{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/3"}],"enrichment":{"capability":"Integrates Papermill notebook execution into Apache Airflow workflows, allowing you to parameterize and run Jupyter notebooks as Airflow tasks.","skillfed_tags":["airflow-provider","notebook-orchestration","workflow-automation"],"use_cases":["Schedule parameterized Jupyter notebooks as recurring Airflow tasks, injecting date ranges or configuration values at runtime.","Orchestrate multi-step ML pipelines where each step is a notebook, capturing metrics and artifacts between steps.","Run exploratory data analysis notebooks on a schedule with different input datasets, storing outputs for review.","Integrate notebook-based reporting into Airflow DAGs, generating reports with dynamic parameters.","Chain notebook execution with other Airflow operators to build hybrid workflows mixing notebooks and Python tasks."],"what_it_does":"This is an Apache Airflow provider package that bridges Jupyter notebooks and Airflow orchestration by wrapping the Papermill library. It lets you define notebook execution as native Airflow tasks, passing parameters into notebooks and capturing their outputs within your DAG. The package depends on Papermill for parameterized notebook execution, Scrapbook for output collection, and standard data-science libraries (pandas, ipykernel, nbconvert) to handle notebook rendering and kernel management.\n\nYou install it alongside an existing Airflow deployment (>=2.11.0) and use its PapermillOperator to run .ipynb files with injected parameters, making notebooks first-class citizens in Airflow workflows. It's designed for teams that want to orchestrate notebook-based analytics, ML experiments, or data processing pipelines without rewriting them as Python scripts.","worth_installing":"Yes, if you run Apache Airflow and want to execute Jupyter notebooks as orchestrated tasks. The package is actively maintained, has no known vulnerabilities, and low install friction. It's the standard way to integrate Papermill into Airflow. Install it only if you have an Airflow deployment already running and a genuine need to parameterize and schedule notebook execution; it adds dependencies (pandas, nbconvert, ipykernel) that are only useful in that context."},"id":"apache-airflow-providers-papermill","links":{"html":"https://skillfed.io/packages/apache-airflow-providers-papermill","md":"https://skillfed.io/packages/apache-airflow-providers-papermill.md","pypi":"https://pypi.org/project/apache-airflow-providers-papermill/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-08","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"apache-airflow-providers-papermill","python_support":"supports_current","summary":"Provider package apache-airflow-providers-papermill for Apache Airflow"},"popularity":{"monthly_downloads":751398,"position":5156,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.13.2"}