Modern data teams are increasingly adopting dbt to bring software engineering practices to analytics. This guide focuses on using dbt with SQL Server, a combination that is less commonly covered than dbt with cloud warehouses like Snowflake or BigQuery. The author walks through a layered architecture—staging, intermediate, and mart layers—and explains the SQL logic behind each model. Key takeaways include how to handle incremental loads, define clear naming conventions, and maintain data quality through tests. For teams still relying on SSIS or stored procedures, this approach offers a more transparent and version-controlled alternative. While the examples are specific to SQL Server, the principles apply broadly to any dbt project. The article is part of a series, so readers may benefit from earlier posts on dbt fundamentals and tool comparisons.
Learn how to structure a data warehouse with dbt and SQL Server using layered modeling. This guide covers design principles and SQL patterns for five core models, helping teams move beyond SSIS and stored procedures.