What is the difference between OLTP and OLAP data modeling considerations?

• A. OLTP uses star schemas; OLAP uses fully normalized.
• B. OLTP emphasizes normalized schemas for data integrity; OLAP emphasizes dimensionally modeled schemas for analytics.
• C. OLTP is for read-heavy; OLAP is for transaction processing.
• D. OLTP uses denormalized structures for speed; OLAP uses normalized.

Answer: (B)

The main idea here is that OLTP and OLAP data modeling are driven by different practical goals. OLTP is built around preserving data integrity and enabling lots of concurrent updates, so its schemas are highly normalized (think 3NF or similar). This minimizes redundancy and keeps updates, deletes, and inserts consistent across the system.

OLAP, by contrast, is designed for fast, complex read queries and aggregations over large histories. To achieve that, data is modeled in a dimensional way (star or snowflake schemas) with fact tables and dimension tables, which denormalizes data to reduce joins and speed up analytics.

So the correct distinction is that OLTP uses normalized schemas for data integrity, while OLAP uses dimensionally modeled schemas for analytics. The other statements mix up which modeling style belongs to which workload (star schemas are typical of OLAP, not OLTP; OLTP is not primarily for read-heavy analytics; OLAP uses denormalized structures for speed, not normalized).