What are hooks in dbt primarily used for?

Hooks in dbt are primarily used to run custom SQL and leverage database-specific actions. They allow you to execute specific commands at different points in the dbt run lifecycle, which can include actions such as creating, dropping, or modifying database objects and configurations that are not directly part of the model's transformation logic.

This capability is particularly useful for tasks such as setting up or tearing down environments, managing indexes, or executing notifications via SQL. Hooks give analytics engineers the flexibility to extend the functionality of their dbt workflows according to the specific requirements of their data operations or the capabilities of the underlying database.

While other options presented in the question touch on different functionalities, hooks specifically target executing custom SQL and handling database interactions, making them a critical tool for customizing the behavior of dbt runs based on project or organizational needs.