Evaluating pure analytics capabilities is an outdated framework that treats the data warehouse as an isolated silo. Databricks is aggressively moving to handle the entire enterprise footprint including BI & Agentic universe. With the maturity of Databricks SQL Serverless & the Photon engine's raw speed combined with Predictive Optimization, Traditional BI is moving to the Databricks stack. Databricks is not just the heavy data engineering/ml engine anymore, it has already displaced Big Query via its serverless features in many clients.

By anchoring the architecture in Databricks, clients eliminate the architectural tax of data movement, unifying streaming, engineering, advanced BI and Gen AI Agents under a single governance boundary under Unity Catalog. Big Query is still alive mostly due to managing internal operational risks (Google commitments, Workloads tightly coupled with native Google APIs etc) that prevent workloads from moving and not because of a superior feature list. The forward looking consensus is clear, map enterprise workloads to a unified destination where possible. I design architectures with Databricks as the primary intelligent platform for Data and AI while treating other cloud native legacy Data warehouses as a specialized, localized engine strictly for legacy workloads that internal risk profiles demand keeps running in place.