What is Data Structure?
Data Structure is a systematic way to store and organize information so that it can be used effectively. For instance, information structures are arrays, Linked List, Stack, Queue, etc. Whether operating system, compiler design, artificial intelligence, graphics, or anything else, data structure is helpful in all computer science aspects. Data structure is linked to algorithms that enable programmers to manage information efficiently and improve application performance.

Each data structure has its own interface representing its supporting activities. Interface offers only the list of activities endorsed and the type of parameters. 

Why Data Structure is required?

Applications are becoming complicated and information is also essential. Therefore, applications are now facing several issues that can be readily solved with data security through the use of data structures. Let's look at the issues that arise in the absence of DS:

Multiple Requests:  Thousands of users can simultaneously search for information on a web server. In such cases, while searching for the data, a fast server also fails.

Data Search: Let's suppose 1 million (106) shop items stock. To search for an item, the application must search for an item in 1 million (106) items each time the search is slowed down. Search will become slower as information expands.

Processor Speed:Although the processor speed is very high, if the information expands to billions of documents, it will be restricted.

A data structure can organize information in such a manner that it may not be necessary to search all objects and it is easy to search for the necessary information immediately.

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Benefits of Data Structure:

It can arrange and store information in a correct way, as we have already discussed. Let's look at the advantages of using DS in our apps:

Reusability: Reusable data structures. Only once have we implemented a DS and then we can use it anywhere else. We can compile a 

DS application into libraries that various customers can use.

Efficiency: If we select the best suited DS for it, a program will be more effective. Let's take an instance to search in a collection of information for a specific record. If we organize that information in an array, the specific record will have to be searched sequentially. That implies it may not be the best option here to use an array. There are better data structures which can make the search method as orderly as possible.

Abstraction: DS is indicated by the abstraction level ADT. The customer program utilizes the information structure only through the interface without going into the details of the execution.

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Basic Terminologies in DS

Entity Set: Entities of similar attributes form an entity set.

Field: Field is a single elementary unit of information representing an attribute of an entity.

Record: Record is a collection of field values of a given entity.

File: File is a collection of records of the entities in a given entity set.

Attribute and Entity: An entity is that which contains certain attributes or properties, which may be assigned values.

Data: Data are values or set of values.

Data Item: Data item refers to single unit of values.

Group Items: Data items that are divided into sub items are called as Group Items.

Elementary Items: Data items that cannot be divided are called as Elementary Items.

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Data Structure Classification

We can classify DS into Premitive and Non-Premitive. The following hierarchy shows the classification in details:

Operations on Data Structures

We can apply following operations on a data structure:

Insertion: the process of adding an element to DS at any location.

Deletion: the process of removing an element from DS from any random location.

Searching: the process of finding the location of an element within DS. We can perform this operation either by using Linear Search or by using Binary Search algorithm.

Sorting: the process of arranging DS in a specific order. There are several algorithms to perform sorting such as insertion sort, selection sort, bubble sort, etc.

Merging: the process of joining two DS of same type to produce the third one.

Traversing: Every DS has its own set of data elements. Visiting each element of DS in order to perform some specific operation like searching or sorting is known as traversing.