Building the Interoperable Lakehouse: A Data Foundation for Enterprise AI
Webinar Speaker: Donald Farmer, Research Fellow
Date: Thursday, May 21, 2026
Time: 9:00 a.m. PT / 12:00 p.m. ET
Enterprise AI is moving from pilots to production, but the bottleneck isn't the model; it's the data underneath it. Fragmented estates, duplicated copies, and brittle pipelines slow every AI initiative. Central teams are facing a familiar false choice between flexible DIY infrastructure and managed platforms that lock them in. Legacy lakehouse approaches, even those built on open source foundations, often reproduce the same silos in new form.
In this webinar, TDWI research fellow Donald Farmer and an expert from Snowflake draw on recent research and real-world implementations to explore how Apache Iceberg and open catalog standards enable true bidirectional interoperability, how declarative pipelines reduce operational burden, and how unified governance turns a storage pattern into an AI-ready foundation.
The goal: giving enterprises agency over their data from any engine, without compromise. Key takeaways:
- How open table formats, catalog design, and engine choice determine what you can actually build
- Why declarative data engineering reduces pipeline maintenance at scale
- How unified governance enables trusted AI deployment across engines
- Why portable semantic context is the missing layer for reliable AI output
Guest Speaker
James Rowland-Jones
Director of Product Management
Snowflake
James Rowland-Jones is a Director of Product Management at Snowflake and a seasoned product leader in modern, open and interoperable data architectures. With more than 25 years of leadership experience, shaping data products at Dremio, Confluent and Microsoft, he's focused on helping organizations build efficient architectures by simplifying data flows, reducing complexity and enabling scalable, future-ready data platforms.
Donald Farmer