All right, thanks everybody for joining us. I'm really excited for this one. It's one I've been looking forward to for a bit here. It's a Confluent, a company that I've known for a long time, and we at RBC use it internally here. And so we're excited to have Jay with us today. And joining Jay on stage, Anurag Sehgal. You can introduce yourself in a second, but he runs kinda all things data for us here at RBC. And so I thought, you know, it'd be a great opportunity to not only talk to Jay about some questions, but also get, you know, sort of a kind of an end user's perspective on the relevancy of Confluent and Kafka and Flink and all things data. And so that was sort of the agenda here today. So maybe, Jay, I'll start with you. Let's just start at a very high level. There's a lot of things that we can talk about following the quarter, but, you know, I think your data, your view on data, data streaming and now data processing, it just resonates with, like, a lot of folks. But the question I still get is, like, isn't this just like TIBCO 2.0? Like, how is this so different than the data piping that I think a lot of us who don't have a lot of hair remember? Yeah. I think it's a great question. You know, so a little bit of the history of the data space, you know, there was a bunch of little products in the storage area that really kinda converged around relational databases. Yep. We ended up with something that was very powerful and general and fully featured, could guarantee correctness, could support complex workloads, you know, really a general-purpose tool in that space. But if you think about the areas of data movement or flow, that didn't really happen there. Yep. And so kinda data at rest, you know, converged, powerful, you know, broad platform, ton of computer science that went into it. Kinda data in motion, it's more like a hodgepodge of, you know, 12 different categories. So you've got these kinda message buses, which are fast but very simplistic, you know, maybe not really scalable or able to support the kind of complex processing. You've got ETL products, other kind of application integration, you know, really just a hodgepodge of different categories. If you kinda squint, they're all sorta doing the same thing, which is handling data as it moves, but they all have kind of pros and cons. You know, some are fast, some are able to do rich processing, some are able to kind of scale out, some support applications, some support, you know, kind of custom software, but none do it all. Yeah. The result is you have this kind of weird set of small $1 billion companies that each serve, you know, a portion of use cases. Yep. From a customer point of view, it's, you know, it's really annoying. Like, each piece of data has to be kinda sent through 12 different pipes. Each of those pipes has different limitations and problems. What happened in the streaming space is actually, you know, I think, starting with a revolution in, you know, some of the underlying computer science of how do you think about this stuff. You know, how can we come up with something that is more general, more scalable, is able to provide strong guarantees around the ordering of data, is able to scale to the size of a company? That suddenly means you can have the same framework handle, you know, the integration between applications, as well as the core database changes that are flowing through, and it can handle delivery into analytics systems, it can handle delivery to other things. And it can be a basis for very rich, sophisticated, real-time processing that can start to have some of the same, you know, guarantees that databases would have. And so, you know, what does that mean? You know, it means basically a convergence, right? You're seeing kind of a very similar thing to what happened in relational databases, but now happening not, you know around data at rest, but for data in motion. And that opens up a whole new set of use cases, as well as just making a set of things that companies have always been doing, you know, to connect applications, to connect different data systems, making that a lot easier and, and, able to be done, you know, kind of at scale in organizations. And that's a really important part of the overall evolution in companies. You know, I think if you go back, whatever, 10, 20 years, a lot of the focus was: How do we build an application? But increasingly now, a lot of the problems that companies are dealing with in software is: How does it all come together? Yeah. You know, how do the things happening in this part of the business feed the customer experience over here? And how does that flow into the AI tools that we're trying to build out over here? And how does that come back into our analytics over here? It's now very much, you know, kind of one system that's all interconnected, and how you do that is the next set of challenges, both in the data space, but also elsewhere, that I think companies are kinda grappling with. In the data space, this has been, you know, a big driver of the rise of streaming and of Confluent. Yeah. Maybe I've got a couple questions, more than a couple, on sort of the results from a week or a week or so ago. Yeah. But maybe before, Anurag, for those that don't know you, introduce yourself quickly, and then maybe give your perspective on what Jay just said and maybe how we think about Kafka and Confluent inside of RBC. Absolutely. Hi, everyone, I'm Anurag Sehgal. I lead our client banking data analytics and digital tech solutions at RBC across Capital Markets. I've been with RBC for about 18 months. Prior to that, I