Start with Apache Airflow
Purpose and Scope
This page orients a new Airflow user before they choose an installation path, write their first DAG, or contribute to the project. Apache Airflow is presented in the official documentation as a workflow orchestration platform: you define workflows, schedule them, run tasks, and inspect their state through Airflow’s runtime and UI. The official Quick Start is intentionally local and temporary; it helps you bootstrap a standalone instance so you can see the scheduler, API-facing services, metadata database behavior, and web UI working together before you make deployment decisions.
The recommended first step is to use the official Quick Start, then move into installation, tutorials, and core concepts once you know which problem you are solving. The official page notes that current Airflow releases require a Python 3 environment and, for Airflow 3.2.x, support Python 3.10 through 3.14. It also frames pip and uv as the officially supported installation methods, with constraints files used to keep dependency resolution reproducible. Treat that quick start as a learning path, not as a production architecture.
Sources: docs/images/documentation_architecture.py
Recommended First Steps
Start by deciding whether you are evaluating Airflow, developing DAGs, or preparing an operational deployment. For evaluation, follow the official Quick Start and create a local standalone instance. For DAG authoring, continue with the tutorials so that DAGs, tasks, schedules, task instances, and the UI views become concrete. For deployment work, read installation and administration material early, because Airflow’s dependency constraints, database, executor, logging, and security choices shape the rest of the system.
A compact local install command from the official docs has this shape:
pip install "apache-airflow[celery]==3.0.0" --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-3.0.0/constraints-3.12.txt"Use that command form as a pattern rather than copying the exact versions blindly. Replace the Airflow version, Python version, and extras with the combination you intend to run. Extras such as celery pull in integration-specific dependencies, while the constraint URL pins a known-compatible dependency set for the selected Airflow and Python versions. If you are only exploring the product, keep the environment disposable; if you are preparing for production, move next to installation constraints, database setup, executor choice, and security configuration.
Core Primitives
Airflow’s first concepts are deliberately small. A DAG is the workflow definition: it describes tasks and their dependency graph. A task is a unit of work created by an operator, a TaskFlow-decorated Python function, or another public authoring API. A DAG run is one scheduled or manually triggered execution of a DAG. Task instances are the concrete runtime attempts for tasks inside a DAG run. The scheduler decides what can run, workers or local execution perform the work, and the UI lets you inspect graph, grid, logs, and state.
The official documentation navigation also gives a useful learning order. After Quick Start, continue through Installation, Security, Tutorials, How-to Guides, UI Overview, Core Concepts, Authoring and Scheduling, Administration and Deployment, Integration, and the Public Interface for Airflow 3.0+. That order matters because Airflow is not only a Python library. It is a system with long-running components, metadata storage, provider integrations, credentials, logs, and operational controls. Learn the authoring model and the operational model together so early examples do not become misleading production patterns.
Sources: docs/images/documentation_architecture.py
Documentation and Community Signals in This Repository
The repository includes a generated documentation architecture diagram that describes how documentation reaches users. The diagram code names GitHub repositories for apache-airflow and apache-airflow-site, a release manager and committer path, a live S3 bucket named live-docs-airflow-apache-org, CloudFront caching, and the public https://airflow.apache.org webserver. For new users, the important signal is that the website is treated as a release artifact and publishing workflow, not merely as incidental markdown. Use the versioned official docs that match the Airflow version you install.
Airflow is also a community project, and the supplied project automation files show how much of the contributor experience is standardized. The apache-magpie override README says local adopter-specific behavior belongs under .apache-magpie-overrides and that the framework snapshot under .apache-magpie should not be modified directly. That separation is a contributor-facing pattern: project-specific policy is kept near the repository, while reusable automation belongs upstream. If your first interaction is a pull request rather than a DAG, read contributor guidance before changing automation-owned files.
Sources: docs/images/documentation_architecture.py, .apache-magpie-overrides/README.md
From User Journey to Repository Signals
The project-specific triage configuration gives a practical view of Airflow’s maintenance scale. It names the apache/airflow-committers team, uses an area: label prefix, defines labels such as ready for maintainer review and suspicious changes detected, and sets stale-review or draft thresholds in days. These details are not user-facing runtime features, but they matter when you rely on Airflow’s documentation and release process: a large integration platform needs repeatable triage, CI categorization, and feedback loops to keep documentation, providers, and core behavior aligned.
The CI-check map and comment templates reinforce the same point. Airflow’s automation classifies static checks, Ruff, mypy, unit tests, documentation builds, Helm tests, Kubernetes tests, image builds, and provider tests, then links failures to contributor documentation. The comment template file also contains Airflow-specific communication placeholders, including Airflow Slack as the project communication channel. For a new user, this means there are two paths into the project: learn the product through the versioned docs, and use the community and contributor workflows when you need help, report issues, or propose changes.
Sources: .apache-magpie-overrides/pr-management-config.md, .apache-magpie-overrides/pr-management-triage-ci-check-map.md, .apache-magpie-overrides/pr-management-triage-comment-templates.md
Relevant Source Files
- docs/images/documentation_architecture.py — Defines the generated diagram for how Airflow documentation is published from GitHub repositories through release-manager and committer actions to live docs infrastructure.
- .apache-magpie-overrides/README.md — Explains the repository-local override directory for apache-magpie framework skills and the rule that local modifications belong here rather than in the framework snapshot.
- .apache-magpie-overrides/pr-management-config.md — Captures Airflow-specific pull request triage identifiers, labels, grace windows, and feedback-delivery settings used by project automation.
- .apache-magpie-overrides/pr-management-triage-ci-check-map.md — Maps CI check name patterns such as static checks, Ruff, mypy, unit tests, docs, Helm, Kubernetes, images, and providers to contributor documentation URLs.
- .apache-magpie-overrides/pr-management-triage-comment-templates.md — Supplies Airflow-specific triage comment placeholders, project URLs, quality criteria links, and community communication wording.
Next Steps
If you are brand new to Airflow, run the Quick Start in a clean Python environment, then read the fundamentals tutorial before building a real pipeline. If you already know the authoring model, go next to installation constraints, DAGs, tasks and operators, scheduling, and connections. If your goal is to contribute, follow the repository’s contributor documentation and expect CI feedback to be categorized by the project automation described here. For help from people rather than docs, use the Airflow Slack community referenced by the official community link and project templates.