How can you ensure data integrity in dbt?

Implementing tests on models to catch issues before deployment is a vital practice in ensuring data integrity in dbt. Tests allow you to define expectations for the data being processed. This can include checks for null values, unique constraints, and referential integrity. By validating your data against these tests, you can identify and resolve problems within your models prior to deploying them to production, thereby enhancing the reliability and accuracy of your datasets.

Using views instead of tables does not inherently ensure data integrity. While views can present real-time data and avoid duplication, they do not provide validation mechanisms for the data. Avoiding model dependencies may lead to an oversimplified architecture but does not address the need to confirm data quality. Running dbt solely in production environments overlooks the essential need for testing and validation that should occur in development or staging environments to safeguard data integrity.