Cosmos Dbt Core

# 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` |

```python
from cosmos import ProjectConfig

_project_config = ProjectConfig(
    dbt_project_path="/path/to/dbt/project",
    # manifest_path="/path/to/manifest.json",  # for dbt_manifest load mode
    # project_name="my_project",  # if using manifest_path without dbt_project_path
    # install_dbt_deps=False,  # if deps precomputed in CI
)
```

## 2. Choose Parsing Strategy (RenderConfig)

Pick ONE load mode based on constraints:

| Load mode | When to use | Required inputs | Constraints |
|-----------|-------------|-----------------|-------------|
| `dbt_manifest` | Large projects; containerized execution; fastest | `ProjectConfig.manifest_path` | Remote manifest needs `manifest_conn_id` |
| `dbt_ls` | Complex selectors; need dbt-native selection | dbt installed OR `dbt_executable_path` | Can also be used with containerized execution |
| `dbt_ls_file` | dbt_ls selection without running dbt_ls every parse | `RenderConfig.dbt_ls_path` | `select`/`exclude` won't work |
| `automatic` (default) | Simple setups; let Cosmos pick | (none) | Falls back: manifest → dbt_ls → custom |

> **CRITICAL**: Containerized execution (`DOCKER`/`KUBERNETES`/etc.)

```python
from cosmos import RenderConfig, LoadMode

_render_config = RenderConfig(
    load_method=LoadMode.DBT_MANIFEST,  # or DBT_LS, DBT_LS_FILE, AUTOMATIC
)
```

---

## 3. Choose Execution Mode (ExecutionConfig)

> **Reference**: See **[reference/cosmos-config.md](reference/cosmos-config.md#execution-modes-executionconfig)** for detailed configuration examples per mode.

Pick ONE execution mode:

| Execution mode | When to use | Speed | Required setup |
|----------------|-------------|-------|----------------|
| `WATCHER` | Fastest; single `dbt build` visibility | Fastest | dbt adapter in env OR `dbt_executable_path` or dbt Fusion |
| `WATCHER_KUBERNETES` | Fastest isolated method; single `dbt build` visibility | Fast | dbt installed in container |
| `LOCAL` + `DBT_RUNNER` | dbt + adapter in the same Python installation as Airflow | Fast | dbt 1.5+ in `requirements.txt` |
| `LOCAL` + `SUBPROCESS` | dbt + adapter available in the Airflow deployment, in an isolated Python installation | Medium | `dbt_executable_path` |
| `AIRFLOW_ASYNC` | BigQuery + long-running transforms | Fast | Airflow ≥2.8; provider deps |
| `KUBERNETES` | Isolation between Airflow and dbt | Medium | Airflow ≥2.8; provider deps |
| `VIRTUALENV` | Can't modify image; runtime venv | Slower | `py_requirements` in operator_args |
| Other containerized approaches | Support Airflow and dbt isolation | Medium | container config |

```python
from cosmos import ExecutionConfig, ExecutionMode

_execution_config = ExecutionConfig(
    execution_mode=ExecutionMode.WATCHER,  # or LOCAL, VIRTUALENV, AIRFLOW_ASYNC, KUBERNETES, etc.
)
`