All right, let's get started. Hi, everyone. I'm Pinjalim Bora, SMID Cap software analyst at J.P. Morgan. I'm delighted to have here with me, Jay Kreps, CEO, Co-founder of Confluent. Jay, welcome to the conference. Thanks for having me. Let's start with a little bit of intro. Maybe briefly introduce yourself and maybe talk a little bit about Confluent for the people in the audience who might not know about it. I'm Jay Kreps. I'm the CEO, one of the Co-founders. Confluent is about eight years old. It was founded around an open source technology called Kafka that I helped to create when I was at LinkedIn. You know, Kafka is a piece of data infrastructure, but it's a little different from most data infrastructure. Like most things that are about data are about kinda how do you store the data, how do you take some pile of data, keep it safe, look up the right bits at, you know, some point in time. Confluent is more about the flow of data. It's about how does data go between things, how do you react and respond to what's happening now in real-time, how do you connect all the parts of an organization. That's a huge part of the data challenge, and it's one that was largely ignored in the early days of data. Early on, it was really about just one app that kinda doesn't have anything it needs to connect to. This, you know, increasingly as software has become a big part of companies, this has become a huge part of the challenge. You know, we founded Confluent to really go solve this problem in the world. You know, we offer a software product and a cloud service that allows our customers to really harness this new paradigm around data in motion. Yeah, that's the 30-second spiel of what Confluent is. maybe double-click on that, on the secular trends. Yeah ... that are kind of driving that change in data architectures, the need for data in motion, right? Just to level set. Well, you know, you can imagine the early days of the adoption of software, it's really about this one app here used by these people, this other app here used by these people. Increasingly there's a whole set of things that are, you know, really driving the scope of software. You know, kind of data and software is kinda moving outside the walls of the company with IoT. You know, it's increasingly driving the, you know, interactions with customers, with the kind of digital customer experiences. It's driving efficiency and operations. There's, you know, machine learning and AI that is kind of increasing the scope of what's addressable by software and where it sits. All of that means that data about the company needs to be not just in one place, but in many places, you know, and up to date and in sync with what's happening in the business as it operates, you know, throughout the day all the time. You know, that's a very different way of thinking about data and software and architecture. You can think, you know, in some sense it's much more like, you know, there's a software side of companies that needs to be fully interconnected. So one metaphor for this area is that it's kind of like the central nervous system. It's the thing that allows you to connect all the parts and, you know, have real-time intelligence on what's happening and act on that as it occurs. You know, those forces have all driven, you know, the rise in popularity of the open source. This is something that's adopted by hundreds of thousands of companies and used as part of their production stack. It's something that's really taken it to scale in some of the largest and most technically sophisticated companies in the world. Really out to the long tail of, you know, all kinds of companies that are really thinking about how to harness data for their business. Yeah, that's a good overview. The two letters that we are hearing nowadays again and again is AI. Yeah. It's a law that any conversation around technology has to come back to AI within five minutes. Exactly. We should talk about it now and then, you know. Get it all out there. ... we'll have five minutes, then we have to come back. Yeah. Wanted to ask you about kind of the significance of real-time data streaming in this age of AI, right? How do you view it? Do you feel like it is actually an accelerant to kind of the real-time streaming movement? Yeah. Yeah, it absolutely is. You know, actually one of the early use cases for the technology at LinkedIn was, you know, not for generative AI, but really powering these kind of machine learning driven applications for relevance, for customer experience. You know, if you look at what's happened recently in this area, the scope and capabilities of that have, you know, increased exponentially. You know, what's the role for streaming? You know, it's really about how a company can take something, you know, like a large language model that has a very general, you know, model of the world and combine it with, you know, its information about that company and about their customers right now, and be able to put those things together to do something for the business. You know, a concrete example of this that I've talked about in the past is, you know, for a large travel company, you know, they wanted to have a chatbot that was interactive and for their customers. You know, this has been the kind of thing lots of companies have tried in the past, but the chatbots were always pretty bad. It's like interacting with like the stupidest person that you've ever talked to. Now you can actually do this really well and, you know, what do you need to make that work? You need to have, you know, kind of the real-time view of all the information about them, their flights, their bookings, their hotel, are they gonna make their connection, et cetera. You need a large language model which can take that information and answer you know, arbitrary questions that the customer might ask. The architecture for them is actually