Database Normalisation Questions Long
Normalization and denormalization are two contrasting techniques used in database design to optimize data storage and retrieval.
Normalization is the process of organizing data in a database to eliminate redundancy and improve data integrity. It involves breaking down a database into multiple tables and establishing relationships between them through keys. The main goal of normalization is to minimize data duplication and ensure that each piece of information is stored in only one place. This helps to maintain data consistency and reduces the chances of data anomalies, such as update anomalies, insertion anomalies, and deletion anomalies. Normalization follows a set of rules, known as normal forms, which define the level of optimization achieved.
On the other hand, denormalization is the process of intentionally introducing redundancy into a database design. It involves combining tables and duplicating data to improve performance by reducing the number of joins required for complex queries. Denormalization is often used in situations where read performance is more critical than write performance or when dealing with large and complex databases. By duplicating data, denormalization can eliminate the need for joins and simplify query execution, resulting in faster response times. However, denormalization can lead to data redundancy and increase the complexity of maintaining data integrity.
In summary, the main difference between normalization and denormalization lies in their objectives and outcomes. Normalization aims to eliminate redundancy and improve data integrity, while denormalization introduces redundancy to enhance query performance. Both techniques have their own advantages and trade-offs, and the choice between them depends on the specific requirements and priorities of the database system.