potiuk commented on code in PR #28300: URL: https://github.com/apache/airflow/pull/28300#discussion_r1046546975
########## docs/apache-airflow/index.rst: ########## @@ -79,6 +79,49 @@ seen running over time: Each column represents one DAG run. These are two of the most used views in Airflow, but there are several other views which allow you to deep dive into the state of your workflows. +Customizing Airflow +=================== + +There are a number of ways to extend the capabilities of Apache Airflow. Some common methods +for extending Airflow include: + +* Writing :doc:`Custom Operators <howto/custom-operator>`_ : Operators are the building blocks of DAGs in + Airflow, and can be used to perform specific tasks within a DAG. By writing custom operators, + you can add new functionality to Airflow, such as the ability to connect to and interact + with external services, or to perform complex data transformations. + +* Writing :doc:`Custom Decorators <howto/create-custom-decorator>`_ : Custom decorators are a way to + extend the functionality of the Airflow ``@task`` decorator. By writing custom decorators, + you can add new functionality to Airflow, such as the ability to connect to and interact + with external services, or to perform complex data transformations. + +* Creating custom :doc:`plugins`: Airflow plugins are extensions to the core system that provide additional + features and functionality. By creating custom plugins, you can add new capabilities to Airflow, + such as adding custom UI components, creating custom macros, creating your own TimeTables, + adding Operator Extra Links, Listeners. + +* Writing custom :doc:`provider packages <apache-airflow-providers:index>`: Providers are the components + of Airflow that are responsible for connecting to and interacting with external services. By writing + custom providers, you can add support for new types of external services, such as databases or + cloud services, to Airflow. + +With all the above methods, you can use the :doc:`public-airflow-api` to write your +custom Python code to interact with Airflow. + +However, you can also extend Airflow without directly integrating your Python code - via the +:doc:`stable-rest-api-ref` and the :doc:`stable-cli-ref`. These methods allow you to interact with +Airflow as an external system: + +Particularly, the doc:`stable-rest-api-ref` of Apache Airflow provides a number of methods for programmatic +accessing and manipulating various aspects of the system. By using the public API, you can build +custom tools and integrations with other systems, and automate certain aspects of the Airflow workflow. + +Overall, there are many different ways to extend the capabilities of Apache Airflow, and the specific +approach that you choose will depend on your specific needs and requirements. +By combining these different methods, you can create a powerful and flexible workflow management +system that can help you automate and optimize your data pipelines. Review Comment: Yep. That was GPT Chat repeating itself. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
