What is denormalization?

Denormalization is normalization's counterpart. Where normalization splits data apart to avoid redundancy, denormalization joins it back together, or pre-computes and stores duplicated values, so that queries need fewer joins.

Why teams denormalize:

• Faster reads fewer joins mean quicker analytical queries.
• Simpler queries wide, flattened tables are easier to slice.
• Reporting fit star schemas denormalize dimensions for BI speed.

The cost is redundancy: the same fact stored in several places must be kept in sync, so writes and maintenance get harder. It is a performance optimisation, chosen when read speed matters more than write simplicity.

iDBQuery reduces the pressure to denormalize just to make data askable. Because it can infer joins and query normalized tables (or several sources) directly with its federation engine, you can get fast plain-language answers without first flattening everything into wide reporting tables. If your data is already denormalized for BI, iDBQuery works with that too, writing the SQL and citing every figure back to its source row either way.