality, and we're now kind of starting to see that in the cloud. So you know, if you think about those three things that Rohan just mentioned, Connect is kind of just hitting that up into the right... Yeah, period in the cloud, and we think that that's gonna be a nice driver through next year. You know, next up is our governance offering that we just released the paid version of that, which has grown faster than any product offering we've ever had. Yeah. And we think that's on a really great trajectory with a lot of exciting features coming. And then last, but far from least... you know, we are launching a Flink offer, and this is in early access. You know, it's a technology called Flink. Yeah. This allows- Right real-time processing of data streams in a very powerful way. And, you know, this is in early access with customers, and we think that that can be as big a portion of our business as the kind of core Kafka offering that we have today. Mm-hmm. We're very excited about where that's going. I would think of each of those as a kind of S- curve where, you know, they're, they're just coming out to the customer base, and, you know, we think will drive growth both, both next year, but, but into the years ahead- Mm-hmm - and be a very substantial driver on their own right, as kind of line items that are, you know, consumption revenue drivers, as well as drivers of the core business. Like, each of these helps bring new streams into the platform- Mm-hmm helps you generate new streams off of that, and so they will generate more Kafka usage as well. Got it. Yeah. So I mean, I think that that really leads to the question I had around Flink and your TAM opportunity. And, you know, we talked, you guys talked about this at your Analyst Day, and just thinking through, you know, the other components of your TAM and what catalyst or unlocks do you see that, to really get there? I know that you just mentioned a lot of those, Connect and Governance and Flink, and how would you see those expanding over time and also maybe even attracting customers? Yeah. Yeah, I, I think there's an amazing opportunity in this space. So, you know, I, I talked about the fundamental setup that, you know, all the investment had gone into data storage, databases. This area of kind of data in motion is kind of largely white space. There had been a connection, you know, like a collection of kind of older point technologies, you know, the message queues and ETL products and application integration layers, and they each kind of solved a little bit of the problem- Mm-hmm ... but in a very limited way. And, when you look at what's happening now, all of these niche categories are coalescing into something that's much more broad and much more powerful, and it draws not just from data movement, but some of the underlying databases that would be used, with that movement. You know, the common pattern people would have is copying a bunch of data into a database, running some batch processing on it, shipping the results somewhere else. And all of that's kind of moving into this continuous kind of data in motion, data streaming world. And, you know, if you put all that together, there's a $60 billion TAM. You know, I think one of the most exciting new data platforms in decades that is emerging. You know, the basis for a whole ecosystem that's emerging around it, of, you know, new technologies to plug into these data streams and use it. I think that's why there's so much excitement about this area among customers, among technologists, just kind of broadly in the industry. Yeah, that, that really leads me to your partner ecosystem and something that you guys have definitely been expanding over the last year or so, even longer. So maybe touch on what's been really beneficial from those partnerships, and also maybe, Rohan, for you, how they've impacted your economics, both on the deployment side and the go-to-market side. Yeah, I can start. I mean, we, you know, have... I would say the early days of strong partnerships across SIs, some of the technology ecosystem around us, and then maybe most importantly, the cloud providers. Mm-hmm. You know, our cloud offering is not, you know, something in its own environment. It's something that's in AWS or in Google or in Azure. So working in a really tight way with, you know, those layers, all of their technologies, top to bottom, and then selling through their marketplace, cooperating well with their go-to-market teams, you know, that's all essential to doing a good job by our customers. And, you know, I think all of those have started to contribute in a meaningful way. You know, you may speak to more of the potential there. Yeah. On the leverage side and how we think about the overall economics, I think in general, if you look at software companies and the partner ecosystem, it helps drive nonlinear leverage as well as top-line growth, if you really get it right. And as Jay mentioned, you know, our partner ecosystem opportunity is probably, you know, in the early innings where we are, pretty early days. And looking ahead, as we think about leverage in the broader business, sales and marketing and go-to-market will be an opportunity, and within that, partner ecosystem will contribute a meaningful part of it over the long term. So yeah, that's how we think about it. So, Jay, I wanted to ask you about TAM adjacencies. I think something that Gilly asked you about, sometimes the Oracle relational database becomes a $50 billion market, otherwise, you have to imagine adjacencies. And the idea is that, what are the other customer problems that you're not solving today, that you could be solving, that are adjacent to the Confluent data streaming platform? What kind of opportunities do you see on