Why Semantic Layers Make Enterprise Text-to-SQL Safer

This article discusses why raw Text-to-SQL fails in enterprise environments due to messy schemas, inconsistent naming, and embedded business logic. It introduces the semantic layer as a solution that provides structured context for AI, boosting query accuracy to 85-95%. The article covers four approaches: Dremio's natively integrated virtual datasets, Snowflake Cortex Analyst with Semantic Views, dbt Semantic Layer with MetricFlow, and how to choose between them. It emphasizes governed access, metadata generation, and performance without data movement, making it a valuable resource for IT professionals seeking reliable AI-driven data querying.