Compare star and snowflake schemas and state which is typically favored for analytics in Clarity environments?

Compare star and snowflake schemas and state which is typically favored for analytics in Clarity environments?

• A. Star schema uses flat, denormalized dimension tables for fast queries; snowflake normalizes dimensions; analytics often favors star schemas.
• B. Snowflake is always faster for analytics.
• C. Star schema normalizes dimensions; snowflake denormalizes.
• D. Both are equally optimized; choice has no impact on performance.

Answer: (A)

For analytics, the goal is fast, scalable queries across large fact tables. A star schema achieves this by keeping dimension tables denormalized and wide, each linked directly to the central fact table. That layout reduces the number of joins a query must perform, so aggregates across many rows come back quickly and the SQL is easier to optimize. Snowflake normalizes dimensions into multiple related tables, which means more joins to assemble complete dimension attributes. While normalization reduces redundancy and can help with data integrity and space efficiency, the extra joins can slow analytic queries, especially in large Clarity environments. Therefore, star schemas are typically favored for analytics. The other statements misstate normalization/denormalization or claim universal speed without considering join costs and trade-offs.

For analytics, the goal is fast, scalable queries across large fact tables. A star schema achieves this by keeping dimension tables denormalized and wide, each linked directly to the central fact table. That layout reduces the number of joins a query must perform, so aggregates across many rows come back quickly and the SQL is easier to optimize. Snowflake normalizes dimensions into multiple related tables, which means more joins to assemble complete dimension attributes. While normalization reduces redundancy and can help with data integrity and space efficiency, the extra joins can slow analytic queries, especially in large Clarity environments. Therefore, star schemas are typically favored for analytics. The other statements misstate normalization/denormalization or claim universal speed without considering join costs and trade-offs.