very simple. They need to put together this real-time view of their customers, what's happening, where are the flights, what's delayed, what's going on, and then they need to be able to call out to, you know, really just a service for the generative AI stuff, feed it this data, feed it the questions from customers, and they can integrate that into their service, which is very significant, right? This is a whole new way of interacting with their customers, and I think that that pattern is very generalizable. You know, when you think about how are companies gonna harness this stuff. It's about taking what you've got, your data, combining it with that model, and being able to, you know, integrate it into some of the ways that you do business, some of the interactions both externally and internally within a company. Have you, have you seen, your customers kind of bring up Confluent in this context as they're thinking of building? Yeah. Yeah, absolutely. Yeah, absolutely. You know, I, I think we're, you know, we're maybe a little bit less well-known in this space, but I think kind of a, an obvious beneficiary when you think about how does data move and what's one of the driving things that's gonna come out of this movement, it's gonna lead to, you know, much more integration of data across the company. I think we're increasingly a de facto way that that happens. You know, kind of a safe bet, in the architecture for the future. Among our customers, they're kind of looking to this architecture, and, you know, as one of the things that's gonna help set them up appropriately, for what they need to do in AI, especially given the uncertainty at other parts in the stack. You know, how are you gonna get this model? How are you gonna train it? Vector databases. There's like four or five things which are all kind of changing all at once. One thing that's not changing is they know they're gonna be able to harness information. You know, they're gonna have to harness information from all over the company to take advantage of that. Yeah. The, I guess the second part of that question is: how are you implementing generative AI within the platform itself, right? You introduced Stream Designer, which actually lowered kind of the learning curve. Now English has become the query language, further democratizing, I guess. Do you think generative AI within Confluent actually drives the usage of Confluent, making it much more easier to use? Yeah. It, it does. Interestingly, you know, it is already. You can actually go to ChatGPT and say, "Hey, I want a Kafka producer that uses the Confluent Schema Registry in this different way. You know, give me the code for that." It does it, 'cause there's a ton of Confluent code out there. Yeah, it actually makes it easier to just kinda get going with that basic starter code, and that's totally non-hypothetical. You know, is there more that we can do to kinda integrate that into the experience and take advantage of it? Yeah, there probably is. You know, it's already possible today because the technology is so prevalent. It's just actually out there in the training set that this stuff is built on. What should we expect from Confluent's product going forward? Is there a roadmap to include generative AI within it? Yeah. Yeah. We'll do some, you know, some light integrations. Our focus is less gonna be like training, you know, large language models where there's other people focused on that. It is really more, how do you get the right data into the right place, into the right systems to actually take advantage of that stuff? One flip side to that, lowering the learning curve is, of course, kind of maintaining open source Kafka, right? You know, bears might say, "Well, that might lower the learning curve of maintaining open source Kafka as well, which might be actually negative for Confluent," something like that, right? How do you answer that question? I don't think that's a huge concern at all. you know, it's true that you can bring to bear a lot of data to, you know, run big managed services more efficiently, but to do that, you have to actually have the data about running thousands of clusters and the, you know, the number of companies that have that is kind of us. We're actually quite sophisticated at how we drive operational efficiencies inside our cloud platform today. You know, I think the opportunities for that increase. You know, I think if you look at the kind of Q&A questions that right now are getting unlocked, that's really on the developer side, like people building against the technology. yeah, I think it doesn't reduce the desire for cloud or managed services at all. If anything, I think it increases the consumption of that. Yeah. Lastly, I'll shut up on AI, right now, but, how are you using it within Confluent to drive efficiency? Yeah. Yeah. You know, we're just starting on this. There's a ton of opportunities. I think this is true of any enterprise company. You, you know, We have a lot of teams that, to some extent, are kind of text in and text out and, you know, that's true of our engineering team, it's true of our legal team, it's true in large part of our support team. People always say it's true of your sales team. I think in reality, the sales team does a whole other set of things as well. Sure, there's definitely some content creation as well. I think there's opportunities across all of that. I think there's opportunities to kind of augment with our unique data. You know, it's still unclear how we're gonna consume this stuff. you know, it'd be nice to have it baked into some of the tools we already use for like contract management rather than us having some separate workflow. yeah, I think there's an opportunity for companies like us to be, you know, more efficient as a result. Yeah. moving on from AI. For five minutes. Let's talk about cloud, right? Cloud is kind of where you are