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.
Why analytics needs a different data model than your application: denormalization, wide tables, and pre-aggregation patterns that make analytical queries fast.
Slowly Changing Dimensions Types 1, 2, and 3 explained with examples: overwrite, add a history row, or add a column, plus how to choose the right SCD type.
Dimensional modeling organizes analytics data into fact tables and dimension tables. Learn how to declare the grain, design both table types, and conform them.
