Lakehouse or Warehouse which one to choose in Fabric ?

Core Concepts Data Warehouse A centralized repository for cleaned, integrated, structured data from multiple sources, using schema-on-write and optimized for SQL analytics and BI. It emphasizes strong data quality, conformed dimensions, historical tracking, and tight governance, typically using ETL or ELT pipelines to transform data before loading. Data Lakehouse An architecture that builds on a data lake (object storage) but adds warehouse-like capabilities —ACID transactions, schema enforcement, indexing, and SQL query performance—over open table formats like Delta, Iceberg, or Hudi. It supports structured, semi-structured, and unstructured data in one platform, enabling both BI and AI/ML workloads without separate lake + warehouse stacks. Architectural Differences Storage & Schema Warehouse Stores data in relational structures (tables, columns, indexes) using schema-on-write—data is conformed to a fixed schema before it’s stored. Often uses proprietary or tightly controlled storage