Metadata-driven programming is a programming technique where the application’s behavior is defined in metadata instead of code. In this approach, the metadata defines the structure and behavior of the application, including input/output formats, data mappings, transformations, and data storage mechanisms. This approach is particularly useful in data engineering, where data formats and storage mechanisms can change frequently.

Using Python dictionaries to store metadata is a simple and effective way to implement metadata-driven programming in PySpark. By using metadata-driven programming, we can make our PySpark code more flexible, maintainable, and reusable, which is essential in data engineering.

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