going. We as investors get mired with kind of the sequential growth every single quarter. Cloud was $50 million run rate 2 years ago, now it's almost $300 million run rate and scaling pretty rapidly. Talk about, I mean, going into this year, are you completely leaning in on the R&D side for cloud? Sales compensation, have you changed anything to kind of lean in on cloud as well? Yeah. We've been leaning in on cloud for a while, and that's gone really well for us. You know, I think in the business that we're in, it's ultimately about connecting the different parts of a company, so we don't get to be choosy about our customers' environments. You know, we knew at the start of the company we would have to support the applications that were in on-premise data centers. We would have to support the applications in AWS, in GCP, and Azure, and then most importantly, they have to all kind of connect to each other. You know, to make that happen, we leaned in really heavily on the managed service. You know, we were committed to really building something that was, you know, thought through as to what does it mean for data streaming to be a cloud service. You know, with really significant investment, even as, you know, a very small company, and that paid off. You know, I think our cloud product is doing really well. I think we've just, you know, very significantly made that shift. It doesn't mean the software offering goes away. It's actually still really important for a lot of our customers when they think about their architecture for cloud adoption. It often involves hooking into older systems that are on-premise and being able to span out into some of the newer environments in the cloud, and then often spanning into other clouds as they think about some of the services that other clouds may offer that can augment, you know, maybe their primary cloud. You know, that full setup is actually a huge differentiator for us. When we think about startup companies, it's very hard for them to try and put together a product in this space 'cause it's a hard space, but even harder to get something that's offered across every cloud and on-premise and do all that well. When we think about, you know, the offerings from cloud providers, it's very hard for them to do a multi-cloud or on-premise offering. So that, you know, that ability to just cover the environments where streaming has to be and knit them together into one thing is definitely one of the unique differentiators for Confluent. As investors look at now the Confluent Platform and the Confluent Cloud, would you say it's at parity? You know, we used to hear role-based access is one thing that's not in cloud. I think you have added that already, but is it at parity now? You know, the early part of the company, it was really about kind of achieving parity for cloud. At this point, the cloud product actually has substantially more functionality. You know, there's a set of things you can do in a cloud service that are, you know, just very hard or too difficult to do on-premise. You know, we're actually okay with that. We wanna have something that all works together as one platform. Customers do understand that a software thing is gonna be different from a cloud service. You know, the interfaces for some of our data governance tools that work with streaming data are purely cloud-based. The next generation stream processing technology that we're releasing is cloud-based. Yeah, it's actually, you know, more than parity in that respect. Got it. Is there any difference? This question I get a lot. Is there any difference between the workloads that are using Confluent Cloud versus Confluent Platform, or is it kind of the similar workloads? Yeah, it's broadly similar. There's some differences in the kind of customers. You know, if you look one misunderstanding that I see as common is people often assume that, you know, our cloud offering is just kind of people, you know, the little guys, like, the little use cases, and then the serious people are kinda doing it themselves. That's actually totally untrue. At this point, you know, we have customers, you know, if you look at our kind of total customer count, 100K+, $1 million-dollar customer base, good representation in each of those cohorts. Some of our very largest customers are cloud customers. You know, the kind of TCO of our offering is positive at every scale now, which is actually a really significant thing when you know, offer some product to customers and there's alternatives, it's great if you can have something that has better features, but it's actually better if it's, like, better features for less money. That's actually a really good combination to have. You know, that kind of TCO story versus doing it yourself at this point is just extremely strong. Like, the savings on hardware and people and just what you would put into trying to do this in-house, you know, it's just much better. It's much better when you're getting started, and it's much better when you're already at large scale. Yeah, you know, all of that kinda adds up to it. Yeah. I thought you did a fabulous job in the earnings call when you kinda lined out the, you know, all the TCO chain differences between... Yeah. Yeah. Our last earnings, we did a deep dive on this, and the reason was just it was such a common question from investors, and they kept saying, "Well, you know, in tighter economic times, which we're in, isn't it gonna be the case that all your customers are gonna leave you for the open source?" I kept saying, "No. Like, that would actually be a bad deal for them because it would cost them more." You know, I felt like, okay, I was saying it, but it didn't really sink in as to why. So we did a much deeper dive on, like, "Hey, what is it that a cloud service replaces?" It's obviously a bunch of, you know, servers in the cloud and networking and observability