Apache Airflow

Apache Airflow is a popular open-source workflow management platform. It is a suitable engine to orchestrate and execute a pipeline authored with Kedro, because workflows in Airflow are modelled and organised as DAGs.

Introduction and strategy

The general strategy to deploy a Kedro pipeline on Apache Airflow is to run every Kedro node as an Airflow task while the whole pipeline is converted to an Airflow DAG. This approach mirrors the principles of running Kedro in a distributed environment.

Each node will be executed within a new Kedro session, which implies that MemoryDatasets cannot serve as storage for the intermediate results of nodes. Instead, all datasets must be registered in the DataCatalog and stored in persistent storage. This approach enables nodes to access the results from preceding nodes.

This guide provides instructions on running a Kedro pipeline on different Airflow platforms. You can jump to the specific sections by clicking the links below, how to run a Kedro pipeline on:

How to run a Kedro pipeline on Apache Airflow with Astronomer

The following tutorial shows how to deploy an example Spaceflights Kedro project on Apache Airflow with Astro CLI, a command-line tool created by Astronomer that streamlines the creation of local Airflow projects. You will deploy it locally first, and then transition to Astro Cloud.

Astronomer is a managed Airflow platform which allows users to spin up and run an Airflow cluster in production. Additionally, it also provides a set of tools to help users get started with Airflow locally in the easiest way possible.


To follow this tutorial, ensure you have the following:

Create, prepare and package example Kedro project

In this section, you will create a new Kedro project equipped with an example pipeline designed to solve a typical data science task: predicting spaceflights prices. You will need to customise this project to ensure compatibility with Airflow, which includes enriching the Kedro DataCatalog with datasets previously stored only in memory and simplifying logging through custom settings. Following these modifications, you will package the project for installation in an Airflow Docker container and generate an Airflow DAG that mirrors our Kedro pipeline.

  1. To create a new Kedro project, select the example=yes option to include example code. Additionally, to implement custom logging, select tools=log. Proceed with the default project name, but feel free to add any other tools as desired:

    kedro new --example=yes --name=new-kedro-project --tools=log
  2. Navigate to your project’s directory, create a new conf/airflow directory for Airflow-specific configurations, and copy the catalog.yml file from conf/base to conf/airflow. This setup allows you to customise the DataCatalog for use with Airflow:

    cd new-kedro-project
    mkdir conf/airflow
    cp conf/base/catalog.yml conf/airflow/catalog.yml
  3. Open conf/airflow/catalog.yml to see the list of datasets used in the project. Note that additional intermediate datasets (X_train, X_test, y_train, y_test) are stored only in memory. You can locate these in the pipeline description under /src/new_kedro_project/pipelines/data_science/pipeline.py. To ensure these datasets are preserved and accessible across different tasks in Airflow, we need to include them in our DataCatalog. Instead of repeating similar code for each dataset, you can use Dataset Factories, a special syntax that allows defining a catch-all pattern to overwrite the default MemoryDataset creation. Add this code to the end of the file:

  type: pandas.CSVDataset
  filepath: data/02_intermediate/{base_dataset}.csv

In the example here we assume that all Airflow tasks share one disk, but for distributed environments you would need to use non-local file paths.

Starting with kedro-airflow release version 0.9.0, you can adopt a different strategy instead of following steps 2-3: group nodes that use intermediate MemoryDatasets into larger tasks. This approach allows intermediate data manipulation to occur within a single task, eliminating the need to transfer data between nodes. You can implement this by running kedro airflow create with the --group-in-memory flag on Step 6.

  1. Open conf/logging.yml and modify the root: handlers section to [console] at the end of the file. By default, Kedro uses the Rich library to enhance log output with sophisticated formatting. However, some deployment systems, including Airflow, don’t work well with Rich. Therefore, we’re adjusting the logging to a simpler console version. For more information on logging in Kedro, you can refer to the Kedro docs.

  handlers: [console]
  1. Package the Kedro pipeline as a Python package so you can install it into the Airflow container later on:

kedro package

This step should produce a wheel file called new_kedro_project-0.1-py3-none-any.whl located at dist/.

  1. Convert the Kedro pipeline into an Airflow DAG with kedro airflow

kedro airflow create --target-dir=dags/ --env=airflow

This step should produce a .py file called new_kedro_project_airflow_dag.py located at dags/.

