What is Normalization of Data in Database?

What is Data Normalization?

Data normalization is the process of reorganizing data within a database so that users can utilize it for further queries and analysis. Simply put, it is the process of developing clean data. This includes eliminating redundant and unstructured data and making the data appear similar across all records and fields.

How Does Data Normalization Work?

Data organization in a database is done by normalization. This entails building tables and linking those tables together in accordance with principles intended to safeguard the data and increase the database's adaptability by removing duplication and inconsistent reliance.

Disk space is wasted by redundant data, and maintenance issues result. If data that already exists in multiple locations needs to be modified, it must be updated in the same manner everywhere. If the information is kept solely in the Customers table and not elsewhere in the database, changing a customer's address is significantly simpler to do.

Whereas it makes perfect sense for a user to search in the Customers database for a specific customer's address, it might not sound right to do so for the worker who phones on that customer's behalf. The wage of the employee must be transferred to the Employees table because it is connected to or dependent upon the employee. Data might become difficult to access as a result of inconsistent dependencies because the path to finding the data may be incomplete or damaged.

Keys in SQL

Before moving on to the different forms of data normalization, you need to first understand the concept of keys in SQL. A key can be a single column or a combination of columns that uniquely identify the rows (or tuples) in the table. It also helps to identify duplicate information and establish relationships between different tables.

Here are the most common type of keys:

• Primary key - A single column used to uniquely identify a table
• Composite key - A set of columns used to uniquely identify the rows in a table
• Foreign key - A key that references the primary key of another table

Data Normalization Forms

Data normalization can be divided into different types of normal forms. The most popular ones are 1NF, 2NF, 3NF, and BCNF. Let us dive into all these normal forms with the help of an example. Assume that a company has a database of all their employees and their key skills as shown in the table below. 

1NF - First Normal Form

The most basic form of data normalization is 1NF which ensures there are no two same entries in a group. For a table to be in the first normal form, it should satisfy the following rules:

• Each cell should contain a single value
• Each record should be unique

The table in 1NF will look like this:

2NF - Second Normal Form

In a 2NF table, all the subsets of data that can be placed in multiple rows are placed in separate tables. For a table to be in the second normal form, it should satisfy the following rules:

• It should be in 1F
• The primary key should not be functionally dependant on any subset of candidate key 

Let’s divide the 1NF table into two tables - Table 1 and Table 2. Table 1 contains all the employee information. Table 2 contains information on their key skills.

Table 1

Table 2

We have introduced a new column called Employee ID which is the primary key for Table 1. The records can be uniquely identified using this primary key.

In Table 2, Employee ID is the foreign key.

3NF - Third Normal Form

For a table to be in the third normal form, it should satisfy the following rules:

• It should be in 2F
• It should not have any transitive functional dependencies 

A transitive functional dependency is when a change in a column (which is not a primary key) may cause any of the other columns to change.

In our example, if there is a name change (male to female), there may be a change in the salutation (Mr., Ms., Mrs., etc.). Hence we will introduce a new table that stores the salutations

Table 1

Table 2

Table 3

Now, there are no transitive functional dependencies and our table is now in 3F. Salutation ID is the primary key in Table 3. Salutation ID in Table 1 is foreign to the primary key in Table 3.

BCNF - Boyce and Codd Normal Form

Boyce and Codd Normal Form is a higher version of 3NF and is also known as 3.5NF. A BCNF is a 3NF table that does not have multiple overlapping candidate keys. For a table to be in BCNF, it should satisfy the following rules:

• It should be in 3F
• For each functional dependency ( X → Y ), X should be a super key

Advantages of Data Normalization

The process of normalizing a database has numerous advantages. The following are a few key benefits:

1. Utilizing database or data redundancy through normalization
2. Duplication may be eliminated.
3. By normalizing, we may reduce null values.
4. Results in a smaller database (since there is less data duplication or zero).
5. Minimize/avoid issues with data modification.
6. It makes the queries easier.
7. The database structure is more comprehensible and straightforward.
8. Existing data can be added to the database without having an impact.
9. Because the table is compact and more rows can fit on the data page, finding, sorting, as well as indexing may be quicker.

Now that we have a better understanding of the terminologies, denormalization, normalization, and denormalization may be distinguished as database systems. By removing duplicated data, normalization reduces insertion, elimination, and update exceptions. In addition, data redundancy is added during the reverse normalization process to enhance application-specific speed, data confidentiality, and integrity.

