YAML vs Python Workflows: Which Is Better for Orchestration?

The debate between using YAML and Python for workflow orchestration is fundamentally about separating structure from execution. YAML, a data serialization format, excels in defining workflow coordination—what runs, when, and under what conditions—due to its readability and compatibility with GitOps practices. It is human-readable without requiring a runtime environment, making it accessible across various roles within a team. Python, on the other hand, is a general-purpose language well-suited for executing tasks that involve data transformation, machine learning, and complex business logic, primarily due to its robust data ecosystem and dynamic capabilities. The ideal approach involves using YAML for workflow definitions and Python for task execution, thereby maintaining clear separation between coordination and execution layers. This separation ensures that workflow definitions remain portable and environment-independent, while the execution logic can leverage Python’s powerful libraries. Kestra exemplifies this approach by using YAML for coordination and allowing Python scripts to run as isolated tasks, thus integrating various tools while maintaining a clean orchestration layer.

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