Hi, everyone. Welcome to the Confluent Q3 2021 Earnings Conference Call. I'm Shane Xie from Investor Relations, and I'm joined by Jay Kreps, Co-founder and CEO, and Steffan Tomlinson, CFO. During today's call, management will make forward-looking statements, including statements regarding our financial outlook for fiscal fourth quarter of 2021, fiscal year 2021, fiscal first quarter and fiscal year 2022, increased adoption of our platform, our ability and position to capitalize on the shift to cloud, growth in revenue, total customers, remaining performance obligations, and dollar-based net retention rate, our market opportunity, and our overall future prospects. These forward-looking statements are subject to risks and uncertainties, some of which are beyond our control, which could cause actual results to differ materially from those anticipated by these statements. Further information on risk factors that could cause actual results to differ is included in our SEC filings, including our Form 10-Q for the quarter ended June 30th, 2021, and Form 10-Q for the quarter ended September 30th, 2021, that will be filed with the SEC. We assume no obligation to update these statements after today's call, except as required by law. As a reminder, certain financial measures used on today's call are expressed on a non-GAAP basis. We use these non-GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes. These non-GAAP financial measures have limitations and should not be considered in isolation from or as a substitute for financial information prepared in accordance with GAAP. Our reconciliation between these GAAP and non-GAAP financial measures is included in our earnings press release and supplemental financials, which can be found on our Investor Relations website at investors.confluent.io. With that, I'll hand the call over to Jay. Thanks, Shane. Welcome everyone to our third quarter earnings call. I'm pleased to say our third quarter results exceeded our expectation and our guidance on all metrics. The outperformance we've seen is a result of great product market fit, the secular tailwinds driving the adoption of data in motion and cloud, and a lot of hard work by our team. I'll get into these drivers in a moment, but first, let me touch on the numbers. Revenue in the third quarter totaled $102.6 million. This is significant for two reasons. First, this is our first quarter ever achieving over $100 million in quarterly revenue, and we've managed to hit this milestone in less than seven years. Second, revenue growth of 67% YoY represents a further acceleration from the growth we saw last quarter. Growth in Confluent Cloud revenue also accelerated, coming in at 245% YoY, well outpacing the growth of the overall business. Confluent Cloud represents 26% of total revenue in the quarter. Our team has continued to execute well, and we feel very good about our position heading into the fourth quarter, which is reflected in our increased guidance for the balance of 2021. Steffan will elaborate further on the financials in a moment. First, I wanna use the opportunity to talk a little bit more about our space. We have a very technical product, and I imagine many of you are still relatively new to our story. I'll spend a little time today on the data in motion paradigm and our platform before we get into more of the specifics from the third quarter. I think the underlying macro change that is driving Confluent's success is that virtually all businesses are becoming defined end-to-end in software. It isn't just that there are more software applications, but increasingly the core activities of the business, the production and distribution of goods and services, and interactions with customers are driven by software. To do this, these software systems must connect end-to-end to the different parts of the business and operate in real time as the business executes out in the real world. This transformation is really being driven by customer demand. In today's world, consumers expect the best digital experiences from the organizations that they interact with. These experiences go beyond simply a user interface or app, but touch everything from logistics to operations to back-end processes that together create a customer experience. At the core of all the software that drives this is data. Data management has traditionally been about storage, that is data at rest. Databases have long been the mainstay of data management, and storage is exactly what they were built to do. They help you safely store your data and look up or process the right bits as you need it. Now the explosion of software and data means that there are more databases, applications, analytics platforms, and SaaS layers. Increasingly all of these systems need to work together in real time to help operate the business. It isn't enough anymore to have all these systems existing as disconnected storage silos. They all have to integrate in real time. Confluent's role is extending support in the infrastructure layer to cover the other half of data management, the management of data in motion that databases have traditionally ignored. This problem is very different from the world of data at rest. It isn't about storing piles of data for individual disconnected applications. Data in motion requires a platform that helps support the real-time flow and processing of data between applications as it is generated to help drive the operations of the business. This new platform forms a key component of the emerging next gen data architecture, which we think over time will be a requirement for every company. By using data in this way, companies can make it far easier to tap into data from any part of the company and operate on it in real time. The streams of data both trigger custom applications to take action, as well as enabling real-time processing and enrichment of the data and integration into other data systems. Data in motion makes it possible to do this in a decoupled way because of the publish-subscribe nature of the usage. Any part of the business can publish the real-time stream of data about its own operation, and any other part of the business can choose to tap into that stream to react or process it. As a result, these capabilities are critical for companies to be able to build around software and data, which is in turn critical for their success in the modern competitive landscape. In this sense, the rise of data in motion is right at the heart of digital transformation. This kind of real-time architecture has long been desirable, but was long believed to be too difficult for all but the most critical applications. However, our platform makes real-time easy. Indeed, our goal is to make processing real-time data streams as easy and ubiquitous as the infrastructure around storage and batch processing. Our product strategy is laser-focused on this goal. To do this, we're building around three key pillars, being cloud native, being a complete offering for data in motion, and being everywhere. These three pillars represent key aspects of making this new paradigm the new default standard, as well as key aspects of differentiation that separate us from competitors. We think this differentiation is key to our success in driving Confluent's high growth, and in