00:00:04 · Neil C. Hughes
[00:00:04] S0: Welcome back to the Tech Talks Daily podcast where this week, I'm recording at the Current twenty twenty six event in San Francisco, and Confluent has announced Streamhouse here, and essentially, it's a new data architecture designed to help businesses work with information as it changes. And, yes, we've heard plenty around AI agents this year, but what happens when those agents are making decisions using information that is several minutes or even hours out of date? And how much existing technology will businesses need to change before they can put these systems into production? Well, joining me today is the CTO of Confluent. We've stepped away from the show floor to discuss what Streamhouse actually does, explore why Lightning Tables matter, and how businesses such as Instacart and Citizens Bank are using real time data to address practical operational problems. And we will also examine that relationship between Confluent and IBM, the role of existing warehouses and lake houses, and what companies should be measuring when deciding whether investment in any infrastructure is worthwhile. But enough from me. Buckle up and hold on tight. It's time for me to beam your ears directly to the show floor. So we are recording here today at Current twenty twenty six in San Francisco. A lot of big announcements here, haven't they, today. But to begin with, can you tell everyone listening and watching a little about who you are and what you do?
00:01:40 · Stephen Deasy, Neil C. Hughes
[00:01:40] S1: Yeah. Thanks, Neil. So I'm Steve Deasy. I'm CTO at Confluent. So I'm responsible for the engineering, the product operations, everything about Confluent Cloud and Confluent Platform. [00:01:50] S0: Awesome. Now one thing we are hearing a lot about at the moment, Steve, is the enormous investment that's going into AI models and infrastructure. But there's another challenge around making sure AI has the right business information at the right time. And, of course, I'm building up here to that big announcement today is Streamhouse. So what problem is Confluent trying to solve with Streamhouse, and why is now the right time to talk about the architecture? [00:02:16] S1: We think that from talking to customers, what we're seeing is that one of the big blockers to taking your agentic workloads into production is the availability of governed fresh data. And the approaches that customers have taken, things like m c p... M c pifying access, things like moving data into lake houses have not been working for various reasons. And so what we see as the next evolution is a Streamhouse to really allow that real time data be available to your business for software to make real time decisions to move your business forward. [00:02:55] S0: And for the non techies listening, now we've got a few business leaders. How would you describe the announcement of Streamhouse today and what it would mean to the the people listening?
00:02:55 · Neil C. Hughes, Stephen Deasy
[00:02:55] S0: And for the non techies listening, now we've got a few business leaders. How would you describe the announcement of Streamhouse today and what it would mean to the the people listening? [00:03:03] S1: Yeah. So what what we think about is use cases. For example, banks are making loan fulfillment decisions. Consumer companies are making ordering decisions and fulfillment and so on. And so, the ability to run those businesses on stale data is no longer feasible. And so, in the business world, the ability to move with real time with the current information and all of your business context available to those systems means that you can move faster, produce better outcomes for your customers, and so it gives you a huge business advantage. [00:03:42] S0: And of course, many organisations have already invested heavily in data warehouses, lake houses and analytics platforms, but where does Streamhouse fit into that picture, and what would you say to a CIO who is concerned that this could mean yet another expensive infrastructure overhaul?
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?