Jay Kreps: Yes, yes, that’s a great question. So yes, I would say generally our expansion is driven by, either new use cases or conversion of existing use cases that are using the open source. There obviously there’s some expansion of existing use cases like there’s more data, but of course there could be less data in some other use case. On net, I don’t know the businesses are just getting bigger at a large rate year-over-year. The other area of expansion is the new product capabilities that we’re adding. We have a consumption model as those layers on customers can kind of in a very frictionless way use more of that. For Confluent Cloud thus far it has been kind of largely driven by Kafka, but we are expecting over time some of those other components to drive.
So those are those are kind of the three vectors expansion in the use of capabilities, expansion in the kind of raw volume of data and expansion and use cases today the spread of use cases is definitely the one that drives is.
Steffan Tomlinson: And then to the New Relic question, yes, you know they they’ve progressed in their deployment and we’re seeing what we would expect out of their adoption.
Raimo Lenschow: Okay, fair enough. Thank you.
Operator: Alright, thanks Raimo. We’ll take our next question from Brad Zelnik with Deutsche Bank followed by Piper Sandler. Brad?
Brad Zelnik: Thanks very much. Nice quarter and Congrats Steffan, we’ll miss you at Confluent, but I couldn’t think of a better, more capable successor than Rohan. So Congrats to you Rohan, really excited to see you in the new role. Jay, on stream processing. It’s good to hear the encouraging early feedback and in particular around the integration points with the rest of your platform you talked about still being in the early stages of S curve adoption. I want to make sure nothing’s changed in terms of the timeline and impact that you initially expected when you did the deal. And if you can remind us of any milestones we should look for in the years ahead to appraise your progress, with Flink?
Jay Kreps: Yes, yes. So yes, we are very pleased with the progress. To go from kind of a standing start to having you know a real like Cloud stream processing layer that customers can use is a big deal. It’s currently in early access, meaning it’s being used by a handful of customers, we’re working with them, getting feedback. It’ll go into you know kind of open availability and then GA and those are kind of the milestones to expect from us. In GA is when it will start to take on production workloads. But to be clear, you know with any cloud infrastructure there is a longer ramp as you kind of reach the full completion of feature sets are available across every cloud with every networking type that will continue. And then of course customers have to ramp their spend to the point where it moves Confluent’s numbers overall and that will happen use case at a time.
And so, a certain amount of patience is important in these areas. This is what we saw with our Kafka business where at first it was a small thing in the cloud and then as we really hit that kind of the right point on that maturity curve, it ramped much faster. I think we can do that a little bit quicker with some of these additional components because they are a natural attached to Kafka, but there still is a curve that ramps and so, yes, to your question, we’re exactly on the schedule that we originally intended which is kind of amazing for such a complicated software project. We’re really pleased with how the team is integrated and I think the product we built is even better than I imagine. So I’m very excited about it, but yes, it will ramp you know over the course of next year and really kind of contribute most meaningfully in 25.
