Stop Fighting Your Fitness Data: Build a Serverless Warehouse with DuckDB and dbt
If you’ve ever tried to reconcile a night of sleep from an Oura Ring , a morning run from a Garmin watch, and active minutes from an Apple Watch , you know the "Dirty Data" struggle is real. Each platform has its own schema, its own definition of "active calories," and its own idiosyncratic export format. In the world of Data Engineering , this is a classic multi-source integration problem. But…
In the realm of wearable fitness trackers, disparate data from devices such as Oura Ring, Garmin, and Apple Watch presents a challenge of varying schemas and export formats. This issue falls under the classic multi-source integration problem in data engineering. However, building a high-performance, serverless data pipeline to clean and normalize this data can be achieved with DuckDB, dbt, and GitHub Actions.
The data flow from the wearables to a clean, queryable state involves Python ingestion into DuckDB Raw, followed by dbt models for normalization. The process culminates in a final Parquet-based personal data warehouse.
To set up this pipeline, prerequisites include DuckDB, dbt-duckdb, and GitHub Actions. A Python script is used to load JSON, CSV, and XML files into a local .duckdb file. dbt is then employed to standardize activity data by merging and prioritizing data from multiple sources.
With GitHub Actions, the entire pipeline is automated and costs $0 to run. The pipeline is triggered on every push to the repository, cleaning the data. While this setup is suitable for personal projects, it requires more robust error handling and schema validation for larger-scale production environments.
DuckDB's speed, portability of Parquet files, and dbt's standardization across devices make this an effective solution for managing health data. This approach not only resolves the dirty data struggle but also creates a high-performance, serverless data warehouse at no cost.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
