00:03:57 · Stephen Deasy
[00:03:57] S1: Yes. We we think that what's happening in the world, what we see, is we're moving from a world of business intelligence. So in business intelligence, data moves in batch. It gets periodically reviewed in dashboards. A decision gets made by a human and fed back into the business. We're moving from that world of business intelligence to a world of artificial intelligence, where decisions are being made continuously. The data is streaming in real time, and that unblocks these types of use cases that previously were maybe not as critical or in an AI world are now expected in the business realm. So the way we think about it is lake houses are not going away. Lake houses help you analyze the business, and they tell you what happened. The Streamhouse is helping you run the business. It's helping you make decisions and run the business rather than analyze the business. So they're very much a complementary setup. So for CIOs, I would think of this as your real time applications unlocking the value of the data that you're already collecting through your services, your SaaS applications, your databases, and making that available to these workloads that are now craving it, and without it are blocking your ability to take them into production or see the return on investment that you're making in that infrastructure over time.
00:05:25 · Neil C. Hughes
[00:05:25] S0: And we're recording this towards the end of twenty twenty six. This year, I've been to 20 tech conferences. Predictably, every single one has mentioned Agentic AI and agents, and of course, agents promise that they can take action on the behalf of a business. That's where everything we've seen this year, but I think we're now moving on to the next phase. And a few people listening might be concerned that an agent making decision using maybe yesterday's information could create very serious problems. So how does Streamhouse help applications, AI agents and indeed employees? Does it keep them working all from the same current view for the business?
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?