How can the basic data model be supplemented to account for additional requirements?

The following approaches are often used to supplement the basic data model to account for additional requirements:

• Adding new fields: expanding existing tables or models with new attributes that reflect additional data.
• Using related tables (relationships): creating new tables related to the basic model through foreign keys, which allows flexible storage of additional information without changing the main structure.
• Inheritance of models: in an object-oriented approach, creating subclasses of the basic model with additional properties.
• Using JSON or other semi-structured data types: storing additional parameters in a single field in JSON format, convenient for dynamic or rarely used data.

An example in Python using SQLAlchemy, where the basic model is extended with a related table:

from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)
    extra_info = relationship('UserExtra', back_populates='user')

class UserExtra(Base):
    __tablename__ = 'user_extra'
    id = Column(Integer, primary_key=True)
    user_id = Column(Integer, ForeignKey('users.id'))
    additional_field = Column(String)
    user = relationship('User', back_populates='extra_info')

This approach allows extending the model without changing the main table and maintains flexibility.