FastAPI Python Tutorial: Build an Analytics API from Scratch

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/codingforentrepren... Sign up for Timescale with my link: https://tsdb.co/justin-api-1 pip install timescaledb-python: https://github.com/jmitchel3/timescal... 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