Many companies using dbt projects often rely on unit tests or data tests, but a robust data quality strategy requires a combination of both, along with data diffs, to address their differing purposes and limitations. dbt unit tests, introduced in dbt Core 1.8, validate model logic with predefined inputs and outputs, allowing for granular testing and isolation of the model code from source data validation. Unlike dbt data tests, which execute assertions on live, ever-changing data, unit tests offer faster execution and less noise, making them ideal for complex SQL logic and critical business scenarios. While dbt data tests, known for their simplicity, can be computationally expensive and noisy, unit tests provide a more stable testing environment during the development phase, especially useful for edge cases or retrospective bug fixes. Integrating dbt unit tests into the CI process is recommended to assess code changes during reviews, but they are not suitable for production due to increased execution time without added benefit.