Apache Airflow supports integration with Papermill. Papermill is a tool for parameterizing and executing Jupyter Notebooks. Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year. Using parameters in your notebook and using the PapermillOperator makes this a breeze.


Creating a notebook

To parameterize your notebook designate a cell with the tag parameters. Papermill looks for the parameters cell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged with injected-parameters with input parameters in order to overwrite the values in parameters. If no cell is tagged with parameters the injected cell will be inserted at the top of the notebook.

Note that Jupyter notebook has out of the box support for tags but you need to install the celltags extension for Jupyter Lab: jupyter labextension install @jupyterlab/celltags

Make sure that you save your notebook somewhere so that Airflow can access it. Papermill supports S3, GCS, Azure and Local. HDFS is not supported.

Example DAG

import airflow

from airflow.models import DAG
from airflow.operators.papermill_operator import PapermillOperator

from datetime import timedelta

args = {
    'owner': 'Airflow',
    'start_date': airflow.utils.dates.days_ago(2)

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

run_this = PapermillOperator(
    output_nb="/tmp/out-{{ execution_date }}.ipynb",
    parameters={"msgs": "Ran from Airflow at {{ execution_date }}!"}

This DAG will use Papermill to run the notebook “hello_world”, based on the execution date it will create an output notebook “out-<date>”. All fields, including the keys in the parameters, are templated.