Data Pipeline & ETL Development

[levelslip]

• Description: Create reproducible data processing pipeline
• Activities:
  • Design data pipeline architecture:
    • Define pipeline stages (extract, transform, load)
    • Plan data flow and dependencies
    • Design for reproducibility and automation
  • Implement data loading:
    • Create functions to load raw data from CSV
    • Handle different data formats and sources
    • Implement error handling
  • Implement data transformation:
    • Create functions for cleaning and preprocessing
    • Create functions for feature engineering
    • Implement feature scaling and encoding
  • Implement data validation:
    • Create data quality checks
    • Validate output against requirements
    • Create data profiling reports
  • Create train/test split strategy:
    • Implement temporal split (train on older data, test on recent)
    • Create stratified splits if needed
    • Document split strategy and rationale
  • Automate pipeline execution:
    • Create main pipeline script
    • Implement logging and error handling
    • Create configuration management
  • Test pipeline:
    • Unit test each pipeline component
    • Integration test full pipeline
    • Validate pipeline output
  • Document pipeline:
    • Create pipeline documentation
    • Document configuration and parameters
    • Create usage guide
• Deliverables: Data pipeline code, pipeline documentation, test results