Normalization in SQL: Benefits and Concepts

Normalization in SQL is the process of organizing data to reduce redundancy and improve efficiency. Without normalization, databases can become cluttered, leading to inconsistencies, data anomalies, and slower performance. 

For example, in a customer database, storing the same customer information in multiple places can lead to errors like mismatched addresses or outdated contact details.

By the end of this blog, you'll be able to structure your database more efficiently, saving time and avoiding errors.

What is Normalization in SQL?

Normalization in SQL is structuring a database to minimize data redundancy and enhance data integrity. It involves dividing large tables into smaller, related tables and defining relationships between them using keys, typically the primary key.

This approach reduces duplicate data by ensuring each piece of information is stored only once, making it easier to maintain and update.  

Purpose of Normalization

Normalization reduces redundancy and improves performance, scalability, and data integrity, ensuring efficient query processing in complex systems with large data volumes.

The main objectives of normalization in SQL include:

• Reducing Redundancy: Eliminates duplicate data, ensuring that each piece of information is stored in one place.
• Improving Data Integrity: Reduces the risk of errors by maintaining consistent, non-repetitive data across the database.
• Enhancing Performance: Streamlines queries and updates by reducing the amount of data the system needs to handle.
• Simplifying Maintenance: Makes database updates, deletions, and modifications more efficient and error-free.

Normalization ensures each data piece is stored in one place, preventing issues like inconsistent customer addresses or outdated contact details across multiple records.

This structure leads to a more organized and reliable database, making it easier to manage over time.

Principles of Normalization

Key principles guide the process of normalizing a database. These principles streamline the database by eliminating redundancy, ensuring consistency, and simplifying data updates.

Let’s take a look at each one:

1. Eliminate Redundancy: 

The goal is to avoid storing the same data in multiple places. This helps save storage space and prevents errors caused by having to update multiple copies of the same information. By eliminating redundancy, you make your database more efficient and easier to manage.

For example, if a customer's address changes, redundancy could cause outdated data in various parts of the system if not properly updated.

2. Avoid Data Inconsistencies: 

When data is repeated across different tables, updating one instance can lead to inconsistencies if other instances are not updated simultaneously. Normalization organizes data into smaller, related tables, ensuring consistency and reducing the risk of discrepancies between records.

A customer’s email address, for instance, should only exist in one place. Failing to do so may lead to different email addresses appearing in different systems when updates aren’t synchronized.

3. Minimize Dependency: 

In a well-normalized database, tables are designed so that each piece of data relies on the smallest possible subset of other data. This reduces unnecessary relationships and dependencies between tables, making your database more flexible and easier to scale as your needs grow.

For example, separating customer orders from the product catalog allows updates to be made independently, preventing issues when adding or removing products without affecting order data.

4. Atomicity: 

This principle states that each data element should be stored in its smallest possible form, meaning it can’t be broken down further. 

For example, instead of storing a full address as a single string, it would be split into separate fields like street, city, and postal code. Atomicity ensures data is clean, precise, and easy to update.

Now that we’ve covered the principles, let’s explore why normalization is crucial for optimizing your database design.

Why is Normalization Important?

Did you know that 40% of database performance issues are caused by inefficient schema design, including problems like data redundancy and poor normalization? As databases grow, normalization is key to reducing redundancy, ensuring uniqueness, and improving performance.

Here are some scenarios where normalization plays a critical role:

• Large, Complex Databases: In a complex inventory management system, normalization ensures that product data is stored in one place, preventing the risk of inconsistent information across multiple tables as the database grows.
• Data Integrity: In a banking system, normalization guarantees that customer account details are stored consistently in one place. This prevents discrepancies that could occur when updating balances or transactions, ensuring financial accuracy.
• Reducing Redundancy in OLTP Systems: In an order management system, normalization helps ensure that customer and order details are stored in separate tables. This reduces duplication and ensures that customer contact information updates are reflected across the entire system without manual intervention.

