And as we enter Q2, most of these trends have continued into month one of Q2, which has informed our guidance for Q2 as well. So that’s the overall context around the consumption patterns.
Pinjalim Bora: Yeah. Thank you for that. One question for you, Jay. We have been picking up on this notion that Flink SQL, being SQL, which is most understood by almost every developer, kind of opens up the aperture versus a skilled set of Java developer or something else. Is — and bringing in more developers to do more Flink and then Flink additionally drives more Kafka, that kind of creates a little bit of a flywheel. Are you starting to see some of that?
Jay Kreps: I like this question. I mean, this question sounds like my answer already. Go ahead. Go ahead.
Pinjalim Bora: No, no, no. Please answer.
Jay Kreps: Are we starting to see that? Yeah, we are. Yeah. I mean, our goal is to open up the full set of APIs. So the first thing we launched was SQL. Our intention is to bring out Java and Python APIs as well. We think they serve different use cases. There’s a set of Quora applications that will probably always be in these more application-oriented programming languages like Java. There’s a set of more dynamic use cases and transformations which are well-suited to SQL. One of the powerful things about Flink is opening up that broad set of tools all on top of a Quora engine. I think that’s one of the things that’s made it the leader in stream processing. And as we do that, yeah, our goal is very much to make this easier and easier to use.
It — for a long time, I think it’s been the case that customers would prefer real-time data. They would rather work with apps that updated in real-time, that reacted in real-time, they would rather be able to connect things in real-time. Nobody wants the data to be slow. It’s actually just been difficult to do that. So making this really easy is a core way of enabling this. Like, there’s an obvious benefit if you can make it not more costly and not more complicated for customers. So when you see us focusing on both this ease of development and TCO-oriented things, that really is the core thing that drives us. And as we do that, we think there’s a huge opportunity for this whole set of batch data movement, batch processing that really needs to move and will move as the alternative becomes appealing because of that ease of use in TCO.
Pinjalim Bora: Got it. Thank you.
Shane Xie: All right. Thanks. So, as a reminder, the Confluent earnings report is now on our IR website. The report contains our earnings infographic, our one-pagers on our technology, the prepared remarks and earnings slides from today’s call. We encourage you to go take a look. And today, our final question will come from Miller Jump with Truist Securities.
Miller Jump: All right. Great. Thank you for taking the question. I will echo my congrats on the strong start. So, just — you talked about the strength in Governance. And I’m just curious, is the need to get your data estate ready for AI driving more conversations there? And then maybe if you could just remind us what that opportunity looks like maybe on a unit economics level, if you’re spending a dollar on streaming, what does that look like for Governance?
Jay Kreps: Yeah. Yeah. It’s a great question. So, yeah, AI is definitely one of the drivers. I would say that there’s a whole set of forces that have driven interest in data governance. One of those is just the kind of rising compliance regime around data. GDPR is the start, but there’s a long list of things that organizations have to do. The second is around just the safety of data. The third is actually around opening it up. Those first two are maybe things you have to do, but in order to really take advantage of data, it has to be the case that the right team can find the right data set, know what it means at the right time. That kind of discovery process, documentation is actually really critical to the integrity of data as something that customers can build around and against.
And then as you say, all of that I think has been supercharged by AI, where you have a set of applications that are much more data rich, draw on many more data sources across an organization than a traditional enterprise app might. But in order for that to work well, you have to know what’s going where and is it up-to-date? Is it getting there in the right way? Is it supposed to be there at all? And managing all of that has just gotten harder and harder. And managing it on top of some frosty set of old bespoke pipelines is trending towards impossible. And I think that’s one of the things that has driven the rise of data streaming. And the nice thing for us is the ability to bring these Governance capabilities right there with the Platform.
So there’s not extra effort to go and adopt this use case by use case. The data is naturally tracked as it flows. You have the lineage of what goes where. You have strong schemas that allow the creation of these data products that are shared across an organization. And this is a really powerful thing for customers as they think about how they use this technology in the large and how they really take advantage of the data they have to better serve customers and be more efficient. And on the unit economics, yeah, this will change over time as that product line develops. The — right now it is kind of a step up with some additional usage as you use it more broadly. I think we’re adding more and more functionality around the encryption of fields of data, around other aspects of how you use and analyze data and I think that will increase the monetization over time.
I think it’s too early to call the final ending state ratio probably for any of these offerings, but we do think that that will be a sizable business for us.
Miller Jump: That is helpful. Thank you. And if I could squeeze in one quick one for Rohan, any gross margin changes to consider as these use cases outside of streaming start to scale?
Rohan Sivaram: Yeah. From a gross margin perspective, what we’ve said, Miller is, we are — essentially our long-term target is 75% plus gross margin. We are operating well above that and it’s been consistently above that. So as we look ahead for say, rest of the year, we expect to be in the zip code of gross margins. So not a whole lot to call out there with respect to any impact one way or the other on gross margins.
Shane Xie: All right. Thanks for all the questions. This concludes our earnings call today. Thanks again for joining us. Bye everyone.
Jay Kreps: Thanks everyone.
Rohan Sivaram: Thank you.
