Apache Airflow
- 1 min read
- 2 chapters
Related on Lably
Through Open-source software, User interface and Python (programming language)
Airflow is written in Python, and workflows are created via Python scripts. Airflow is designed under the principle of "configuration as code". While other "configuration as code" workflow platforms exist using markup languages like XML, using Python allows developers to import libraries and classes to help them create their workflows.
Overview
Airflow uses directed acyclic graphs (DAGs) to manage workflow orchestration. Tasks and dependencies are defined in Python and then Airflow manages the scheduling and execution. DAGs can be run either on a defined schedule (e.g. hourly or daily) or based on external event triggers (e.g. a file appearing in Hive). Previous DAG-based schedulers like Oozie and Azkaban tended to rely on multiple configuration files and file system trees to create a DAG, whereas in Airflow, DAGs can often be written in one Python file.
Sign in to keep reading
One more chapter is free with a Lably account.
Managed providers
Three notable providers offer ancillary services around the core open source project.
- Astronomer has built a SaaS tool and Kubernetes-deployable Airflow stack that assists with monitoring, alerting, devops, and cluster management.
- Cloud Composer is a managed version of Airflow that runs on Google Cloud Platform (GCP) and integrates well with other GCP services.
- Amazon Web Services offers Managed Workflows for Apache Airflow starting from November 2020.

