In this module, we will dive into the essentials of working with FastAPI, starting with creating your first application. You will learn how to configure a clean and maintainable setup, follow best practices for code formatting, and implement core features of a social media API. By the end of this section, you will also know how to manage a scalable codebase using APIRouter for modular development.

In this module, we will explore pytest, a robust testing framework for Python. You will start with its basics and then focus on writing and implementing tests for your FastAPI applications. By the end of this section, you will have built a suite of tests to ensure the reliability of the posts and comments features in your API, laying the groundwork for quality assurance in your projects.

In this module, we will delve into the integration of asynchronous databases with FastAPI. You will learn how to install necessary dependencies, create configuration files using Pydantic, and manage multiple environments. Additionally, you will set up an async database, handle connections using lifespan events, and integrate database operations into your API routers to enable seamless data persistence.

In this module, we will focus on implementing robust logging mechanisms in FastAPI applications. You will explore Python’s logging framework, configure multiple loggers, and add file handlers to persist logs. Advanced techniques like using filters, obfuscating sensitive data, and implementing correlation IDs will also be covered. Additionally, you’ll learn how to integrate and manage cloud-based logging tools like Logtail, enabling efficient monitoring and debugging in production.

In this module, we will cover user authentication in FastAPI, from the basics of creating a users table to advanced features like token generation and password hashing. You will implement user registration, retrieve the current user using tokens, and apply dependency injection for streamlined user management. Additionally, the module explores integrating user relationships with other tables and optional features like OAuth Password Bearer and Swagger for secure and well-documented APIs.

In this module, we will explore the implementation of many-to-many relationships in FastAPI using SQLAlchemy. You will learn how to set up a table for post likes, create API routes for interacting with posts, and streamline database operations with reusable queries. The module also covers advanced techniques like using query string arguments and Enums to enable data sorting and filtering, enhancing the functionality and user experience of your application.

In this module, we will implement user email confirmation in FastAPI. You will learn how to generate and decode confirmation tokens, create a confirmation endpoint, and enforce email verification for authenticated requests. Additionally, the module guides you through setting up Mailgun, sending confirmation emails, and optimizing email delivery using background tasks to ensure a seamless and efficient user experience.

In this module, we will focus on enabling file uploads in your FastAPI application. You'll learn how to set up and integrate Backblaze B2 as a file storage solution, implement a dedicated file upload endpoint, and write tests to ensure seamless and secure operation.

In this module, you will learn how to implement image generation features in FastAPI using background tasks. You will modify your application’s models and databases, configure DeepAI as a third-party service, and use background tasks to optimize image generation. By the end, you will have fully integrated image generation functionality into your FastAPI endpoints.

In this module, you will learn to deploy and manage FastAPI applications effectively. You will update your project to Pydantic v2, deploy your app to Render, and configure a PostgreSQL database. Additionally, the module covers integrating Sentry for error tracking and setting up GitHub Actions for continuous integration, ensuring a streamlined deployment and management process.