What role do 'seeds' play in dbt?

Seeds in dbt play a pivotal role in enabling users to upload and incorporate CSV files into their projects. When a CSV file is defined as a seed, dbt treats it as a source of data that can be transformed and utilized just like any other models created from SQL transformations. By using seeds, you can easily integrate pre-existing data into your dbt workflows, which is particularly useful for incorporating reference tables, static datasets, or any supplementary data that enhances your models.

Seeds are defined typically in the data directory, and dbt automatically generates a model from each seed file upon execution. This allows users to have access to the data in the same way they would access data from a database model, facilitating effective data management and utilization throughout the project.

The other options, although related to data handling in different capacities, do not accurately describe the primary function of seeds. Temporary data storage for calculations isn't specifically the purpose of seeds, nor do they involve executing transformations from raw SQL files or managing environmental changes in a dbt project.