What is Normalization in Database? Example, Use, Types, Pros and Cons

What is Data Normalization in Database? 

Database normalization is a process used to organize data within a database to reduce redundancy and improve efficiency. By structuring data into smaller, related tables, normalization helps avoid duplicate data and ensures that updates or changes only need to be made in one place. This process is especially useful for keeping data accurate and making the database easier to maintain. In simple terms, normalization arranges data neatly and logically, allowing it to be accessed and managed without unnecessary repetition.

Database Normalization Example

• Imagine you have a table that stores information about students, including their names, classes, and teachers.
• Without normalization, each student’s record might repeat the teacher’s name and class details, leading to duplicate data.
• Normalization would separate this information into multiple tables: one for students, one for classes, and one for teachers.
• Each table has a unique ID (like a student ID or class ID) to link related information across tables.
• Now, if a teacher’s information changes, you only update it in one place, keeping the data organized and reducing redundancy.

Why Do We Use Normalization?

Normalization is important because it helps organize data, reducing problems like redundancy and inconsistency that can arise when storing information in a database. Let’s look at the main reasons we need normalization, along with examples of common anomalies:

1. Normalization organizes data to reduce redundancy.
2. It prevents insertion anomalies (problems adding data),
3. It helps avoid deletion anomalies (problems deleting data),
4. It reduces updation anomalies (problems updating data).

What is Functional Dependency?

Functional dependency is a key concept in database normalization that describes a relationship between columns in a table. It means that the value of one column (or set of columns) determines the value of another column. This is important for organizing data to avoid redundancy and maintain data integrity.

Types of Functional Dependencies

There are different types of functional dependencies like:

Trivial Functional Dependency

• Definition: If a column or set of columns determines itself, it’s called a trivial dependency.
• Example: In a table with columns A and B, if A → A or B → B, this is a trivial dependency because each column naturally determines itself.

Non-Trivial Functional Dependency

• Definition: If a column (or set of columns) determines another column, and that dependency is not self-evident, it’s a non-trivial dependency.
• Example: If A determines B (written as A → B), and B is not part of A, it’s a non-trivial dependency. This means B relies on A but isn’t part of A.

Partial Dependency

• Definition: This occurs when a column depends on only part of a composite key (a primary key made up of multiple columns).
• Example: If a table’s primary key is a combination of StudentID and CourseID, and StudentName only depends on StudentID, this is a partial dependency because StudentName does not depend on the full composite key.

Transitive Dependency

• Definition: When a non-key column depends on another non-key column instead of directly on the primary key.
• Example: If A → B and B → C, then A → C is a transitive dependency. In this case, C is indirectly dependent on A through B.

Multivalued Dependency

• Definition: When one column determines multiple values of another column independently of other columns in the table.
• Example: In a table where a student can have multiple skills and attend multiple clubs, there would be a multivalued dependency if skills and clubs were listed as separate entries for each student.

Join Dependency

• Definition: A situation where a table can be split into multiple tables that can be joined back together without losing information.
• Example: If a table contains data that can be divided into separate tables, like customer, order, and product tables, join dependency ensures you can recombine them without losing details.

Advantages of Functional Dependency

• Reduces Data Redundancy: Functional dependency helps identify and organize data so that each piece of information is stored only once. By removing duplicate data, it saves storage space and makes the database more efficient. For example, instead of storing a customer’s address multiple times for each order, we only store it once in a related table.

• Improves Data Integrity: With functional dependency, each piece of data has a clear dependency, meaning it depends on other specific data (like a student ID linked to a student’s name). This makes it easier to ensure accuracy since updates or changes only need to be made in one place, reducing the risk of inconsistent or outdated information.

• Simplifies Data Updates: Functional dependency makes it easier to update or modify data. By organizing data into related parts, updates in one part of the database automatically reflect wherever that data is linked, avoiding manual adjustments across multiple tables. This is especially helpful for managing large databases where data needs frequent updates.

Normal Forms in DBMS

In DBMS (Database Management Systems), Normal Forms are a set of rules that help organize data to reduce redundancy (duplicate data) and improve efficiency. There are several normal forms, each with its own set of rules.

• 1NF: No repeating groups; each column holds only one value.
• 2NF: No partial dependency; all non-key columns depend on the full primary key.
• 3NF: No transitive dependency; all non-key columns depend only on the primary key.
• BCNF: Every determinant is a candidate key.
• 4NF: No multi-valued dependencies; avoid mixing independent data in one table.

Advantages of DBMS Normalization

• Reduces Data Duplication: Normalization helps avoid storing the same information in multiple places. This makes the database more efficient, as you only store each piece of data once. For example, instead of repeating a customer’s address in every order record, you store it in a separate “Customers” table and link it to the orders.

• Ensures Data Consistency: With normalization, related data is stored in separate tables, which makes it easier to update or change information. If a customer’s address changes, you only need to update it once, preventing errors where some records might still show the old address.

• Makes Data Easier to Maintain: Since the data is organized into smaller, related tables, it’s easier to manage and modify. Adding new information, like a new customer or product, is simpler and doesn’t require changes to multiple places in the database. This makes the system more flexible and reduces the chances of mistakes.

Disadvantages of DBMS Normalization

• Increased Complexity: When you normalize a database, you break it down into smaller, related tables. While this reduces redundancy, it can make the database more complex to manage, as there are more tables to deal with. This might require more effort to write and maintain queries, especially for people who are new to the database structure.

• Slower Queries: Since data is spread across multiple tables, retrieving information often requires combining data from different tables using joins. This can slow down the performance of certain queries, especially in large databases with complex relationships between tables.

• Higher Maintenance Effort: While normalization makes the database more organized, it can increase the maintenance workload. For example, adding or updating data often requires inserting or modifying information in multiple tables. This can be time-consuming and more prone to errors if not done carefully.

Conclusion about Normalization of Database

Database normalization is an essential process that helps organize data efficiently by reducing redundancy and ensuring consistency. By breaking down large, complex tables into smaller, related ones, it improves data integrity and makes it easier to maintain the database. However, normalization can also increase complexity and slow down queries, especially in large databases. Despite these challenges, the benefits of better data organization and reliability make normalization a crucial step in database design, ensuring that the system remains flexible, accurate, and efficient in the long run.

FAQS – Normalization in a Database