How to: Libraries, Hooks, Utilities
- [Domino](https://github.com/Tauffer-Consulting/domino) - Domino is an open source Graphical User Interface platform for creating data and Machine Learning workflows (DAGs) with no-code, visually intuitive drag-and-drop actions. It is also a standard for publishing and sharing your Python code so it can be automatically used by anyone, directly in the GUI.
- [Airflow-Helper](https://github.com/xnuinside/airflow-helper) - setting up Airflow Variables, Connections, and Pools from a YAML configuration file.
- [AirFly](https://github.com/ryanchao2012/airfly) - Auto generate Airflow's dag.py on the fly.
- [DEAfrica Airflow](https://github.com/digitalearthafrica/deafrica-airflow) - Airflow libraries used by [Digital Earth Africa](https://digitalearthafrica.org/), an humanitarian effort to utilize satellite imagery of Africa.
- [Airflow plugins](https://github.com/airflow-plugins/) - Central collection of repositories of various plugins for Airflow, including mailchimp, trello, sftp, GitHub, etc.
- [fileflow](https://github.com/industrydive/fileflow) - Collection of modules to support large data transfers between Airflow operators through either local file system or S3.  This addresses a gap where data is too large for XCOMs but too small or inconvenient for loading directly in the operator.  Built by [Industry Dive](https://www.industrydive.com/).
- [fairflow](https://github.com/michaelosthege/fairflow) - Library to abstract away Airflow's Operators with functional pieces that transform the data from one operator to another.
- [airflow-maintenance-dags](https://github.com/teamclairvoyant/airflow-maintenance-dags) - [Clairvoyant](http://clairvoyantsoft.com/) has a repo of Airflow DAGs that operator on Airflow itself, clearing out various bits of the backing metadata store.
- [test_dags](https://gist.github.com/criccomini/2862667822af7fae8b55682faef029a7) - a more complete solution for DAG integrity tests ([first Circle of Data’s Inferno are the first](https://medium.com/@ingwbaa/datas-inferno-7-circles-of-data-testing-hell-with-airflow-cef4adff58d8).
- [dag-factory](https://github.com/ajbosco/dag-factory) - A library for dynamically generating Apache Airflow DAGs from YAML configuration files.
- [whirl](https://github.com/godatadriven/whirl) - Fast iterative local development and testing of Apache Airflow workflows.
- [airflow-code-editor](https://github.com/andreax79/airflow-code-editor) - A plugin for Apache Airflow that allows you to edit DAGs in browser.
- [Pylint-Airflow](https://github.com/BasPH/pylint-airflow) - A Pylint plugin for static code analysis on Airflow code.
- [afctl](https://github.com/qubole/afctl) - A CLI tool that includes everything required to create, manage and deploy airflow projects faster and smoother.
- [Dag Dependencies viewer](https://github.com/ms32035/airflow-dag-dependencies) - A plugin which creates a view to visualize dependencies between the Airflow DAGs
- [Airflow ECR Plugin](https://github.com/asandeep/airflow-ecr-plugin) - Plugin to refresh AWS ECR login token at regular intervals. This is helpful where DockerOperator needs to pull images hosted on ECR.
- [AirflowK8sDebugger](https://github.com/Javier162380/AirflowKuberentesDebugger) - A library for generate k8s pod yaml templates from an Airflow dag using the KubernetesPodOperator.
- [Oozie to Airflow](https://github.com/GoogleCloudPlatform/oozie-to-airflow) - A tool to easily convert between [Apache Oozie](http://oozie.apache.org/) workflows and Apache Airflow workflows.
- [Airflow Ditto](https://github.com/angadsingh/airflow-ditto) - An extensible framework to do transformations to an Airflow DAG and convert it into another DAG which is flow-isomorphic with the original DAG, to be able to run it on different environments (e.g. on different clouds, or even different container frameworks - Apache Spark on YARN vs Kubernetes). Comes with out-of-the-box support for EMR-to-HDInsight-DAG transforms.
- [gusty](https://github.com/chriscardillo/gusty) - Create a DAG using any number of YAML, Python, Jupyter Notebook, or R Markdown files that represent individual tasks in the DAG. gusty also configures dependencies, DAGs, and TaskGroups, features support for your local operators, and more. A fully containerized demo is available [here](https://github.com/chriscardillo/gusty-demo).
- [Meltano](https://www.meltano.com) - Open source, self-hosted, CLI-first, debuggable, and extensible ELT tool that embraces [Singer](https://www.singer.io) for extraction and loading, leverages [dbt](https://www.getdbt.com) for transformation, and [integrates with Airflow for orchestration](https://meltano.com/#orchestration).
- [DAG checks](https://github.com/politools/dag-checks) - The dag-checks consist of checks that can help you in maintaining your Apache Airflow instance.
- [Airflow DVC plugin](https://github.com/covid-genomics/airflow-dvc) - Plugin for open-source version-control system for data science and Machine Learning pipelines - [DVC](htt