Project Notes
Building a reproducible macroeconomic risk pipeline
A project note on separating data engineering, feature construction and modelling across Rust and Python.
One of the most useful architectural decisions in a data project is deciding where each kind of work should live.
Two languages, distinct responsibilities
In my macroeconomic downturn project, Rust handles CSV ingestion, validation and feature engineering, while Python handles modelling, linear-algebra analysis and the interactive dashboard. The point is not to use two languages for novelty; it is to keep a clear boundary between deterministic preprocessing and exploratory/model-facing work.
Why the boundary matters
A reproducible pipeline should make it obvious where a value came from and which transformation produced it. Separating responsibilities makes it easier to test the engineered dataset before modelling begins.
The larger lesson
For me, this project sits between data science and software engineering. That intersection is increasingly important in scientific computing too: a model is only as trustworthy as the pipeline that produced its inputs.