From Messy Health Data to Actionable Insights: Building a Personal Knowledge Graph with Neo4j and Apache Hop

We are living in the golden age of "Quantified Self." Between your Apple Watch, Oura Ring, and Whoop strap, you probably have more data on your heart rate than a 1980s ICU. But here’s the problem: Your data is trapped in silos. Apple Health speaks one language, Oura speaks another, and none of them talk to each other. If you want to know how a specific workout affected your REM sleep three days later, a simple dashboard won't cut it. You need a Personal Health Knowledge Graph . In this tutorial, we’ll build an end-to-end Data Engineering pipeline to integrate multi-source health data into Neo4j , using Apache Hop for ETL and Pandas for the heavy lifting. The Architecture: Why a Knowledge Graph? Relational databases struggle with the "n+1" problem when you start asking complex questions about correlations over time. A Knowledge Graph treats relationships as first-class citizens. Data Flow Overview graph TD A[Apple Health XML] -->|Pandas Parsing| B(Cleaned CSVs) C[Oura Ring API/Export] -