the horizon to do things? Yeah, I, I would think about this in three phases, right? You know, the... First of all, this core hub of all the streaming data in a company, that kind of core central nervous system, that's a huge opportunity in its own right. Mm-hmm. And we're pretty early in just capturing that, even just going out to all the existing open source users- Mm-hmm Getting them on our cloud offering. It's not like that open source user base is fixed in time. You know, it's growing, right? Mm-hmm. So that's the first opportunity. The next opportunity is expanding that functionality, like bringing real-time processing to bear, making this, you know, something that it is a central part of your governance strategy for data across the organization, hooking into all the parts and unlocking it.... That's, that's that second wave of functionality that we talked about that's coming out now. You know, if you look at what's next after that, it's really getting into the use cases that people are adopting this for. Mm-hmm. You know, this is a key component in the stack for real-time analytics, for security, you know, for working with kind of IoT projects or things that bridge out into the edge or the real world, projects around logistics, projects around all the kind of intelligence about the operations of your business, projects around, you know, your customer and personalization and how you interact there. I think there's opportunities across all of that for us to add more value and grow into that. Mm-hmm. And of course, there's also partners who are, you know, helping to complete that picture with our customers. So I think about, you know, our growth in kind of three layers, really completing each of those, and each of that is kind of in a different phase. You know, the core of Kafka is, you know, broadly adopted and becoming a kind of de facto standard. We're well into the commercialization journey there. Mm-hmm. That next wave of infrastructure is just kind of reaching the market, and beyond that, I think there's a lot we can pull in on top. Yeah. So as people build custom applications on the platform, does it become easier to upgrade to the cloud, or is it a little tougher? How do you go through the process of, Yeah. Upgrading to the cloud? I think everything we do adds to the argument for our cloud offering. You know, the... As we add more functionality, each of this is more things that the next customer gets- Mm-hmm. out of the box Mm-hmm As they adopt. You know, as we started, we had something that was just kind of a bare bones thing around Kafka. You know, that was already pretty good for a lot of people because they just want something that's managed for them. Yeah. As we've expanded that with more capabilities, it's become increasingly compelling and, you know, there's now many reasons to move towards it. And so, you know, I think that journey continues as we have more functionality. Mm-hmm. Customers will, of course, build directly around the APIs in our platform. Yeah. That's not a bad thing. It's actually a great thing. It's one of the things that makes us sticky and durable, and it's also one of the things that teaches us what to build next. You know, if you look at your smartest customers, whenever they're building some kind of internal layer- Mm-hmm ... around you, there's a good chance that many other customers would need that same thing, and that's something you should turn into kind of a fully supported area of product. Mm-hmm. Got it. Confluent Cloud, where do you see the... I know that at this point in time, we have a paywall. It's for a certain set of use cases. At what point does that become the default cloud or default Confluent product that customers naturally gravitate to? Yeah, yeah- When they do their first evaluation. Yeah, you're asking like, you know, when is our cloud kind of the default starting point? I think increasingly that's happened. Yeah. But you know, I think a lot of this is kind of an overall mindset shift in tech, much more than anything Confluent orchestrated on our own. You know, I think it used to be that customers went and looked at open source things they could kind of download and build a team around and run internally. I think increasingly, that seems like a lot of work, and customers are looking for a managed service that just kind of gives them some, you know, superpower around data that they can have instantly, that's going to be world-class, that's going to be available around the world, that will be operated perfectly, and they don't want to wait for any of that. And so, you know, I, I think that's a shift that we've helped enable with our, you know, in our products by making our product meet that bar. But I think it's a mindset shift that's happening in every techno... you know, every company that builds around technology- Mm-hmm ... that's actually made it more of a pull than a push. Mm-hmm. Go ahead. I had a quick question for you around generative AI. Yeah. Yes. You know- Thank you ... we had the last 30 minutes on, Thank you ... on a software session without talking about generative AI, so I think that's a new record. And I know that it's not necessarily a direct benefit to Confluent in the way that you've spoken about it, but I'm curious to get an update from you guys in the sense of if that's changed the conversations or the nature of the conversations that you're having with customers as they're reevaluating the data that they need and the real-time nature of these LLMs and outputs that they're looking for. Yeah, I think that's exactly the reason this is a driver for us is, you know, companies are looking at the opportunities with these large language models, and they're looking at all the data they have locked up