Summary
Your organization needed to transform raw staging data into clean, structured datasets that analysts and downstream systems can rely on. In this module, you applied T-SQL techniques in a Fabric warehouse to address that challenge.
You started by writing queries to filter, join, aggregate, and reshape data using the SQL query editor and the Visual Query Editor. You then created views to encapsulate reusable transformation logic, hiding complexity behind simple, queryable objects. With stored procedures, you automated repeatable data processing tasks using parameterized logic and loading patterns like full refresh, incremental load, and merge. Finally, you implemented dimensional tables that form the foundation for semantic models and analytics.
These T-SQL transformation patterns give you a complete, repeatable approach to preparing warehouse data. The dimensional model you built is ready to serve as the source for Power BI semantic models, cross-database queries, and AI workloads.