Airflow is an open-source platform used for orchestrating complex workflows and data pipelines. It allows users to define, schedule, and monitor workflows as a series of interconnected tasks. Airflow is particularly valuable for managing data-related tasks, ETL (Extract, Transform, Load) processes, and job scheduling in a flexible and scalable manner.
When is Airflow useful?
Airflow is useful when you want to extract data from specific sources on a recurring basis and / or run some transformations on it. The core capability of Airflow being smooth management of the scheduling, workers and ensuring reliability of the process, helps data teams that have constant data requirements, simplify experience.
Why is Airflow popular?
Airflow was created within Airbnb in 2014 to manage their data pipelines and then donated to the open source community in ~2016.
The core reason for Airflow widespread adoption has been it’s versatile Python framework that allows you to create workflows that can seamlessly integrate with a wide range of technologies. Additionally, it provides a web-based interface to effectively oversee the status and progress of your workflows.
Challenges with Airflow and alternate options
Some teams have had challenges in scaling with Airflow due to multiple reasons, some being:
- Difficult debugging: Managing dependencies between tasks and handling task failures can get complex in Airflow at scale. The below diagram explains the architecture design of Airflow.
- Learning Curve: Onboarding is non-trivial and requires some dedicated time allocated for it. In case you do not have any team member deeply experienced in Airflow, it can take some time and effort to get used to it.
- Lack of Version Control: Airflow doesn’t come with a version control by default.
Alternative options to Airflow include Dagster (YC startup), Luigi (donated by Spotify), Apache NiFi and Prefect.
Read more about Airflow: https://airflow.apache.org/
