Should Python Data Pipelines be Function based or Object-Oriented (OOP)?
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FEBRUARY 10, 2025
1. Introduction 2. Data transformations as functions lead to maintainable code 3. Objects help track things (aka state) 3.1. Track connections & configs when connecting to external systems 3.2. Track pipeline progress (logging, Observer) with objects 3.3. Use objects to store configurations of data systems (e.g., Spark, etc.) 4. Class lets you define reusable code and pipeline patterns 4.1.
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