Learning Path: Researchers and Analysts

This path is for you if you want to use a Dorieh warehouse — query curated health data, understand what each column means and how trustworthy it is — rather than build pipelines yourself.

At the end you will be able to run the complete Medicare example on synthetic data, query the resulting warehouse, read its quality-control dashboard, and trace any column back to the raw files it came from.

The path

Skim Why a data platform? — five minutes to understand what problem Dorieh solves and why reproducibility drives its design.
Run the Medicare example end to end. It uses a publicly available synthetic dataset, so no data use agreement is needed; you will load a full warehouse into PostgreSQL on your own machine.
Read Medicare: Building a Data Warehouse from ResDac Files — now that the tables exist in your database, this page explains what each of them is: the Bronze/Silver/Gold layers, the beneficiaries, enrollments and admissions tables, and the QC aggregates.
Learn to query it: How to query the database for the mechanics, Querying Medicaid Data for the health-data-specific patterns and caveats, and Using HLL for the approximate distinct counts the QC tables rely on.
Open the QC dashboard — the Superset section of the Medicare example walks you through importing it. Every consistency percentage on it is an ordinary SQL measure you can now recompute yourself.

Going deeper

Data dictionary and lineage for Medicare processing — look up the meaning and derivation of any column, down to the raw file and line number.
The Dorieh approach — the concepts behind what you have been using: disambiguation rules, journaling, fine-grained lineage.
Terms and acronyms — when the vocabulary gets ahead of you.