As data teams scale, one of the biggest pain points is runaway compute and storage cost from platforms designed for heavy workloads but misused for everyday analytics. Databricks shines at large‑scale ML and advanced analytics, but using it as a catch‑all BI engine can quickly inflate budgets.

In this talk, we’ll show how layering Trino’s high‑performance federation with Databricks provides a cost‑optimized analytics strategy. Rather than keeping expensive SQL warehouses or large clusters hot, we can offload exploratory queries, BI workloads, and cross‑system joins to Trino. Delta Lake tables in Databricks remain accessible through Trino, but now joined seamlessly with data in S3, Kafka, or cloud warehouses — at lower cost.

We’ll walk through real patterns where Trino reduced compute bills while still enabling Databricks to focus on what it does best: advanced ML/AI and scalable storage. Attendees will learn a pragmatic approach to balancing cost, performance, and flexibility by leveraging Trino and Databricks together instead of in silos.