How do you decide which tests to write for a DBT model? Are alerts needed?

When selecting tests for a DBT model, it is important to focus on:

• Data integrity: checking for unique keys, absence of duplicates.
• Absence of gaps: checking for NULLs in mandatory fields.
• Logical constraints: for example, values within a certain range, compliance with business rules.
• Relationships between tables: checking foreign keys and data consistency.

Alerts are necessary to respond quickly to changes in data quality. If tests start failing, it signals problems in data sources or transformation logic. Setting up alerts helps maintain trust in analytics and correct errors in a timely manner.

Example: if a model requires uniqueness on user_id, the test will verify this, and upon violation, it will notify the team.