dbt has become a standard tool in modern data stacks, but its adoption with SQL Server has been slower due to historical limitations. This series addresses that gap by walking through a real project that builds a layered data warehouse on SQL Server using dbt. The first article explains what dbt is—a transformation tool that enables analytics engineers to write modular SQL models with testing, documentation, and version control—and outlines the typical workflow from local development to production deployment. Key stages include project setup, model development, testing, documentation generation, and scheduling via dbt Cloud or CLI. For teams stuck with SQL Server as their primary warehouse, this series demonstrates that dbt can still bring engineering rigor to their transformation layer. The practical focus on a real-world scenario makes it a useful reference for data engineers and analysts looking to modernize their SQL Server workflows.
A practical guide to using dbt with SQL Server for data warehousing, covering core concepts and project lifecycle from development to deployment.