However, there are scenarios where normalization might not be necessary, or where denormalization could offer advantages, such as:

• Read-Heavy Applications: For systems with a high number of read queries and few updates, denormalization can speed up retrieval times by reducing the need for complex joins.
• OLAP (Online Analytical Processing) Systems: In analytical environments like OLAP systems where complex queries are frequently run, denormalization is often preferred to speed up query performance, as the trade-off in data consistency is acceptable.
• Performance-Critical Environments: In databases where speed is essential—such as high-frequency trading or real-time systems—denormalization may be used to optimize performance by reducing query complexity.

Ultimately, the decision to normalize or denormalize depends on the system’s purpose, query patterns, and data volume.

Avoiding Data Redundancy

Imagine you’re managing a customer database for an e-commerce company. Each time a customer places an order, their shipping address is stored in the order record. Now, imagine that same customer places five orders. 

Without normalization, their shipping address might be stored five times, once for each order. Not only does this waste storage space, but if the customer moves and updates their address, you'd have to manually update it in all five places. If you miss one, you risk sending their order to an outdated address.

Normalization solves this problem by ensuring that the customer's address is stored only once, in a dedicated table, and each order links back to that address via a unique identifier. This removes redundancy and ensures consistency across the entire system.

Improving Data Integrity

Think of a school database where student records, such as their name and contact details, are stored across multiple tables. Without normalization, if a student’s contact information needs to be updated, several entries across various tables might need to be updated.  

Normalization eliminates such issues by ensuring each piece of data is stored in only one place. This eliminates update anomalies, where changes are inconsistently reflected, and insertion anomalies, where incomplete data could be added.  

Enhancing Query Performance

Consider a reporting system in a large retail store that needs to pull sales data from multiple tables. In a non-normalized database, sales records might contain redundant information, like the store’s location or employee name, repeated across every transaction.

Normalization removes redundant data, and each piece of information is stored in the most compact form possible. Queries can access data more efficiently because the system can no longer sift through duplicated information.  

Normalization in SQL with Example

Let’s take a look at an example of normalizing a customer and order table to improve efficiency and reduce redundancy. 

Below is the unnormalized table that stores customer orders, which includes customer details and order information all in one table.

Unnormalized Table: Customer_Orders

Problem in the Unnormalized Table:

• Data Redundancy: The customer’s name and address are repeated across multiple rows, leading to inefficient storage.
• Update Anomalies: If Aarav Patel moves to a new address, his address would need to be updated in multiple rows, risking inconsistency.
• Deletion Anomalies: Deleting one order entry could accidentally remove important customer details.

Now, let’s normalize this into two separate tables—one for the customer information and another for orders. This structure removes redundancy and allows for easier maintenance.

After Normalization: Customers and Orders Tables

Customers Table

Orders Table

Explanation of the Normalized Structure:

• Redundancy Removal: Customer data is stored only once in the Customers table, reducing repetitive information.
• Data Integrity: Any updates to customer details (like changing the address) need to be done only in one place.
• Efficient Querying: Querying orders for a specific customer becomes simpler, and joins between the two tables enable efficient data retrieval.

Normalization is achieved in steps through various normal forms, each progressively eliminating redundancy and improving data integrity. Let's look at the types of normalization in SQL.

Types of Normal Forms

In relational databases, normal forms are a set of guidelines used to organize data efficiently and minimize redundancy. Each successive normal form (1NF, 2NF, 3NF, etc.) builds on the previous one, addressing specific types of data anomalies and ensuring the database structure is optimal for storage, integrity, and performance.

Let’s now look at the different normal forms and their role in database design: 

First Normal Form (1NF)

First Normal Form (1NF) is the foundation of normalization. It ensures that the data in a table is atomic (i.e., indivisible) and that there are no repeating groups or arrays. 

A table is considered to be in 1NF when:

• Each column contains only atomic (indivisible) values.
• There are no repeating groups or arrays in any column.
• Each row is unique.
  Let’s start with a table in an unnormalized state and then convert it to 1NF.

Unnormalized Table: Customer_Orders

Here, we have a repeating group in the Orders column where multiple products are listed for each customer in one cell. This violates the atomicity rule of 1NF and introduces redundancy.

