Seven common data modeling mistakes and how to avoid them : from naming conventions to governance integration.
A deep dive into Data Vault modeling : Hubs, Links, and Satellites explained with examples, and how to present Data Vault output for analytics consumption.
When and why to flatten your data : denormalization techniques, trade-offs, and how virtual denormalization via views avoids physical data duplication.
How to optimize data models for analytical queries instead of transactional workloads : denormalization strategies, wide tables, and pre-aggregation patterns.
