How are data redundancy and anomalies handled through normalization?

Normalization handles data redundancy and anomalies by organising data into tables and establishing relationships between them to ensure data integrity and consistency.

Data redundancy and anomalies are common issues in database management. Normalization is a systematic approach of decomposing tables to eliminate data redundancy and undesirable characteristics like insertion, update and deletion anomalies. It is a multi-step process that puts data into tabular form, removing duplicated data from the relation tables.

Data redundancy occurs when the same piece of data is held in two separate places. It's not only a waste of storage space, but it can also cause inconsistencies if the data is updated in one place but not the other. Normalization addresses this by ensuring that each piece of data is stored in just one place. This is achieved by dividing larger tables into smaller tables and linking them using relationships. For example, instead of having a customer's name and address repeated in every order they make, normalization would involve creating a separate table for customers, with a unique customer ID. This ID can then be used in the orders table, reducing redundancy.

Anomalies are problems that can occur in poorly planned, un-normalised databases where all the data is stored in one table, leading to confusion and anomalies. There are three types of anomalies: update, insertion, and deletion. Update anomalies occur when data is changed in some places but not others. Insertion anomalies occur when certain attributes cannot be inserted into the database without the presence of other attributes. Deletion anomalies occur when a record is deleted, all its associated data is also lost.

Normalization handles these anomalies by dividing the database into two or more tables and defining relationships between the tables. The main aim of this division is to avoid redundancy and anomalies. This ensures that the database remains consistent and accurate, which in turn makes the database more efficient and reliable.

In conclusion, normalization is a crucial process in database design to handle data redundancy and anomalies. It ensures data integrity, reduces data complexity, and improves data consistency by organising data into tables and establishing relationships between them.