BI Serving Pointers; Maximizing for Performance and TCO

This post discusses Databricks' full stack approach for optimizing BI workloads through physical data layout optimization, semantic layers, and query efficiency techniques.

• •Star schema dimensional modeling with denormalized dimension tables and surrogate keys provides optimal join paths for BI query performance
• •Unity Catalog managed tables with Predictive Optimization, liquid clustering, and metadata caching deliver 22% average performance improvement
• •Metric Views create a single governed semantic layer where business metrics are defined once and consumed consistently across multiple BI tools and AI agents
• •Metric View materialization automatically maintains pre-aggregated results with intelligent query rewriting that transparently routes queries to the fastest execution path