Need for Normalization –
While managing data belonging to big organizations, there are many attributes with many records. For example, if we are managing a database of a multinational company, there are hundreds of employees and a lot of information related to them is to be maintained such as their phone number, address, name, employee id, dept_no, etc. So, keeping such a large information in a single relation makes the relation very large which will lead to problems such as data repetition. This data repetition may result in –
- Making the relations very large
- Maintaining and updating data is difficult since it necessitates searching through numerous related entries.
- Wastage and inefficient use of resources and disc space.
- Errors are more likely to happen.
So, to avoid these problems, the big relations are analyzed and decomposed into smaller relations keeping in mind that none of the data loss occurs while doing this. This decomposition of relations is called normalization.
What is Normalization?
- The process of structuring the data in the database is called normalization.
- Redundancy from a relation or collection of relations is reduced through the use of normalization. The removal of unwanted traits like Insertion, Update, and Deletion Anomalies is another purpose for it.
- The larger table is split into smaller ones during normalization, and they are connected through relationships.
Also read: Difference between DBMS and RDBMS
The reason behind normalization
There are anomalies occurring in the database. So, to remove these anomalies we need to split the relations into smaller ones which are called normalization.
Data modification anomalies can be divided into three types –
Insertion Anomaly – Insertion Anomaly is the term used when there is insufficient data to allow the insertion of a new tuple into a relationship.
Deletion Anomaly – The term “deletion anomaly” describes a circumstance in which some important data is unintentionally lost when some other data is deleted.
Updation Anomaly – When numerous rows of data must be updated in order to update a single data value, this is known as an updation anomaly.
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Advantages of Normalization in DBMS
- Normalization reduces data redundancy.
- Improved database organization in general
- Improved data inconsistency
- Greater flexibility in database design
- Maintains relational integrity
Disadvantages of normalization in DBMS
- Before creating the database, you must first determine the demands of the user.
- When the relationships are normalized to higher normal forms, the performance degrades.
- Careless data decomposition may lead to data loss.
- It is a time-consuming process to decompose into higher normalization forms.
So, in this tutorial, we learned about data normalization and its advantages and disadvantages. In other articles, we will learn about different forms of normalization.
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