Abstract
76%
senior dev or higher
1:11
speaker ratio
60+
practitioners
QCon London 2025 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Slides & video
Slides Not available
Part of the track
Modern Data Architectures Hosted by Fabiane Nardon Data Expert, Java Champion & Data Platform Director @totvsFrom the same track
Wednesday 9 April
10:35 Whittle (3rd Fl.) Session Data Architecture Reliable Data Flows and Scalable Platforms: Tackling Key Data Challenges Matthias Niehoff Head of Data and Data Architecture @codecentric AG, iSAQB Certified Professional for Software Architecture There are a few common and mostly well-known challenges when architecting for data. For example, many data teams struggle to move data in a stable and reliable way from operational systems to analytics systems. 11:45 Whittle (3rd Fl.) Session AI/ML Achieving Precision in AI: Retrieving the Right Data Using AI Agents Adi Polak Director, Advocacy and Developer Experience Engineering @Confluent, Author of "Scaling Machine Learning with Spark" and "High Performance Spark 2nd Edition" In the race to harness the power of generative AI, organizations are discovering a hidden challenge: precision. 13:35 Fleming (3rd Fl.) Session Panel: Modern Data Architectures 14:45 Mountbatten (6th Fl.) Session AI/ML The Data Backbone of LLM Systems Paul Iusztin Senior ML/AI Engineer, MLOps, Founder @Decoding ML Any LLM application has four dimensions you must carefully engineer: the code, data, models and prompts. Each dimension influences the other. That's why you must learn how to track and manage each. The trick is that every dimension has particularities requiring unique strategies and tooling. 15:55 Whittle (3rd Fl.) Session Data Architecture Beyond the Warehouse: Why BigQuery Alone Won’t Solve Your Data Problems Sarah Usher Data & Backend Engineer, Community Director, Mentor Many organizations mistake the adoption of a data warehouse, like BigQuery, as the golden ticket to solving all their data challenges. But without a robust data strategy and architecture, you’re simply shifting chaos into the cloud.