This project is part of the 42 School Common Core and focuses on
data validation, structured modeling, and business rule enforcement using Python.

Building on previous modules, this project introduces advanced concepts such as
data validation frameworks, schema enforcement, and real-world data integrity rules.

The goal of this module is to understand and apply:

• data modeling with pydantic
• field validation using constraints (Field)
• advanced validation using @model_validator
• handling complex data relationships (nested models)
• enum usage for controlled values
• error handling and validation feedback
• clean architecture for data pipelines

Each exercise simulates a real-world system where data must be validated before use,
resulting in a robust data validation pipeline.

• 🛰️ Exercise 0 - Space Station Validation
  Basic data validation with constraints

• 👽 Exercise 1 - Alien Contact Protocol
  Advanced validation rules and conditional logic

• 🚀 Exercise 2 - Space Mission Control
  Complex nested validation and business rules

ex0/
└── space_station.py

ex1/
└── alien_contact.py

ex2/
└── space_crew.py

Introduces structured data validation using Pydantic models.

• Defines a SpaceStation model
• Validates fields using:
  • string length constraints
  • numeric ranges (ge, le)
• Uses default values and optional fields
• Handles validation errors safely

Concepts: data validation, schema definition, field constraints
👉 Built using BaseModel and Field

Focuses on advanced validation logic and conditional rules.

• Introduces Enum for controlled values (ContactType)
• Implements complex validation using @model_validator
• Applies rules such as:
  • ID format enforcement
  • conditional validation based on contact type
  • cross-field dependency validation

Concepts: business logic validation, enums, conditional constraints
👉 Uses @model_validator(mode="after") for full-object validation

Implements nested models and complex validation scenarios.

• Defines multiple models:
  • CrewMember
  • SpaceMission
• Uses nested data structures (list[CrewMember])
• Validates:
  • crew composition (roles required)
  • experience distribution
  • mission constraints based on duration
  • active status of crew members

Concepts: nested validation, aggregation rules, system integrity
👉 Demonstrates real-world data validation pipelines

• pydantic simplifies robust data validation
• Data integrity must be enforced at model level
• Validation can go beyond fields into full object logic
• Enums prevent invalid categorical data
• Nested models reflect real-world structured data
• Clean validation logic improves maintainability

• Always validate external or user-provided data
• Use clear and explicit constraints (Field)
• Keep validation logic readable and separated
• Avoid side effects (like print) in validation functions
• Prefer returning validated objects instead of raw data
• Handle validation errors gracefully

• Written for Python 3.10+
• Uses type hints and follows flake8
• Focuses on data integrity and validation patterns
• Designed to simulate real-world backend/data engineering scenarios
• Outputs match the subject expectations