What does the term 'seed' refer to in dbt?

The term 'seed' in dbt refers to static data files, typically in CSV format, that are loaded into the data warehouse as tables. This functionality allows analytics engineers to quickly and efficiently manage small datasets that are essential for analyses, testing, or providing consistent reference data.

When you use seeds, dbt reads the content of these CSV files and creates tables in your data warehouse. This is particularly beneficial for providing lookup tables or datasets that do not change frequently, ensuring uniformity and enabling easier data access for downstream analytics.

The other options relate to different aspects of dbt's functionality. Dynamic queries generated at runtime involve a different concept focused on querying behavior depending on user inputs or the current context. Temporary tables created during testing refer to the mechanisms dbt uses for testing data transformations, which do not encapsulate the concept of a 'seed.' Model definitions stored in YAML format are related to the configuration and documentation of dbt models but do not correspond to the concept of seeds. Overall, the connection between seeds and static data files showcases how dbt facilitates structured data management in analytics workflows.