Airflow Concepts and Architecture – Fundamentals Study Guide
This domain accounts for roughly 30% of the exam and establishes what Airflow is, its core vocabulary, and how its components fit together. The Fundamentals exam is conceptual — it tests whether you understand DAGs, tasks, the scheduler/executor split, and when Airflow is the right tool. Nail this vocabulary and the rest of the exam builds on it cleanly.
What Airflow Is
Apache Airflow is a platform to programmatically author, schedule, and monitor workflows — pipelines defined as code (Python). Its defining role is orchestration: it coordinates tasks that usually run on external systems (databases, Spark, cloud services), rather than doing heavy data processing itself.
"Pipelines as code" matters because DAGs are then version-controllable, testable, dynamic (parameterizable), and maintainable with normal software practices.
Core Concepts
| Concept | Meaning |
|---|---|
| DAG | Directed Acyclic Graph — tasks with directed dependencies and no cycles (the workflow) |
| Task | A unit of work, created by instantiating an operator in the DAG |
| Operator | A template defining a unit of work (PythonOperator, BashOperator, …) |
| DAG run | One execution of a DAG for a specific logical/execution date |
| Task instance | One run of a task within a DAG run (has its own state) |
The nesting: a DAG contains tasks; a DAG run contains task instances for a given date.
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5 more sections (about 283 words) continue below. The first domain's study guide is free for every Apache Airflow 3 Fundamentals learner with a free account.