in different systems spread across their organization, and they're thinking about how to put those two things together. And, you know, these AI use cases really start with that data unlock. And increasingly, you know, the way that you would combine the two does involve data that's up to date about your business, about the context of the customer that you're interacting with, and that does require this kind of real-time streaming. So, you know, a good example of a customer that, that has done this, that we shared in a past earnings, you know, was a, a major travel provider that was integrating all the information from their systems about where you were, you know, what flights you'd booked, what was on time, what was late, and be able to combine that with a large language model to answer questions intelligently with their customers. This is a lot better than kind of waiting on hold for a long time to talk to a human. But it's very important that that information be accurate, you know, not hallucinated, that it be up to date with your business. You're seeing a whole set of technologies that we, you know, partner very closely with the kind of vector databases and different data stores that would ingest these streams and try and serve it up for models. You know, that's a very tight partnership and integration we have that's showing up in all of these different architectures to try and enable this next generation of, you know, AI-enabled enterprise use cases. Okay. How important are vector databases and vector search capabilities for Confluent? Do you see yourself getting involved in that? No, right now we're just partnering. You know, there's a broad set of different providers that are trying to add vector indexing, some of the existing databases like Elastic or MongoDB, and then, you know, a bunch of specialized databases, the Pinecone and Weaviate of the world. You know, rather than try and, you know, beat them all- Mm-hmm ... we want to integrate into all of those. If you think about what we're enabling for customers, it's first of all, unlocking data by connecting to all the systems you have, enabling that flow. Mm-hmm. ... being able to process it into the right form at the right time, and then being able to hook into all these technologies so that- Yeah. Even for our customers, they don't have to pick the winner. Yeah. in a very confusing space. Yeah. They can use what works for them today- Yeah. But they can switch in the future if something new comes along. Got it. Any question from you guys? We still have a minute. If you have a question, just raise your hand. Mic's coming up. Yes, there's a question here on the other side of the room. Here. Yeah, there you-- Thank you. Hello. Thank you very much for coming to the forum and spending the time. I have a question on the competitive landscape. So I think, like, just recently, MongoDB has announced their own streaming product, and then I think there is a smaller player called Redpanda, which are claiming to be cheaper than Confluent. So it seems like there are a bunch of people coming at, you know, a different angle into this market. So would love to see... love to hear how you think about their entrance and how does that impact our business down the road? Yeah, yeah. You know, honestly, broadly, it hasn't had much impact. The, you know, it's natural as an area gets big and exciting, you're gonna see more people try and get some part of it in different ways. You know, there, there's been a series of kind of early startups, including Redpanda, that are just kind of at the beginning of the monetization journey, that will have products in the space. There's been a sequence of those that would include technologies like NSQ or Pulsar. You know, most of them haven't really gotten to the level of traction that Kafka has, and none has gotten anywhere close in terms of commercial scale to Confluent. In the stream processing space, you know, this is an area where we partner very closely with MongoDB. You know, there's very commonly architectures where data is flowing from us into them. There's processing that can happen, you know, on both sides. The power of our platform and our model is twofold, right? To make this kind of streaming data easy, you know, easy enough to make it the default, it's really important to combine the processing with the stream itself. That's what gives you the transactional processing guarantees. That's what makes it easy to kind of secure end to end. That's, you know, what has made databases so successful, right? Database combines storage with data processing. In the streaming world, it's about combining the stream with the stream processing. So I think that's something that pulls that into our layer. I think the other thing that naturally encourages this is that data doesn't just go to one place, it goes to many places, right? Data goes to MongoDB. It may also go to a data warehouse or a data lake or to a generative AI service, or out into different SaaS services. And so when you think about the kind of cleanup and processing of data, it doesn't really make sense to try and replicate that in each of the destinations. It's much more likely that that pulls upstream so that these reusable streams can flow to all of those destinations. Those are you know, the two biggest reasons that the stream processing world has maintained its very close attachment to Kafka, and, you know, the reason I think that'll be a very successful part of our business as well. On that note, we're out of time, but thank you for the question. Thank you, Jay and Rohan, for attending the conference. Thank you so much. Thank you. Good to keep the discussion. Mm-hmm. Much appreciated, and have a wonderful rest of this evening, and thank you.