Articles

• What the conceptual layer must define to derive a Data Vault

  A first look at what a conceptual layer must define, from business concepts and keys to timelines and source mappings, so the Data Vault can be derived.

• The key to full data warehouse automation, and the role AI plays

  Today's data warehouse automation stops at the two hardest tasks. A conceptual layer closes the gap, and AI makes writing it faster. Here is how the pieces fit.

• The data warehouse rebuild cycle: why technology changes alone will not break it

  Most organizations rebuild their Data Warehouse when it grows too complex to change. This article explains why that rebuild becomes a cycle, and why a new platform alone never breaks it.

• Your data warehouse as code, with SQL on business concepts

  Write SQL on business concepts, not Data Vault tables. The conceptual model stays text, versioned and reviewed like code, and the implementation is derived from it.

• Automation or mechanization? What real data warehouse automation would take

  Most data warehouse automation speeds up the manual work instead of removing it. Real automation starts from a business definition, not the Data Vault model.