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.
How data modeling changes in a lakehouse: schema-on-read, Iceberg partition evolution, and a virtual Medallion Architecture built as SQL views over open tables.
