✳flâneur — a map of the web's best reading
Introduction to Airflow decorators | Astronomer Documentation
docs.astronomer.io · 2,260 words · saved by 1 readers
An overview of Airflow decorators and how they can improve the DAG authoring experience.
> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://www.astronomer.io/docs/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://www.astronomer.io/docs/_mcp/server. # Introduction to the TaskFlow API and Airflow decorators > An overview of Airflow decorators and how they can improve the DAG authoring experience. The *TaskFlow API* is a functional API for using decorators to define DAGs and tasks, which simplifies the process for passing data between tasks and defining dependencies. You
Explore this link on the map →related reading
- Manage task and task group dependencies in Airflow | Astronomer Documentationastronomer.io
- What I know about Apache Airflow so Faraaronlelevier.github.io
- Pass data between tasks | Astronomer Documentationastronomer.io
- Airflow Architecture: Key Components & Best Practiceshevodata.com
- What we learned after running Airflow on Kubernetes for 2 years | by Alexandre Magno Lima Martins | Apache Airflow | Mediummedium.com
- A guide on Airflow best practicesayc-data.com
- Airflow Executors | Astronomer Documentationastronomer.io
- Using Apache Airflow to monitor data pipelinesbiomadeira.github.io
- Cookbookcookbook.openai.com
- Executor - Airflow 3.3.0 Documentationairflow.apache.org
- Running Airflow Locally with Docker: A Technical Guidestackabuse.com
- Dag Runs - Airflow 3.3.0 Documentationairflow.apache.org