How can you filter models when running dbt?

Using tags or specifying model names in the run command is a fundamental way to filter models in dbt. This functionality allows analysts to run specific models or groups of models that are tagged with certain attributes, streamlining the workflow and making it more efficient.

When you specify model names directly in the command, you're instructing dbt to only execute those particular models, which can save time and resources. Tagging models provides an additional layer of flexibility, as you can easily group related models and run all of them at once, enhancing organization and clarity in larger projects.

Other methods mentioned, like an SQL WHERE clause, are not applicable to dbt's model execution process. dbt commands typically do not support SQL clauses in this manner. Similarly, adjusting dbt profile configurations or setting environment variables does not directly filter which models are run; these configurations primarily focus on connection settings and general runtime behavior, rather than controlling the specific models to be executed. Thus, using tags or specifying model names effectively meets the needs of filtering models during a dbt run.