Disadvantages of Data Normalization

The process of normalizing a database has numerous disadvantages. The following are a few drawbacks:

1. The need to link tables increases when information is spread over more tables, lengthening the task. The database also gets more interesting to recognize.
2. Since rewritten data will be saved as lines of numbers instead of actual data, tables will contain codes rather than actual information. As a result, it is necessary to always consult the query table.
3. The data model proves to be incredibly challenging to inquire about because the information model is created for programs, not for ad hoc questioning. It is composed of a SQL that has been amassed over time and is typically performed by working framework cordial query devices. As a result, it is challenging to display knowledge and understanding without first understanding the client's needs.
4. The show moves at a steadily slower pace than the standard structure type does.
5. Accurate knowledge of the different typical structures is necessary in order to complete the standardization cycle successfully. Unwise use can lead to a terrible plan with significant abnormalities and data inconsistencies.

Difference Between Normalization And Denormalization

The process of efficiently organizing the data in a database is called normalization. It entails building tables and establishing connections between those tables in accordance with predetermined principles. These guidelines can make it much more flexible by removing redundant and erratic dependencies.

The opposite of normalization is called denormalization, which results in a redundant information-filled version of the normalized schema. Utilizing redundancy and maintaining the consistency of the redundant data enhances performance. Denormalization is necessary because an overly normalized structure causes query processor overhead.

Important distinctions between denormalization and normalization are:

1. In order to eliminate data redundancy and inaccuracy and to ensure data integrity, normalization is the technique of separating the data into numerous tables. Denormalization, on the other hand, is a method of consolidating the information into a single table to speed up data retrieval.
2. In an OLTP system, normalization is utilized to speed up the insert, delete, and update abnormalities. In contrast, the OLAP system uses denormalization, which focuses on accelerating search and analysis.
3. While data integrity is easier to maintain during normalization, it is more difficult during denormalization.
4. When normalization is done, redundant data is reduced, and when denormalization is done, redundant data is increased.
5. Tables and joins are added during normalization. Denormalization, in contrast, minimizes the number of tables and joins.
6. Denormalization wastes disc space since the same data is kept in several locations. On the other hand, a normalized table optimizes disc space.

Need for Data Normalization

The way that data is organized when it is present in large quantities becomes more crucial as data's value to all kinds of businesses increases. It is obvious that when Data Normalization is carried out successfully,

• Overall business performance improves as a result
• From ensuring email delivery to reducing misdials
• Enhancing group analysis without worrying about duplication.

Think about what would happen if you neglected to organize your data and lost out on important growth chances because a website wouldn't load or a vice president didn't receive your notes. Nothing of this shouts success or advancement. Making the decision to standardize data is one of the most important things you can do for your business right now.

Who Would Require Normalized Data?

• Every business that wants to grow and flourish must regularly implement data normalization. Eliminating errors is one of the most important things you can do to make Data Analysis less complicated and time-consuming.
• These problems frequently occur when changing, adding, or removing system information. When data input error is removed, an organization will have a well-running system that is full of beneficial, useful data.
• With normalization, a company may maximize its data and engage in data collecting at a higher, more effective level. It becomes simpler to examine data to enhance a company's operations, particularly when cross-examining.
• For people who frequently combine and access information from Software-as-a-Service(SaaS) applications, in addition to those who collect data from a variety of source materials such as Social Media, Internet Sites, and more, Data Normalization becomes an invaluable tool.
• In the process of creating AI systems, data is used in one of the most prevalent and significant ways. Artificial neural networks are used by contemporary artificial intelligence systems to produce their output. These neural networks also learn from data, just like the human brain.
• Businesses that employ supply chain, logistics, IoT, automation, proactive maintenance, inventory systems, and other solutions all deal with a lot of data every day. By employing data normalization procedures and standardizing their data, these firms stand to gain greatly.

The Data Normalization Process

A database's columns, attributes, and tables, or relationships, are organized in accordance with a set of normal form rules during normalization. These normal forms act as a kind of check and balance system to preserve the integrity of relationships between the characteristics and relations and are what direct the normalization process. Through a set of guidelines (called "normal forms"), the normalization process seeks to guarantee that the consistency of the database is maintained regardless of whether any data is changed, added, or destroyed.

Benefits of Data Normalization

As data becomes more and more valuable to any type of business, data normalization is more than just reorganizing the data in a database. Here are some of its major benefits:

• Reduces redundant data
• Provides data consistency within the database
• More flexible database design
• Higher database security
• Better and quicker execution 
• Greater overall database organization

A company can collect all the data it wants from any source. However, without data normalization, most of it will simply go unused and not benefit the organization in any meaningful way.

Here’s What You Can Do Next

Data normalization is of top priority as organizations continue to use data on a scale like never before. Now that you have a basic understanding of data normalization, it is time to take a deep dive into the depths of this topic. As databases get more complex, there are higher-level normal forms that you need to learn. Simplilearn’s Big Data Engineer Master’s Program features masterclasses by IBM experts to help you learn job critical skills via interactive live sessions, practical labs, and industry projects. Get started with this course today to get a leg up in your career.