particular, is the driving factor behind the outsized growth of our cloud offering. Let me go through each of these three pillars in more depth. First, being cloud native. Confluent Cloud was architected from the ground up to operate as an elastically scalable multi-tenant cloud service. These capabilities can sound superficial, but a true cloud native service is far more than just putting open source Apache Kafka in the cloud. Cloud services are fundamentally different from solutions that might have been designed for an on-premise environment, and different in a way that is transformative for the customers who adopt them. This flexibility gets customers out of a world in which their infrastructure dictates their pace of innovation. What's more, this difference isn't just added features. It cuts right to the heart of how these systems are designed and built. By being cloud native, we're able to offer the same protocol of Kafka on a platform that is completely serverless and elastic, and also integrates natively with many other software solutions and technologies from the major cloud providers to help customers manage their data easily and securely. Developing these capabilities has required some of our deepest R&D investments and has enabled us to add a feature set that is a generation ahead of any competitor. Our cloud native capabilities are particularly relevant for customers in tech who are often operating at large scale. Two great examples are Square and Gainsight, both of which have turned to Confluent Cloud for their data streaming platform. Square is a rapidly growing financial services, software, and payments organization with an ever-increasing volume of payments and new customers joining daily. They needed a scalable, reliable, and secure data streaming solution. When a merchant receives a customer's order through their website, or they need to process payroll, a lot needs to happen in real time. We're helping power Square's seller business with a number of use cases that connect the point of sale to payment processing to reporting. Gainsight, a SaaS service that helps businesses build long-lasting relationships with customers, uses Confluent for real-time data integration to bring together the insights that help maximize the customer experience and trigger actions that ensure their success. Our cloud native foundation isn't just a factor for larger companies, though. Confluent Cloud is seeing success across customers of all sizes. Our cloud-focused commercial sales organization was one of our fastest-growing segments this quarter, which further speaks to the strong product market fit of Confluent Cloud for digital native organizations. A great example of this is iFood, a mobile food delivery leader in Brazil, which has recently migrated to our fully managed cloud service running on AWS. They now manage more than 4 million orders per month. Everything from order placement to payment processing to delivery ETAs are handled with Confluent to make sure the right food gets to the right customer as fast as possible. The second pillar of our product differentiation is completeness. In order for customers to be able to truly harness the power of data in motion, they need a complete offering that makes developing in this new paradigm easy. Kafka is a great foundational layer for building around data streams, but it's just one layer in a modern data in motion stack. Trying to assemble this stack in bits and pieces and operate all of these is a huge challenge for companies, and that is why we've brought together the essential elements of this stack into a complete unified product. Our product gives customers a robust set of connectors that allow them to plug into the many systems holding data in their organization. We also give them a rich streaming SQL layer, KSQL, which allows them to extend the database skill set their team already has to the new world of real-time stream. One solution which brings together many of these capabilities is helping customers migrate and modernize their analytics platform. We recently launched our first priority partner solution focused on cloud data warehouse modernization. A key challenge for organizations is feeding these modern warehouses with data that may come from cloud environments or legacy systems on premise. As part of that, organizations need a solution that connects data from any system while maintaining the flexibility and security to work across multi-cloud and hybrid environments. Our cloud data warehouse modernization solution enables customers to extract data in real time, transform it into the right structures to support analytics, and transport it across cloud environments and regions to any one of a number of next generation analytics technologies, including Google BigQuery, Amazon Redshift, Snowflake, or Azure Synapse. This solution truly illustrates the completeness of our platform. It is built on our ecosystem of over 120 connectors and our stream processing capabilities that make it easy to capture, transform, and transparently deliver real-time data streams from existing databases, infrastructure layers, and SaaS APIs. How we take this solution to market is a great example of our partnership with the major cloud providers. The ability to unlock data for ingestion into these next generation data warehouses is a key blocker for the growth of these next generation analytics services, and a great point of collaboration and joint go-to-market between Confluent and the cloud providers. Our third pillar is being everywhere, and in Q3, we have continued to strengthen the capabilities that support this pillar. Most recently with the launch of Cluster Linking for Confluent Cloud. Organizations today are becoming increasingly geographically distributed with data living in a number of different heterogeneous environments throughout the business. Managing data securely across these environments is a huge challenge. Cluster Linking introduces a new way to transparently link together Kafka clusters in different geographies, cloud providers or environments to create a unified fabric for data in motion that seamlessly spans all the parts of the company. An early user of this functionality is Namely. Namely is a fast-growing HR software company, and they have architectural requirements that span geographies in multiple cloud environments to serve their diverse customer base. Cluster Linking makes real-time global data sharing, disaster recovery, and workload migrations easy for them. A great example that puts all three of these pillars together is Instacart. Instacart experienced record growth at the onset of the pandemic, which required them to be able to scale elastically to meet the need. They evaluated a handful of different data in motion options and selected Confluent Cloud due to the combination of cloud-native capabilities that would let them scale elastically to support the growth of their business, the completeness of our offering, and our ability to be everywhere, spanning the environments and availability zones their product required. They have complex problems to solve in real time with inventory, customer service, and staffing. We believe our platform and the larger movement around data in motion represents a significant step change in making