Cosmos + dbt Fusion: Implementation Checklist

Cosmos + dbt Fusion: Implementation Checklist

Execute steps in order. This skill covers Fusion-specific constraints only.

Version note: dbt Fusion support was introduced in Cosmos 1.11.0. Requires Cosmos ≥1.11.

Reference: See reference/​cosmos-config.md for ProfileConfig, operator_args, and Airflow 3 compatibility details.

Before starting, confirm: (1) dbt engine = Fusion (not Core → use cosmos-dbt-core), (2) warehouse = Snowflake or Databricks only, (3) ExecutionMode.LOCAL is acceptable (no containerized/​async/​virtualenv).

Fusion-Specific Constraints

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1. Confirm Cosmos Version

CRITICAL: Cosmos 1.11.0 introduced dbt Fusion compatibility.

# Check installed version
pip show astronomer-cosmos

# Install/​upgrade if needed
pip install "astronomer-cosmos>=1.11.0"

Validate: pip show astronomer-cosmos reports version ≥ 1.11.0

---

2. Install the dbt Fusion Binary (REQUIRED)

dbt Fusion is NOT bundled with Cosmos or dbt Core. Install it into the Airflow runtime/​image.

Determine where to install the Fusion binary (Dockerfile / base image / runtime).

Example Dockerfile Install

USER root
RUN apt-get update && apt-get install -y curl
ENV SHELL=/​bin/​bash
RUN curl -fsSL https://public.cdn.getdbt.com/​fs/​install/​install.sh | sh -s -- --update
USER astro

Common Install Paths

Validate: The dbt binary exists at the chosen path and dbt --version succeeds.

---

3. Choose Parsing Strategy (RenderConfig)

Parsing strategy is the same as dbt Core. Pick ONE:

from cosmos import RenderConfig, LoadMode

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

---

4. Configure Warehouse Connection (ProfileConfig)

Reference: See reference/​cosmos-config.md for full ProfileConfig options and examples.

from cosmos import ProfileConfig
from cosmos.profiles import SnowflakeUserPasswordProfileMapping

_profile_config = ProfileConfig(
    profile_name="default",
    target_name="dev",
    profile_mapping=SnowflakeUserPasswordProfileMapping(
        conn_id="snowflake_default",
    ),
)

---

5. Configure ExecutionConfig (LOCAL Only)

CRITICAL: dbt Fusion with Cosmos requires ExecutionMode.LOCAL with dbt_executable_path pointing to the Fusion binary.

from cosmos import ExecutionConfig

_execution_config = ExecutionConfig(
    dbt_executable_path="/​home/​astro/​.local/​bin/​dbt",  # REQUIRED: path to Fusion binary
    # execution_mode is LOCAL by default - do not change
)

What "Local-Only" Means

---

6. Configure Project (ProjectConfig)

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
    # install_dbt_deps=False,  # if deps precomputed in CI
)

---

7. Assemble DAG / TaskGroup

Option A: DbtDag (Standalone)

from cosmos import DbtDag, ProjectConfig, ProfileConfig, ExecutionConfig, RenderConfig
from cosmos.profiles import SnowflakeUserPasswordProfileMapping
from pendulum import datetime

_project_config = ProjectConfig(
    dbt_project_path="/​usr/​local/​airflow/​dbt/​my_project",
)

_profile_config = ProfileConfig(
    profile_name="default",
    target_name="dev",
    profile_mapping=SnowflakeUserPasswordProfileMapping(
        conn_id="snowflake_default",
    ),
)

_execution_config = ExecutionConfig(
    dbt_executable_path="/​home/​astro/​.local/​bin/​dbt",  # Fusion binary
)

_render_config = RenderConfig()

my_fusion_dag = DbtDag(
    dag_id="my_fusion_cosmos_dag",
    project_config=_project_config,
    profile_config=_profile_config,
    execution_config=_execution_config,
    render_config=_render_config,
    start_date=datetime(2025, 1, 1),
    schedule="@daily",
)

Option B: DbtTaskGroup (Inside Existing DAG)

from airflow.sdk import dag, task  # Airflow 3.x
# from airflow.decorators import dag, task  # Airflow 2.x
from airflow.models.baseoperator import chain
from cosmos import DbtTaskGroup, ProjectConfig, ProfileConfig, ExecutionConfig
from pendulum import datetime

_project_config = ProjectConfig(dbt_project_path="/​usr/​local/​airflow/​dbt/​my_project")
_profile_config = ProfileConfig(profile_name="default", target_name="dev")
_execution_config = ExecutionConfig(dbt_executable_path="/​home/​astro/​.local/​bin/​dbt")

@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def my_dag():
    @task
    def pre_dbt():
        return "some_value"

    dbt = DbtTaskGroup(
        group_id="dbt_fusion_project",
        project_config=_project_config,
        profile_config=_profile_config,
        execution_config=_execution_config,
    )

    @task
    def post_dbt():
        pass

    chain(pre_dbt(), dbt, post_dbt())

my_dag()

---

8. Final Validation

Before finalizing, verify:

• Cosmos version: ≥1.11.0
• Execution mode: LOCAL only
• Fusion binary installed: Path exists and is executable
• Warehouse supported: Snowflake or Databricks only
• Secrets handling: Airflow connections or env vars, NOT plaintext

Troubleshooting

If user reports dbt Core regressions after enabling Fusion:

AIRFLOW__COSMOS__PRE_DBT_FUSION=1

User Must Test

• The DAG parses in the Airflow UI (no import/​parse-time errors)
• A manual run succeeds against the target warehouse (at least one model)

---

Reference

• Cosmos dbt Fusion docs: https://astronomer.github.io/astronomer-cosmos/configuration/dbt-fusion.html
• dbt Fusion install: https://docs.getdbt.com/docs/core/pip-install#dbt-fusion

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Related Skills

• cosmos-dbt-core: For dbt Core projects (not Fusion)
• authoring-dags: General DAG authoring patterns
• testing-dags: Testing DAGs after creation