Airflow

1. Architecture Overview

   Scheduler, executor, workers, triggerer, metadata database and webserver/API server.

2. DAGs

   DAG definition, dependencies, scheduling, catchup, DAG runs and DAG bundles.

3. Tasks and Task Instances

   Task lifecycle, states, retries, timeouts and trigger rules.

4. Operators and Hooks

   Operators as templates, hooks, providers and reusable connections.

5. Sensors

   Poke versus reschedule mode, waiting on external conditions.

6. TaskFlow API

   @task decorator, Pythonic DAG authoring and automatic XCom passing.

7. XComs

   Passing small data between tasks and custom backends.

8. Dynamic Task Mapping

   expand and partial, runtime-generated parallel tasks.

9. Scheduling, Timetables and Assets

   Cron schedules, timetables, data intervals, asset-aware (dataset) scheduling.

10. Deferrable Operators and Triggers

    Triggerer, async waiting, freeing worker slots.

11. Executors

    Local, Celery and Kubernetes executors and parallelism.

12. Pools and Concurrency

    Pools, max_active_tasks, parallelism limits and priority weights.

13. Connections and Variables

    Managing credentials and config, secrets backends.

14. Backfill and Catchup

    Reprocessing historical intervals and clearing task states.

15. Best Practices

    Idempotency, top-level code, testing and DAG design.

16. Logging and Monitoring

    Task logs, metrics, alerts and observability.

17. Security

    Authentication, RBAC, secrets and hardening.

Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.