data accessible across an organization. In order to truly unlock customers' data, it isn't enough just to make it easy. We also have to make it safe. As data powers more of the operations of a company, the quality, correctness, compliance, and lineage of data becomes an existential concern. This is a difficulty that is compounded by the explosion of new data types as well as an explosion in the level of regulation and risk around data. When organizations had one monolithic team that did everything and only a few places where data resided, it was much easier to control data integrity and governance. Customers now need to do this across a diverse organization that is innovating and changing in many areas in parallel, which is a substantial challenge. The key to addressing this challenge is a product offering we announced in Q3 called Stream Governance. This takes the best practices for decentralized federated data governance and applies it automatically with Confluent. Stream Governance is offered as a fully managed cloud solution and delivers a simple self-service experience that enables customers to discover, understand, and trust their data flows. This new suite of capabilities establishes trust in the real-time data moving throughout our customers' businesses and empowers teams across any organization to quickly put event streams to work. There are three key capabilities of Stream Governance. The first is our Stream Catalog, which helps users find the data they need. This allows individuals across teams to collaborate within a centralized, organized digital library for data in motion, which allows any user at any skill level to put data to use right away. Our customers like Care.com really value this. When it comes to finding, managing, and paying for family care, data confidence is critical. Next is Stream Lineage. This is like Google Maps for your data flows, created in real time off the actual flow of data. It provides an always up-to-date version of how data is flowing in the organization and who is consuming it. With a better understanding of where data originated and where it's going, how it's transformed, and when it arrives, developers can move projects forward with assurance their work won't cause negative or unexpected downstream impact. Take Ritchie Bros., for example. The global industrial asset auctioneer, and one of our great customers, depends on Stream Lineage for their real-time online bidding system. It makes buying and selling industrial equipment like giant earth-moving trucks easier. Finally, Stream Quality, which helps us avoid bad data, enforces schemas, and ensures compatibility, which enables organizations to scale data integrity without adding operational complexity. We believe that standardizing around well-defined and agreed upon schema structures allows teams to develop resilient data in motion pipelines prepared for safe, compatible evolution over time. We're really excited about Stream Governance as it extends the completeness of our platform with capabilities that are essential for a successful data in motion strategy, and it represents one of the single most requested features from our customers. With better visibility into and control over data governance, customers can leverage our data in motion platform for mission-critical use cases without having to invest scarce software engineering resources into building tools for monitoring and managing the quality of their data. Stream Governance also serves to illustrate a more general point I'd like to make about our space. Data in motion is a genuinely new category, and though building a new category and paradigm comes with a number of challenges, it has one very big advantage, which is that unlike most of the data stack, all the white space around data in motion is not yet filled in. There are literally hundreds of use cases and product opportunities around data in motion that we see our customers and the larger ecosystem developing. Because that ecosystem is just coming into being, Confluent has an enormous opportunity to expand our position and scope into these additional capabilities. This is quite different from a more mature space where all these niches have been filled in by at-scale mature competitors. This is something that for me makes our future product roadmap very exciting. You can ask yourself the question, if there was a central nervous system which captured the real-time flow of data in a company, what else would naturally grow in and around that stack? The possibilities are endless. If we execute quickly and effectively, many of the key elements of this ecosystem can be Confluent products. Stream Governance is one small step into this larger problem of helping companies rebuild around real-time data. It's a good illustration of how we can go beyond the raw infrastructure of moving and processing bytes and deeper into the larger problem of managing and using data in motion. As we continue to build a market leading product in a new category, we've made a number of key hires to support our expansion. One of the recent notable hires was Chad Verbowski, our new SVP of Engineering, who joined us from Google. Chad was head of BigQuery Engineering at Google and had extensive experience leading and scaling Google's cloud data analytics platforms, tools, and machine learning services. Together with Ganesh Srinivasan, our Chief Product Officer, the team will continue to innovate and build great products for our customers to set their data in motion. Finally, I'm really pleased with the interest and participation we've seen at Kafka summits around the world. More than 51,000 people registered to attend the three summits we held in America, EMEA, and APAC this year, an increase of over 40% YoY. This momentum is also evident in the talent market. According to LinkedIn Talent Insights, more than 60,000 job postings globally today are targeting those skilled in Kafka. This has been noted by industry observers as well. Thoughtworks, a premier technology consultancy and analyst, recently noted in their Radar publication on tech trends that, quote, "Kafka continues towards its status as a de facto standard for asynchronous publish-subscribe messaging in volume." They further noted that they were seeing increasing standardization around these key platforms like Kafka, Kubernetes, and the cloud service providers, and far less interest in newer challengers to these emerging standards. This matches our observations. We think it's a very positive sign for the larger movement towards data in motion. With that, I'll turn the call over to Steffan to walk through the financials. Thanks, Jay. Good afternoon, everyone. Q3 was another strong quarter as we exceeded the high end of our guidance on all metrics and continued to see accelerating growth in revenue, RPO, and a robust net retention rate. These results underscore the power of our industry-leading platform for data in motion and our ability to execute and capture the large and growing market opportunity in front of us. Turning to our customer metrics, we added approximately 190 net new customers this quarter, bringing total customer count to approximately 3,020, up 75% YoY. Our growth in large customers continues to be robust. We ended the third quarter with 664 customers