was at Credit Suisse, where I also worked with Confluent. We had a really good partnership with Confluent at CS as well. I mean, and I'm sure everyone's aware, but there's just an exponential demand for, you know, data analytics. You know, we talk about GenAI, and, you know, a lot of that has to do with data. As the demand for, you know, analytics has grown, the demand for more real-time analytics has grown as well, right? I mean, there's so much real-time data that you only can get an edge if you're tapping into that data, whether it's real-time client analytics or whether it's alternative data-driven analytics for our research teams, for our, you know, for our clients, or for, you know, if you look at hedge funds or long-only investment managers, in financial services, a lot of them are looking at real-time insights and companies or macroeconomic equations. And so- And by the way, they're doing that because there is so much real-time data available now, either through, you know, mobile apps or through, you know, e-commerce platforms, or through, satellite imagery, or through, you know, everything that's going on around us with IoT devices, connected cars. And, you know, all these devices are generating billions and billions of events that are going somewhere, and they need, like, that sort of data in motion to get to what you really need to see as an outcome from that data. And so, as that demand's grown, you know, really, Kafka as a real-time streaming, you know, service is just continuing to grow in many forms. And so, we at RBC in Capital Markets leverage, you know, almost five different services, as you mentioned, for real-time capabilities. And, you know, we use even within the Kafka domain, we have, we have a lot of teams that are using open source Kafka. There are teams that historically leveraged Hortonworks Kafka, and a lot of teams are now moving to more and more resilient needs that they have, are moving more and more towards Confluent. And so I think because of the resiliency and the administrative capability, and many other wrappers that Confluent provides around Kafka, and that's been the mover for people to move towards it. You know, there's still a long way for us to go because we also have Solace and many other technologies over the last 20 years that have sort of grown exponentially. And so I think there is a path ahead for us in how we think about this space. And as we look at moving more towards the cloud as well, I think Confluent will play an even bigger role for us in the coming years. Super helpful. A couple of comments from the quarter the other week or so ago. You know, Jay, on the call, you noticed there was a particular customer that is moving back on-prem, and I think that surprised a lot of folks. And Anurag and I talked about this yesterday. I think, Anurag, you said, you know, I mean, sometimes it's a negotiation tactic that customers will use against vendors. But maybe on that point of that customer, can you talk about, you know, who has the ability to actually do. I mean, we use Kafka internally, but who has the ability to run completely Kafka sort of standalone without some sort of a paid version? Ultimately, do you think that customer is that a potential win-back customer, when you look at an on-prem platform opportunity? Yeah. I mean, there's still a good chunk of open-source usage in the kind of large tech companies, and so, you know, they have the capability. You know, it may not make sense. Certainly in the cloud, the TCO of these managed offerings, I think, is just much better. You know, building out a team of highly paid engineers to do it all yourself is not that compelling. You know, the movement back into data centers, I don't think that's a broad trend. I think that was specific to this company. We haven't seen that across our customers. Yeah, I do think it's a potential win-back, but this isn't about. You know, it wasn't a decision that was based on Kafka or Confluent. You know, it was a decision about their overall environment, and it was born out of a pretty significant set of outages they had with another vendor, as well as a pretty dramatic change in cost profile they're trying to orchestrate. And, you know, I don't think moving out of the cloud is necessarily a good way to save cost. You know, there's probably a whole other set of companies that are moving into the cloud as part of a cost optimization, but at least in this case, that was the cause of it. Interesting. The other question that I get from a lot of folks, too, is, you know, are there things that Confluent is thinking about doing to make it even more advantageous to move to the paid version? Yeah. In other words, you know, you know, I know you want to try to drive as much functionality in Kafka as you can, 'cause it, it's a great funnel into Confluent, but are there things that, you know, perhaps in the future, you know, might make it even more challenging for customers to sort of trade down to Kafka? Yeah. I mean, the areas where we've built a lot of advantage and continue to invest is, like, first of all, just build a true cloud-native offering. This is particularly true as in the managed service. Yeah. But that ability to have something that just kind of scales up and down elastically, where you kinda just pay for what you use, where you can really treat it as a utility. You know, it may, it may sound like a small thing, but the difference between a Snowflake and a Teradata is really that kind of cloud-native operations. Yeah. Yeah, that's the first pillar, which I think is a huge thing, and it's just impossible