How to think like a data engineer : principles over tools, the Five Questions Framework, systems thinking, and designing for failure first.
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.
