What does "denormalization" trade off, and when might it be appropriate?
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Incorrect.
The correct answer is B) Denormalization intentionally duplicates data across tables to reduce joins needed for reads, trading increased storage and more complex writes (to keep redundant data consistent) for faster reads — useful in read-heavy analytics
Correct Answer
Denormalization intentionally duplicates data across tables to reduce joins needed for reads, trading increased storage and more complex writes (to keep redundant data consistent) for faster reads — useful in read-heavy analytics
Denormalization is a deliberate performance trade-off: duplicating or pre-aggregating data avoids costly joins on read-heavy paths at the cost of update complexity and storage, common in data warehouses and reporting systems.