What is the primary goal of normalization in database design?a.Maximizing update anomaliesb.Maximizing redundancyc.Minimizing redundancy and update anomaliesd.Minimizing data integrity

The primary goal of normalization in database design is c. Minimizing redundancy and update anomalies.

Here's why:

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 by removing duplicated data from the relation tables.

Normalization is used for mainly two purposes:

1. Eliminating redundant (useless) data.
2. Ensuring data dependencies make sense i.e data is logically stored.

So, the main goal of normalization is to reduce redundancy and remove Insert, Update and Delete Anomaly. It also helps in improving the integrity and consistency of the database.

The primary goal of normalization in database design is c. Minimizing redundancy and update anomalies.

Here's why:

Normalization is a process in database design that organizes data to minimize redundancy and dependency of data. It divides larger tables into smaller ones and links them using relationships to increase the clarity in organizing data.

1. Minimizing redundancy: Without normalization, a database system may have duplicate data in multiple places. Normalization helps eliminate this redundancy by ensuring that each piece of data is stored in only one place, reducing the amount of space a database consumes. It also improves the consistency and accuracy of the data.

2. Minimizing update anomalies: An update anomaly is a data inconsistency that results from data redundancy and a partial update. For example, if a piece of data is duplicated in several places, updating the data in one place but not the others leads to data inconsistency. Normalization solves this problem by ensuring that each piece of data is stored in just one place.

So, the primary goal of normalization in database design is to minimize redundancy and update anomalies, which leads to more efficient data management.