Data Structures Tutorial

Data Structures Tutorial Asymptotic Notation Structure and Union Array Data Structure Linked list Data Structure Type of Linked list Advantages and Disadvantages of linked list Queue Data Structure Implementation of Queue Stack Data Structure Implementation of Stack Sorting Insertion sort Quick sort Selection sort Heap sort Merge sort Bucket sort Count sort Radix sort Shell sort Tree Traversal of the binary tree Binary search tree Graph Spanning tree Linear Search Binary Search Hashing Collision Resolution Techniques

Misc Topic:

Priority Queue in Data Structure Deque in Data Structure Difference Between Linear And Non Linear Data Structures Queue Operations In Data Structure About Data Structures Data Structures Algorithms Types of Data Structures Big O Notations Introduction to Arrays Introduction to 1D-Arrays Operations on 1D-Arrays Introduction to 2D-Arrays Operations on 2D-Arrays Strings in Data Structures String Operations Application of 2D array Bubble Sort Insertion Sort Sorting Algorithms What is DFS Algorithm What Is Graph Data Structure What is the difference between Tree and Graph What is the difference between DFS and BFS Bucket Sort Dijkstra’s vs Bellman-Ford Algorithm Linear Queue Data Structure in C Stack Using Array Stack Using Linked List Recursion in Fibonacci Stack vs Array What is Skewed Binary Tree Primitive Data Structure in C Dynamic memory allocation of structure in C Application of Stack in Data Structures Binary Tree in Data Structures Heap Data Structure Recursion - Factorial and Fibonacci What is B tree what is B+ tree Huffman tree in Data Structures Insertion Sort vs Bubble Sort Adding one to the number represented an array of digits Bitwise Operators and their Important Tricks Blowfish algorithm Bubble Sort vs Selection Sort Hashing and its Applications Heap Sort vs Merge Sort Insertion Sort vs Selection Sort Merge Conflicts and ways to handle them Difference between Stack and Queue AVL tree in data structure c++ Bubble sort algorithm using Javascript Buffer overflow attack with examples Find out the area between two concentric circles Lowest common ancestor in a binary search tree Number of visible boxes putting one inside another Program to calculate the area of the circumcircle of an equilateral triangle Red-black Tree in Data Structures Strictly binary tree in Data Structures 2-3 Trees and Basic Operations on them Asynchronous advantage actor-critic (A3C) Algorithm Bubble Sort vs Heap Sort Digital Search Tree in Data Structures Minimum Spanning Tree Permutation Sort or Bogo Sort Quick Sort vs Merge Sort Boruvkas algorithm Bubble Sort vs Quick Sort Common Operations on various Data Structures Detect and Remove Loop in a Linked List How to Start Learning DSA Print kth least significant bit number Why is Binary Heap Preferred over BST for Priority Queue Bin Packing Problem Binary Tree Inorder Traversal Burning binary tree Equal Sum What is a Threaded Binary Tree? What is a full Binary Tree? Bubble Sort vs Merge Sort B+ Tree Program in Q language Deletion Operation from A B Tree Deletion Operation of the binary search tree in C++ language Does Overloading Work with Inheritance Balanced Binary Tree Binary tree deletion Binary tree insertion Cocktail Sort Comb Sort FIFO approach Operations of B Tree in C++ Language Recaman’s Sequence Tim Sort Understanding Data Processing Applications of trees in data structures Binary Tree Implementation Using Arrays Convert a Binary Tree into a Binary Search Tree Create a binary search tree Horizontal and Vertical Scaling Invert binary tree LCA of binary tree Linked List Representation of Binary Tree Optimal binary search tree in DSA Serialize and Deserialize a Binary Tree Tree terminology in Data structures Vertical Order Traversal of Binary Tree What is a Height-Balanced Tree in Data Structure Convert binary tree to a doubly linked list Fundamental of Algorithms Introduction and Implementation of Bloom Filter Optimal binary search tree using dynamic programming Right side view of binary tree Symmetric binary tree Trim a binary search tree What is a Sparse Matrix in Data Structure What is a Tree in Terms of a Graph What is the Use of Segment Trees in Data Structure What Should We Learn First Trees or Graphs in Data Structures All About Minimum Cost Spanning Trees in Data Structure Convert Binary Tree into a Threaded Binary Tree Difference between Structured and Object-Oriented Analysis FLEX (Fast Lexical Analyzer Generator) Object-Oriented Analysis and Design Sum of Nodes in a Binary Tree What are the types of Trees in Data Structure What is a 2-3 Tree in Data Structure What is a Spanning Tree in Data Structure What is an AVL Tree in Data Structure Given a Binary Tree, Check if it's balanced

