The first part of our course is all about getting to know each other, the Maven platform, and discuss the general flow and format of what we'll learn together. I'm excited to meet you here in this one!

This lecture will be the first code-focused section of the course. We'll begin studying the foundations of FastAPI: Python type hints.

Pydantic is a core element of both FastAPI (our main focus and API layer) driving an insane amount of functionality there, as well as the core window into MongoDB using Beanie. Suffice it to say, it's quite important and this chapter will be all about Pydantic.

In this chapter, you'll learn about the key ideas behind Python's asyncio programming model. When we can benefit from it and some of the limitations to be aware of.

Perhaps you've heard that async programming is hard. But with Python's asyncio, many of those challenges are avoided entirely. In this chapter, you will see several examples of concrete async code written using Python's async and await keywords. Our examples will show you can gain massive performance with minimal code changes using asyncio.

Document databases, and MongoDB in particular, are powerful tools that allow us to write code exactly matching the structure in our databases. With end-to-end Pydantic models using Beanie + FastAPI, these for a great pairing for your maintainable and fast web app.

Beanie is a great ODM (Object-Document mapper) with async support. In this chapter, we will talk about getting started with Beanie as well as the standard boiler-plate topics of setting up DB connections, and related tasks.

Beanie is based on Pydantic models making it a good pair for FastAPI. We model our data and speak to the database using Pydantic models.

When using Beanie, we speak to the database (MongoDB) using Pydantic models and a clever async query interface. That query interface is the topic of this chapter. You'll find it a dream to work with.

With all of the foundational topics covered, it's time to focus on building a great API with FastAPI. In this course, we will build an API that exposes and models real pypi.org data. We will build an API to explore details and run searches across the top Python packages.

Our previous foray into FastAPI resulted in a powerful API over the PyPI data. But it was read-only. In this chapter, we begin accepting data from API clients. This will introduce is to a whole host of new concepts and good data processing practices around APIs.

So you've built an API. How many users can it handle? How large of a server do you need to deploy it to? How many servers? In this chapter, we'll provide concrete tooling to answer those and related questions.

That's it, you've made it to the end! We'll do a quick review and more importantly, a final Q&A session to close any loose ends.