Why Avoiding Unnormalized Data is Crucial in RDBMS?

Avoiding Unnormalized Data

Overview:
Why Avoiding Unnormalized Data is Crucial in RDBMS? Top 8 Bad Practice We Must Stop Doing. Normalization is a database design technique used to organize data efficiently and reduce redundancy. The goal is to eliminate data anomalies and ensure data integrity. When dealing with unnormalized data, information is duplicated across multiple records, leading to inconsistencies and difficulties in maintaining the database.

Consequences of not avoiding unnormalized data.

Data Redundancy: Unnormalized data leads to redundant storage of information, wasting space and making updates error prone.
Data Inconsistency: Inconsistencies arise when changes are not propagated consistently across all instances of duplicated data.
Increased Complexity: Unnormalized structures make queries and updates more complex, affecting performance and maintainability.

Example SQL Implementation:

1- Bad Way – Denormalization with Redundant Columns

-- Bad: Redundant columns storing duplicated data
CREATE TABLE Employees (
    EmployeeID INT PRIMARY KEY,
    EmployeeName VARCHAR(255),
    DepartmentName VARCHAR(255),
    ManagerName VARCHAR(255),
    DepartmentLocation VARCHAR(255)
);

In this example, both DepartmentName and DepartmentLocation are redundant since they can be derived from the Department table.

2-Bad Way –Unnecessary Use of Arrays

-- Bad: Using arrays to store multiple values
CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    ProductIDs INT ARRAY,
    Quantities INT ARRAY
);

Storing multiple product IDs and quantities in arrays violates the first normal form.

3-Bad Way –Storing Comma-Separated Values

-- Bad: Comma-separated values in a column
CREATE TABLE Students (
    StudentID INT PRIMARY KEY,
    Courses VARCHAR(255)
);

Storing multiple courses as a comma-separated list in a single column violates the first normal form (1NF).

4-Bad Way –Duplicating Information Across Tables

-- Bad: Duplicating department information in multiple tables
CREATE TABLE Employees (
    EmployeeID INT PRIMARY KEY,
    EmployeeName VARCHAR(255),
    DepartmentName VARCHAR(255)
);

CREATE TABLE Departments (
    DepartmentID INT PRIMARY KEY,
    DepartmentName VARCHAR(255),
    DepartmentLocation VARCHAR(255)
);

Duplicating department information in both the Employees and Departments tables can lead to data inconsistencies.

5-Bad Way –Unnormalized Hierarchical Structure

-- Bad: Unnormalized hierarchical structure
CREATE TABLE Categories (
    CategoryID INT PRIMARY KEY,
    CategoryName VARCHAR(255),
    ParentCategoryID INT
);

Storing hierarchical data in a flat structure without using a recursive relationship violates normalization principles.

6-Bad Way –Composite Keys with Redundant Information

-- Bad: Composite key with redundant information
CREATE TABLE Orders (
    OrderID INT,
    ProductID INT,
    ProductName VARCHAR(255),
    Quantity INT,
    PRIMARY KEY (OrderID, ProductID)
);

Including ProductName in the composite key is redundant as it can be derived from the Product table.

7-Bad Way – Inconsistent Naming Conventions

-- Bad: Inconsistent column names
CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    FirstName VARCHAR(255),
    LastName VARCHAR(255),
    EmailAddress VARCHAR(255)
);

CREATE TABLE Orders (
    OrderID INT PRIMARY KEY,
    CustomerID INT,
    FirstName VARCHAR(255),
    LastName VARCHAR(255)
);

Inconsistent column names (FirstName and LastName) across tables can lead to confusion and maintenance challenges.

8-Bad Way –Using Unnecessary Flags

-- Bad: Using unnecessary flags
CREATE TABLE Products (
    ProductID INT PRIMARY KEY,
    ProductName VARCHAR(255),
    IsAvailable BOOLEAN
);

Storing availability as a boolean flag can be unnecessary, as the availability status can be inferred from inventory or order information.

These examples illustrate various ways in which unnormalized data can manifest in a relational database, leading to redundancy, inconsistency, and decreased maintainability.

Conclusion:
Avoiding unnormalized data through normalization is a fundamental principle in relational database design. It promotes data integrity, efficiency, and ease of maintenance, ensuring a solid foundation for robust and scalable database systems.