{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring"}],"enrichment":{"capability":"Apache Airflow's core runtime that schedules, executes, and monitors data pipelines defined as directed acyclic graphs (DAGs), with a REST API server and task triggering engine.","skillfed_tags":["workflow-orchestration","data-pipeline","distributed-computing"],"use_cases":["Schedule and execute recurring ETL jobs that extract data from multiple sources, transform it, and load it into a data warehouse.","Orchestrate machine learning pipelines with dependencies between data preparation, model training, and evaluation stages.","Monitor and retry failed batch jobs across distributed systems with automatic alerting and logging.","Coordinate multi-team workflows where different teams own different pipeline stages and need visibility into upstream/downstream dependencies.","Build event-driven data ingestion pipelines that react to external triggers and fan out work across worker nodes."],"what_it_does":"Apache Airflow Core is the runtime engine for Apache Airflow, a platform for authoring, scheduling, and monitoring workflows. It provides the scheduler that reads DAG definitions and triggers tasks on schedule, the REST API server for external interaction, the DAG file processor that parses workflow definitions, and the triggerer component for event-driven task execution. It is designed for data engineers and DevOps teams building complex, multi-step data pipelines that may span multiple systems and require visibility into execution state.\n\nThe package depends on a large ecosystem of providers and utilities\u2014fastapi for the web server, SQLAlchemy-based ORM layers for metadata storage, jinja2 for templating, cryptography for secure credential handling, and cron scheduling libraries. It targets modern Python versions and is classified as production-stable. Installation is straightforward from a wheel, though running Airflow in practice requires external infrastructure: a database to store DAG metadata and execution history, and typically a message broker or Kubernetes cluster for distributed task execution.","worth_installing":"Yes. Apache Airflow Core is the foundation of a mature, widely-adopted orchestration platform with 46489 repository stars and monthly downloads in the millions. It is actively maintained, permissively licensed, and production-stable. Install it if you need to build or run Airflow workflows; it is a prerequisite for any Airflow deployment. Be aware that it brings 65 dependencies and requires external infrastructure to operate at scale."},"id":"apache-airflow-core","links":{"html":"https://skillfed.io/packages/apache-airflow-core","md":"https://skillfed.io/packages/apache-airflow-core.md","pypi":"https://pypi.org/project/apache-airflow-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"apache-airflow-core","python_support":"supports_current","summary":"Core packages for Apache Airflow, schedule and API server"},"popularity":{"monthly_downloads":9668710,"position":1509,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.3.1"}