Criteria for Achieving 1NF:

To achieve First Normal Form (1NF), we need to ensure the following:

1. Atomicity: Each column must hold atomic (indivisible) values. In the above example, the "Orders" column contains pairs of products and quantities, which need to be separated into individual rows.
2. No Repeating Groups: We must eliminate repeating groups, ensuring each field holds only a single value.
3. Unique Rows: Every row must be unique. In our case, there should be a unique record for each order.

Table Representation: Unnormalized vs. 1NF

Unnormalized Table: Customer_Orders

After Normalization to 1NF: Customer_Orders_1NF

Step-by-Step Example: Converting Data to 1NF

Let’s walk through the transformation of the unnormalized table into First Normal Form (1NF): 

-- Create the normalized 1NF table
CREATE TABLE Customer_Orders_1NF (
    CustomerID INT,
    CustomerName VARCHAR(100),
    Product VARCHAR(100),
    Quantity INT
);
-- Insert normalized data
INSERT INTO Customer_Orders_1NF (CustomerID, CustomerName, Product, Quantity) VALUES
(1, 'Aarav Patel', 'Laptop', 2),
(1, 'Aarav Patel', 'Mouse', 1),
(2, 'Priya Sharma', 'Phone', 1),
(2, 'Priya Sharma', 'Charger', 2);

Output of 1NF Table: Customer_Orders_1NF

Explanation:

• Atomicity: Each column now holds a single value (e.g., product and quantity are separated into individual rows).
• No Repeating Groups: The products and their quantities for each customer are now stored in separate rows, not in a single cell.
• Unique Rows: Each row is unique, and there’s no duplication of the customer’s details or orders.

Second Normal Form (2NF)

Second Normal Form (2NF) builds upon First Normal Form (1NF) by addressing partial dependencies—where a non-prime attribute (a column not part of a primary key) depends only on a part of a composite primary key rather than the entire key. Achieving 2NF requires that:

1. The table is already in 1NF.
2. There are no partial dependencies.

What are Partial Dependencies?

A partial dependency occurs when a non-prime attribute is dependent on a part of the composite primary key, rather than the whole key. This typically happens in tables where the primary key is composed of more than one column. In such cases, some attributes may depend only on a subset of the key, leading to data redundancy and inefficiencies.

2NF aims to remove partial dependencies by splitting the table into multiple tables where each non-prime attribute depends on the whole primary key, not just part of it.

Example Table: (Breaking Down Partial Dependencies)

Let’s consider an example of a table in 1NF that violates 2NF because of partial dependencies.

Table in 1NF: Student_Courses

Here, the primary key is a composite of StudentID and CourseID.

Issues with 2NF:

• The InstructorName and InstructorContact depend only on CourseID, not on the entire composite key (StudentID, CourseID).
• This results in partial dependencies because the instructor information is tied only to the course, not the student.

Moving to 2NF: Breaking Down Partial Dependencies

To move this table into 2NF, we separate the attributes based on their dependencies.

Normalized Tables in 2NF:

1. Student_Courses: Stores only student-course relationships.
2. Course_Instructors: Stores course-instructor details. 

-- Create Student_Courses table
CREATE TABLE Student_Courses (
    StudentID INT,
    CourseID VARCHAR(10),
    PRIMARY KEY (StudentID, CourseID)
);
-- Insert data into Student_Courses
INSERT INTO Student_Courses (StudentID, CourseID) VALUES
(1, 'CSE101'),
(1, 'CSE102'),
(2, 'CSE101'),
(3, 'CSE103');
-- Create Course_Instructors table
CREATE TABLE Course_Instructors (
    CourseID VARCHAR(10) PRIMARY KEY,
    InstructorName VARCHAR(100),
    InstructorContact VARCHAR(15)
);
-- Insert data into Course_Instructors
INSERT INTO Course_Instructors (CourseID, InstructorName, InstructorContact) VALUES
('CSE101', 'Dr. Rajesh', '9876543210'),
('CSE102', 'Dr. Mehta', '9123456789'),
('CSE103', 'Dr. Sharma', '9345678901');

Tables After 2NF

Table 1: Student_Courses

Table 2: Course_Instructors

Explanation:

• Student_Courses now contains only the relationship between students and courses, and the composite primary key (StudentID, CourseID) is used.
• Course_Instructors now contains the instructor details, with CourseID as the primary key.
• By splitting the data into these two tables, we eliminate the partial dependency and move to 2NF.