costs and people costs. That's kind of the pool of things that they would otherwise spend. When you, when you run a cloud service, you have a bunch of knobs that build efficiency on that. You know, one is about multi-tenancy and being able to pool and drive high utilization. The other is about just kind of deeply building for cloud systems so that you can build something that's just, like, hyper-efficient at large scale. There's a lot of work that goes into doing that well, really kind of closing some of the feedback loops of, you know, the actual usage, you know, what that we see happening, you know, out in production to be able to optimize the placement of data and customers. You know, there's a whole set of things that you do to kind of drive that, and then all of that adds up to this kind of TCO advantage, where you can take something to customers that is, you know, a better deal for them and a, you know, and a better resulting product. Is there a way to quantify that TCO advantage? You talked a lot of qualitative. Yeah. Yeah. ...laid out, but is there a way to kind of understand, again, like, for, like, workload? Yeah, absolutely. Absolutely. I, you know, I went into this in some detail and, you know, it does differ at different scales. It'll be slightly different, you know, at small scale, a lot of the savings is typically people, 'cause you're gonna have to hire some people even for your first use case just to run it. At large scale, it's gonna be about infrastructure because you're gonna be using just a lot of servers. You know, some of the tidbits that we gave on the operational side, we believe that we have more than a 1,000x cost advantage versus our customers' cost structure. You know, we run, tens of thousands of these clusters in the cloud. We don't have tens of thousands of employees babysitting them. You know, these kind of big distributed systems, typically you would have like a team of people whose job is to, like, babysit the system and make sure it's up and operational and deal with upgrades and monitoring and observability and on-call rotation. We do that with a, you know, a very small on-call team for the system we, you know, that we run. How do we do that? Well, we don't do it by just, like, you know, typing faster. We've actually built a substantially different piece of software that runs our cloud service that's built to run thousands of clusters that's, you know, run by software, not by, you know, humans. That deals with every single thing that can go wrong from slow disks on a server that don't fail, but don't perform the way you want, you know, to how, you know, new changes get rolled out. How can you do that in a way that's safe and that's automated and that's efficient? That, that's where that advantage comes from. When you look at how this plays out for individual customers, we do this analysis based on their cost structure and their usage. A good example of this would be, Michelin. You know, this is a customer that was, you know, running Kafka themselves. You know, Michelin is, they do tires and they do restaurant reviews, and then a bunch of things in between those two, which are, you know, both very different. They're really trying to bring data to bear in their business and how they interact with customers all the way from the manufacturing side to the customer interaction and kind of e-commerce side of things. You know, really spanning the business. You know, they were very serious about just like, "Hey, you know, what's the cost advantage of us doing it ourselves versus not?" You know, I think they said that it was more than 35% savings, you know, in that analysis. Mm-hmm. We've seen similar things even for very technically sophisticated companies. We, you know, we sell in tech, and some of the earliest Kafka users were these tech companies that are at very large scale, and they're good at this stuff. It turns out, you know, those high-end Silicon Valley engineers are not cheap. You know, when you're talking about large pools of hardware, the efficiency advantages add up quite significantly in that area of just how you use networking, et cetera. All of that adds up to a pretty significant savings, you know, as people move to our platform. Yeah. Let's talk about macro for a second. When you're talking about cloud consumption trends, I think you talked about a dip in March and a bounce back in April, right? Has that trend kind of continued into May? Or if you can comment on that. Yeah. Yeah. Yeah. We feel pretty good about, you know, what we gave in the earnings call. We were saying we were expecting kind of a sequential add of cloud revenue that would be between $7.5 million and $8 million. You know, I think that's about what we expected. When you're talking to customers, what's kind of the broad sense of the macro environment at this point? Yeah. You know, it's been similar for us over the last few quarters. You know, we've definitely seen just more scrutiny on spend overall. You know, there's fewer net new software projects happening. You know, for us then that means we lean a little bit more heavily into the open source Kafka conversion, which is kind of the other lever for growth that doesn't require a new project. You know, so that's happening. That scrutiny. You know, it's not, I wouldn't say it's geography or industry specific, though there's definitely some industries that have their own particular dynamics at the moment. You know, we've actually seen ourselves be quite successful through it, but it does tend to, you know, elongate sales cycles. We saw a little bit of shortening of contract duration, you know, in the last quarter. We don't worry too much about the kind of duration, you know, for this kind of production data system. If you're using it, you're probably gonna keep using it. Whether you're committing for 3 years or 2 years is kinda very, very similar. You