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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:
- Sign up for Timescale with my link:
- pip install timescaledb-python:
- Railway Templates: fastapicontainer.com & jupytercontainer.com
- FastAPI
- 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
✅ Producti
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2025/03/22
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