About

AI is changing what enterprise data platforms need to deliver but most architectures are still built around moving and copying data. Every new AI application, dashboard, or operational system adds another pipeline, another duplicate dataset, and another opportunity for data to become stale.

The result is growing complexity, rising costs, and AI systems that struggle to access trusted, real-time information.

In this session, we’ll explore why the future of data architecture lies beyond the traditional lakehouse. You’ll learn how streaming technologies and open table formats such as Apache Iceberg are converging to reduce data movement, eliminate unnecessary ETL, and create a single, continuously updated data foundation for operational applications, analytics, and AI.

If your organisation is building more pipelines to support AI, it may be time to stop – and rethink the architecture instead.

Takeaways:

• Understand why AI is exposing the limitations of traditional ETL and batch-oriented data architectures.
• Learn how streaming data and open table formats like Apache Iceberg can reduce data movement and eliminate unnecessary duplication.
• Discover architectural patterns for building a single, real-time data foundation that serves operational systems, analytics, and AI.
• Leave with practical ideas for simplifying your data platform while improving data freshness, scalability, and AI readiness.