Which practice deliberately duplicates data to speed up reads at the cost of potential inconsistencies?

Which practice deliberately duplicates data to speed up reads at the cost of potential inconsistencies?

• A. Normalization
• B. Graph Database
• C. Composite Index
• D. Denormalization

Answer: (D)

Duplicating data to speed up reads is denormalization. By intentionally storing the same information in multiple places or precomputing joins, you can answer read queries very quickly without needing to traverse multiple tables or perform complex joins. The trade-off is that when a piece of data changes, every copy must be updated to stay consistent. If updates miss a copy, you get inconsistencies, so you often need extra logic or eventual consistency approaches to keep everything in sync. Normalization, in contrast, reduces duplication by splitting data into related tables, which keeps data consistent but can require joins to assemble a full view. A composite index speeds up lookups by indexing multiple fields but doesn’t create redundant data. A graph database focuses on connected nodes and relationships rather than duplicating data for reads.

Duplicating data to speed up reads is denormalization. By intentionally storing the same information in multiple places or precomputing joins, you can answer read queries very quickly without needing to traverse multiple tables or perform complex joins. The trade-off is that when a piece of data changes, every copy must be updated to stay consistent. If updates miss a copy, you get inconsistencies, so you often need extra logic or eventual consistency approaches to keep everything in sync.

Normalization, in contrast, reduces duplication by splitting data into related tables, which keeps data consistent but can require joins to assemble a full view. A composite index speeds up lookups by indexing multiple fields but doesn’t create redundant data. A graph database focuses on connected nodes and relationships rather than duplicating data for reads.