/cosmos-dbt-fusion

name: cosmos-dbt-fusion
description: Run a dbt Fusion project with Astronomer Cosmos. Use when running a dbt Fusion project with Astronomer Cosmos (Cosmos 1.11+, ExecutionMode.LOCAL on Snowflake/Databricks). Before implementing, verify dbt engine is Fusion (not Core), the warehouse is supported, and local execution is acceptable. Does not cover dbt Core.

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](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, Databricks, Bigquery and Redshift only.

Fusion-Specific Constraints

| Constraint | Details | |------------|---------| | No async | `AIRFLOW_ASYNC` not supported | | No virtualenv | Fusion is a binary, not a Python package | | Warehouse support | Snowflake, Databricks, Bigquery and Redshift support [while in preview](https://github.com/dbt-labs/dbt-fusion) |

---

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

| Environment | Typical path | |-------------|--------------| | Astro Runtime | `/home/astro/.local/bin/dbt` | | System-wide | `/usr/local/bin/dbt` |

**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:

| Load mode | When to use | Required inputs | |-----------|-------------|-----------------| | `dbt_manifest` | Large projects; fastest parsing | `ProjectConfig.manifest_path` | | `dbt_ls` | Complex selectors; need dbt-native selection | Fusion binary accessible to scheduler | | `automatic` | Simple setups; let Cosmos pick | (none) |

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](reference/cosmos-config.md#profileconfig-warehouse-connection)** 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
from cosmos.constants import InvocationMode

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

---

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,