Analytics API with FastAPI and TimescaleDB
Own your data pipeline with Python and Postgres
0:00
3/21/2025
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.
š¹ļø Key Tech:
- Python
- FastAPI
- SQLModel + SQLAlchemy
- TimescaleDB
- Docker
š 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/
š Topics Covered:
- ā Development Environment Setup - Install Python 3, create virtual environments, and set up your workspace properly. All from scratch.
- ā FastAPI Fundamentals - Build your first API endpoints in minutes with Python basics.
- ā Containers with Docker & Docker Compose - Create optimized containers for both development and production environments
- ā Data Schemas with Pydantic - ensure valid incoming and outgoing data using the powerful Pydantic for serialization and validation.
- ā Use SQLModel to connect FastAPI to PostgreSQL with type-safe database operations based on Pydantic and SQLAlchemy.
- ā Time Series Optimization - Transform regular postgres tables into Timescale hypertables for efficient time-based queries and support massive data ingestion.
- ā Advanced Data Aggregation - Implement time bucket queries that analyze patterns across different time intervals
- ā Production Deployment - Deploy your API to Railway in minutes with public and private connections
- ā TimescaleDB Cloud Integration - Connect to managed database services optimized for time series workloads and saving time and headache managing production databases
- ā Secure your API and data with private networking
- ā And more
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