Airflow

Managing data workflows is essential for data scientists and involves processes such as data preparation and model building pipelines. The complexity of such management has highlighted the inadequacies of traditional orchestration tools like CRON. To address these challenges, Airbnb developed Airflow in 2014. Airflow is an open-source Python library designed for task orchestration, enabling the creation, deployment, and monitoring of complex workflows.

Airflow represents complex data workflows as directed acyclic graphs (DAGs) of tasks. It acts as an orchestrator, scheduling tasks based on their interdependencies while offering a user-friendly web interface for workflow visualization. The library's flexibility in handling various task types simplifies the automation of data processing tasks, contributing to Airflow's popularity in contemporary data management.

For data scientists, setting up workflows with steps like data preprocessing, model training, and performance evaluation can become cumbersome with intricate Bash scripts, which are hard to maintain. Airflow provides a more maintainable solution with its built-in monitoring and error handling capabilities.

While Airflow is a popular choice, there are alternatives suited to specific needs. For instance, Dagster facilitates direct data communication between tasks without the need for an external storage service. Kubeflow Pipelines offers specialized ML operators and is geared towards Kubernetes deployment but has a narrower community due to its ML focus. Meanwhile, DVC caters to the experimental phase, providing pipeline definitions and integration with experiment tracking, though it may not be ideal for production environments.

OUR PERSPECTIVE

We recommend Airflow for the robust orchestration of diverse tasks, including production-level Machine Learning pipelines. For developmental stages and model iteration, tools like DVC are preferable due to their superior experiment tracking features.