with at least $100,000 in ARR, up 48% YoY, and 74 customers with at least $1 million in ARR, up 90% YoY. The growth in our customers is driven by the complete cloud native and everywhere aspects of our product differentiation, the network effects of data in motion, and our continued improvement in go-to-market with broad-based momentum across enterprise and commercial segments, coupled with our strong partnership with cloud service providers. Driven by strong gross retention and expansion across both of our product offerings, Q3 dollar-based net retention rate, or NRR, was greater than 130% for the second quarter in a row, exceeding our near term target threshold of 120%, and was in line with our long-term target of above 130%. Our thesis is that over time, Confluent Cloud, with its elasticity and consumption-based model, should have a higher NRR than Confluent Platform. The thesis played out nicely this quarter. As we noted in our last earnings call, we expect fluctuation in NRR to continue. With that said, we're pleased with the progress we've made in increasing NRR, and we remain operationally focused on driving it consistently above our near-term threshold and long-term target. Turning to revenue. Q3 total revenue was $102.6 million, growing 67% YoY, an acceleration from 64% in Q2. Subscription revenue was $92.4 million, accelerating to 70% growth YoY, and it accounted for 90% of total revenue. The two components of our subscription revenue continue to exhibit strong growth. Confluent Platform revenue was $65.6 million, up 40% YoY, and accounted for 64% of total revenue. Confluent Cloud revenue was $26.8 million, accelerating to 245% growth YoY, and accounted for 26% of total revenue, up from 22% of total revenue last quarter, and up from 13% in the year ago quarter. Our strong performance in Confluent Cloud revenue was driven by the themes Jay discussed earlier, including the secular trend of cloud migration, the enhancements in features we've added to our cloud product in recent quarters, and great execution by our go-to-market team. Turning to the geographic mix of revenue, we saw robust demand for our Data in Motion platform worldwide, with revenues from outside the U.S. outpacing total revenue growth. Revenue from the U.S. grew 59% YoY to $66.3 million, representing 65% of total revenue. Revenue from outside the U.S. grew 82% YoY to $36.3 million, representing 35% of total revenue, up from 32% in the year ago quarter. Turning to remaining performance obligations or RPO. We ended the third quarter with $385 million in RPO, up 75% YoY. Current RPO, which is estimated to be 67% of RPO, was approximately $256 million, up 65% YoY, an acceleration from 63% last quarter. The main driver of RPO growth was the broad-based strength across both of our subscription offerings. We also saw a modest increase in multi-year deals, which is an endorsement of how our value proposition is resonating with customers. Before turning to gross margins and profitability, I'd like to note that I'll be discussing non-GAAP results unless otherwise noted. Q3 total gross margin was 69.4%, down from 71.6% a year ago. Subscription gross margins were 76.8%, down from 78.9% a year ago. The decline in gross margin was anticipated due to the higher mix and strong growth in Confluent Cloud revenue. As discussed on our last earnings call, Confluent Cloud has a lower gross margin profile than Confluent Platform. We've seen continued improvement in our cloud gross margin, and we remain in our early days of achieving leverage and scale for the infrastructure that supports our cloud offering. As Confluent Cloud continues to scale and account for a larger share of total revenue, we anticipate total gross margin to fluctuate near our midterm target of approximately 70%. Turning to profitability, operating loss was $42.6 million, representing an operating margin of -41.6% compared to -32.1% a year ago. Free cash flow margin was -20.1% compared to -16.7% a year ago. Net loss per share was -$0.17, using 259.2 million basic and diluted weighted average shares outstanding. As a reminder, our profitability in FY 2021 is being impacted by our plan to catch up on hiring given the pause we prudently took in FY 2020. Additionally, we're continuing to invest for growth driven by our robust competitive positioning in the large market and the strong unit economics in our model. Moving on to the balance sheet, we ended the third quarter with $1.03 billion in cash equivalents, and marketable securities. Turning now to guidance. For the fourth quarter of 2021, we expect revenue to be in the range of $108 million-$110 million, representing growth of 54%-56% YoY. Non-GAAP operating loss in the range of -$59 million to -$57 million, and non-GAAP net loss per share in the range of -$0.23 to -$0.21, using approximately 264 million weighted average shares outstanding. For fiscal year 2021, we are raising our guidance and now expect revenue to be in the range of $376 million-$378 million, representing growth of 59%-60% YoY. Non-GAAP operating loss in the range of -$170 million to -$168 million, and non-GAAP net loss per share in the range of -$0.92 to -$0.90, using approximately 189 million weighted average shares outstanding. I'd also like to provide some modeling points which remain consistent with what we discussed last quarter. We expect FY 2021 non-GAAP taxes to be in the range of $2 million- $3 million, and FY 2021 capital expenditures and amounts capitalized for internal use software costs to be approximately 2%-3% of total revenue. Finally, while we're still early in the planning process for 2022, I'd like to provide an initial read on our expectations for revenue growth next year. For fiscal year 2022, we expect revenue to grow approximately 36% to approximately $511 million from our FY 2021 revenue guidance, which has been raised to $377 million at the midpoint this afternoon. From a seasonality standpoint, we expect Q1 2022 sequential revenue growth to be roughly flat. As our business continues to grow, we expect to see more pronounced seasonality with Q4 2022 being our strongest sequential quarter. In closing, we're pleased with our third quarter results, which reflect our team's ability to execute to capitalize on our significant opportunity, coupled with the growing market adoption and secular tailwinds for data in motion. We remain confident in our ability to build on our momentum in the quarters ahead. With that, Jay and I will take your questions. Thank you, Steffan. To ask your question, please use the Raise Hand feature on your Zoom screen. We will now take our first question from Mark Murphy of JP Morgan, followed by Morgan Stanley. Hi, Mark, are you there? Thank you. Thank you, Shane. Sorry about the delay. It took it a minute to add me as a panelist. First off, thank you for taking my question, and congratulations on just a tremendous growth rate. Jay, I'm wondering if you're possibly seeing any effect from global supply chain issues on the positive side. What I mean by that is, perhaps driving retailers to more continual or real-time processes to just try to get a better handle on inventories, right? Which have been fluctuating so much. Maybe reworking some of their, you know, distribution centers to try to handle these challenges