Twelve Tests Every Production dbt Project Should Have on Day One
The essential quality checks every analytics engineering team should standardize before scaling pipelines.
Why Testing Matters in dbt Projects
Production dbt environments constantly evolve as teams introduce new models, sources, and transformations. Without automated testing, small issues can silently propagate into dashboards and business reports.
Core Tests Every Team Should Implement
- Unique tests
- Not null tests
- Relationship tests
- Freshness tests
- Accepted value tests
Example Use Case
For example, freshness tests can quickly identify delayed upstream ingestion pipelines before stakeholders notice reporting issues.
Building Reliable Data Pipelines
Strong testing practices help analytics engineering teams scale production systems with greater reliability, visibility, and confidence.
