This is a wonderfully complete end-to-end data engineering project built around a delightfully nerdy premise: pulling your own chess.com game history, running every move through the Stockfish engine to score it, and then turning the results into a polished analytics dashboard. It's the kind of personal project that doubles as a portfolio piece, because it touches nearly every layer of a modern data stack.

The architecture is a textbook example of the contemporary BI pipeline:

• Extraction with dlt — pulling games from the chess.com public API into Postgres.
• Enrichment with Stockfish — calculating per-move evaluations, which is the genuinely interesting bit. Most chess analytics projects stop at win/loss; this one actually quantifies move quality.
• Transformation with dbt-core — modeling the raw data into clean marts on Postgres.
• Presentation with Metabase and Streamlit — two complementary front-ends, one for dashboarding and one for interactive exploration.

What makes this interesting isn't just the chess angle — it's that the same skeleton works for any personal-data project. Swap chess.com for Strava, GitHub events, or your bank's CSV exports, and you've got a reusable template for the dlt → Postgres → dbt → Metabase pattern that's become something of a de facto standard for small-to-medium analytics work.

Who would benefit:

• Aspiring data engineers looking for a concrete, runnable reference of the modern open-source BI stack without wading through corporate boilerplate.
• dbt learners who want a non-trivial models folder to study — chess move data has enough structure to demonstrate real transformations.
• Chess improvers who already track their games and want richer analytics than chess.com's built-in insights.