Airflow
Important
This documentation is for the now deprecated Data Engineering Airflow service. This service is now in updates only mode, and will be shuttered on July 8th 2025.
Please refer to Analytical Platform Airflow for more information on our new Airflow service.
Airflow is a platform to programmatically author, schedule and monitor workflows
Important links
- AWS control panel: to login to AWS and access AP tools including the Airflow dev and prod UI
- Airflow dev UI: for running and monitoring development and training workflows on the Airflow UI (you will need to login to AWS first)
- Airflow prod UI: for running and monitoring production workflows on the Airflow UI (you will need to login to AWS first)
- Airflow repo: Github repo to store Airflow DAGs and roles
- Airflow template for Python: Github template repository for creating a Python image to run an Airflow pipeline
- Airflow template for R: Github template repository for creating an R image to run an Airflow pipeline
- Support: contact the Analytical Platform team on #ask-analytical-platform
To find out more
- Airflow pipeline concepts: What is Airflow and why you should use it
- Airflow pipeline instructions: Step by step guide for creating an example Airflow pipeline and related resources
- Troubleshooting Airflow pipelines: Common pitfalls
This page was last reviewed on 7 April 2025.
It needs to be reviewed again on 7 April 2026
by the page owner #ask-analytical-platform
.
This page was set to be reviewed before 7 April 2026
by the page owner #ask-analytical-platform.
This might mean the content is out of date.