Mastering DBT: The Secret to Seamless and Reliable Data Workflows

Understanding dbt build, dbt run, and dbt test: A Quick Comparison

In dbt, understanding the difference between build, run, and test commands is key to streamlining your data workflows.

• dbt run: Executes your models to transform raw data into tables or views but doesn't perform any data quality checks. Use this when you want to focus on data transformation alone.
• dbt test: Validates your models by running tests for data quality (e.g., checking for uniqueness or null values). It’s a crucial step to ensure that your transformations produce reliable and accurate results.
• dbt build: The most comprehensive command. It runs your models, performs tests, and executes freshness checks. Importantly, if any test fails during dbt build, the process rolls back, ensuring that no bad data is written to your tables. This safeguard helps maintain the current state of your data without disruptions or incomplete updates.

Example: While transforming a customer model, you could use dbt run to build it, followed by dbt test to check data integrity. However, with dbt build, both steps are combined, and if a test fails, the previous data remains unaffected.

This feature ensures a smooth, reliable deployment process!