A concise guide to caching in C#, covering in-memory and distributed caching, their benefits, and when to use each for optimal performance and scalability.

The CAP Theorem states that a distributed system can only achieve two out of three characteristics: Consistency, Availability, and Partition Tolerance. In this article, we will explain what this means and provide practical examples.

Explore the implementation of the main data structures, focusing on fundamental concepts such as static and dynamic arrays, linked lists, and stacks. The practical and detailed examples provide a solid foundation for understanding and applying these essential structures in programming.

Big O notation is used to describe the efficiency of algorithms, especially in terms of time and space, as the size of the input grows. It helps us understand how an algorithm behaves with large volumes of data, highlighting its performance in the worst-case scenario.

Parallelism in C# offers powerful ways to execute tasks simultaneously, improving software efficiency. In this article, we will briefly explore Tasks, Asynchrony, and Concurrency, three fundamental approaches to understanding and applying parallelism in your C# projects.

Explore the relevance of Myers' Rule 10 in software testing, emphasizing how this rule is crucial for ensuring the reliability of software products, highlighting its importance in testing practice.