out there in the environment. Yeah, that's. It's a great point. We definitely see a set of use cases around retail that have this much more real-time ability to know what's where, when, how, and act faster off of it. We don't see any immediate impact from this in the short term, right? You know, this is generally the case, I think, with these infrastructure layers that, you know, it's not the next day after this that you know, rework all your inventory management. It often does provoke, you know, a longer-term reaction and reflection on how this will be done going forward. We saw that with the pandemic, where, you know, maybe Zoom, you would buy it the day after. Confluent and this larger investment in the infrastructure to support the digital side of the business, that comes a little bit later. As you're really thinking through that strategy, it takes a little bit more time, but in some cases can be a little bit longer lasting 'cause even, you know, after people are back at work or even after the ports are unclogged, you still, this is the architecture you want to have and build against going forward. You know, it's certainly possible that we'll see some positive impact from it, but nothing immediate. Okay, great. Just as a quick follow-up, Steffan, is there any effect in these results where you would see, you know, someone who decides they don't want to run or they don't wanna manage Kafka internally, it compels them, you know, to migrate to Confluent Cloud. If so, is there any type of conversion ratio or uplift that you're seeing, right, in terms of, I think presumably their annual spend in that scenario would see some increase? Well, we see a couple of different angles to that question. The first is we see our pay-as-you-go customers that contribution's robust, and those are folks who are typically Kafka users who then migrate to our platform for the first time. We make it easy, frictionless to sign up. Once we land those pay-as-you-go customers, we have a customer journey that we've outlined for our customer base and investors to see them progress. That would naturally lead to more expansion dollars over time, and that's definitely a factor. You know, for the other angle, which is customers, who may be running some Kafka and some Confluent at the same time, in this environment we are seeing folks migrate over to us from the free version to Confluent, mainly due to all of the product feature and functionality enhancements that the team has been working on. Candidly, over the last five years, but in particular over the last year, all of the things we've put into that Confluent Cloud product in particular is making it very, very easy for customers to come over to us and have a very valuable experience. Great. Thank you. All right. Thanks, Mark. Our next question comes from Sanjit Singh of Morgan Stanley, followed by UBS. All right. Thank you very much, and congrats to the team, Jay and Steffan, on another really strong quarter. That 245% cloud growth is very impressive. My question was sort of thinking about where we are against what I think are the big opportunities, and correct me if I'm wrong. If I sort of bucket, Jay, the big sort of opportunities you're going for, I think one would be sort of reimagining the data pipeline with streaming data, that being one. The second, a whole new class of streaming apps on sort of the other side of Kafka, those downstream applications. Third, maybe using Confluent as a data store and analytics engine as well. The two questions there is, one, do I have those buckets right, A? Secondly, like across those three buckets, where are you seeing the most traction today, and what do you think could be emerging opportunities as you guys advance the product roadmap? I think that's a great way of bucketing it. You know, the first two I think are very mainstream. We see companies building out, you know, pipelines for real-time flow of data between data systems, SaaS layers, whatever needs to connect. We see a whole set of real-time applications being built. You know, those are very common in our customers. You know, the prevalence might vary from one customer to another, but virtually anybody at scale has both use cases. You know, if you have this stream of sales that are occurring in the business, there are both data systems that might need to be delivered to, maybe your data warehouse. There's also real-time activity and processing applications that work off of that. Those are very common. You know, the newer thing is certainly what you described, which is, you know, we have in our platform the ability to store data indefinitely and, you know, particularly for more kind of database data sets like the changes of all user profiles, really store that complete data set and be able to do not just real-time processing, but actually load the historical data and work off of that. Yeah, that is a pattern that we see in customers. I would say that's kind of the, maybe the second wave. People start, you know, for the real time, and then that retention and longer term use case becomes very natural 'cause you're just kind of turning the knob on what you're retaining and how, you know, as use cases demand it. Makes total sense. Jay, you devoted a good part of your script talking about the new capabilities of the platform. You mentioned sort of the governance being a particular one of notable importance. I wanted to get your particular view as what is this going to mean for the existing customer base as either a conversion from Kafka to paid Confluent and paid Confluent Cloud, or just migrating from platform to cloud rather. How key are these developments in terms of that larger enterprise opportunity? Yeah. I mean, these are key features, right? I mentioned Cluster Linking. Virtually every company needs to span geographies, environments, cloud providers, hybrid setups. That's incredibly important. Stream Governance. You know, as I mentioned, this is probably the most requested, you know, feature set in our customer base, and it makes very natural sense. If you're gonna unlock data, you have to do it in a way that's safe. By doing that, it actually encourages the use of this platform, that growth into a central nervous system. It's only possible if the mechanism is really safe and well-governed in the modern world. Yeah, you know, both of these capabilities are unique to Confluent and are great reasons to adopt our product, right? They would fall under that complete and everywhere bucket. These are kind of key reasons you would want what we have. Yeah, we think it absolutely adds to that. Understood. Thank you very much, Jay. Congrats to the team. Yeah. Thank you. Thank you. Our next question comes from Karl Keirstead of UBS, followed by Barclays. Okay, great. Thanks, Shane. Steffan, maybe I'll direct this one to you and see if you might be able to elaborate on that comment you made when you were talking about seasonality next year, about Q1 maybe being sequentially flat and maybe having a little bit more of a Q4 skew. I think most people on the line are used to hearing that from the likes of Workday and Salesforce, much larger organizations. I'm curious. Why you would be seeing it and whether the fact that you're hiring a lot of sales reps today, and maybe they're hitting full