know, and we saw good growth on kinda CRPO, but you would see some impact in total RPO. Yeah. Want to ask a couple of questions on stream processing since you kind of expanded your time with Immerok, going into Flink as your main kind of stream processing engine, I guess, underneath. I want to ask you in terms of the value a customer gets from stream processing versus using the real-time stream, streaming platform itself, right? How do you delineate between that? You know, the purpose of these streams of data is partially to get data, you know, from point to point, and then partially to be able to act on it, the applications that do smart stuff in reaction to it. There's capabilities within Kafka that'll allow them to, you know, do some smart stuff in their application. What we're doing at Confluent is kind of pulling more of that application logic into our platform and making it easier and easier to build that kind of application. You know, we have a couple of paths for that, but one of the biggest ways is a technology called Flink, which is one of the most, you know, it's probably the second most popular open source thing in the streaming space after Kafka. We're adding that to our cloud offering. We made an acquisition that brought in a lot of the core people in that space. You know, we think that's a great opportunity to extend our reach, monetize more of the application development around the streaming data, and just make it, you know, easier to add more and more use cases in this area. I think it will be an accelerant for us in a number of different dimensions, you know, making it easier for customers to get the thing they were gonna do done faster, as well as allowing us to more completely monetize the application that they were building, as well as creating more incentive, you know, to become a Confluent customer and use our offering. Yeah. Understood. now you do have a few... You had a few stream processing capabilities already. Yep. ksqlDB Yeah. Yep. Kafka Streams as well, right? Yep, yep. How do you kind of think about unifying that from a sales perspective? Yeah. Yeah. You know, Kafka Streams was the kind of capability that was, you know, there in Kafka. It's probably the best, like, embedded in your application solution. This is like a complete framework and now cloud offering that will allow you to kind of just run that application in a fabric which takes over the resilience and scaling and so on. Yeah, what does Flink bring? You know, it's a complete cloud solution, you know, for stream processing as we add it to our offering. You know, it's just more complete in terms of vision. It covers SQL and different programming languages, different interfaces programmers would have for streaming data. It. You know, it's probably the most complete kind of community and technology for accessing streaming data. It was a very natural thing for us to add because it had such a kind of high attach rate to, you know, among our customer base, among the open source Kafka users. You know, we felt we could do kind of a uniquely good job at, you know, adding this into the platform and creating a unified product around it. Yep. Understood. I want to ask you a high-level growth question on kind of sustainability of growth, right? You're in a large market which is kind of gaining relevance. There is a low-hanging fruit of free Kafka users that you can go after. You're expanding the TAM with stream processing recently. How do you think about the organic growth of the business in the medium term, right? You're guiding to at somewhere in the thirties, I think about 30%, for this year. Is that kind of the zone that you think is sustainable for a few years? Would you say the business is in a state of maturity at this point that sustainable growth might be a little bit lower? How do you answer that question? Yeah. I think that there's, you know, a set of tailwinds. You hinted at some of them, right? Which are pretty powerful for us, right? You know, even if you just look at the conversion of open source Kafka users to Confluent customers, we're still in the very early days of that. That pool of open source Kafka users is growing rapidly. You know, if you just look at our NRR, you know, last quarter, even in a, you know, tighter macro, 130%. That obviously, you know, gives you kind of a base that makes sustaining growth easier. You know, we believe that this is, you know, really emerging. This area of data streaming is emerging as one of the major data platforms in a company. You know, both the number of companies that are gonna have it and the importance and prevalence of it within each of those companies is gonna continue to grow. Right now, we have the leading technology in that space by far. You know, all of those are kind of important tailwinds when you think about, "Okay, how long can they keep it going for?" We'll get a little bit more into, you know, some of the modeling and et cetera in our investor day, which is coming up in June. Those who wanna kinda dive into it with our CFO, that's a, that's a great place to tune in for. Sounds good. Let me see, if there are questions in the audience. Can we get a mic? Thank you. Good morning. Jay, one big picture question for you. Just the value prop that you're articulating seems really compelling. Just looking at history, there have been very few big open source companies created, right? Maybe Red Hat and arguably MongoDB are the only two. By the same token, there have been, I think few, if any, big data integration companies created. You know, you look at MuleSoft, Alteryx, whoever you wanna choose. Just looking forward for your path to scale, you know, as an open source data integration company, what has changed that would, you know, produce a different outcome from what we've seen over the last 20, 30 years? Yeah, that's a really good question. I think there's actually