Introduction of Database Normalization (original) (raw)

Last Updated : 13 Jan, 2025

Normalization is an important process in database design that helps improve the database’s efficiency, consistency, and accuracy. It makes it easier to manage and maintain the data and ensures that the database is adaptable to changing business needs.

• Database normalization is the process of organizing the attributes of the database to reduce or eliminate data redundancy (having the same data but at different places).
• Data redundancy unnecessarily increases the size of the database as the same data is repeated in many places. Inconsistency problems also arise during insert, delete, and update operations.
• In the relational model, there exist standard methods to quantify how efficient a databases is. These methods are called normal forms and there are algorithms to covert a given database into normal forms.
• Normalization generally involves splitting a table into multiple ones which must be linked each time a query is made requiring data from the split tables.

Why do we need Normalization?

The primary objective for normalizing the relations is to eliminate the below anomalies. Failure to reduce anomalies results in data redundancy, which may threaten data integrity and cause additional issues as the database increases. Normalization consists of a set of procedures that assist you in developing an effective database structure.

• **Insertion Anomalies: Insertion anomalies occur when it is not possible to insert data into a database because the required fields are missing or because the data is incomplete. For example, if a database requires that every record has a primary key, but no value is provided for a particular record, it cannot be inserted into the database.
• **Deletion anomalies: Deletion anomalies occur when deleting a record from a database and can result in the unintentional loss of data. For example, if a database contains information about customers and orders, deleting a customer record may also delete all the orders associated with that customer.
• **Updation anomalies: Updation anomalies occur when modifying data in a database and can result in inconsistencies or errors. For example, if a database contains information about employees and their salaries, updating an employee’s salary in one record but not in all related records could lead to incorrect calculations and reporting.

Read more about Anomalies in Relational Model.

Before Normalization: The table is prone to redundancy and anomalies (insertion, update, and deletion).
After Normalization: The data is divided into logical tables to ensure consistency, avoid redundancy and remove anomalies making the database efficient and reliable.

Prerequisites for Understanding Database Normalization

In database normalization, we mainly put only tightly related information together. To find the closeness, we need to find which attributes are dependent on each other. To understand dependencies, we need to learn the below concepts.

Keys are like unique identifiers in a table. For example, in a table of students, the student ID is a key because it uniquely identifies each student. Without keys, it would be hard to tell one record apart from another, especially if some information (like names) is the same. Keys ensure that data is not duplicated and that every record can be uniquely accessed.

Functional dependency helps define the relationships between data in a table. For example, if you know a student’s ID, you can find their name, age, and class. This relationship shows how one piece of data (like the student ID) determines other pieces of data in the same table. Functional dependency helps us understand these rules and connections, which are crucial for organizing data properly.

Once we figure out dependencies, we split tables to make sure that only closely related data is together in a table. When we split tables, we need to ensure that we do not loose information. For this, we need to learn the below concepts.

Dependency Preserving Decomposition Lossless Decomposition in DBMS

Features of Database Normalization

• **Elimination of Data Redundancy: One of the main features of normalization is to eliminate the data redundancy that can occur in a database. Data redundancy refers to the repetition of data in different parts of the database. Normalization helps in reducing or eliminating this redundancy, which can improve the efficiency and consistency of the database.
• **Ensuring Data Consistency: Normalization helps in ensuring that the data in the database is consistent and accurate. By eliminating redundancy, normalization helps in preventing inconsistencies and contradictions that can arise due to different versions of the same data.
• **Simplification of Data Management: Normalization simplifies the process of managing data in a database. By breaking down a complex data structure into simpler tables, normalization makes it easier to manage the data, update it, and retrieve it.
• **Improved Database Design: Normalization helps in improving the overall design of the database. By organizing the data in a structured and systematic way, normalization makes it easier to design and maintain the database. It also makes the database more flexible and adaptable to changing business needs.
• **Avoiding Update Anomalies: Normalization helps in avoiding update anomalies, which can occur when updating a single record in a table affects multiple records in other tables. Normalization ensures that each table contains only one type of data and that the relationships between the tables are clearly defined, which helps in avoiding such anomalies.
• **Standardization: Normalization helps in standardizing the data in the database. By organizing the data into tables and defining relationships between them, normalization helps in ensuring that the data is stored in a consistent and uniform manner.

Normal Forms in DBMS

Read more about Normal Forms in DBMS.

Advantages of Normalization

• Normalization eliminates data redundancy and ensures that each piece of data is stored in only one place, reducing the risk of data inconsistency and making it easier to maintain data accuracy.
• By breaking down data into smaller, more specific tables, normalization helps ensure that each table stores only relevant data, which improves the overall data integrity of the database.
• Normalization simplifies the process of updating data, as it only needs to be changed in one place rather than in multiple places throughout the database.
• Normalization enables users to query the database using a variety of different criteria, as the data is organized into smaller, more specific tables that can be joined together as needed.
• Normalization can help ensure that data is consistent across different applications that use the same database, making it easier to integrate different applications and ensuring that all users have access to accurate and consistent data.

Disadvantages of Normalization

• Normalization can result in increased performance overhead due to the need for additional join operations and the potential for slower query execution times.
• Normalization can result in the loss of data context, as data may be split across multiple tables and require additional joins to retrieve.
• Proper implementation of normalization requires expert knowledge of database design and the normalization process.
• Normalization can increase the complexity of a database design, especially if the data model is not well understood or if the normalization process is not carried out correctly.

Conclusion

Database normalization is a key concept in organizing data efficiently within a database. By reducing redundancy, ensuring data consistency, and breaking data into well-structured tables, normalization enhances the accuracy, scalability, and maintainability of a database. It simplifies data updates, improves integrity, and supports flexible querying, making it an essential practice for designing reliable and efficient database systems.