Flowchart: Process of Moving from 1NF to 2NF

Here’s a visual flowchart to illustrate the steps in transforming a table from 1NF to 2NF:

Explanation:

• The process starts with a table in 1NF.
• We identify any partial dependencies based on the composite key.
• We break down the table into multiple tables to eliminate partial dependencies.
• The result is a set of normalized tables that follow 2NF, where each non-prime attribute depends on the whole primary key.

Third Normal Form (3NF)

Third Normal Form (3NF) goes a step further than 2NF by addressing transitive dependencies. A transitive dependency occurs when a non-prime attribute (a column not part of the primary key) depends on another non-prime attribute, rather than directly depending on the primary key. This type of dependency can lead to data redundancy and inefficiency.

In 3NF, we aim to eliminate these transitive dependencies, ensuring that non-prime attributes depend directly on the primary key and not on other non-prime attributes. Achieving 3NF improves data integrity by eliminating unnecessary relationships that could lead to anomalies during data updates, inserts, or deletions.

What is a Transitive Dependency?

A transitive dependency happens when one non-prime attribute indirectly relies on the primary key through another non-prime attribute. For example, if we have a table with student information where the StudentCity depends on StudentState, which in turn depends on StudentID, the StudentCity is transitively dependent on StudentID.

Example: Converting a Table to 3NF

Let’s consider a table in 2NF that violates 3NF due to a transitive dependency.

Table in 2NF: Student_Courses

Issues with 3NF:

• The StudentCity column is transitively dependent on the StudentID through the StudentState. If we store the state in this table as well, updating a city might require modifying several rows.
• The InstructorName is dependent on the CourseID, but this is fine because it’s directly related to the primary key (StudentID, CourseID).

Converting to 3NF

To eliminate the transitive dependency between StudentCity and StudentID through StudentState, we split the table into two:

1. Students: Stores information about students.
2. Courses: Stores details about the courses.

Normalized Tables in 3NF:

1. Students (contains only student details) 

CREATE TABLE Students (
    StudentID INT PRIMARY KEY,
    StudentName VARCHAR(100),
    StudentCity VARCHAR(100),
    StudentState VARCHAR(100)
);
-- Insert student data
INSERT INTO Students (StudentID, StudentName, StudentCity, StudentState) VALUES
(1, 'Aarav Patel', 'Delhi', 'Delhi'),
(2, 'Priya Sharma', 'Mumbai', 'Maharashtra'),
(3, 'Riya Verma', 'Pune', 'Maharashtra');

2. Student_Courses (stores student-course relationships) 

CREATE TABLE Student_Courses (
    StudentID INT,
    CourseID VARCHAR(10),
    InstructorName VARCHAR(100),
    PRIMARY KEY (StudentID, CourseID),
    FOREIGN KEY (StudentID) REFERENCES Students(StudentID)
);
-- Insert course data
INSERT INTO Student_Courses (StudentID, CourseID, InstructorName) VALUES
(1, 'CSE101', 'Dr. Rajesh'),
(1, 'CSE102', 'Dr. Mehta'),
(2, 'CSE101', 'Dr. Rajesh'),
(3, 'CSE103', 'Dr. Sharma');

Tables After 3NF

Table 1: Students

Table 2: Student_Courses

Explanation:

• The Students table now stores only student-specific data, removing the transitive dependency between StudentCity and StudentID.
• The Student_Courses table maintains the relationship between students and courses, without unnecessary information about cities or states.

Visual Representation: 3NF in Action

This transformation illustrates how splitting data ensures that each piece of information depends directly on the primary key, eliminating transitive dependencies and improving the database's efficiency and consistency. 

