00:00:04 · Neil C. Hughes, Steffen Hoellinger
[00:00:04] S0: What happens when an AI agent makes a decision using information that's already out of date? And how much money our business is wasting by throwing increasingly powerful AI models at problems that could be solved with much simpler technology? Well, today, I'm recording at Current twenty twenty six in San Francisco where Confluent has announced its Streamhouse architecture. And I'm joined by their field CTO for Across EMEA to talk about the challenges facing European businesses as they try to move their AI projects into production from fragmented information and data sovereignty to the growing importance of governance and accountability. My guest today will share some fascinating examples of technology running in unexpected places, including racing yachts, factory floors, and retail stores that need to keep operating when those cloud connections fail. And with so many businesses planning their AI investments for 2027, I wanted to know what they should actually be prioritizing right now. And with that scene set, let me officially beam your ears all the way to the show floor here in San Francisco. For everyone watching and listening, can you tell them a little about who you are [00:01:29] S1: and what... Steffen Hoellinger. I'm the field CTO with Confluent, particularly focused on Flink and AI related use cases. I'm pretty excited about today's announcement, particularly around Streamhouse and the additions to Confluent platform.
00:01:29 · Steffen Hoellinger, Neil C. Hughes
[00:01:29] S1: and what... Steffen Hoellinger. I'm the field CTO with Confluent, particularly focused on Flink and AI related use cases. I'm pretty excited about today's announcement, particularly around Streamhouse and the additions to Confluent platform. [00:01:42] S0: There has been a lot of announcements. So we've spent the last few years hearing about AI pilots, proofs of concept, and ambitious investment plans. But from the conversations with enterprise leaders across Europe and everything you're seeing, how much progress are organizations making in turning those experiments into genuinely... Or or something generally that really improves business outcomes?
00:02:04 · Steffen Hoellinger, Neil C. Hughes
[00:02:04] S1: Yeah. I think there is a big change currently underway in regards to how organizations deal with data and particularly around how they process data. So historically, Kafka was always used as a system to distribute data but to kind of not transform it while it's traveling in the stream. I think the requirement of AI and of AI agents in particular induces the need to not only like move data from A to B but also like to increase the value while the data is traveling in the stream. And this is also why our strategy around Streamhouse and around Flink is particularly focused on transforming the data, joining the data, enriching the data, but also filtering the data which is incredibly important when you combine it with AI agents. So only the right data hits the agent at the right time in the right possible shape, size, and form as the ideal context, fresh context for these agents to run. And batch processing in this world of AI is just not good enough anymore. So this is why I think there is a need to introduce the category of Streamhouse as a new principle of data architecture of how you build these operational use cases, particularly focused with the needs and requirements of AI. [00:03:15] S0: And one of the reoccurring challenges from businesses that we keep hearing is that their data is spread across so many different systems, departments, and locations. So how big a barrier is that fragmentation, especially when companies want to introduce things like AI agents that need to understand exactly what's happening right across the organization?
00:03:15 · Neil C. Hughes, Steffen Hoellinger
[00:03:15] S0: And one of the reoccurring challenges from businesses that we keep hearing is that their data is spread across so many different systems, departments, and locations. So how big a barrier is that fragmentation, especially when companies want to introduce things like AI agents that need to understand exactly what's happening right across the organization? [00:03:34] S1: Yeah. I think it's a big problem, and it also shows why particularly around batch way of doing things in terms of moving data in a batch pipeline and a batch process takes long, is expensive, and is particularly not good enough for these AI, agentic AI use cases to work with that fresh data. So AI really needs fresh continuously aggregated data all the time to make good decisions and recommendations. And this is why batch fails in the in the world of AI. [00:04:04] S0: And of course, we are two European guys in The US at the moment. And Europe just has its own challenges around data sovereignty, privacy Yep. And indeed regulation. Do do you think these requirements could slow AI adoption down, or could they ultimately encourage European businesses to build more trustworthy and reliable systems? Which way do you see this?
00:04:23 · Steffen Hoellinger
[00:04:23] S1: Yeah. I think there is definitely a big requirement and big interest at the moment for sovereignty in that space which is also why we are building on open source and on open standards in that sense. So Kafka itself is the technology as you know that Confluent developed and the creators of of Kafka back in the days at LinkedIn are kind of the same people that started then in the company Confluent and then we adopted further open standards and open source technologies like Flink for stream processing as a leading technology to process data while it's traveling in the stream. In the world at the moment, a huge adoption for Flink particularly in China but also in Silicon Valley for moving data around but also processing it whilst moving in the stream. And then OpenTable formats like Iceberg and Delta. So all of this is kind of built on open source technology, open standards, but still presented as a fully integrated platform. So companies can actually rely on that they can run these systems in all kinds of ways in different parts and also in different scenarios in operational mission critical use cases and scenarios wherever you you require this this compute and this processing to to happen. So we have lots of scenarios where people run Kafka and Flink in hybrid cloud scenario where they run it on prem, where they run it in the battlefield at the edge. There are places where you wouldn't expect Kafka to run. And, of course, also, server end infrastructure to run those those systems.
00:05:55 · Neil C. Hughes, Steffen Hoellinger
[00:05:55] S0: And I'm curious you'll... You dangled a carrot there. You don't have to mention any names. Some of those scenarios where you said you you wouldn't expect them to run. Is there anything you can share around that? [00:06:04] S1: Yeah. Actually, there are there are few very interesting use cases also over the last few years. You have people ran Kafka, for example, in America's Cup cluster linking like two boats with each other. You had like the the race boat and you had like a a separate boat that was actually the boat where all the team members had like making the decisions and then you had cluster link... Linking between those two boats exchanging information at low latency. There was just one example of a scenario where you wouldn't expect people to run a Kafka cluster on. [00:06:31] S0: Really? Incredibly cool. I'm curious, are you seeing European organizations or some reconsidering where their data, where their AI workload should live, particularly when it comes to public cloud versus private infrastructure. Are you are you hearing a lot of those conversations?