Apache Airflow Workflow Management Platform

Context:
- It can (typically) be composed for Airflow Components, such as:
  - Scheduler, Executors, Metadata Database, and Web Server.
- It can (often) be used for Complex Data-Intensive Workflows.
- It can create Airflow DAGs (represented as Airflow DAG files).
- It can (typically) follow a Workflow as Code philosophy (which makes the platform unsuitable for non-developers).
- It can (typically) be written in a Python language.
- …
Example(s):
- Apache Airflow v2.5.0 (2022-12-02) [1].
- Apache Airflow v2.1.0 (2021-05-21) [2].
- Apache Airflow v1.8.2 (2017-06-22) [3].
- …
- https://github.com/apache/incubator-airflow/releases.
- …
Counter-Example(s):
- Apache NiFi.
- Argo Workflow Platform.
- Apache Oozie.
- cron.
- Jenkins.
- AWS SQS, AWS Lambda.
- Zapier Platform.
See: GitHub, Data Streaming Framework, Airflow Workflow.

References

HTTP://chat.openai.com
- A: ... Apache Airflow is a platform for managing and scheduling workflows. It is designed to allow users to define, schedule, and monitor the execution of workflows, typically in the context of data processing pipelines or other batch processing tasks. It includes a web-based interface for managing and monitoring workflows, as well as a rich set of features for defining and scheduling tasks, including support for dependencies and error handling. ...
  Overall, Apache Airflow and Zapier are both useful tools for managing and automating workflows, but they have different capabilities and are intended for different use cases. Apache Airflow is better suited for complex, data-intensive workflows, while Zapier is more focused on automating tasks and processes across multiple web-based applications and services.

https://airflow.apache.org/docs/apache-airflow/2.1.0/
- QUOTE: Airflow is a platform to programmatically author, schedule and monitor workflows.
  Use Airflow to author workflows as Directed Acyclic Graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
  When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.

https://airflow.incubator.apache.org/
- QUOTE: Airflow is a platform to programmatically author, schedule and monitor workflows.
  Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
  …
  Airflow is not a data streaming solution. Tasks do not move data from one to the other (though tasks can exchange metadata!). Airflow is not in the Spark Streaming or Storm space, it is more comparable to Oozie or Azkaban. Workflows are expected to be mostly static or slowly changing. You can think of the structure of the tasks in your workflow as slightly more dynamic than a database structure would be. Airflow workflows are expected to look similar from a run to the next, this allows for clarity around unit of work and continuity.