Boyce-Codd Normal Form (BCNF)

Boyce-Codd Normal Form (BCNF) is a stricter version of Third Normal Form (3NF). While 3NF ensures that there are no transitive dependencies, BCNF goes a step further by ensuring that every determinant is a candidate key. In other words, BCNF addresses situations where a non-prime attribute (a column not part of the primary key) determines another attribute, even if no transitive dependency exists.

The key difference between 3NF and BCNF lies in the following:

• 3NF allows a non-prime attribute to determine another non-prime attribute, as long as there is no transitive dependency.
• BCNF, however, requires that every determinant (an attribute that determines other attributes) must be a candidate key.

In simple terms, BCNF removes certain types of anomalies that 3NF does not address, making it a stricter version of normalization, ideal for completely eliminating redundancy and dependency issues in complex databases.

BCNF Example

Let’s take a table that is in 3NF but violates BCNF due to a partial dependency. Consider the following table that tracks employees, their departments, and the department heads.

Table in 3NF: Employee_Department

Here:

• The DepartmentHead depends on DepartmentID.
• The DepartmentName depends on DepartmentID.

Although the table is in 3NF, it still violates BCNF because DepartmentID determines DepartmentHead and DepartmentName, but DepartmentID is not a candidate key—EmployeeID could also serve as the primary key.

Fixing the Table to BCNF

To bring this table into BCNF, we need to split it into two tables. DepartmentID becomes a candidate key in the new table, and the department-related information is moved to a separate table.

Step 1: Create the Department Table 

-- Create the Department table
CREATE TABLE Department (
    DepartmentID VARCHAR(10) PRIMARY KEY,
    DepartmentName VARCHAR(100),
    DepartmentHead VARCHAR(100)
);
-- Insert department data
INSERT INTO Department (DepartmentID, DepartmentName, DepartmentHead) VALUES
('D101', 'Engineering', 'Dr. Rajesh'),
('D102', 'Marketing', 'Mr. Mehta');

Step 2: Modify the Employee_Department Table 

-- Create the Employee_Department table
CREATE TABLE Employee_Department (
    EmployeeID INT PRIMARY KEY,
    EmployeeName VARCHAR(100),
    DepartmentID VARCHAR(10),
    FOREIGN KEY (DepartmentID) REFERENCES Department(DepartmentID)
);
-- Insert employee data
INSERT INTO Employee_Department (EmployeeID, EmployeeName, DepartmentID) VALUES
(1, 'Aarav Patel', 'D101'),
(2, 'Priya Sharma', 'D101'),
(3, 'Riya Verma', 'D102');

Tables After BCNF

Table 1: Department

Table 2: Employee_Department

Explanation:

• The Department table now holds information about departments and their heads.
• The Employee_Department table links employees to departments, with DepartmentID acting as a foreign key.
• The violation of BCNF is resolved by ensuring DepartmentID is the candidate key for determining department-related information, and EmployeeID remains the key for employee data.

Case Study: Netflix's Transition to BCNF for Database Optimization

Problem:

Netflix, as it scaled, faced challenges with data consistency, query performance, and complex relationships in its original database schema. A large number of tables were only partially normalized, and issues with redundancy and dependencies were not fully resolved, leading to slow queries and updates.

Solution:

Netflix realized that their database schema was not fully normalized, especially when it came to handling customer interactions with movies, recommendations, and ratings. The data was heavily interdependent, leading to redundancy and data anomalies. 

By adopting BCNF, they were able to restructure their tables so that each table was fully normalized, removing any dependencies between non-prime attributes and ensuring that every determinant was a candidate key.

Process of Execution:

1. Identifying Dependencies: The first step was to identify all the functional dependencies in the database schema.
2. Decomposing Tables: They split large tables into smaller, more efficient tables. For instance, they moved data related to user preferences, movie details, and ratings into separate tables.
3. Data Migration: They migrated their existing data to the newly structured tables, ensuring that each table adhered to BCNF.
4. Optimization: The process also involved optimizing queries by reducing unnecessary joins and ensuring more efficient data retrieval.

Lessons Learned:

• Efficiency: By moving to BCNF, Netflix was able to significantly improve query performance and reduce the amount of redundant data.
• Data Integrity: BCNF eliminated anomalies that could have affected data consistency, especially with user ratings and recommendations.
• Scalability: The transition to BCNF made it easier for Netflix to scale their database as they added more content and users, ensuring that their data model could handle larger volumes of data without performance degradation.

