What is the difference between data normalization and denormalization?

What is the difference between data normalization and denormalization?

• A. Normalization increases redundancy; denormalization reduces redundancy.
• B. Normalization moves data into a single table; denormalization uses many tables.
• C. Normalization reduces redundancy by organizing data into related tables; denormalization introduces redundancy for faster reads.
• D. Normalization and denormalization are synonyms.

Answer: (C)

The main idea here is how database design choices handle redundancy and data integrity versus performance. Normalization organizes data into related tables with clear relationships, splitting data into logical units to minimize duplication. This reduces redundancy, so updates are consistent and anomalies are less likely because each fact is stored in one place and linked via keys. Denormalization does the opposite on purpose: it duplicates data or combines related tables to reduce the number of joins needed for reads, speeding up query performance in read-heavy workloads. The trade-off is more redundancy, which can lead to potential inconsistencies if data isn’t kept synchronized. So the correct explanation is that normalization reduces redundancy by organizing data into related tables, while denormalization introduces redundancy to speed up reads. The other statements misstate the effects on redundancy, the number of tables involved, or treat the terms as synonyms.

The main idea here is how database design choices handle redundancy and data integrity versus performance. Normalization organizes data into related tables with clear relationships, splitting data into logical units to minimize duplication. This reduces redundancy, so updates are consistent and anomalies are less likely because each fact is stored in one place and linked via keys.

Denormalization does the opposite on purpose: it duplicates data or combines related tables to reduce the number of joins needed for reads, speeding up query performance in read-heavy workloads. The trade-off is more redundancy, which can lead to potential inconsistencies if data isn’t kept synchronized.

So the correct explanation is that normalization reduces redundancy by organizing data into related tables, while denormalization introduces redundancy to speed up reads. The other statements misstate the effects on redundancy, the number of tables involved, or treat the terms as synonyms.