Data quality testing: how Bruin and dbt take different paths to the same goal

If you've built data pipelines for any length of time, you know the drill: the pipeline runs fine, the table gets created, and three days later someone discovers that half the rows have null IDs. The transformation was correct, the data just wasn't what you assumed. Both Bruin and dbt have built-in systems for catching these problems. They solve the same problem, but in genuinely different ways. dbt treats tests as separate nodes in the DAG. Bruin embeds quality checks directly into asset definitions. Both approaches work, and the trade-offs between them are worth understanding regardless of which tool you use. What we mean by "data quality testing" Before comparing the tools, let me define the scope. I'm talking about checks that answer: "Does the data this pipeline just produced actually look right?" Common examples: Is this column unique? Are there nulls where there shouldn't be? Are all values in a column within an expected set? Does a custom business rule hold? (e.g., total debits