The move to BCNF was critical for Netflix's ability to handle complex relationships between users, content, and interactions while ensuring that their system remained performant and scalable.

Now, let’s dive into the step by step normalization process to efficiently structure your database.

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Step-by-Step Normalization Process

Normalization is key to structuring databases efficiently and ensuring data integrity. It refines data through 1NF, 2NF, 3NF, and BCNF, eliminating redundancy and anomalies at each step.

 Let’s go through this process, starting from unnormalized data and progressively moving toward higher normal forms.

Step 1: Identifying Unstructured Data

Unnormalized data is often messy, with repeating groups or redundant information scattered across columns. The goal at this stage is to identify these problems, such as multiple values stored in a single column or rows that have unnecessary repetition of information.

Unnormalized Table: Employee_Project

Issues in the Unnormalized Table:

• Repetition of Employee Information: Employee details such as EmployeeName are repeated across multiple rows.
• Multiple Projects in One Row: An employee can be associated with multiple projects, but the table stores multiple project details in individual rows for each employee.

At this point, this table is considered unnormalized, and the next step is converting it into 1NF.

Step 2: Applying First Normal Form (1NF)

To convert the unnormalized data into 1NF, we need to eliminate repeating groups and ensure that each column contains atomic values (indivisible). We separate multi-valued attributes (like multiple projects per employee) into individual rows.

Table in 1NF: Employee_Project_1NF

Explanation:

• We have ensured that each column contains atomic values.
• 1NF is achieved by removing repeating groups and storing each project-employee relationship in a separate row.

 Step 3: Achieving Second Normal Form (2NF)

To achieve 2NF, we need to eliminate partial dependencies. Partial dependencies occur when a non-prime attribute (a column not part of the primary key) depends only on part of the primary key. 

Since EmployeeID and ProjectID together form the primary key in this table, EmployeeName and ManagerName should not depend on only part of this key.

Table in 2NF: Employee_Project and Project_Manager

1. Employee_Project Table

CREATE TABLE Employee_Project (
    EmployeeID INT,
    ProjectID VARCHAR(10),
    HoursWorked INT,
    PRIMARY KEY (EmployeeID, ProjectID)
);
INSERT INTO Employee_Project (EmployeeID, ProjectID, HoursWorked) VALUES
(1, 'P101', 40),
(1, 'P102', 30),
(2, 'P101', 35),
(2, 'P103', 20),
(3, 'P102', 40),
(3, 'P104', 25),
(4, 'P101', 30);

2. Project_Manager Table 

CREATE TABLE Project_Manager (
    ProjectID VARCHAR(10) PRIMARY KEY,
    ProjectName VARCHAR(100),
    ManagerName VARCHAR(100)
);
INSERT INTO Project_Manager (ProjectID, ProjectName, ManagerName) VALUES
('P101', 'Data Mining', 'Dr. Gupta'),
('P102', 'Web Design', 'Ms. Mehta'),
('P103', 'Marketing', 'Mr. Kumar'),
('P104', 'Finance', 'Mr. Sharma');

Tables After 2NF: 

Employee_Project

Project_Manager

Explanation:

• The Employee_Project table now stores only the relationship between employees and projects, eliminating partial dependencies.
• The Project_Manager table stores the project details along with the manager information, ensuring that the non-prime attributes depend on the entire primary key.

Step 4: Converting to Third Normal Form (3NF)

To achieve 3NF, we must eliminate transitive dependencies—when a non-prime attribute depends on another non-prime attribute. In this case, the ManagerName in the Project_Manager table depends on ProjectName, but ProjectName depends on ProjectID, which is part of the primary key.

We will create a separate table for Manager to remove the transitive dependency.

