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Building Scalable Python Projects: What I Wish I Knew Earlier
- The article discusses lessons learned from building a marketing analytics dashboard with a Large Language Model (LLM) using Python.
- Understanding the fundamentals of Flask, Uvicorn, and APIs is crucial before delving into complex project aspects.
- Organizing the project structure is essential for scalability, with separate modules for routes, models, services, and utilities.
- Flask Blueprints are recommended for organizing routes based on features or resources.
- Services act as intermediaries between routes and models, handling business logic efficiently.
- Clear pipeline design is crucial for multi-step processes, ensuring clarity on input and output of main functions.
- Type hints, prefixed underscores for internal functions, and proper package structuring aid in code maintainability.
- Naming conventions like using underscores for internal functions help define clear API boundaries and prevent namespace pollution.
- Structured packaging, understanding __init__.py usage, and avoiding circular imports contribute to cleaner code organization.
- Proper packaging also facilitates writing tests and running code in different contexts efficiently.
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