{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/2"}],"enrichment":{"capability":"dag-factory builds Apache Airflow DAGs declaratively from YAML configuration files, eliminating the need to write Python code for DAG construction.","skillfed_tags":["airflow-plugin","workflow-orchestration","infrastructure-as-code"],"use_cases":["Define and version-control multiple similar Airflow workflows using YAML templates without duplicating Python code.","Enable data engineers or domain experts without Python expertise to create and modify Airflow DAGs.","Rapidly prototype and iterate on workflow definitions by editing YAML files instead of redeploying Python modules.","Manage large numbers of DAGs with consistent structure and defaults across an organization.","Reduce maintenance burden by centralizing DAG logic in configuration rather than scattered Python files."],"what_it_does":"dag-factory is a library that lets you define Apache Airflow DAGs using YAML configuration files instead of writing Python code. It reads YAML files and generates fully functional Airflow DAGs, making it possible to construct workflows without needing to understand Airflow's Python API or object model. The library supports scheduling, dynamically mapped tasks, custom operators, callbacks, and KubernetesPodOperator, covering most common workflow patterns.\n\nIt's designed for teams that want to avoid repetitive DAG boilerplate, enable non-Python developers to define workflows, or maintain DAG definitions in a more declarative, version-control-friendly format. The package depends on Apache Airflow, packaging, pathspec, pyyaml, and typer, and is actively maintained with support for modern Python versions (3.10\u20133.14) and Airflow 2.9+ including Airflow 3.","worth_installing":"Yes. dag-factory is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem\u2014reducing boilerplate and enabling declarative DAG definition\u2014for Airflow users. Install it if you manage multiple similar Airflow workflows or want to let non-Python developers define DAGs; skip it only if your DAGs are highly custom or require complex Python logic that YAML cannot express."},"id":"dag-factory","links":{"html":"https://skillfed.io/packages/dag-factory","md":"https://skillfed.io/packages/dag-factory.md","pypi":"https://pypi.org/project/dag-factory/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-07","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dag-factory","python_support":"supports_current","summary":"Dynamically build Apache Airflow DAGs from YAML files"},"popularity":{"monthly_downloads":679361,"position":5370,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}