Tables After 3NF

1. Employee_Project Table (same as in 2NF)
2. Project_Manager Table 

CREATE TABLE Project_Manager (
    ProjectID VARCHAR(10) PRIMARY KEY,
    ProjectName VARCHAR(100)
);
INSERT INTO Project_Manager (ProjectID, ProjectName) VALUES
('P101', 'Data Mining'),
('P102', 'Web Design'),
('P103', 'Marketing'),
('P104', 'Finance');

3. Manager Table 

CREATE TABLE Manager (
    ManagerID INT PRIMARY KEY,
    ManagerName VARCHAR(100)
);
INSERT INTO Manager (ManagerID, ManagerName) VALUES
(1, 'Dr. Gupta'),
(2, 'Ms. Mehta'),
(3, 'Mr. Kumar'),
(4, 'Mr. Sharma');

4. Project_Manager_Assignment Table (links project to manager) 

CREATE TABLE Project_Manager_Assignment (
    ProjectID VARCHAR(10),
    ManagerID INT,
    PRIMARY KEY (ProjectID),
    FOREIGN KEY (ProjectID) REFERENCES Project_Manager(ProjectID),
    FOREIGN KEY (ManagerID) REFERENCES Manager(ManagerID)
);
INSERT INTO Project_Manager_Assignment (ProjectID, ManagerID) VALUES
('P101', 1),
('P102', 2),
('P103', 3),
('P104', 4);

Tables After 3NF: 

Employee_Project (unchanged from 2NF)
Project_Manager

Manager

Project_Manager_Assignment

Explanation:

• The Manager table is created to hold manager information independently, removing transitive dependencies.
• The Project_Manager_Assignment table links projects to managers, eliminating redundancy and ensuring data integrity.

Step 5: Refining with BCNF

To convert to BCNF, we need to ensure that every determinant in the database is a candidate key. If a table has a non-prime attribute determining another non-prime attribute, it violates BCNF.

However, in this case, the tables are already in BCNF because all determinants (such as ProjectID and ManagerID) are candidate keys in their respective tables. There is no violation of BCNF.

Now, let's explore advanced normalization techniques for improved data consistency and performance.

Advanced Normalization Techniques

While BCNF is highly effective, more advanced normalization forms exist to handle complex data dependencies. These advanced normalization techniques are typically used in specialized cases where the data structure becomes more complex. 

Let’s dive deeper into these advanced concepts and how they apply to real-world database design.

Fourth Normal Form (4NF)

4NF addresses multivalued dependencies, a scenario where one attribute in a table depends on another, but where both attributes are independent of each other. 

In simple terms, a multivalued dependency happens when a single entity is associated with multiple values of different attributes, but these associations don't depend on each other.

Definition: A table is in 4NF if it is in BCNF and has no multivalued dependencies.

Example of Applying 4NF

Consider the following table where a student can take multiple courses and have multiple skills:

In this table:

• A student can take multiple courses.
• A student can have multiple skills.
• However, Course and Skill are independent of each other, creating a multivalued dependency.

To bring this table into 4NF, we split it into two tables:

1. Student_Courses (StudentID, Course)
2. Student_Skills (StudentID, Skill)

This ensures that there are no multivalued dependencies in either table.

Also Read: What Is Programming Language? Syntax, Top Languages, Examples

Fifth Normal Form (5NF)

5NF deals with join dependencies. A table is in 5NF when it can’t be decomposed into smaller tables without losing information. The concept of join dependency relates to situations where you can reconstruct the table by joining other smaller tables without losing any data or causing anomalies.

Definition: A table is in 5NF if it is in 4NF and every join dependency is a consequence of the candidate keys.

Example of Applying 5NF

In industries like finance, 5NF is useful for managing complex transactions, such as derivatives or multi-party contracts, ensuring that each fact is represented only once in the database and can be reconstructed via joins when needed.

Imagine a table where a project is assigned to a department, with multiple employees working on it, as follows:

In this table:

• A project is assigned to multiple departments.
• A department has multiple employees.

However, it’s unnecessary to store this information all in one table. To reach 5NF, we break it into three smaller tables:

1. Project_Department (ProjectID, Department)
2. Project_Employee (ProjectID, EmployeeID)
3. Department_Employee (Department, EmployeeID)

Now, the information is fully decomposed, and any join between these tables will correctly reconstruct the original table without any redundancy or ambiguity.

