A hands-on walkthrough of analytics engineering with dbt on Databricks SQL. Covers key dbt project files, model layering strategy, folder and naming conventions, documentation and YAML patterns, and materialization decision-making—built from real project experience.

This blog shares real-world experience using dbt with Databricks SQL to transform raw data into analytics-ready datasets, covering analytics engineering fundamentals, governance, and scalable practices that improve data quality, speed, and confidence in business decisions.