Data Validation Toolkit: Data Validation Guide

Data Validation Guide Why Validate Data? Invalid data causes crashes, corrupted databases, and security vulnerabilities. A structured validation layer catches problems at the boundary — before bad data propagates through your system. Validation Layers External Input │ ▼ ┌─────────────┐ │ Type Check │ Are fields the right Python types? ├─────────────┤ │ Schema Check │ Do values satisfy constraints (length, range, pattern)? ├─────────────┤ │ Business │ Do cross-field rules hold (dates, dependencies)? │ Rules │ ├─────────────┤ │ File Check │ Are uploaded files valid (size, format, extension)? └─────────────┘ │ ▼ Application Building a Pipeline Combine validators for defence in depth: from src.pipeline import ValidationPipeline from src.validators.type_validator import TypeValidator from src.validators.schema_validator import SchemaValidator from src.validators.business_rules import BusinessRuleValidator pipeline = ValidationPipeline ( mode = " collect_all " ) pipeline . add ( TypeValidato