Are you tired of your health data being trapped in "walled gardens"? Your Apple Health data lives in your iPhone, your Oura Ring stats are in a separate app, and your Garmin running metrics are somewhere else entirely. As engineers, we hate data silos. We want a unified view to answer the ultimate question: How does my late-night coding session actually affect my HRV and deep sleep?
In this tutorial, we are diving deep into Health Data Engineering. We will use InfluxDB as our high-performance time-series engine, Grafana for stunning visualizations, and Airflow to orchestrate the ETL (Extract, Transform, Load) process. By the end of this guide, you’ll have a professional-grade Quantified Self dashboard that correlates sleep, activity, and recovery metrics in one place.
The Architecture 🏗️
To build a robust pipeline, we need to handle disparate APIs and inconsistent data formats. Here is how the data flows from your wrist to your dashboard:
graph TD