productivity in the second half might be contributing to that dynamic. Maybe it's the fact that you're signing a lot more multi-year deals, and that sort of pushes you into a fourth quarter cadence. Anyway, love to hear a little bit more color on that. We're still in the early stages of planning for FY 2022. But the way that our hybrid rev rec model works is we have a portion of our business for Confluent Platform that gets recognized upfront as folks know. Then the hybrid component of our model, we have consumption-based that's coming off of Confluent Cloud. Depending on the deal's structure in the deals that we have in the pipeline, Q4 tends to be a seasonally strong quarter for us. That's applicable to both Confluent Platform and Confluent Cloud. When you look at other companies in the enterprise space, even with a similar model of ours, like a MongoDB as an example, they see similar dynamics between a Q4 to Q1 bridge. You know, we'll see how Q4 ends, and you know, we'll give updated guidance, you know, post our Q4 earnings call around what obviously Q1 looks like and an update for the year. The second part of your question around seeing Q4 even being kind of more pronounced next year in terms of sequential growth, we have made a lot of investments in the sales organization. It does take roughly four quarters for folks to get fully productive. As part of the modeling and capacity analysis that we do, we're basically kind of forecasting that to really see the seasonality pattern increase in Q4 next year, and that's why we called it out on this call. Yep. That seems pretty reasonable. Steffan and Jay, congrats on amazing numbers. Thanks so much, Karl. Our next question comes from Raimo Lenschow of Barclays, followed by Goldman Sachs. Hey, thank you. Congrats from me as well. Two quick questions. First, for Jay. If you think about the real-time as the kind of the emerging new normal, what's the appetite from your customers around all the kind of more established kind of use, you know, like, kind of batch processing use cases to modernize that? At the moment, you know, we're hearing a lot around, like, reimagining things around real time, et cetera, but there's a whole new world out there that is kind of crazy and grown over 20, 30 years, where you think, like, you could probably do it a lot better in the new way. Maybe speak to that one. For Steffan, like, if I look at the numbers, like everything accelerated this quarter, I kind of just did the bookings math as well, which looked like very, very strong. Just one simple question, was there anything specific in terms of one large deal or anything that drove that? Because this looks like a very, very amazing quarter, all around. Thank you. Yeah. I'll take the first part of that. You know, I think the question that you're kinda getting at is around. Can you remind me the- Oh, yeah. Like, if you think about, like, this whole world of batch, like, but like, you know, what's the appetite to kind of think about kind of modernizing that world? Yeah. That's a good question. You know, I would say there's really no reason you want things to be processed in batch. Like, it doesn't. There's nothing in the world that happens in batch. You know, things in the world happen all the time, you know, continuously and in real time. You know, the migration happens as there's new demands. You know, what's the new need that that kind of drives this? As it gets easier and easier to do it this way. As this platform has gotten more mature, as the set of people who know how to use it expands, then more things get migrated. Then also, you know, the kind of driving force, I touched on this a little bit in the call, is, you know, it's kind of just the digitization of business. Like, as the actual operation of the business moves into software, not just the analysis, not just the report you get in the morning, but the actual carrying out of the business as it plugs into e-commerce things, as it drives operations out in the world, as IoT bridges into other parts of the world, as machine learning kind of closes the loop on some of the decision-making and processes, that's really where you have to do it. Like you don't, you know. It's always better, but that's where you must. It's a combination of both. I think over time, yeah, I think the majority of that will move. Thanks. Yeah. On the second question, we really saw broad-based strength across the board. There was not one large deal that came in and kind of swayed the numbers. You look across the details, Raimo, and we look at our commercial and enterprise segments. They did incredibly well. They both did incredibly well. We look at our products. Confluent Cloud continues to really shine for us at 245% growth YoY. It basically doubled its revenue mix from a year ago. That's really a testament to the investments we've made in two parts of the organization. The first is in our product and eng org. That team has delivered really great products and features and functionality that our customers want, so that's resonating. In our go-to-market organization, from sales to marketing to field operations, we've been finely tuning you know the machine relative to how to pitch it, how to go in and win big business, and also win small. What we've seen in terms of dollar-based net retention rate being above 130% for a second quarter in a row, it's really a testament to the ecosystem working. So we're very pleased with the broad-based strength. Well done. Congrats. Thank you. Our next question comes from Kash Rangan of Goldman Sachs, followed by Wells Fargo. Hi. Thank you very much. Sorry, my Zoom crashed all of a sudden here. Jay, as I listen to prepared remarks, there are two things that struck me among several things. One was how you're getting tied into the data warehousing ecosystem. Can you talk about what could likely be Confluent's involvement in that ecosystem? Do you see yourself ultimately being a data integrator of choice to that ecosystem, maybe even start to do some light analytical workloads? Also, the other thing that caught my attention was you had a chart on the left-hand side, there were data infrastructure use cases and business applications use cases. You made a very fleeting observation that you could see yourself being in one of these product areas or some of these could contribute to growth opportunities in the future. Can you just expand on that one for Steffan? The long-term deferred revenue is certainly much better than expected. Despite the rise of the cloud in relative proportion, one would think that the opposite would happen, but long-term deferred revenues went up very nicely. What's driving that dynamic? Thank you so much. Yeah. To the first question, yeah, we see it as very complementary. You know, there's a number of really amazing next generation data warehouse and analytics platforms, and, you know, there's actually a number of strong players there, and we partner with all of them. So any streams that go into Confluent that can power not just real-time applications, not just, you know, the kind of operational side of the business, it can flow into some of the analytical systems as well. And that strengthens the value of our platform and the data that flows through it. And it's obviously a huge win for these