If you’ve ever tried to click that "Export Health Data" button on your iPhone, you know the feeling of pure dread that follows. You expect a clean CSV; you get a bloated, multi-gigabyte XML file that looks like it was designed by a chaotic deity. When building high-performance AI models for health tech, Apple Health data is a goldmine—but only if you can navigate the minefield of data engineering challenges. We’re talking about massive data volumes, duplicate entries from overlapping devices (iPhone vs. Apple Watch), and inconsistent sampling frequencies that would make any data scientist cry. In this tutorial, we are going to build a robust Data Pipeline using Polars , Apache Hop , and S3 to transform "dirty" XML exports into a standardized, high-performance Parquet Lakehouse . Pro-Tip: If you are looking for advanced architectural patterns for health-tech scaling, I highly recommend checking out the production-ready examples over at WellAlly's Engineering Blog .…