Sixth Normal Form (6NF)

6NF deals with temporal data, which involves situations where you need to store historical data or track the validity of data over time. It is particularly useful in situations where data changes frequently and where each change needs to be tracked with precise accuracy.

In healthcare, 6NF is used to handle patient records over time, capturing changes in medical history, treatments, and diagnoses efficiently, while ensuring that the database can store historical changes without redundancy or inconsistency.

Definition: A table is in 6NF if it is in 5NF and it handles temporal constraints by decomposing the data into minimal units that can be independently updated.

When to use 6NF:

• 6NF is often used in databases that deal with time-sensitive or versioned data. This is commonly seen in applications like financial systems, patient records, and systems that need to track historical changes at a fine level of granularity.

Real-Life Scenarios of 6NF

1. Financial Systems: In banking or investment systems, records often change frequently.  
   Example: A transaction history table where each change (like deposits, withdrawals, and account balances) is tracked over time. 6NF allows each transaction to be stored with its timestamp, ensuring that the record reflects the exact state at every point in time.
2. Healthcare Systems: Patient records, including medical history, prescriptions, and treatment plans, require precise tracking over time. 

For instance, a patient’s medical condition might change frequently, and their treatment plan may be adjusted multiple times. 6NF ensures that each version of the patient’s record is stored separately, allowing for efficient querying of the state at any given time.

Also Read: Mastering Data Normalization in Data Mining: Techniques, Benefits, and Tools

Now, let’s explore the advantages and disadvantages of normalization to understand when it's the right approach and when it might not be ideal.

Advantages and Disadvantages of Normalization

When designing a database, balancing normalization with performance needs is essential. While normalization has clear benefits in terms of data integrity and organization, it can introduce some performance challenges. 

Here’s a look at the pros, cons, and potential workarounds.

Balancing data integrity with performance needs ensures that your database remains both efficient and scalable.

Now, let’s examine best practices for database normalization to ensure that you're implementing it effectively and efficiently. 

Best Practices in Database Normalization

Applying normalization principles wisely requires understanding when to use them and when alternatives like denormalization are more beneficial. This section explores best practices, helping you strike the right balance between performance and data organization.

When to Normalize vs. When to Denormalize?

Knowing when to normalize and when to denormalize is key to maintaining database performance while ensuring data integrity. Normalization is ideal for transactional systems, where data consistency and integrity are crucial. 

However, denormalization can be more effective in performance-critical applications, such as data warehouses or read-heavy systems, where speed is prioritized over minimal storage use.

Also Read: A Sample Roadmap for Building Your Data Warehouse

How to Optimize Query Performance in Normalized Databases?

In normalized databases, queries often involve complex joins, which can degrade performance. However, there are strategies you can use to optimize these queries without sacrificing the benefits of normalization.

1. Indexing:

Create indexes on columns that are frequently queried or used in JOIN operations. Indexes speed up search operations by reducing the time needed to locate data. 

CREATE INDEX idx_employee_name ON Employee(EmployeeName);

2. Query Optimization:

Write optimized queries by selecting only the necessary columns rather than using SELECT *. This reduces the amount of data being processed.

• Use EXPLAIN PLAN to analyze and optimize query execution plans.
• Break complex queries into smaller parts to reduce load on the database.

3. Partitioning:

Divide large tables into smaller, more manageable partitions, particularly for time-series data, reducing query processing time.

4. Caching:

Implement caching for frequently accessed data to avoid repeated querying of the same information.

5. Materialized Views:

Precompute and store complex query results, reducing the need for repeated heavy computations.

Also Read: List of Operators In SQL [With Examples]

Conclusion

To effectively manage your database, start by applying normalization principles to minimize redundancy and ensure data integrity. Once your structure is stable, assess your query patterns. Strategic denormalization may be necessary to boost performance. Regularly review your database design to strike the right balance between efficiency and scalability.

For many, database design can be overwhelming, particularly when balancing data integrity with system performance. Without proper guidance, it's easy to end up with a database that’s either inefficient or too complex. 

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References:

https://techjury.net/blog/big-data-statistics/