Which statement best captures the trade-offs between normalization and denormalization for reporting in Clarity?

Which statement best captures the trade-offs between normalization and denormalization for reporting in Clarity?

• A. Normalization reduces update anomalies and storage redundancy; denormalization simplifies queries and improves performance but can duplicate data; a hybrid approach is common.
• B. Normalization always increases performance; Denormalization never duplicates data.
• C. Denormalization reduces storage but complicates queries.
• D. Normalization is outdated; Denormalization is always best.

Answer: (C)

The trade-off between normalization and denormalization in reporting is about balancing data integrity with query performance. Normalization aims to reduce data duplication and update anomalies, keeping data consistent as updates happen. That often means more tables and more joins when you run a report, which can slow queries. Denormalization, on the other hand, introduces redundancy to make reads faster and queries simpler by avoiding many joins, but it increases storage use and raises maintenance overhead because changes must be applied to multiple copies of the same data. In practice, a hybrid approach is common: keep normalized structures to preserve data integrity, and selectively denormalize parts of the model (often into star schemas or materialized views) to accelerate reporting. So the best description among the options is that normalization reduces update anomalies and storage redundancy; denormalization simplifies queries and improves performance but can duplicate data; a hybrid approach is common. The other statements are inconsistent with how normalization and denormalization actually behave (for example, denormalization generally increases storage, not reduces it, and normalization isn’t inherently always faster).

The trade-off between normalization and denormalization in reporting is about balancing data integrity with query performance. Normalization aims to reduce data duplication and update anomalies, keeping data consistent as updates happen. That often means more tables and more joins when you run a report, which can slow queries. Denormalization, on the other hand, introduces redundancy to make reads faster and queries simpler by avoiding many joins, but it increases storage use and raises maintenance overhead because changes must be applied to multiple copies of the same data.

In practice, a hybrid approach is common: keep normalized structures to preserve data integrity, and selectively denormalize parts of the model (often into star schemas or materialized views) to accelerate reporting.

So the best description among the options is that normalization reduces update anomalies and storage redundancy; denormalization simplifies queries and improves performance but can duplicate data; a hybrid approach is common. The other statements are inconsistent with how normalization and denormalization actually behave (for example, denormalization generally increases storage, not reduces it, and normalization isn’t inherently always faster).