Lineage support is very experimental and subject to change.

Airflow can help track origins of data, what happens to it and where it moves over time. This can aid having audit trails and data governance, but also debugging of data flows.

Airflow tracks data by means of inlets and outlets of the tasks. Let’s work from an example and see how it works.

from airflow.operators.bash_operator import BashOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.lineage.datasets import File
from airflow.models import DAG
from airflow.utils.dates import days_ago
from datetime import timedelta


args = {
    'owner': 'Airflow',
    'start_date': days_ago(2)

dag = DAG(
    dag_id='example_lineage', default_args=args,
    schedule_interval='0 0 * * *',

f_final = File("/tmp/final")
run_this_last = DummyOperator(task_id='run_this_last', dag=dag,
    inlets={"auto": True},
    outlets={"datasets": [f_final,]})

f_in = File("/tmp/whole_directory/")
outlets = []
for file in FILE_CATEGORIES:
    f_out = File("/tmp/{}/{{{{ execution_date }}}}".format(file))
run_this = BashOperator(
    task_id='run_me_first', bash_command='echo 1', dag=dag,
    inlets={"datasets": [f_in,]},
    outlets={"datasets": outlets}

Tasks take the parameters inlets and outlets.

Inlets can be manually defined by the following options:

  • by a list of dataset {"datasets": [dataset1, dataset2]}

  • can be configured to look for outlets from upstream tasks {"task_ids": ["task_id1", "task_id2"]}

  • can be configured to pick up outlets from direct upstream tasks {"auto": True}

  • a combination of them

Outlets are defined as list of dataset {"datasets": [dataset1, dataset2]}. Any fields for the dataset are templated with the context when the task is being executed.


Operators can add inlets and outlets automatically if the operator supports it.

In the example DAG task run_me_first is a BashOperator that takes 3 inlets: CAT1, CAT2, CAT3, that are generated from a list. Note that execution_date is a templated field and will be rendered when the task is running.


Behind the scenes Airflow prepares the lineage metadata as part of the pre_execute method of a task. When the task has finished execution post_execute is called and lineage metadata is pushed into XCOM. Thus if you are creating your own operators that override this method make sure to decorate your method with prepare_lineage and apply_lineage respectively.

Apache Atlas

Airflow can send its lineage metadata to Apache Atlas. You need to enable the atlas backend and configure it properly, e.g. in your airflow.cfg:

backend = airflow.lineage.backend.atlas.AtlasBackend

username = my_username
password = my_password
host = host
port = 21000

Please make sure to have the atlasclient package installed.

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