Top 10 Programming Blunders & Common Mistakes, You Can’t Afford to Make That Could Cost You Huge Real Money Loss (Millions of dollars)

Top 10 Programming Blunders

Top 10 Programming Blunders & Common Mistakes –

Top 10 Programming Blunders & Common Mistakes – We are giving example in a specific programming language, but this idea and concept applied in any other language out there in the market.

Blogs Overflow – We expose truths and safe-guard community form huge losses. We know the pain of loss.

1-Incomplete Input Validation

  • Example: Accepting user input without proper validation, leading to SQL injection.
  • Consequence: Compromised database security, potential data loss, and unauthorized access.

Bad Way (PYTHON):

user_input = input("Enter your username: ")
# No validation, allowing SQL injection
query = "SELECT * FROM users WHERE username = '" + user_input + "';"

Directly passing user input in database query statements is not recommended and very dangerous.

Good Way (PYTHON):

import sqlite3

user_input = input("Enter your username: ")
# Use parameterized queries to prevent SQL injection
query = "SELECT * FROM users WHERE username = ?;"
cursor.execute(query, (user_input,))

Above user input has been parameterized and it is safe to pass to database query statements.

Real Incident & Consequence – [Reference – Equifax Data Breach]

  • Incident: In 2017, the Equifax data breach occurred due to incomplete input validation in a web application, allowing attackers to execute a SQL injection attack.
  • Consequence: Personal information of 147 million individuals was exposed, leading to identity theft concerns.
  • Loss Amount: Estimated at hundreds of millions in damages and settlements.

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

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 WayDenormalization 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

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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