to do with a kind of do-it-yourself open source or any, really any other software self-managed offering. The second is, you know, in this space, there is really a convergence of capabilities that is. You know, if you think of Kafka as the pure stream of data, you know, increasingly, companies need all the connectors that plug that in, the kind of real-time processing capabilities, where we're bringing Flink into our platform so you can do kind of rich SQL query, SQL being the language of traditional databases, but also programmatic access to really build rich applications around it. This has become an essential capability for companies as they're working with real-time streaming, and we wanna make that really easy for their engineers. As well as the kind of governance of streaming data across. So that kind of complete data streaming platform, I think that's the other area of difference. And then, you know, making this something that's available across these different environments so that, you know, companies that are hybrid, that have some presence in their data centers, have something in the public cloud, maybe even across multiple clouds, that that can all link together into one fabric for, you know, real-time streaming. Those are all the areas of kind of differentiation. I, I think it's quite substantial. So, like, when I think about, hey, overall, you know, you can have. With any of these open-source offerings, you know, you could have somebody who kind of stays with the open source or, you could compete with different managed services. You know, for us at Confluent, I think the focus is really very much on that managed service area. I think over time, you know, the idea of kind of doing these big data services and systems in-house, I think it becomes less and less appealing to all but the very largest tech companies that really want to kind of, you know, do that from scratch with very heavy investment. And even for them, the question is: Are they really winning out? And I think you're seeing that trend more broadly. I mean, even within the digital native cohort that had, you know, very significant open source Kafka usage, I mean, we are seeing a move of that class of companies into our managed service, and it's because it ultimately isn't that cost-effective to try and, you know, run everything yourself. You know, notwithstanding this one company that's actually moving out of the cloud, I mean, in the cloud, why would you want to try and replicate, you know, one of these massive, multi-tenant services? Like, there's huge, economies of scale of doing this across customers, both in how it operates and your ability to build it. And so, you know, I think that's the ultimate driver, so I think that's a positive force for any of these offerings that are built around open source. You know, traditionally, on-premise, you might look at the overall conversion from open source to paid as maybe being around 5%. I mean, it sounds like, you know, even at RBC, right? There's some Confluent stuff, and there's some other stuff, but, you know, it's really a mishmash. I think in the cloud, that changes quite significantly. You know, I think it is TCO negative, you know, effectively at any scale in the cloud to try and do it yourself, and over time, that proposition becomes better and better. Like, the investment in R&D for a managed service, that goes up, whereas the open source is kind of still just the open source. And for each company to try and replicate that internally, you know, it gets harder and harder to match, right? You know, maybe that was something that was doable when Confluent had 50 engineers working on the cloud service. It gets much harder when it's, you know, 700 engineers working on the cloud service to kind of match that level of investment and the quality of the product and set of capabilities. And so, yeah, I, I think overall, like, we're in a world where the commercialization of open source is going from something that maybe traditionally was kind of like 5% of users to something that's probably gonna be closer to, you know, 90+. Yeah. Maybe excepting some of these very large companies that, for whatever reason, wanna kind of, you know, stick it on their own, but even those, I think we're seeing changing. I think they will, right? Because at the end of the day, like, the question to be asked is, if you. You know, let's say the cost factor is one factor, but beyond the cost factor, is there anything differentiating, or is this just like, you know, why do you need to apply or, you know, hire an engineering resource on doing something and engineering a solution around open source when you have something that is as robust and resilient, right? Like, that's, that's a question that we ask ourselves on every single aspect of this. Even if you look at Airflow or Spark, like, it's the same question- Same thing ... that comes up, right? Yep. So, so that's one part, but then the second part is, okay, well, it's gonna be more costly. Okay, well, when you add up the pieces of maintenance, you know, and vulnerability management and upgrades and like, we don't always think about that as cost- Oh, hidden costs ... when you look at- Yeah ... you know, okay, well, we have people that do that work. But when you actually look at the total cost of ownership of these things, there's no way that, you know, going for something that provides all of that as a service and, you know, takes the headache out, is absolutely the right thing to do. I guess my question for you, Jay, would be more around pricing and how you think about pricing