two questions there. The first is around open source, the second is around integration. I think the answers are actually different between the two. On the open source side, you know, the big thing that's changed is the cloud, and it's actually a very substantial change in business model. If you look at some of the earlier open source companies, you know, they were effectively trying to create an offering around software that was freely available. If you think about, hey, how can you build a kind of sustainable moat and competitive advantage that allows you to capture a lot of value that others can't if they can have your software? I would say that's actually very hard to do, right? It's not impossible. Red Hat somehow did it, but effectively nobody else did it. If you look at some of the companies that tried, like maybe a Cloudera, you saw kind of exactly what you would expect from like an Econ 101 point of view, which is competitors develop that have more or less exactly the same offering. You know, the price is kind of competed down to the cost of offering it. It basically doesn't work. What's different now in the cloud is actually a cloud service is totally different. It's totally different from a, like, single server software download thing. What runs our cloud offering is a massive chunk of extremely differentiated software. You can read about it. We did like a blog post that dives into this. That back-end engine Kora, you know, it's a phenomenal piece of technology, and it's significantly better. We have a lot of the advantages of open source, which is like, hey, we're doing something quite different. This category is new. If we were kind of going door to door trying to convince people to think about data in a new way, that would be a very difficult, you know, proposition to make successful. Open source really, like, helps us attach to use cases that are real, that are out there in the world, that are happening, and then the cloud service allows us to monetize that. If you look at this next generation of companies that are coming, you know, I think MongoDB has done a great job. I think there's a number of others that are kind of maybe that late stage private and earlier, they're actually doing quite well. It is actually just a completely different business model than Red Hat, even though they both involve some element of open source. You know, we're not selling an offering that's based around support. We're selling an offering that's based around software. That, that's the answer on the, you know, on the open source side. On the integration side, this is another good point. Like, there's been a bunch of technologies that move data in some way. You know, you could look at the kinda TIBCOs that were maybe doing real-time data at small scale, low latency between custom applications. You know, then you would have maybe your Informaticas, which are doing, you know, relational data at large scale, very high latency, you know, like once a day, between databases. Then you would have maybe your MuleSofts, which are integrating, you know, API-driven stuff in real time, not a very large scale, you know, not handling transactional data. It's like each one of these data movement things had a lot of asterisks. It could do parts of the problem, but it couldn't do the general problem. If you think about this particular space, that's actually terrible, right? The whole point is, you know, let's say you're a retailer, and you have the stream of what's selling. Is that going to go into analytic systems like, ETL product would solve? Yes. Is it gonna impact your production applications and need to, you know, go into some of those? Yes. Is it gonna feed into operational databases? Yes. Is it gonna go impact SaaS applications? Yes. You need to reuse that data across all of this. Having, you know, whatever it is, a dozen pointwise technologies that do part of that problem is not good. You know, what's happened in this space is the revolution in distributed computing has allowed a much more powerful approach to this that actually, you know, is real-time, is scalable, can handle integration with batch stuff, and is a platform for, you know, very rich processing of data and application development in a way that none of the previous stuff was at all. You know, that's the answer of how you go from a bunch of little segments that all have, you know, $2 billion in revenue to something that's much more significant. You can see that in both the adoption statistics, in the usage patterns, how it's used, the role in the companies that have adopted this at scale, and then just Confluent growth as well. You know, I think all of those are data points that would support that. Jay, you talk about architecture of the future. Just thinking about that, I get how Flink moves you up into the application stack. How do you think about moving down into the database layer? Yeah. You know, in many ways, you know, can you look at this kind of area of data in motion as parallel to, like, data in rest. Where it was like, you know, kinda file systems, databases, et cetera. I think this stack is very similar to that. It's solving a set of application development needs, you know, kind of the flow of data. Yeah, I do think a chunk of what it ends up taking spend from is these kind of integration technologies. A chunk is also databases, which are used either for batch processing, you know, used for kind of application, you know, development. Some of those are now moving into this kind of real-time streaming world, and that's a chunk. If you look at our TAM, there's a chunk of it that's absolutely attributable to that. If you look at our customers and you say, "Hey, you know, where'd this money come from?" That's the budget it comes from. All right. I think we are out of time. Thank you so much, Jay, for all this. Yeah. My, my pleasure. Thank you, everyone.