platforms whose goal is, of course, fill themselves up with the useful data and be able to drive analysis on top of that. You know, so it's great. Obviously, as part of that, there is a fair amount of processing of data as it needs to land, and that's the stream processing component. Of course, some of those pipelines need to be purely real time and live around our platform already today. Yeah, there is a role for, you know, some real-time analysis as well. We see customers building out solutions around that feed off of Confluent for sure. There's actually a, you know, a whole portfolio in the ecosystem of data stores that really specialize in trying to do, you know, exactly that thing. Yeah. I think that's a, you know, it's a fantastic area for us. On the long-term revenue question, Kash, it grew 59% YoY. Some of the drivers of that go back to the large multi-year deals that we've done. For Confluent Platform deals that are multi-year in nature, that will definitely positively impact long-term deferred revenue. To give you a sense, though, I mean, proportionally speaking, we have $202 million of total deferred revenue, and long-term deferred revenue is about $22.4 million. It's a relatively small portion, but you know, when we see more multi-year deals coming in for Confluent Platform, that's gonna impact long-term deferred revs. Thanks, guys. The second part of your question, you know, you asked a little bit about the growth into the ecosystem. Yeah, it's a bit of a nuanced point, but I think it's important, which is, you know, in an established category, everything that's adjacent to the category is filled in by, you know, a strong competitor. In a new category, it's not. It's open. You know, historically, if you look at an older data platform like Oracle databases, you know, they could kind of jump relatively easily up into a lot of the business applications that were driving that and have offerings in HR and financials and all kinds of other things, right? Now, of course, today, if you make a next gen operational database, you know, maybe you're MongoDB, you don't necessarily get to be Workday, right? Like, there's already somebody doing that. You know, that's the difference, I think, in a category that's emerging versus a category that's established. What's interesting in this space is there's so much excitement and use cases getting built in customers around data in motion. I think that's just a very target-rich environment for us to grow into. I think it's something that's exciting about the space. It's something that makes me excited about the opportunities for the company. I just wanted to share that point. Great. Thanks, Kash. Our next question comes from Michael Turrin of Wells Fargo, followed by JMP Securities. Hey there. Thanks. Congrats on another impressive quarter. On the retention rates, the long-term target of more than 130%, it's actually higher than the near-term target of 120%. Steffan, you mentioned the mix towards cloud as a key driver there, but can you also expand on the confidence that gives you in the durability of the revenue growth profile? Given you're saying the long-term targets are more than 130%, it's not something we often see in software, and I think that's unique and worth highlighting. Well, we are very pleased with the progress we've made over the last several quarters in operationally being focused on driving NRR higher. If you recall, you know, going back to even our IPO, the quarter in which we went public, our NRR for Q2 was 117%. Or, you know, sorry, for Q1 was 117%. We had work to do, and there were some crosscurrents that were going on, you know, back in those times when NRR was fluctuating in the lower levels. There was a transition in our cloud business to usage-based billing. We had some timing of large initial deal sizes, not, you know, expanding within a 12-month time period. There's a real testament to, I think, the go-to-market team and the product team on emphasizing operationally how we can get customers through the life cycle and journey of their deployment of Confluent. When we look at the near term target of greater than 100%, we've been operating for two quarters in a row now above 120%. We're actually at our longer term target of above 130%. The profile of like in the dynamics of NRR as it relates to our two main products, Confluent Platform and Confluent Cloud, our thesis is that Confluent Cloud will have a higher NRR profile over time because it's elastic, it's consumption-based, and there's very little friction in terms of expansion. Whereas with Confluent Platform, you know, we're re-renegotiating deals, et cetera. When we look at the profile of our revenue base and the durability of revenue growth for cloud, I don't think there is, you know, very much debate that cloud is where it's at going forward. We also have to be mindful to be the central nervous system, we have to be both on-prem and in the cloud. There's gonna be a dynamic at play where we have both platform and cloud. We think cloud from a durability standpoint is gonna be very strong, and that should be a tailwind for NRR, which is why we have, you know, all of that kind of sum up into basically saying we have this 120% near term target. We've been above that for two quarters in a row. You know, we want to be above 130 consistently, but it takes time. Before we set new benchmarks in place, we wanna see, you know, call it three, four quarters in a row of great performance in NRR before we look to revisit the targets. Just to provide a little bit about, you know, the product space that I think kinda helps drive that. You know, the question is like, okay, you know, is that expansion, you know, whether it's 120, 130 now, you know, can you have high expansion over a very long period of time in a customer base? I think one of the things to understand about this domain is infrastructure is a little bit slow. We talked about this with the supply chain stuff, right? Like, you don't just put it in tomorrow, and you're done. In reality, what happens is usage is driven by each application that moves over. You know, the batch process that moved into real time, right? How quickly do enterprises turn over their set of applications? Well, I mean, it's over decades in reality, right? Like a lot of these things, you know, end up lasting much, much longer than any would like. That does mean that, you know, the bad news is we can't just come in and install the central nervous system over the weekend. The good news is we can, you know, grow as long as it, you know, we're successful, and we remain the platform of choice in this space. We can grow in these customers over a very long period of time. That's great. Congrats to the team. Thank you. Thank you. Our next question comes from Patrick Walravens of JMP Securities, followed by Cowen. Great. Thanks, Shane. Let me add my congratulations. Jay, as you look out to 2022, what are your top two or three priorities for Erica to get done and for the sales organization to get done? Well, Erica has her own fantastic list of priorities. You know, there's a lot happening in our sales organization. I would say this move to a consumption model for our cloud product, which we undertook a little over a year ago, you know, that's great. That's been a boon to