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)
);
