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Dev Tutorial: Rust and ClickHouse Power High-Performance Apple Health XML Pipeline

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A software developer has published a technical tutorial on DEV Community detailing how to build a high-performance ETL pipeline for processing Apple Health export files, which can exceed 5GB in size. The pipeline uses Rust's quick-xml crate for streaming XML parsing, avoiding memory crashes that occur when standard parsers attempt to load the entire file at once. Apache Arrow serves as a zero-copy data transport layer between Rust and Python, while Polars handles data transformation without duplicating memory. Processed records are then bulk-inserted into ClickHouse, a columnar OLAP database suited for fast analytical queries. The architecture is designed to run on consumer-grade hardware, making it applicable for personal health dashboards as well as larger population health platforms.

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