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Denormalization can improve database performance by reducing the need for complex joins, which can speed up query response times. It simplifies data retrieval by consolidating related information into fewer tables, making it easier for applications to access the data they need. Additionally, denormalization can enhance read-heavy workloads, as it often results in fewer disk I/O operations. However, it may lead to data redundancy and increased complexity in ensuring data consistency.
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What is the difference between normalization and denormalization?
Normalizing data means eliminating redundant information from a table and organizing the data so that future changes to the table are easier. Denormalization means allowing redundancy in a table. The main benefit of denormalization is improved performance with simplified data retrieval and manipulation.
What is the purpose of denormalization?
Denormalization is done to increase the read performance of a database by reducing the number of joins needed to retrieve data. It involves duplicating data across tables to minimize the need for complex joins, which can result in faster query processing. However, denormalization can lead to data redundancy and potential data inconsistency risks.
What normalisation is used in IEEE 754 format?
none, it uses denormalization.
When do you perform denormalization?
Generally to optimize the performance of select query. To minimize joins used in the query.
What is the conflict between design efficiency information requirements and processing speed are often resolved through?
compromises that include denormalization
Find out functional dependencies?
A constraint between two sets of attributes is known as functional dependency in relational database. Determination of functional dependencies is vital in database denormalization, normalization and relational model.
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