us. It's something our customers love. It allows us to land and expand faster. I think we're just in the very early days of taking advantage of that and really building out this journey from, you know, the lightest, lowest friction possible lands in the product to, you know, expansion over time, this central nervous system vision that can take many years. You know, we've built a lot of supporting elements to that. What services do people need as they get to scale? You know, what do we need to do at the very top of the funnel to, you know, land in volume and make that really easy to get going with? How do people progress, and what are the trigger points? I think, you know, despite all that, we're a young company, and there's a lot more we can do there. As we do, I think we, you know, we can land in higher volume. We can, you know, expand faster with customers. All of that is gonna make us better and better. That's certainly one of the areas that's really on all our minds is how to do that really well. You know, along with just the continued expansion and tuning of the business as we grow. Can I just ask, I mean, your international was growing faster than the U.S., right? Are there different requirements there? Yeah. From a go-to-market point of view? Yeah. I mean, there's obviously a lot to operate in all these countries. You know, I think we're lucky that we have a product which is inherently very international. Kafka usage is kinda everywhere. In many ways, it's really just on us in execution to get into all those areas and make sure that we have a sales team that speaks the language and is able to operate on the right accounts in those areas. We're still in the early days of a lot of that expansion, which is why you see the faster growth there. We would expect that to continue until monetization looks a little bit closer to the adoption that we see internationally. Great. All right. Thank you. Thank you. Our next question comes from Derrick Wood of Cowen, followed by Bank of America. Great. Thanks. Congrats as well from me. I'd like to ask just about, you know, to new IPO companies. You know, often an IPO can be a catalyst for brand awareness. Have you guys seen any change in inbound interest, lead generation or any discernible impact to win rates, or is it too early to tell? Yeah. That, it's hard, of course, to pinpoint anything to, you know, to something that's a little bit softer like this, but I think for us it is a bonus. I think, because it's a new category that we're trying to bring awareness of, especially in the kind of senior technology leadership ranks, of a larger set of companies, I think just the raising the profile of the company, is really important. We've, you know, we've tried to do a little bit to aid that. You know, Steffan joined as our CMO, you know, not long before the IPO and has really invested that both as part of the IPO and since. You know, I think that awareness is actually a real boon to us, and we're just starting to get to the scale where that's possible. You know, as an early company, it's hard to really have broad awareness. But as we do, I think that's really important. I think with new categories, there's often you know, an early company that becomes closely associated with that. I think, you know, Confluent is becoming that for data in motion. Sure. A quick follow-up, you know, it is a tough hiring environment out there. How are you guys feeling about attracting and retaining good talent and driving new onboards to productivity as you go into next year? Yeah. Yeah. It's as competitive as I have seen it in the tech world for talent. You know, we feel good. You know, we're on track with our plans, but you know, it's definitely work. There is a silver lining on the war for talent on the technical side, which is every company in the world is struggling to hire engineers. The natural complement to hiring a bunch of distributed systems experts to run open source is cloud services. We're definitely seeing that shift in some of our customers, where they're like, "Okay, we only have so many brilliant people in the organization and all of their salaries are going up quite significantly, and it's really hard to hire more. How can we move those onto the parts of the business that are unique to us, and how can we get some of the other stuff for a reasonable price?" That's definitely a tailwind that's driving, I think, all of these cloud services, but certainly us. That's great. Congrats again. Thanks. Yeah. Thanks, Derrick. Our next question comes from Brad Sills of Bank of America. Oh, great. Hey, guys. Thanks so much, and congratulations on a real nice quarter here. I wanted to ask about the cloud. I think the focus there was on really driving that SMB or, you know, lower TCO kind of initial footprint to kinda get your foot in the door with some mid-market accounts. Is that what's driving the success here, would you say? Or is it a balance of that plus larger organizations kinda, you know, getting started with the cloud, you know, moving data in motion more to the cloud as well? It really is the full journey. You know, for this kind of product, you know, it isn't the case that the cloud offering is for the kind of low-end, commercial companies only and big companies, what they really want is to do it all in their data centers. There was certainly a period of time where that was true for cloud infrastructure, but that's changed. If we look at this last quarter, you know, a good chunk of our largest deals were cloud. You know, surprising chunk of them. I think that's great. Again, you know, the whole journey matters. Of course, yes, we want people to be able to start quicker with lower friction, but we want that because we want them to graduate to where this is really a strategic platform in the company over time. You know, the challenging part for us is to really nail all the steps in that journey and make it happen consistently and regularly. Yeah, it is a mix. We saw great success at the top end of the market. We saw great success at the low end. Our commercial sales organization really killed it this quarter. Yeah, it really is a mix of company types. Great to hear, Jay. Thanks so much. One more if I may, please. I know the global SI channel has been a big focus here. Can you remind us kinda where you are in that development? How critical is that channel to delivering on some of these bigger expand deals? You're obviously already seeing really good net revenue retention and expand activity. Could we anticipate that as a potential catalyst as that channel becomes more online? Yeah. I think we're, you know, we're still early, right? We are putting effort into that. I think over time, this is gonna be really important for us to grow into the role that we imagine with our customers. We're certainly investing in it, and I think it could contribute a lot over time, but it's still in the early days for us now. Okay. All right. Thanks, Brad. This concludes the Q&A portion of our call. We will now turn it back over to Jay for closing remarks. Thanks everyone for being with us on the call today. You know, huge thanks to our team, to our partners, to our investors, and especially to our customers. Thank you all for being part of the journey, and we'll see you again next quarter.