Modularizing and Testing Pipelines
etl_module.py
test_etl_module.py
Best practices for modular code and test-driven development in data pipelines
- Define each ETL step as a separate, well-named function;
- Organize related steps into modules or packages for easier reuse and maintenance;
- Avoid hardcoding file paths, credentials, or configuration—use parameters or environment variables;
- Write unit tests for every transformation and edge case before deploying changes;
- Run tests automatically as part of your development workflow;
- Document function inputs, outputs, and expected behavior clearly;
- Refactor duplicated code into shared utility functions;
- Use small, composable steps so that each function does one thing well.
Building modular pipelines with thorough test coverage ensures your data processes are reliable, maintainable, and ready to adapt as requirements grow or change.
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Can you give examples of how to structure modules for a data pipeline?
What tools are recommended for automating tests in data pipelines?
How do I handle sensitive information like credentials securely in my pipeline?
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etl_module.py
test_etl_module.py
Best practices for modular code and test-driven development in data pipelines
- Define each ETL step as a separate, well-named function;
- Organize related steps into modules or packages for easier reuse and maintenance;
- Avoid hardcoding file paths, credentials, or configuration—use parameters or environment variables;
- Write unit tests for every transformation and edge case before deploying changes;
- Run tests automatically as part of your development workflow;
- Document function inputs, outputs, and expected behavior clearly;
- Refactor duplicated code into shared utility functions;
- Use small, composable steps so that each function does one thing well.
Building modular pipelines with thorough test coverage ensures your data processes are reliable, maintainable, and ready to adapt as requirements grow or change.
Grazie per i tuoi commenti!