strategy and cloud on-prem, like, how are you evolving that for especially for large clients like us that are in this sort of diverse equation? Yeah, so, especially for the cloud, one of the things we've started to do is, you know, really exactly what you described, which is just work with customers to come up with kind of a shared view of, "Hey, what's it cost for you to do this with the open source, and what does it cost to get the managed service?" And, you know, if you fit in that envelope, it becomes very compelling for most customers of like: "Hey, you know, I get something that has a lot more capabilities, and I get it for less money than I would spend doing it myself on cloud infrastructure and people and everything else." And then, our pricing, we wanna make it clear, you know, what the cost structure is and make sure that we kind of fit within that, and I think that's the, you know, that's the thing that ultimately makes it an easy decision. If it's something where it's better product, more money, or, you know, worse product, less money, then you kind of have to weigh the pros and cons. I think if it's a better product for less money, then that becomes something that's a bit more of a no-brainer. So it's a good segue into. I think something that I. You know, after your earnings call, I think there was a lot of confusion about kind of your go-to-market evolution. Yeah. 'Cause I think a lot of people interpret it as, as revolutionary or massive disruption. Yeah. Maybe can you level set us for what's changing? Yeah A nd maybe more importantly, what's not changing? Yeah. 'Cause I think, you know, the reality of it is, I think it's the right thing to do, and I just think it's, you know, my view, it's less disruptive, and I think the market, you know sort of, you know, read it. Yeah. I think that's probably true. So, you know, all of the cloud infrastructure companies have gone through a change to be more oriented around the consumption of customers. And so the, you know, the first part of that change for us was, you know, really having a consumption model where customers pay us for, you know, what they use, and they have the ability to lock in a commitment to some amount in exchange for discounts. And so we made that kind of business model change, you know, maybe 3.5 years ago or something prior to going public, and that was a very big change, right? Because just how you're taking money from customers changes everything. All your systems change, how your billing changes. Yeah. The change we're making now is much smaller than that but is actually quite important, which is how does our go-to-market internally function? Are they motivated, you know, incentivized around selling the kind of commitment? you know, the, "Hey, you're committed to $X in spend," or are they you know, motivated and pulled around the actual projects and, you know, consumption coming online? And this is a change that, you know, I think all the infrastructure companies have made, to positive effect. And it, it tends to be good for customers because the value for them comes when they realize a successful project, and they're kind of getting value out of it. It's good for Confluent because that is actually when the revenue shows up for us, and so you wanna have your go-to-market focused on that. It's definitely a change in that, you know, it encompasses both sales compensation as well as kind of the systems that, you know, manage and run pipeline and that you run the business by, but it's not a, you know, revolutionary change in our business model or something like that. And it ultimately aligns to what it is that we want to do, which is help customers build a lot of successful projects around the technology and, you know, successfully take those to production. So, you know, I think that's the change we're making. Companies have done this in different ways, but I think all have seen positive impact. And, you know, we factored some disruption into the first half of next year for this, 'cause anytime you're making go-to-market changes, that has impact. But it's not a, like, significant thing where we need to make big personnel changes or we're, you know, somehow changing our business model or there's some multi-year shift. It's just kind of a, ultimately a change in what the end goal is, that the go-to-market orientation. One of the, you know, a skeptic would say, "Is it, is it a demand problem?" You know, "Is it, is it customers just don't, you know, you know, don't wanna make these big commits, and therefore, it's sort of reactionary to what they're saying? Yeah, I think here's what we've seen over the course of this year. You know, I think a lot of these vendors have made this change, and I think some customers did end up over-committing to certain either cloud providers or other vendors. And they were unhappy with the result. We didn't have that problem, so, like, when we look at utilization against our commits, we've consistently seen, you know, consumption above that commit amount. But nonetheless, you know, over the course of this year, I think there was a lot of pressure on IT budgets and a lot of scrutiny. And so if your motion is, "Hey, flesh out all the Kafka, you know, projects that may happen over the course of the year or migrations that may occur," and then size some very large commit that will cover all that for the next two years, and then I bring that to your desk, you know, the first question you're gonna have is like: Well, how sure are we about all of this? Like, are these things really gonna happen? Do we know exactly how much Confluent