astronomer/dag-factory

Health v2 · maintenance only

Adoption not part of the score

1453 stars · 238 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

dag-factory is a Python library for Apache Airflow that lets users define DAGs declaratively via YAML configuration files instead of writing Python code. It supports custom operators, dynamic task mapping, TaskFlow pipelines, callbacks, and is compatible with Airflow 2.9+ and Airflow 3.

Use cases

• define airflow dags in yaml instead of python
• generate airflow workflows from configuration files
• let non-python users author data pipelines
• reduce duplicated boilerplate across airflow dags
• manage many similar dags from one config
• configure kubernetes pod operator tasks via yaml

When to choose

• your team maintains many similar Airflow DAGs with repetitive boilerplate
• pipeline authors know YAML but not Python or Airflow internals
• you want DAG definitions separated from code for review or governance
• you need Airflow 3 compatibility with declarative DAG generation

When to avoid

• your DAGs require complex dynamic logic that YAML cannot express
• you have only a handful of simple DAGs already written in Python
• you use an Airflow version older than 2.9

Facets

library · maturity stable

workflow-automation configuration-management developer-tools developer-tools python windows self-hosted apache-airflow yaml dags orchestration declarative-configuration data-pipelines data-engineering automation linux macos docker

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