What is the primary benefit of using the `dbt test` command?

The primary benefit of using the dbt test command is to check for data quality and integrity issues in models. This command allows analytics engineers to define tests that validate assumptions about their data, such as ensuring that no values are null when they should have a value or that certain fields meet specified criteria. By running these tests, you can identify discrepancies or issues in the dataset before they propagate through your data pipeline, thus ensuring higher data reliability and quality in your business intelligence efforts. This proactive approach helps maintain trust in the analytics produced from the data models.

The other options do not capture the essential purpose of the dbt test command. Compiling models into raw SQL is a function handled by other dbt commands, scheduling runs pertains to task orchestration tools, and performance metrics are usually assessed through other means within dbt or external monitoring tools.