Models are where your developers spend most of their time within a dbt environment. Models are primarily written as a select statement and saved as a .sql
file. While the definition is straightforward, the complexity of the
execution will vary from environment to environment. Models will be
written and rewritten as needs evolve and your organization finds new
ways to maximize efficiency.
SQL is the language most dbt users
will utilize, but it is not the only one for building models. Starting
in version 1.3, dbt Core and dbt Cloud support Python models. Python
models are useful for training or deploying data science models, complex
transformations, or where a specific Python package meets a need — such as using the dateutil library to parse dates.