Pipeline Patterns and Best Practices

  • Linear Pipelines: Connect modules in sequence for step-by-step processing
  • Branching Pipelines: Split data flows to perform parallel processing
  • Merging Pipelines: Combine multiple data sources
  • Data Transformation Chains: Use multiple modules to progressively transform data

Performance Optimization

  • Memory Usage: Be mindful of sending large dataframes or objects
    • Chunking: Process large datasets in smaller chunks
    • Sampling: Use data sampling during development
  • Processing Efficiency: Optimize algorithms for faster execution
  • I/O Operations: Minimize unnecessary data transfers

More tips

  1. Log Strategically

    • Include informative log messages at key points
    • Log input and output sizes/shapes
    • Use appropriate log levels (INFO, WARNING, ERROR)

  2. Versioning Discipline

    • Update module versions when making significant changes
    • Document version changes
    • Consider backward compatibility

  3. Test Thoroughly

    • Test modules individually before adding to pipelines
    • Test with representative data
    • Test edge cases and error conditions

  4. Handle Errors Gracefully

    • Add try/except blocks for robust error handling
    • Provide clear error messages
    • Fail early and explicitly

  5. Security Considerations

    • Follow GitLab permission best practices
    • Don’t hardcode credentials in your code
    • Use environment variables or secrets management for sensitive information