Enterprises today struggle with sprawling data estates — Delta Lake in Databricks for ML workloads, Snowflake for BI, and legacy relational systems still running critical operations. The default answer is usually another pipeline, another data copy, and yet another layer of complexity.

In this session, we’ll share how combining Trino’s federated query engine with Databricks’ lakehouse platform creates an agile data fabric that eliminates unnecessary ETL. With Trino, we can query Delta tables in Databricks directly alongside Snowflake, S3, or even legacy RDBMS — all through one SQL interface. This means faster time to insight, fewer brittle pipelines, and a unified view across silos.

We’ll explore architectural patterns, performance considerations for querying Delta Lake via Trino, and how this approach empowers teams to democratize analytics without duplication. Attendees will leave with practical examples of making Databricks a first‑class participant in a broader federated architecture, powered by Trino.