Database Design

Database Design

Database Blunders: What You Must Avoid in RDBMS and Why-Avoid common RDBMS Pitfalls.

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Database Blunders: What You Must Avoid in RDBMS and Why-Avoid common RDBMS Pitfalls

In a relational database management system (RDBMS), there are several bad practices and database blunders what you must avoid in RDBMS and why for optimal performance and data integrity:

Common RDBMS Pitfalls – Database Blunders

1-Avoiding Unnormalized Data – Storing redundant data or not normalizing your database can lead to data inconsistencies, increased storage requirements, and difficulties in maintaining data integrity.

Example: Consider a database for a library. Instead of having a separate table for authors and storing author details in each book entry, normalize the data. Create an “Authors” table with author information and establish a relationship with the “Books” table using author IDs. This prevents redundant author data and ensures consistency.

Real Life Example: Consider an e-commerce platform where product details are duplicated in every order. If the product information changes, updating each order becomes cumbersome. Normalizing the data by having a separate “Products” table avoids redundancy.

Consequence of Not Following: Without normalization, a change in product details would require updating every order record, leading to data inconsistency and increased maintenance efforts.

Bad Way:

-- Storing redundant author information in every book entry
CREATE TABLE Books (
    BookID INT PRIMARY KEY,
    Title VARCHAR(255),
    AuthorName VARCHAR(255),
    Genre VARCHAR(50)
);

Good Way:

-- Normalizing data with a separate Authors table
CREATE TABLE Authors (
    AuthorID INT PRIMARY KEY,
    AuthorName VARCHAR(255),
    Bio TEXT
);

CREATE TABLE Books (
    BookID INT PRIMARY KEY,
    Title VARCHAR(255),
    AuthorID INT,
    Genre VARCHAR(50),
    FOREIGN KEY (AuthorID) REFERENCES Authors(AuthorID)
);

For detailed information follow this link: Why Avoiding Unnormalized Data is Crucial in RDBMS? Top 8 Bad Practice We Must Stop Doing.

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Why Avoiding Unnormalized Data is Crucial in RDBMS? Top 8 Bad Practice We Must Stop Doing.

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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)
);

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