they're gonna use? And it kind of gets picked apart and gets harder and harder to get done. An easier motion is, you know, prove out the TCO and help bring workloads on and take the commit up, you know, at a customer-driven pace. You know, some customers may wanna commit ahead for a bigger discount. Some may wanna be more conservative 'cause there's risk in their own, you know, timelines and plans, and letting them drive that is much more successful. How does that help us? Well, it basically orients our go-to-market around the useful thing, which is actually finding these workloads where we can add value making sure that we're attached to those, making sure that they succeed and get out there. That's the thing that ultimately is gonna be valuable to our customers, and it's the thing that ultimately, like, brings Confluent the revenue. If we're spending all our time negotiating some big upfront thing we're just slowing down the rest of the works. I think to a customer like RBC, we may not even notice a difference, but I guess when you hear Jay talk about that philosophy of the incentivizing sales, how does that resonate with you, Anurag? I mean, it totally resonates, right? Like, if you think about any of the, you know, tech partners that we operate with, the early stages of that, there's no way we're gonna commit to a multi-year deal. We have to get familiar with actually, like, leveraging the technology, understanding the partner, being able to influence the partner on their product roadmap, having a stronger relationship overall, and being able to sort of crawl, walk, run, right? And be able to scale from one program or product to multiple products. And so you can't really think about multi-year commits upfront. Like, that just doesn't make any sense. So invariably, we take that approach, crawl, walk, run, and at some point, as we are starting to get the jog a bit, we start thinking about, "Okay, how do we scale this relationship now?" Because we think there's significant demand for us for this capability and that we've actually, you know, penetrated training and awareness and, you know, knowledge sharing across this capability. And so I think that's absolutely the way to go. I mean, you would never be able to commit to something upfront for many of these things. So it seems to me that the way I interpret it is that it's a potential accelerant of new business, but that for some of these big customers that are already strategic, like, they're using most of their commits anyway, at this point. Yeah, I think that's exactly right. So, you know, we, prior to doing this, you know, we went through the first year of this year. You know, we're basically accelerating the next two years and kind of pulling that in. We talked to the peer companies that had been through it, and so each of them saw this as, like, net positive for growth and then net positive in just their kind of interaction with customers. Like, customers are happier you know, higher success rate, et cetera. So, you know, I think there's little doubt about the outcome. You know, when we talk to companies about, like, how disruptive was the change, that varies. Yeah. And so for some companies, it was very smooth. For some companies, there was you know, a quarter or two where they're kind of getting their arms around the new system. You know, obviously, our intention is to make it very smooth, but when we plan, we wanna be conservative and factor in- And consumption ... you know, some impact from that, and so that's how we've thought about it. Just, you know, we only have five minutes left. We had to jam, like, 30 minutes into five. Competition is one that comes up a ton in my conversation with investors, and we all know, you know, the hyperscalers and some of the cloud data stores, but, you know, even smaller companies like Redpanda comes up a lot. From an investor perspective, could you address, maybe some of those questions around, you know, is the point, like, just take Red Hat. They're, you know, smaller vendor, but, like, how does your technology stack up, you know, to some of these other competitive offerings that, you know, maybe would consider, you know, not down to Kafka, but, you know, maybe an alternative? Yeah. I mean, probably the surprising thing in this space has been how uncompetitive it is, actually. You know, if you look at the database market, it's kind of sliced up into 101 vendors, and there's probably a new database company every six months. The streaming area hasn't been that way, you know. Kafka's been extremely dominant, you know, since this kind of whole paradigm emerged. There's always been something new that comes around, you know? So a few years ago it was Pulsar, and probably the same conversation and everybody would be like: "Oh, this Pulsar thing is gonna kill you, and it'll." You know, and it didn't happen. And, you know, I think it didn't happen for a whole set of reasons, which is the world is kind of standardized around Kafka. The new vendors now have given up on any kind of real innovation outside of that, and they're just implementing the Kafka protocol, which is kind of a weak position to come in from. And at the same time, the set of capabilities that people want is now Kafka plus other stuff. Yeah. You know, to a successful streaming platform is now Kafka, the connectors, governance, and kind of Flink and stream processing capabilities all working together in a holistic way. So it's a much bigger product, and I think it's harder for a startup to go after one slice of that and do it really well, because ultimately people want all those