Normalization is a procedure used to reduce dependency and redundancy in a relational database. This method ensures that the data stored in a database is well-organized, accurate, and easy to maintain. Normalization in SQL involves dividing a database into two or more tables and defining the relationship between the tables. If you work with databases and want to understand what normalization is, you must go through this blog. Let’s explore normalization in detail.
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What is Normalization in SQL?
In SQL, the process of structuring and resolving data in a database is known as normalization. It is achieved by applying a set of rules and procedures to a database, known as normalization forms.
There are several normalization forms, including the First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), Boyce-Codd Normal Form (BCNF), and the Fourth Normal Form (4NF). Each form builds on the previous one, providing a set of guidelines for organizing data in a way that meets the specific requirements of a database.
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Types of Normalization in SQL
There are various levels of normalization, each with its own set of regulations. The most commonly used types of normalization in SQL are the First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), Fourth Normal Form (4NF), and Boyce-Codd Normal Form.
First Normal Form (1NF)
Data normalization in a relational database starts with the creation of the First Normal Form (1NF). Each column in a table must follow the 1NF specification, and only include atomic values, or indivisible data units. In other words, a table should only have a single value in each column, not a collection of values or a list of values. This lessens data redundancy and helps ensure data consistency.
First Normal Form (1NF) Example:
A table with columns for “Name” and “Address” would be considered in 1NF if each row in the table contains a single value for “Name” and a single value for “Address”, rather than a combination of values or a list of values.
Once a table is in 1NF, it can be further normalized to meet the requirements of higher normal forms, such as the Second Normal Form (2NF) and Third Normal Form (3NF).
Second Normal Form (2NF)
The Second Normal Form (2NF) is the next step in normalizing data in a relational database after achieving the First Normal Form (1NF). 2NF requires that each non-key column in a table depends solely on the primary key.
In other words, a table is in 2NF if each non-key column’s values completely depend on the primary key’s values and not on any other non-key columns. By doing this, data inconsistency and redundancy are reduced.
Second Normal Form (2NF) Example
If a table has columns for “Customer ID” (the primary key), “Customer Name”, “Address”, and “Phone Number”, then the table would be in 2NF if the “Address” and “Phone Number” values depend only on the “Customer ID” values, and not on the “Customer Name” values.
Once a table is in 2NF, it can be further normalized to meet the requirements of higher normal forms, such as the Third Normal Form (3NF).
Third Normal Form (3NF)
The Third Normal Form (3NF) is a higher level of normalization in a relational database after the Second Normal Form (2NF). 3NF requires that each non-key column in a table depends solely on the primary key and that there are no transitive dependencies between non-key columns.
Transitive dependencies occur when one non-key column depends on another non-key column, rather than on the primary key.
Third Normal Form (3NF) Example
If a table has columns for “Product ID” (the primary key), “Product Name”, “Supplier Name”, and “Supplier Address”, the table would not be in 3NF if the “Supplier Address” depends on the “Supplier Name”, instead of the “Product ID”.
The goal of 3NF is to remove any transitive dependencies in data and reduce the chances of data anomalies and inconsistencies. A table in 3NF will have a more optimal data structure, allowing for more efficient data storage and retrieval.
Once a table is in 3NF, it can be further normalized to meet the requirements of higher normal forms in SQL, such as the Boyce-Codd Normal Form (BCNF) and Fourth Normal Form (4NF).
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Boyce-Codd Normal Form (BCNF)
After obtaining the Third Normal Form in a relational database, the Boyce-Codd Normal Form (BCNF) is the next level of normalization.
In other words, BCNF requires that each non-key column in a table depends solely on the superkey and that there are no non-trivial functional dependencies between non-key columns. A functional dependency occurs when the values in one column determine the values in another column.
Boyce-Codd Normal Form (BCNF) Example
If a table has columns for “Order ID” (the primary key), “Order Date”, “Customer Name”, and “Product Name”, the table would be in BCNF if the “Order Date” and “Customer Name” depend only on the “Order ID”, and not on each other.
The goal of BCNF is to ensure that each table in a relational database is free of functional dependencies, making it easier to manage, and reducing the chances of data anomalies and inconsistencies. A table in BCNF will have a more optimal data structure, allowing for more efficient data storage and retrieval.
BCNF is considered one of the strongest normal forms in the normalization process.
Fourth Normal Form (4NF)
The Fourth Normal Form (4NF) is a higher level of normalization in a relational database after achieving the Third Normal Form (3NF). 4NF requires that a table be free of multi-valued dependencies which occur when two or more non-key columns in a table depend on each other, but not on the primary key.
A multi-valued dependency occurs when the values in one non-key column determine the values in another non-key column, and vice versa.
Fourth Normal Form (4NF) Example
If a table has columns for “Employee ID” (the primary key), “Employee Name”, “Skill”, and “Certification”, the table would not be in 4NF if the “Skill” and “Certification” values are dependent on each other, but not on the “Employee ID”.
The goal of 4NF is to remove any multi-valued dependencies in data, making it easier to manage and reducing the chances of data inconsistencies. A table in 4NF will have a more optimal data structure, allowing for more efficient data storage and retrieval.
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Benefits of Normalization
Normalization provides several benefits to database design and management. Following are some of the benefits of normalization:
Improved data consistency
Normalization helps to improve data consistency by eliminating data anomalies. This ensures that data is well-organized, making it easier to maintain and update.
Improved data security
Normalization helps to improve data security by reducing the amount of data that needs to be stored in a single table. This in turn reduces the risk of data loss or corruption.
Reduced data redundancy
Normalization helps to eliminate data redundancy by breaking down the data into small, manageable tables. This helps to reduce the amount of duplicate data stored in the database and improves the overall efficiency of the database.
Ease of maintenance
Normalization makes it easier to maintain a database by reducing the amount of data that needs to be updated. Making changes to the database without affecting the other parts also becomes easy.
Ease of querying
Normalization makes it easier to query data from a database by reducing the amount of data stored in a single table. This makes it easy to retrieve the data that is needed for a specific task.
How to achieve Normalization in SQL?
Achieving Normalization in SQL involves following a set of rules and procedures to ensure that the data in a database is organized optimally. Below are the steps to achieve normalization in SQL:
Identify the entities and relationships
Start by identifying the main entities and relationships in your database, and creating a rough table structure which reflects these dependencies.
Choose a primary key
Select a unique identifier for each table in your database, which will serve as the primary key.
Remove repeating groups
Look for any repeating groups in your tables, and remove them by creating separate tables for each group and establishing relationships between the new tables and the original table.
Eliminate redundancy
Remove any redundant data in your tables by ensuring that each piece of data is stored only once.
Apply normalization rules
Apply the rules for the First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), and the higher forms, such as BCNF or 4NF, to ensure that your tables are organized optimally and consistently.
Test and refine
Test your database design by inserting and retrieving data, and refine the architecture as needed to ensure that your data is organized in a way that meets your requirements.
Document your design
Document your database design, including the structure of each table, the relationships between tables, and any normalization rules that were applied.
Conclusion
Normalization is an essential aspect of database design in SQL. By organizing data in a structured and consistent manner, normalization helps to improve data integrity and reduces the chances of data anomalies and inconsistencies. It is important to understand the various normalization forms and how to apply them to your data.
By taking the effort to understand and use normalization in SQL, you can make sure that your database is set up in a way that meets your requirements and serves as a strong foundation for your data-driven applications.
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