Deployment process with Astro CLI

In this section, you will start by setting up a new blank Airflow project using Astro and then copy the files prepared in the previous section from the Kedro project. Next, you will need to customise the Dockerfile to enhance logging capabilities and manage the installation of our Kedro package. Finally, you will be able to run and explore the Airflow cluster.

  1. To complete this section, you have to install both the Astro CLI and Docker Desktop.

  2. Initialise an Airflow project with Astro in a new folder outside of your Kedro project. Let’s call it kedro-airflow-spaceflights

    cd ..
    mkdir kedro-airflow-spaceflights
    cd kedro-airflow-spaceflights
    astro dev init
  3. The folder kedro-airflow-spaceflights will be executed within the Airflow container. To run the Kedro project there, you need to copy several items from the previous section into it:

  • the /data folder from Step 1, containing sample input datasets for our pipeline. This folder will also store the output results.

  • the /conf folder from Steps 2-4, which includes our DataCatalog, parameters, and customised logging files. These files will be used by Kedro during its execution in the Airflow container.

  • the .whl file from Step 5, which you will need to install in the Airflow Docker container to execute our project node by node.

  • the Airflow DAG from Step 6 for deployment in the Airflow cluster.

    cd ..
    cp -r new-kedro-project/data kedro-airflow-spaceflights/data
    cp -r new-kedro-project/conf kedro-airflow-spaceflights/conf
    mkdir -p kedro-airflow-spaceflights/dist/
    cp new-kedro-project/dist/new_kedro_project-0.1-py3-none-any.whl kedro-airflow-spaceflights/dist/
    cp new-kedro-project/dags/new_kedro_project_airflow_dag.py kedro-airflow-spaceflights/dags/

Feel free to completely copy new-kedro-project into kedro-airflow-spaceflights if your project requires frequent updates, DAG recreation, and repackaging. This approach allows you to work with kedro and astro projects in a single folder, eliminating the need to copy kedro files for each development iteration. However, be aware that both projects will share common files such as requirements.txt, README.md, and .gitignore.

  1. Add a few lines to the Dockerfile located in the kedro-airflow-spaceflights folder to set the environment variable KEDRO_LOGGING_CONFIG to point to conf/logging.yml to enable custom logging in Kedro (note that from Kedro 0.19.6 onwards, this step is unnecessary because Kedro uses the conf/logging.yml file by default) and to install the .whl file of our prepared Kedro project into the Airflow container:

ENV KEDRO_LOGGING_CONFIG="conf/logging.yml" # This line is not needed from Kedro 0.19.6

RUN pip install --user dist/new_kedro_project-0.1-py3-none-any.whl
  1. Navigate to kedro-airflow-spaceflights folder and launch the local Airflow cluster with Astronomer

cd kedro-airflow-spaceflights
astro dev start
  1. Visit the Airflow Webserver UI at its default address, http://localhost:8080, using the default login credentials: username and password both set to admin. There, you’ll find a list of all DAGs. Navigate to the new-kedro-project DAG, and then press the Trigger DAG play button to initiate it. You can then observe the steps of your project as they run successfully:

  1. The Kedro project was run inside an Airflow Docker container, and the results are stored there as well. To copy these results to your host, first identify the relevant Docker containers by listing them:

docker ps

Select the container acting as the scheduler and note its ID. Then, use the following command to copy the results, substituting d36ef786892a with the actual container ID:

docker cp  d36ef786892a:/usr/local/airflow/data/ ./data/
  1. To stop the Astro Airflow environment, you can use the command:

astro dev stop

Deployment to Astro Cloud

You can easily deploy and run your project on Astro Cloud, the cloud infrastructure provided by Astronomer, by following these steps:

  1. Log in to your account on the Astronomer portal and create a new deployment if you don’t already have one:

  2. Use the Astro CLI to log in to your Astro Cloud account:

astro auth

You will be redirected to enter your login credentials in your browser. Successful login indicates that your terminal is now linked with your Astro Cloud account:

  1. To deploy your local project to the cloud, navigate to the kedro-airflow-spaceflights folder and run:

astro deploy
  1. At the end of the deployment process, a link will be provided. Use this link to manage and monitor your project in the cloud:

How to run a Kedro pipeline on Amazon AWS Managed Workflows for Apache Airflow (MWAA)

Kedro project preparation

MWAA, or Managed Workflows for Apache Airflow, is an AWS service that makes it easier to set up, operate, and scale Apache Airflow in the cloud. Deploying a Kedro pipeline to MWAA is similar to Astronomer, but there are some key differences: you need to store your project data in an AWS S3 bucket and make necessary changes to your DataCatalog. Additionally, you must configure how you upload your Kedro configuration, install your Kedro package, and set up the necessary environment variables.

