Why and when should I use Stack or Queue data structures instead of Arrays/Lists?

Stacks and queues are specialized data structures that offer different ways to manage data compared to more general structures like arrays or lists. Choosing between using a stack, a queue, or an array/list depends on the specific requirements of the application, particularly how data needs to be accessed, stored, and manipulated.

Why Use a Stack?

A stack is a Last In First Out (LIFO) data structure, meaning the last element added to the stack is the first one to be removed. This characteristic makes stacks ideal for certain types of data processing:

1. Backtracking Applications: Stacks are useful in scenarios where you need to backtrack or reverse actions, such as in the undo mechanisms in software applications or navigating between pages in web browsers[1][3].
2. Expression Evaluation: Stacks are used in algorithms that evaluate or parse expressions, especially those involving nested structures like arithmetic expressions or HTML/XML parsing[1][3].
3. Function Call Management: In programming, stacks are used to keep track of function calls. Each call to a function creates a new frame on the stack, and returning from the function pops the frame off the stack[1][3].
4. Depth-First Search (DFS): In graph and tree traversal algorithms, stacks are used to achieve DFS traversal, allowing the algorithm to explore a path fully before backtracking[3].

Why Use a Queue?

A queue is a First In First Out (FIFO) data structure, where the first element added is the first to be removed. Queues are suitable for:

1. Ordering Processes: Queues are used in scheduling algorithms where the order of tasks is important, such as in operating system processes, task scheduling, and service systems like ticket counters[3][5].
2. Buffering: Queues can serve as buffers in data communication, where data packets are stored and forwarded in the order they arrive[3].
3. Breadth-First Search (BFS): In graph and tree traversal, queues are used to achieve BFS, ensuring that all nodes at the present depth are explored before moving on to nodes at the next depth level[3].
4. Asynchronous Data Handling: Queues are used in scenarios where data is produced at a different rate than it is consumed, such...