Most data teams can see exactly what is wrong with their data platform. The dashboards are full, cost reports flag overruns, and AI assistants will even draft query rewrites. Yet platform spend keeps climbing and the optimization backlog keeps growing.
The gap is not knowledge. It is execution. In this on-demand session, we unpack why the most common approaches stop short and what it takes to actually close the gap across Databricks, Snowflake, and BigQuery.
See where AI is finally doing meaningful work on the other side of the recommendation. Watch now and learn how to turn insight into validated fixes in production.
In this 30-minute session plus live Q&A, Head of Artificial Intelligence Prajakta Kalmegh and VP of Product Eric Chu will guide you through:
- Why common approaches stall: Learn why observability platforms, compute rightsizing, AI copilots, and in-house tools keep producing more recommendations, not more fixes.
- What real context changes: Discover how an AI system with context across queries, pipelines, compute, and storage turns a suggestion into a safe, validated production change.
- Autonomous operations in practice: See the work happening today on Databricks, Snowflake, and BigQuery, and the early results teams are seeing.
