cosmos-dbt-core

from cosmos import RenderConfig, LoadMode
 
_render_config = RenderConfig(
    load_method=LoadMode.DBT_MANIFEST,  # or DBT_LS, DBT_LS_FILE, AUTOMATIC
)

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

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",
        profile_args={"schema": "my_schema"},
    ),
)

from cosmos import ProfileConfig
 
_profile_config = ProfileConfig(
    profile_name="my_profile",
    target_name="dev",
    profiles_yml_filepath="/path/to/profiles.yml",
)

from cosmos import RenderConfig, TestBehavior
 
_render_config = RenderConfig(
    test_behavior=TestBehavior.AFTER_EACH,
)

_operator_args = {
    # BaseOperator params
    "retries": 3,
 
    # Cosmos-specific params
    "install_deps": False,
    "full_refresh": False,
    "quiet": True,
 
    # Runtime dbt vars (XCom / params)
    "vars": '{"my_var": "{{ ti.xcom_pull(task_ids=\'pre_dbt\') }}"}',
}

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()
_render_config = RenderConfig()
 
my_cosmos_dag = DbtDag(
    dag_id="my_cosmos_dag",
    project_config=_project_config,
    profile_config=_profile_config,
    execution_config=_execution_config,
    render_config=_render_config,
    operator_args={},
    start_date=datetime(2025, 1, 1),
    schedule="@daily",
)

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, RenderConfig
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()
_render_config = RenderConfig()
 
@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def my_dag():
    @task
    def pre_dbt():
        return "some_value"
 
    dbt = DbtTaskGroup(
        group_id="dbt_project",
        project_config=_project_config,
        profile_config=_profile_config,
        execution_config=_execution_config,
        render_config=_render_config,
    )
 
    @task
    def post_dbt():
        pass
 
    chain(pre_dbt(), dbt, post_dbt())
 
my_dag()

import os
from datetime import datetime
from pathlib import Path
from typing import Any
 
from airflow import DAG
 
try:
    from airflow.providers.standard.operators.python import PythonOperator
except ImportError:
    from airflow.operators.python import PythonOperator
 
from cosmos import DbtCloneLocalOperator, DbtRunLocalOperator, DbtSeedLocalOperator, ProfileConfig
from cosmos.io import upload_to_aws_s3
 
DEFAULT_DBT_ROOT_PATH = Path(__file__).parent / "dbt"
DBT_ROOT_PATH = Path(os.getenv("DBT_ROOT_PATH", DEFAULT_DBT_ROOT_PATH))
DBT_PROJ_DIR = DBT_ROOT_PATH / "jaffle_shop"
DBT_PROFILE_PATH = DBT_PROJ_DIR / "profiles.yml"
DBT_ARTIFACT = DBT_PROJ_DIR / "target"
 
profile_config = ProfileConfig(
    profile_name="default",
    target_name="dev",
    profiles_yml_filepath=DBT_PROFILE_PATH,
)
 
 
def check_s3_file(bucket_name: str, file_key: str, aws_conn_id: str = "aws_default", **context: Any) -> bool:
    """Check if a file exists in the given S3 bucket."""
    from airflow.providers.amazon.aws.hooks.s3 import S3Hook
 
    s3_key = f"{context['dag'].dag_id}/{context['run_id']}/seed/0/{file_key}"
    print(f"Checking if file {s3_key} exists in S3 bucket...")
    hook = S3Hook(aws_conn_id=aws_conn_id)
    return hook.check_for_key(key=s3_key, bucket_name=bucket_name)
 
 
with DAG("example_operators", start_date=datetime(2024, 1, 1), catchup=False) as dag:
    seed_operator = DbtSeedLocalOperator(
        profile_config=profile_config,
        project_dir=DBT_PROJ_DIR,
        task_id="seed",
        dbt_cmd_flags=["--select", "raw_customers"],
        install_deps=True,
        append_env=True,
    )
 
    check_file_uploaded_task = PythonOperator(
        task_id="check_file_uploaded_task",
        python_callable=check_s3_file,
        op_kwargs={
            "aws_conn_id": "aws_s3_conn",
            "bucket_name": "cosmos-artifacts-upload",
            "file_key": "target/run_results.json",
        },
    )
 
    run_operator = DbtRunLocalOperator(
        profile_config=profile_config,
        project_dir=DBT_PROJ_DIR,
        task_id="run",
        dbt_cmd_flags=["--models", "stg_customers"],
        install_deps=True,
        append_env=True,
    )
 
    clone_operator = DbtCloneLocalOperator(
        profile_config=profile_config,
        project_dir=DBT_PROJ_DIR,
        task_id="clone",
        dbt_cmd_flags=["--models", "stg_customers", "--state", DBT_ARTIFACT],
        install_deps=True,
        append_env=True,
    )
 
    seed_operator >> run_operator >> clone_operator
    seed_operator >> check_file_uploaded_task

from cosmos import DbtDag, DbtResourceType
from airflow.sdk import task, chain
 
with DbtDag(...) as dag:
    @task
    def upstream_task():
        pass
 
    _upstream = upstream_task()
 
    for unique_id, dbt_node in dag.dbt_graph.filtered_nodes.items():
        if dbt_node.resource_type == DbtResourceType.SEED:
            my_dbt_task = dag.tasks_map[unique_id]
            chain(_upstream, my_dbt_task)

postgres://0.0.0.0:5434/postgres.public.orders

postgres://0.0.0.0:5434/postgres/public/orders

AIRFLOW__COSMOS__ENABLE_MEMORY_OPTIMISED_IMPORTS=True

from cosmos.airflow.dag import DbtDag  # instead of: from cosmos import DbtDag

AIRFLOW__COSMOS__REMOTE_TARGET_PATH=s3://bucket/target_dir/
AIRFLOW__COSMOS__REMOTE_TARGET_PATH_CONN_ID=aws_default

from cosmos.io import upload_to_cloud_storage
 
my_dag = DbtDag(
    # ...
    operator_args={"callback": upload_to_cloud_storage},
)

AIRFLOW__COSMOS__DBT_DOCS_DIR="path/to/docs"                   # local path or S3/GCS/Azure/HTTP URI; defaults to the dbt target/ folder
AIRFLOW__COSMOS__DBT_DOCS_CONN_ID="my_conn_id"                 # optional; for cloud storage
AIRFLOW__COSMOS__DBT_DOCS_INDEX_FILE_NAME="static_index.html"  # optional; only if docs built with --static

AIRFLOW__COSMOS__DBT_DOCS_PROJECTS='{
    "my_project": {
        "dir": "s3://bucket/docs/",
        "index": "index.html",
        "conn_id": "aws_default",
        "name": "My Project"
    }
}'