Introducing the Metadata and Config-Driven Python Framework for Data Processing with Spark! This powerful framework offers a streamlined and flexible approach to ingesting files, applying transformations, and load data into a database. By leveraging metadata and a configuration file, this framework enables efficient and scalable data processing pipelines. With its modular structure, you can easily adapt the framework to your specific needs, ensuring seamless integration with different data sources, file formats, and databases. By automating the process and abstracting away the complexities, this framework enhances productivity, reduces manual effort, and provides a reliable foundation for your data processing tasks. Whether you are dealing with large-scale data processing or frequent data updates, this framework empowers you to effectively harness the power of Spark and achieve efficient data integration, transformation, and loading. 

Here’s an example of a metadata and config-driven Python framework for data processing using Spark to ingest files, transform data, and load it into a database. The code provided is a simplified implementation to illustrate the concept. You may need to adapt it to fit your specific needs.

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