What is the purpose of the dbt seed command?

The dbt seed command is specifically designed to load CSV files into your data warehouse, making it a crucial tool for integrating static data into your dbt project. This command is particularly useful when you need to incorporate reference data that isn't generated through transformations or isn't present in your source data.

When you run the seed command, dbt reads the CSV files specified in your project and creates corresponding tables in the data warehouse, which can then be utilized in your models. This allows you to have a centralized and version-controlled approach to managing reference datasets alongside your dbt models.

This functionality distinguishes dbt seed from other operations such as generating snapshots, creating mock data, or refreshing existing tables, which are outside its scope. The primary focus of the seed command is on data ingestion from CSV files, making it essential for those who need to augment their analytical capabilities with additional reference data.