Apache Airflow

Apache Airflow is a platform created by the community to programmatically author, schedule and monitor workflows using Python DAGs.

Use it when

• •You need to orchestrate complex, multi-step machine learning workflows and data pipelines.
• •Your team prefers Python-native workflow definition with version control capabilities.
• •You require robust monitoring, alerting, and retry mechanisms for production ML operations.
• •You need tool-agnostic orchestration that can integrate with any MLOps tool with an API.
• •You want to convert existing ML scripts into scheduled, monitored workflows using TaskFlow API.
• •Your workflows are mostly static and slowly changing (not streaming data).
• •You need dynamic pipeline generation and complex dependency management.
• •You require extensive integration with cloud providers (AWS, GCP, Azure) and MLOps tools.

Watch out

• ⚠Installation complexity: Direct pip install often fails; requires constraint files for repeatable installations.
• ⚠Python expertise required: Not suitable for teams without strong Python programming skills.
• ⚠Resource intensive: Can consume significant memory and CPU; requires proper resource configuration and monitoring.
• ⚠Not for streaming: Designed for batch processing, not real-time streaming data.
• ⚠Docker challenges: Custom dependencies and GPU support require specialized Docker expertise.
• ⚠Production deployment complexity: Requires careful setup of monitoring, security, and infrastructure management.
• ⚠Learning curve: Understanding DAGs, operators, and Airflow concepts takes time.
• ⚠Overhead for simple tasks: May be overkill for basic scheduling needs.

Available in stages

Installation

Example stacks