Are you a "data hoarder" when it comes to your health? Between Apple Health exports, Oura Ring logs, and Garmin CSVs, I found myself sitting on nearly 10 million rows of biometric data. Trying to analyze a multi-year trend of Heart Rate Variability (HRV) or Resting Heart Rate (RHR) in Excel is a one-way ticket to "Application Not Responding" hell. 📉
In this tutorial, we are diving into the world of Quantified Self data engineering. We will leverage DuckDB—the Swiss Army knife of OLAP—and Apache Superset to build a lightning-fast, local-first biometric dashboard. We'll explore how to turn messy JSON/CSV exports into high-performance insights using dbt for modeling and DuckDB for compute. If you've been looking for a way to master Data Engineering for personal use, this is the ultimate "learn in public" project! 🚀
The Architecture: From Raw Export to Real Insights
Before we write a single line of SQL, let’s look at how the data flows. We want a system that is modular, fast, and stays entirely on our local machine (privacy first, right? 🥑).
graph TD
