Airflow Fundamentals study guide
Airflow Fundamentals · Work through your exam, one topic at a time.
The Level 1 Apache Airflow 3 Fundamentals credential is an entry-level certification offered by Astronomer, the company behind the Astro platform, through its Astronomer Academy. It is designed for practitioners with roughly three or more months of hands-on Airflow experience who want to demonstrate that they understand core Airflow concepts and can build, schedule, and monitor basic pipelines. The Airflow Fundamentals study guide has 9 sections and about 3,058 words.
01The Airflow UI
The Airflow web UI is the fastest way to answer operational questions: is a DAG paused, when did it last run, why did a task fail, and what is its log? The DAGs view lists every DAG the scheduler has parsed, with toggles for pause state and links to trigger a run manually.
357 words, about 2 min02The Airflow CLI
The airflow command-line tool wraps the same metadata database and components the UI uses, which makes it essential for automation and debugging on servers without a browser. Core subcommands a candidate should recognize: airflow dags list - DAGs the scheduler has parsed; airflow dags list-runs -d my dag - run history.
334 words, about 2 min03Airflow Use Cases
Airflow is a batch orchestration platform: it schedules and coordinates work, but it does not process data itself. Its sweet spot is multi-step pipelines with dependencies, retries, and observability - ETL/ELT loads, nightly model training, report generation, data quality checks, and cross-system synchronization.
320 words, about 2 min04Writing, Scheduling & Debugging DAGs
A DAG is a Python file that defines a DAG object (or uses @dag ) with a dag id , start date , schedule , and catchup setting. Tasks are operator instances; dependencies are set with or set upstream / set downstream .
289 words, about 1 min05DAG Dependencies
Candidates must separate intra-DAG dependencies from inter-DAG dependencies. Intra-DAG dependencies are edges between tasks inside one DAG: task a task b means B waits for A.
292 words, about 1 min06DAG Runs
A DAG run is one execution of a DAG for a specific logical date (also called execution date). Each run contains task instances , one per task, each with its own state: queued , running , success , failed , upstream failed , skipped , or up to date .
321 words, about 2 min07Airflow Architecture Components
Modern Airflow separates concerns across several processes sharing one metadata database (Postgres or MySQL): Scheduler - parses DAG files, creates DAG runs and task instances, and queues tasks whose dependencies are met. DAG processor - in newer versions, a separate process that parses DAG files and writes them to the database, lightening the scheduler.
270 words, about 1 min08Basic Operators (e.g. PythonOperator)
An operator is a template for a task. Instantiating it inside a DAG creates a task node.
295 words, about 1 min09Basics of XCom
XCom (cross-communication) lets tasks exchange small pieces of data through the metadata database. A task pushes a value with ti.xcom push(key=..., value=...) or by returning it (TaskFlow does this automatically); another task pulls it with ti.xcom pull(task ids=..., key=...) .
580 words, about 3 min