FastAPI Video Series - Lesson 3

Welcome to Lesson 3 of our FastAPI video series!

This lesson dives into data validation and schema modeling—critical skills for ensuring the integrity and reliability of your APIs.

You’ll learn how to define, enforce, and document the structure of your data using modern tools that integrate seamlessly with your framework of choice.

🔍 Overview

APIs often exchange complex data structures between clients and servers.

Without proper validation and consistent schema definitions, subtle bugs and security issues can creep in.

In this lesson, you’ll discover how to:

• Define clear data schemas for requests and responses
• Automatically validate incoming data against those schemas

🎯 What You Will Learn

1. Defining Schema Models
   • Create reusable data models using Pydantic.
   • Handle nested and optional fields for real‑world payloads.
2. Automatic Validation
   • Enforce type checks and required fields without extra boilerplate.
   • Provide clear error messages to API consumers.
3. Custom Validators & Model Configuration
   • Implement business rules with custom validation functions.
   • Customize field aliases, default behaviors, and schema options.
4. Documentation & Testing
   • Leverage auto‑generated docs (Swagger/OpenAPI) to explore schemas.
   • Test schema validation with both valid and invalid data samples.

🔑 Key Takeaways

• Consistent Schemas: Define clear, reusable models to represent your data.
• Automatic Safety: Let your framework enforce data integrity with minimal code.
• Custom Rules: Extend validation logic to fit your business requirements.
• Self‑Documenting APIs: Benefit from up‑to‑date interactive docs generated from your models.

Let's Code

GitHub Repository: https://github.com/nunombispo/FastAPI-Series---Lesson-3

🚀 What’s Next?

In Lesson 4, we’ll explore Dependency Injection & Project Structure, showing you how to organize your codebase for maintainability and scale.

See you there!