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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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Exploring the Best Programming Languages for Back-End Web Development: Factors, Advantages, and Trends

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Exploring the Best Programming Languages for Back-End Web Development Factors, Advantages, and Trends

It’s important to note that the “best” language depends on various factors such as project requirements, team expertise, and specific use cases. Developers often choose languages based on their familiarity, the needs of the project, and the ecosystem surrounding each language. To get the most current and context-specific information, refer to recent surveys and industry reports from reputable sources, as mentioned in the previous response. Keep in mind that preferences and trends can change over time.

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Top 10 Critical Thinking Skills Every Great Data Scientist Must Master

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Top 10 Critical Thinking Skills Every Great Data Scientist Must Master

Discover the top 10 critical thinking skills every data scientist must master to excel in analytics, machine learning, and data-driven decision-making.

APIs in PHP CI3 -CodeIgniter 3 API Development Tutorial: Learn to Build RESTful APIs

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Blogs Overflow help you to creating APIs (Application Programming Interfaces) in CodeIgniter 3 (CI3) involves defining routes, controllers, and handling data appropriately. Here’s a basic guide to creating APIs in CodeIgniter 3 using RESTful principles:

  1. Install CodeIgniter
    Ensure that you have CodeIgniter 3 installed on your server. You can download it from the official website and follow the installation instructions.
  2. Configure Routes:
    Open the application/config/routes.php file and set up routes for your API. For RESTful APIs, you can use the resources method to map HTTP verbs to controller methods. 
$route['api/users']['get'] = 'api/users/index';
$route['api/users/(:num)']['get'] = 'api/users/view/$1';
$route['api/users']['post'] = 'api/users/create';
$route['api/users/(:num)']['put'] = 'api/users/update/$1';
$route['api/users/(:num)']['delete'] = 'api/users/delete/$1';

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Comparing HashiCorp Vault and AWS Secrets Manager: Which is the Ultimate Secret Management Solution?

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HashiCorp and AWS Secret Manager are two popular solutions for managing secrets in the cloud. While HashiCorp offers a comprehensive suite of tools for secret management, AWS Secret Manager provides a seamless integration with other AWS services. Both options have their strengths and weaknesses, making it crucial to evaluate your specific requirements before making a decision.

Embarking on the Stock Trading Journey: A Novice’s Perspective

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Embarking on the Stock Trading Journey: A Novice's Perspective

Welcome, fellow financial enthusiasts and aspiring traders, to the inaugural post of [Your Website Name]! I am thrilled to share the excitement of diving into the dynamic world of stock trading with you. As a newcomer myself, I understand the mix of anticipation and curiosity that accompanies the decision to venture into stock trading. The …

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Bollywood Movie Recommendations: Math vs. Machine Learning (Cosine Similarity in Action!)

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AI vs. math in Bollywood movie recommendations - Cosine similarity visualization

How do platforms recommend Bollywood movies? Is it pure math or AI magic? This blog deciphers cosine similarity, comparing traditional and machine learning-based movie recommendation methods. Dive into Bollywood’s data-driven world and see how your next movie pick is determined!

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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