Apache Airflow Workflow Management Platform
An Apache Airflow Workflow Management Platform is an open source Python-based DAG-based automated workflow management platform by Apache Software Foundation.
- AKA: Apache Airflow, Airflow Platform, Apache Airflow Orchestration Platform.
- Context:
- It can (typically) compose Apache Airflow Core Components through Apache Airflow service-oriented architecture:
- Apache Airflow Scheduler for Apache Airflow task scheduling.
- Apache Airflow Executors for Apache Airflow distributed task execution.
- Apache Airflow Metadata Database for Apache Airflow state persistence.
- Apache Airflow Web Server for Apache Airflow UI serving.
- Apache Airflow DAG Processor for Apache Airflow workflow parsing.
- It can (typically) enable Apache Airflow Developers to create Apache Airflow DAGs through Python code definition.
- It can (typically) manage Apache Airflow Task Dependencys through directed acyclic graph structure.
- It can (typically) execute Apache Airflow Workflows through scheduled triggers and event-based triggers.
- It can (typically) monitor Apache Airflow Task Executions.
- It can (typically) support Apache Airflow DAG Versioning.
- ...
- It can (often) orchestrate Apache Airflow Data Pipelines through ETL operators.
- It can (often) integrate Apache Airflow External Systems through provider packages.
- It can (often) enable Apache Airflow Dynamic Workflows through task mapping.
- It can (often) provide Apache Airflow Workflow Debugging through dag.test() method.
- It can (often) facilitate Apache Airflow Team Collaboration through role-based access control.
- ...
- It can range from being a Simple Apache Airflow Workflow Management Platform to being a Complex Apache Airflow Workflow Management Platform, depending on its Apache Airflow deployment scale.
- It can range from being a Standalone Apache Airflow Workflow Management Platform to being a Distributed Apache Airflow Workflow Management Platform, depending on its Apache Airflow executor configuration.
- It can range from being a Basic Apache Airflow Workflow Management Platform to being an Enterprise Apache Airflow Workflow Management Platform, depending on its Apache Airflow feature utilization.
- It can range from being a Traditional Apache Airflow Workflow Management Platform to being a Modern Apache Airflow Workflow Management Platform, depending on its Apache Airflow version generation.
- ...
- It can integrate with Cloud Storage Services for data persistence.
- It can connect to Container Orchestration Platforms for scalable execution.
- It can interface with Monitoring Systems for observability.
- It can communicate with Version Control Systems for workflow versioning.
- It can synchronize with Authentication Providers for security management.
- ...
- It can (typically) compose Apache Airflow Core Components through Apache Airflow service-oriented architecture:
- Example(s):
- Apache Airflow v3.0.2 (2025-06-10), featuring memory leak fixes and enhanced log masking.
- Apache Airflow v3.0.1 (2025-05-12), with improved value masking and dashboard enhancements.
- Apache Airflow v3.0.0 (2025-04-22), introducing service-oriented architecture and React-based UI.
- Apache Airflow v2.5.0 (2022-12-02), representing the last 2.x series release.
- Apache Airflow v2.1.0 (2021-05-21), with enhanced scheduler performance.
- Apache Airflow v1.10.0 (2018-08-20), marking significant architectural improvements.
- Apache Airflow v1.8.2 (2017-06-22), during Apache incubation phase.
- ...
- Counter-Example(s):
- Apache NiFi, which focuses on real-time data flow rather than batch workflow orchestration.
- Argo Workflow Platform, which specializes in Kubernetes-native workflows rather than Python-based workflows.
- Jenkins, which targets continuous integration rather than data pipeline orchestration.
- Zapier Workflow Automation Platform, which provides no-code automation rather than code-based workflow definition.
- AWS Step Functions, which uses JSON state machines rather than Python DAG definitions.
- See: Workflow Management Platform, DAG-based System, Python Framework, Data Pipeline Orchestration, Apache Software Foundation Project, Workflow as Code, Task Scheduling System.
References
2022
- 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.
- 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. ...
2021
- 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.
- QUOTE: Airflow is a platform to programmatically author, schedule and monitor workflows.
2017a
- 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.
- QUOTE: Airflow is a platform to programmatically author, schedule and monitor workflows.
2017b
- https://medium.com/airbnb-engineering/airflow-a-workflow-management-platform-46318b977fd8
- QUOTE: … Architecture
… While you can get up and running with Airflow in just a few commands, the complete architecture has the following components:
- The job definitions, in source control.
- A rich CLI (command line interface) to test, run, backfill, describe and clear parts of your DAGs.
- A web application, to explore your DAGs definition, their dependencies, progress, metadata and logs. The web server is packaged with Airflow and is built on top of the Flask Python web framework.
- A metadata repository, typically a MySQL or Postgres database that Airflow uses to keep track of task job statuses and other persistent information.
- An array of workers, running the jobs task instances in a distributed fashion.
- Scheduler processes, that fire up the task instances that are ready to run.
- QUOTE: … Architecture