A Level Computer Science: Relational Databases
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Frequently Asked Questions
What is the purpose of normalisation in relational databases?
Normalisation aims to reduce data redundancy by breaking down larger tables into smaller ones that are related to each other. This process improves data integrity, eliminates inconsistencies, and enhances overall database performance.
What are the advantages of using normalisation in database design?
Using normalisation in database design offers several advantages. It helps to improve data integrity by eliminating redundant information, ensures efficient use of storage space, enhances data consistency, simplifies data updates and modifications, and facilitates faster and more accurate data retrieval.
What are the common stages or forms of normalisation?
The common stages or forms of normalisation are often referred to as normal forms. These include First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), Boyce-Codd Normal Form (BCNF), and Fourth Normal Form (4NF). Each normal form has specific criteria that a table must meet to achieve that level of normalisation.
Can normalisation ever have disadvantages or drawbacks?
While normalisation is generally beneficial, it can have some drawbacks in certain situations. Over-normalisation, where tables are excessively broken down, can lead to complex queries and join operations, which may impact performance. Additionally, normalisation can sometimes result in the need for more complex database designs and can require additional effort during database maintenance and updates.
Are there any alternatives to normalisation in database design?
Although normalisation is a widely accepted and effective approach to database design, there are alternative strategies available. One alternative is denormalisation, where the focus is on optimizing database performance by reintroducing some level of redundancy. Denormalisation is often used in scenarios where read performance is critical, and data integrity can be ensured through other means, such as careful application logic or data validation techniques.