FastAPI Python Tutorial: Build an Analytics API from Scratch

Own your own data pipeline and built an Analytics API from scratch in this tutorial. We'll go step-by-step building a production-ready API microservice so you can harness time-series data to analyze traffic of any web application. 

🕹️ Key Tech:
- Python
- FastAPI
- SQLModel + SQLAlchemy
- TimescaleDB
- Docker

🔗 Links & References
- Course Code: https://github.com/codingforentrepreneurs/analytics-api
- Sign up for Timescale with my link: https://tsdb.co/justin-api-1
- pip install timescaledb-python: https://github.com/jmitchel3/timescaledb-python
- Railway Templates: fastapicontainer.com & jupytercontainer.com
- FastAPI https://fastapi.tiangolo.com/
- SQLModel: sqlmodel.tiangolo.com/

👉 Topics Covered:
✅ Development Environment Setup - Install Python 3, create virtual environments, and set up your workspace properly. All from scratch.
✅  FastAPI Fundamentals - Build your first API endpoints in minutes with Python basics.
✅  Containers with Docker & Docker Compose - Create optimized containers for both development and production environments
✅  Data Schemas with Pydantic - ensure valid incoming and outgoing data using the powerful Pydantic for serialization and validation. 
✅  Use SQLModel to connect FastAPI to PostgreSQL with type-safe database operations based on Pydantic and SQLAlchemy.
✅  Time Series Optimization - Transform regular postgres tables into Timescale hypertables for efficient time-based queries and support massive data ingestion.
✅  Advanced Data Aggregation - Implement time bucket queries that analyze patterns across different time intervals
✅  Production Deployment - Deploy your API to Railway in minutes with public and private connections
✅  TimescaleDB Cloud Integration - Connect to managed database services optimized for time series workloads and saving time and headache managing production databases
✅  Secure your API and data with private networking
✅  And more

Chapters
00:00:00 Welcome
00:03:21 Demo
00:06:45 Tools
00:10:31 Setup Development Environment
00:12:15 Download & Install Python 3
00:15:40 Create a Python Virtual Environment
00:20:24 Install Python Packages
00:27:53 FastAPI Hello World
00:33:42 Docker Desktop & Docker Compose
00:44:04 Production Dockerfile for FastAPI
00:52:47 Build & Run FastAPI Container
00:57:54 Development Mode with Docker Compose
01:11:36 Section Wrap Up
01:14:27 Routing & Data Validation
01:16:51 Our First API Endpoint
01:21:11 FastAPI Routing Module
01:26:03 Verify API Endpoint
01:29:21 Basic Data Types
01:37:10 List Data Types
01:41:41 POST Method to Send our API Data
01:51:47 Incoming Data Validation with Pydantic Schemas
01:57:56 Optional Values with Pydantic
02:04:30 Section Wrap Up
02:06:13 Storing Data with SQLModel
02:07:48 Postgres or TimescaleDB with Docker Compose
02:16:39 Load Environment Variables with Python
02:24:07 Pydantic to SQLModel
02:26:35 First SQL Table with SQLModel
02:33:50 Create Database Tables with FastAPI Lifespan
02:40:44 Database Connection Issues
02:45:59 Store Data using SQLModel Sessions
02:51:48 SQLModel Query for List View
02:57:01 Detail Lookup via SQLModel
03:01:50 Update Data with SQLModel
03:05:30 Adding a Datetime Field
03:10:39 Updated At Timestamp Field
03:14:12 Section Wrap Up
03:15:30 Time Series Data in Postgres
03:17:33 SQLModel to TimescaleModel
03:22:04 Creating Hypertables
03:25:33 Chunks & Retention in Hypertables
03:30:56 Verify Hypertables with PopSQL
03:36:34 SQLModel Queries in Notebooks
03:42:06 Aggregate Data with Time Buckets
03:52:46 Time Bucket Aggregations with FastAPI & Timescale
04:01:08 Web Traffic Data and More Aggregations
04:12:43 Section Wrap Up
04:14:02 Deploy
04:15:04 Add CORS to FastAPI
04:17:17 Fork the Analytics FastAPI Project
04:20:34 First Deploy on Railway
04:22:35 Provision Database on Timescale Cloud & Deploy
04:28:35 Test Data to Production Endpoint
04:30:17 Analytics API with Private Networking
04:41:41 Section Wrap Up
04:43:14 Thank you

YouTube

Learn how to build an analytics API service using FastAPI, Python, and TimescaleDB. The tutorial covers creating a microservice to ingest and store time series data, deploying the application using Docker, and ensuring it runs in a containerized cloud environment with Railway. The step-by-step guide helps in setting up the development environment, writing code for the API, working with Postgres optimized for time series, and finally deploying the setup for production.