Tools change every few years, principles do not. Learn a five-question framework, systems thinking, failure-first design, and anti-patterns to avoid.
Seven data modeling mistakes and fixes: undefined grain, cryptic names, skipped conceptual models, over-normalizing, thin docs, and ignored governance.
Data Vault modeling explained: Hubs hold business keys, Links hold relationships, and Satellites hold history, plus how to expose Data Vault data for analytics.
Denormalization adds deliberate redundancy to cut joins and speed up reads. Learn when it helps analytics, when to avoid it, and how views do it virtually.
