00:05:59 · Stephen Deasy, Neil C. Hughes
[00:05:59] S1: Very much what what we see is the investment in your data streaming platforms to date is what unlocks this. And the really nice thing about the Streamhouse that we unveiled today is that this is something that is building on that investment already. So you're streaming data, the governance of that data, and so we've been saying for quite a while that the ability to shift left that governance to where the data is created unlocks the safe use of this data later. You have forward and backward compatibility. You're able to govern access to the data. You can stream it to where those use cases are necessary. And so we think that that is really the key unblock today with one of our key additionals. One of the other big announcements today was the query side with our Lightning Tables. So the introduction of that, which augments where we already have connecting to your data, streaming your data on the best Kafka platform out there wherever your workloads are, being able to process it now in real time with things like Flink, and then query it through things like Lightning Tables or land it in open formats like Iceberg and Delta really allow you to unlock your entire ecosystem rather than any one piece of it. [00:07:19] S0: And for the techies listed, what excites you about that second announcement? What really excites you then?
00:07:19 · Neil C. Hughes, Stephen Deasy
[00:07:19] S0: And for the techies listed, what excites you about that second announcement? What really excites you then? [00:07:24] S1: Yeah. I I think there's a couple of things really that stood out to me in in in our announcement day. Obviously, Lightning Table is super exciting to me. So we had... And we still have a real time context engine, which allows access to your Kafka stream of data in a... Think of it in a table format. Now, with Lightning queries, you can access a true SQL, true APIs, and you can access the data with very low latency, and we're able to govern that schema for you. So as your application teams evolve, your downstream consumers can take advantage of that really, really quickly. Couple of other standouts for me today. Was very exciting to see our webhook connector. So a big part of our data ecosystem is being able to connect in where that data is. And one of the big unlocks with a webhook connector is that producers of events can now put it onto the stream without ever needing to interact with a Kafka cluster. They simply provide the endpoint, and from there, you can stream in that data from first or third parties, and now everybody downstream can take advantage of it. So there were a lot of exciting announcements today. There were a couple that stood out for me.
00:08:36 · Neil C. Hughes
[00:08:36] S0: And, of course, no matter how exciting that tech is, increasingly, every tech project has got to go through the ROI test case. There's a... CFOs will be paying very close attention to that after a lot were burned on those early AI experiments. So on that side of things, could you walk me through a very real world scenario where this architecture could make a real measurable difference inside an organisation? Could be something financial services, customer experience, supply chain management, where speed and accuracy of a decision directly affect revenue and cost?
00:09:08 · Stephen Deasy, Neil C. Hughes
[00:09:08] S1: Sure. So a a couple of great examples come to mind. You know, in in in our keynote this morning, we had some great demos of, you know, mocking up things like airlines and flights and delays. But maybe a real world example that came up that we're all very familiar with is customer of ours, Instacart, who during COVID, all of a sudden, the idea for for listeners that are not familiar with Instacart, think of it as grocery delivery, fulfillment, things like that. And so during COVID, you can imagine you had a massive spike in demand pretty much overnight. And in there, you have to manage everything from orders to inventory to fulfillment to shipping and returns, purchasing all... And that all has to happen in real time because if I'm purchasing my groceries now, I don't want to find out later that you didn't have it in stock. Or the delivery guy left and my... I won't be arriving for twenty four hours, things like that. So real world examples of needing that real time data connected up across these different systems. So that's one example that stands out of really bringing this to to bear in a real meaningful return way. [00:10:22] S0: And I guess what we should also mention is also that relationship between Confluent and IBM now. Now many large enterprises operate across public clouds, private infrastructure and decades of existing technology. Dare I say a fair amount of legacy tech as well, but how does an open hybrid approach help the businesses listening modernize without adding another layer of complexity or vendor dependency?
00:10:22 · Neil C. Hughes, Stephen Deasy
[00:10:22] S0: And I guess what we should also mention is also that relationship between Confluent and IBM now. Now many large enterprises operate across public clouds, private infrastructure and decades of existing technology. Dare I say a fair amount of legacy tech as well, but how does an open hybrid approach help the businesses listening modernize without adding another layer of complexity or vendor dependency? [00:10:46] S1: I think there's there's a couple of things in there, but I think you you really hit it. When we think about our approach around open standards, so we have Kafka, which is, you know, the the open standard for data streaming. We have Apache Flink, which is the open standard for processing. And now with products like Tableflow, which today we announced the GA of Tableflow on GCP, so it's across all of the clouds now. Producing that in things like Iceberg and Delta format and integrating with the catalogs you already have mean you don't need to go through an entire technology refresh to take advantage of these real time use cases. We already plug in where you are, and so that's very much the philosophy that we've had and will continue with.
00:10:46 · Stephen Deasy, Neil C. Hughes
[00:10:46] S1: I think there's there's a couple of things in there, but I think you you really hit it. When we think about our approach around open standards, so we have Kafka, which is, you know, the the open standard for data streaming. We have Apache Flink, which is the open standard for processing. And now with products like Tableflow, which today we announced the GA of Tableflow on GCP, so it's across all of the clouds now. Producing that in things like Iceberg and Delta format and integrating with the catalogs you already have mean you don't need to go through an entire technology refresh to take advantage of these real time use cases. We already plug in where you are, and so that's very much the philosophy that we've had and will continue with. [00:11:32] S0: Love it. And there's also a lot of excitement around AI agents, what they will eventually be able to do, but businesses still need to justify that tech spending today. Anything else a company could achieve with Streamhouse right now? What would it struggle to achieve with its existing data architecture? Maybe just to help people listening and watching understand what that before and after picture might look like.