cosmos-dbt-core
Convert dbt Core projects into Airflow DAGs or TaskGroups using Astronomer Cosmos.
- Supports three assembly patterns: standalone DbtDag, DbtTaskGroup within existing DAGs, and individual Cosmos operators for fine-grained control
- Choose from eight execution modes (WATCHER, LOCAL, VIRTUALENV, KUBERNETES, AIRFLOW_ASYNC, and others) based on isolation and performance needs
- Offers three parsing strategies (dbt_manifest, dbt_ls, dbt_ls_file, automatic) to balance speed and selector complexity
- Configures warehouse connections via Airflow connections with ProfileMapping classes or existing profiles.yml files; supports testing behavior modes (AFTER_EACH, BUILD, AFTER_ALL, NONE)
- Requires upfront verification of dbt engine (Core only), warehouse type, Airflow version (2.x or 3.x), execution environment, and manifest availability
Cosmos + dbt Core: Implementation Checklist
Execute steps in order. Prefer the simplest configuration that meets the user's constraints.
Version note: This skill targets Cosmos 1.11+ and Airflow 3.x. If the user is on Airflow 2.x, adjust imports accordingly (see Appendix A).
Reference: Latest stable: https://pypi.org/project/astronomer-cosmos/
Before starting, confirm: (1) dbt engine = Core (not Fusion → use cosmos-dbt-fusion), (2) warehouse type, (3) Airflow version, (4) execution environment (Airflow env / venv / container), (5) DbtDag vs DbtTaskGroup vs individual operators, (6) manifest availability.
1. Configure Project (ProjectConfig)
| Approach | When to use | Required param |
|---|---|---|
| Project path | Files available locally | dbt_project_path |
| Manifest only | dbt_manifest load |
manifest_path + project_name |
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