What happens if you change an existing model's schema in dbt?

Changing an existing model's schema in dbt requires careful consideration and management of the potential impacts on downstream models and tests. When the schema of a model is altered—whether that involves changing column names, data types, or any structural aspect—it does not trigger an automatic update for dependent models. Instead, those dependent models may break or yield errors if they rely on the former schema structure.

This means that an analyst or engineer must meticulously evaluate how the schema change affects any models that reference the modified model. Additionally, tests that validate the integrity and correctness of the data may also need to be revised or recreated to ensure they are aligned with the updated schema. Thus, proper migration steps and testing are critical to maintain the reliability and accuracy of the data pipeline.

The choices that suggest automatic updates or deletion misconstrue dbt's functioning; it emphasizes manual oversight and deliberate management of dependencies, reducing the chances of unforeseen issues in data workflows. Furthermore, a claim that a schema change has no effect on the dbt project disregards the interconnected nature of models within dbt’s architecture. Therefore, the understanding that careful migration is essential reflects the nuanced reality of dbt workflows.