Scheduling

Because Cosmos uses Airflow to power scheduling, you can leverage Airflow’s scheduling capabilities to schedule your dbt projects. This includes cron-based scheduling, timetables, and data-aware scheduling. For more info on Airflow’s scheduling capabilities, check out the Airflow documentation or check out the Astronomer documentation.

Time-Based Scheduling

To schedule a dbt project on a time-based schedule, you can use Airflow’s scheduling options. For example, to run a dbt project every day starting on January 1, 2023, you can use the following DAG:

from cosmos import DbtDag

jaffle_shop = DbtDag(
    # ...
    start_date=datetime(2023, 1, 1),
    schedule="@daily",
)

Data-Aware Scheduling

By default, Cosmos emits Airflow Datasets when running dbt projects. This allows you to use Airflow’s data-aware scheduling capabilities to schedule your dbt projects. Cosmos emits datasets in the following format:

Dataset("DBT://{connection_id}/{project_name}/{model_name}")

For example, let’s say you have:

• A dbt project (project_one) with a model called my_model that runs daily

• A second dbt project (project_two) with a model called my_other_model that you want to run immediately after my_model

Then, you can use Airflow’s data-aware scheduling capabilities to schedule my_other_model to run after my_model. For example, you can use the following DAGs:

from cosmos import DbtDag, get_dbt_dataset

project_one = DbtDag(
    # ...
    start_date=datetime(2023, 1, 1),
    schedule="@daily",
)

project_two = DbtDag(
    # for airflow <=2.3
    # schedule=[get_dbt_dataset("my_conn", "project_one", "my_model")],
    # for airflow > 2.3
    schedule=[get_dbt_dataset("my_conn", "project_one", "my_model")],
    dbt_project_name="project_two",
)

In this scenario, project_one runs once a day and project_two runs immediately after project_one. You can view these dependencies in Airflow’s UI.