Splitting a unique csv file data set to different tables inside Fabric

Hi filipenovaims20 ,

Thanks for using Fabric Community.

1. Cloning Data Flows:

While cloning existing Data Flows can be convenient, it can lead to code duplication and maintenance challenges. Instead, I recommend these options:

• Modular Data Flows: Design reusable Data Flow components for common transformations (e.g., date formatting, address cleaning). These can be used across different dimensions, reducing redundancy.
• Parameterization: Instead of cloning, modify the same Data Flow with different parameters (e.g., column names, transformation rules) for each dimension. This ensures flexibility and centralizes changes.
• Chained Data Flows: If transformations are truly unique, consider separate Data Flows chained together. This improves maintainability by keeping each dimension's logic isolated.

2. Data Splitting:

Yes, splitting data into separate tables before individual Data Flow transformations is a good practice. This avoids unnecessary processing on irrelevant data for each dimension.

• Pre-split Data Flow: Create a dedicated Data Flow to split the original data based on dimension-specific criteria. This pre-processed data can then be fed into separate Data Flows for each dimension, improving efficiency.
• Lakehouse Partitioning: Utilize M.Fabric's lakehouse partitioning feature to group relevant data together based on dimension identifiers. This allows Data Flows to efficiently access only the required data for each dimension.

Additional Tips:

• Documentation: Clearly document your chosen approach for transformation and data organization to improve project clarity and avoid future confusion.
• Testing and Monitoring: Regularly test and monitor your Data Flows to ensure they are operating correctly and efficiently.

Remember: There is no one-size-fits-all solution, and the best approach will depend on your specific data, transformations, and preferences.

I hope this helps! Feel free to ask any further questions you may have.