Managing static data in a data warehouse can be streamlined with dbt seeds, part of the Data Build Tool (dbt) framework, which modernizes traditional methods by integrating them into broader data infrastructure. dbt seeds are typically small, static CSV files that are easy to create, edit, and version control, allowing data teams to manage static data in the same way they handle code. This method enhances simplicity and consistency in data operations, as dbt seeds can be loaded into a warehouse during a dbt run and then used like any other table for joining with transformed data, modeling, or analysis. Unlike larger, frequently changing datasets that require custom ETL pipelines or tools like Fivetran and Airbyte, static data managed with dbt seeds benefits from a standardized approach, ensuring consistency and transparency through version control, pull requests, and peer reviews.