What's the difference between normalized and denormalized data?
Normalized Data ⇒ Smaller, focused tables. Optimal for quick writes and storage, but query performance may suffer as it requires more JOINs. Great for OLTP (online transaction processing). We typically want to get the data in Data Cloud to a normalized state before we begin building models for analytical purposes.
Denormalized Data ⇒ Big messy “wide tables”. Optimal for quick reads (less JOINs), not great for write and storage performance. Great for OLAP (online analytical processing). After having performed ingestion and unification, we will typically move back to denormalized structures like Calculated Insights and Segments for business intelligence or automation use cases.