FastAPI Python Tutorial Build an Analytics API from Scratch

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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

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