things to really work like one thing, and they want it across, you know, the full spectrum of environments. And so, you know, doing one of those things well is hard and takes time. Doing the full package together is harder and, you know, takes investment at some scale. And doing that across the different cloud providers and on-prem well, as a first-class cloud product and software offering, is harder still. But that's kind of where the value is, and that's what customers need and want. And, you know, I think that's ultimately the, the kind of, barrier we have, is, like, a lot of hard software that we've done and taking that out across the environments where customers needing it, need it to, you know, make them successful. The other thing that I think is really intriguing about the story right now is the opportunity to accelerate growth. You noted a number of things on the call, Flink, FedRAMP, obviously. You know, anniversarying this slight go-to-market change. What are, what are. You know, what would you tell folks out there in terms of, you know, like, if, if, if you stack all these things, Gen AI it goes without saying. You know, what are you most excited about for the future that we're sitting here a year from now, and we're like: "Wow, you know, that was a real opportunity to look at this story a little bit differently with these new opportunities"? Yeah. I mean, look, the, the exciting thing in this area is there's a major new paradigm around the use of data that, you know, wraps up a whole bunch of problems people had and a lot of spend with a $60 billion TAM that is really just coming into being now and is getting broad and rapid adoption across virtually every type of company, across a broad set of use cases, and that doesn't happen that often. You know, when you have new kind of data platforms, they're usually either, you know, the next gen of an existing thing, where it's a little better, but it's kind of the same thing you had before, or it's relatively narrow on the edge, and sure, it has a couple use cases, but it's not broad. So this is, yeah, this is one of the few examples where there's kind of deep value across a broad set of use cases. And, I think that's a great position to be in. I think we're incredibly well-positioned in it. And then, as you said, yeah, mechanically, there's a bunch of things coming online in the business that we're super excited about. You know, the kind of rest of the data streaming platform around Kafka, this has seen a ton of early success, and we're very excited about what that brings to the business. Some of the security unlocks for highly regulated customers, you know, FedRAMP in the cloud for, you know, kind of public sector, but a whole set of things across financial services and elsewhere, as you know, a lot of the banks are shifting spend into the public clouds. You know, that's a huge deal for us. And yeah, the kind of AI tailwinds is probably harder to put a you know, a firm number on, but, like, the reality is there's a ton of investment in data flow and making use of data and a whole new set of applications that are kind of springing up in our customer base around that, and I think that's very exciting. So yeah, you know, there's, you know, a couple different moving parts, but I think that set of tailwinds is really impressive. So yeah, maybe just to wrap up here in the last minute, obviously, the reaction to the print was pretty extreme. I think a lot of people, it doesn't. You know, when I talk to investors, there's still this very bullish view on, you know, you being an important conduit for the future of data and movement and everything that sits on top of that data streaming process. Yeah, you know, if we were to sort of, like, you know, sort of wrap with, like, a final comment on, like, your bullishness towards the future, and that, you know, this is not just this decelerating growth story from here, how would you leave it with investors around the future? Yeah. Well, look, I talked a little bit about the market. You know, I think that's a really unique opportunity, and I think we're uniquely positioned to go get it. Like, there's no other company that's as well positioned as Confluent to really capture that by a lot. And so the. You know, with any new market, of course, there's more uncertainty, and there'll be a tendency to draw, you know, a line between the last two data points and, you know, project it out to infinity. I think that's natural because it's not an established thing yet, but I think at this point it's becoming established enough. And so you can talk to the customers, you can talk to the users, you can talk to the technologists, you can hear about the use cases. Yeah. You can look at the overall growth in the space and kind of convince yourself that there's something really remarkable happening here, and that that's a foundation for, you know, a major new data platform and a ton of innovation around that over time. I think if we get that right, that's an incredible opportunity for Confluent to grow for, you know, decades to come. 30 seconds, Anurag, does that resonate with you as an IT professional? Absolutely. Yeah. Absolutely. Absolutely. I mean, look, I think we're gonna talk more with Jay and team on some of the consolidation opportunities we see, and I think there's a lot to be done here. Excellent. Well, that was the fastest 30 minutes we needed to 2x that. But Jay, really from all of us at RBC and Anurag and the IR team, which is somewhere around here, maybe in the back. Thank you, guys, and best of luck to you in the future. Yeah, thank you.