  1. Complete steps 1-4 from the Create, prepare and package example Kedro project section.

  2. Your project’s data should not reside in the working directory of the Airflow container. Instead, create an S3 bucket and upload your data folder from the new-kedro-project folder to your S3 bucket.

  3. Modify the DataCatalog to reference data in your S3 bucket by updating the filepath and add credentials line for each dataset in new-kedro-project/conf/airflow/catalog.yml. Add the S3 prefix to the filepath as shown below:

  type: pandas.CSVDataset
  filepath: s3://<your_S3_bucket>/data/01_raw/companies.csv
  credentials: dev_s3
  1. Set up AWS credentials to provide read and write access to your S3 bucket. Update new-kedro-project/conf/local/credentials.yml with your AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY and copy it to the new-kedro-project/conf/airflow/ folder:

    aws_access_key_id: *********************
    aws_secret_access_key: ******************************************
  1. Add s3fs to your project’s requirements.txt in new-kedro-project to facilitate communication with AWS S3. Some libraries could cause dependency conflicts in the Airflow environment, so make sure to minimise the requirements list and avoid using kedro-viz and pytest.

  1. Follow steps 5-6 from the Create, prepare and package example Kedro project section to package your Kedro project and generate an Airflow DAG.

  2. Update the DAG file new_kedro_project_airflow_dag.py located in the dags/ folder by adding conf_source="plugins/conf-new_kedro_project.tar.gz" to the arguments of KedroSession.create() in the Kedro operator execution function. This change is necessary because your Kedro configuration archive will be stored in the plugins/ folder, not the root directory:

    def execute(self, context):
        with KedroSession.create(project_path=self.project_path,
                                 env=self.env, conf_source="plugins/conf-new_kedro_project.tar.gz") as session:
            session.run(self.pipeline_name, node_names=[self.node_name])

Deployment on AWAA

  1. Archive your three files: new_kedro_project-0.1-py3-none-any.whl and conf-new_kedro_project.tar.gz located in new-kedro-project/dist, and logging.yml located in new-kedro-project/conf/ into a file called plugins.zip and upload it to s3://your_S3_bucket.

zip -j plugins.zip dist/new_kedro_project-0.1-py3-none-any.whl dist/conf-new_kedro_project.tar.gz conf/logging.yml

This archive will be later unpacked to the /plugins folder in the working directory of the Airflow container.

  1. Create a new requirements.txt file, add the path where your Kedro project will be unpacked in the Airflow container, and upload requirements.txt to s3://your_S3_bucket:


Libraries from requirements.txt will be installed during container initialisation.

  1. Upload new_kedro_project_airflow_dag.py from the new-kedro-project/dags to s3://your_S3_bucket/dags.

  2. Create an empty startup.sh file for container startup commands. Set an environment variable for custom Kedro logging:

export KEDRO_LOGGING_CONFIG="plugins/logging.yml"
  1. Set up a new AWS MWAA environment using the following settings:

S3 Bucket:
DAGs folder
Plugins file - optional
Requirements file - optional
Startup script file - optional

On the next page, set the Public network (Internet accessible) option in the Web server access section if you want to access your Airflow UI from the internet. Continue with the default options on the subsequent pages.

  1. Once the environment is created, use the Open Airflow UI button to access the standard Airflow interface, where you can manage your DAG.

How to run a Kedro pipeline on Apache Airflow using a Kubernetes cluster

The kedro-airflow-k8s plugin from GetInData | Part of Xebia enables you to run a Kedro pipeline on Airflow with a Kubernetes cluster. The plugin can be used together with kedro-docker to prepare a docker image for pipeline execution. At present, the plugin is available for versions of Kedro < 0.18 only.

Consult the GitHub repository for kedro-airflow-k8s for further details, or take a look at the documentation.