#kimball

Apr 16, 2026

The GINO Crisis: How Ambiguous Models Break Humans and LLMs — and the Case for Kimball 2.0

This article focuses on good database schema design. It highlights an emerging anti-pattern (GINO - "Gold in Name Only") whereby data engineering teams have "polished up" bronze data and moved it into Gold, with multi grain facts, normalised structures, outer joins etc, and left it to the Data Analyst to sort out. This is bad enough but it breaks when LLMs are brought in. The article then covers 6 straightforward techniques to simplify and optimise data models to increase success of NL2SQL platforms such as FDAs