Understanding Data Processing



In our everyday lives, any task that we perform online is related to data. Millions of pieces of data are produced every second across the globe. Data production is largely credited to social media networking, business, financial transactions, and research projects.

Data is collected from the source and primarily doesn’t have any specific structure or format. This raw data can be of any form like images, videos, songs, files, and organised data like records that are stored in a database, excel files, or any other. Raw data is not useful since it is in an unstructured format. So, to make it readable and make the data accessible to the company, the raw data is processed.

Data Processing?

Data processing is the process of collecting raw data and making this data into specific useful information. In simple words, first the data is in the raw form and, using several techniques like extraction and machine learning algorithms, the data is analysed and processed. This processed data is now useful and in readable formats like documents, graphs, and charts, and can be used by humans or software companies to build applications for extracting information and drawing conclusions.

Understanding Data Processing

Since there is a large chunk of data being produced and handling this data is not a cup of tea, So, to perform this data processing, a well-trained data scientist or a group of data engineers will work on this data to translate it into useful information.

Data Processing Cycle

There are different stages for performing this data processing, and every stage must be in a specific order, and this process of steps is done in cycles.

In this process, a set of raw data is taken as an input and processed in a system that produces useful information. After the completion of one whole cycle, the output of that cycle is considered as an input for the next cycle of processing, and this process continues in a cycling manner.

The stages involved in the data processing cycle are

1. Data Collection

2. Data Cleaning

3. Input

4. Data Processing

5. Output

6. Storage

Data Collection

The primary step in the data processing cycle is data collection. At this stage, raw data needs to be collected in such a way that the data collected must be precise and unambiguous. So, it is better to collect data from accurate sources, which include data like cookies from websites, company profit or loss information, user actions, and financial statements.

Data Cleaning

This step is also called data preparation. In this step, the raw data is verified and checked thoroughly for errors. The data is sorted and filtered so that any missing, duplicate, or wrongly calculated data is cleaned and transformed for further analysis and processing. Then unwanted and irrelevant data is removed from the raw data and the data is cleaned completely. This process makes sure that only high-quality data is sent for processing.


This step involves conversion of raw data into machine-understandable data and sent for data processing. The data is taken through a scanner, a keyboard, a mouse or any other input source.

Data processing

The most important step in the data cycle is data processing. In this step, the raw data is transformed into desired actionable insights by performing different algorithms of artificial intelligence and machine learning. This step shows partial results as the input is varied and depends upon the sources like data lakes, warehouses, or connected database devices.


After data processing, the desired output is transmitted in the form of dictionaries, graphs, charts, papers, tables, visual images, videos, vector files, songs, graphs, and others. This process has another name, which is data interpretation.

Data Storage

The last step in the data processing cycle is to store the output. This output is further used for processing of the next cycle. Data and metadata are stored for easy retrieval and quick interaction with the information whenever required.

Types of Data Processing

Based on the type of source input and process steps for finding the result, there are several types of data processing. A few of them into account are

  1. Online processing
  2. Real time processing
  3. Multi-processing
  4. Batch processing
  5. Time sharing

Online processing

  • As soon as the data is available, it is sent to the CPU.

Real time processing

  • This processing is for small data sets. Data is processed after giving the input.


  • Also called "parallel processing," the data is divided into frames and then processed by more than two CPUs on a single computer system.

Batch processing

  • This processing is for large volumes of data.

Time sharing

  • In this processing the data is processed in time slots with several users sharing data simultaneously.

The Data processing methods

The three methods for data processing are

1. Manual

2. Electrical

3. Mechanical

Manual data processing

Data is manually processed by humans without any interference from mechanical or electronic devices. It is a cheap method and doesn’t require many tools, but there is a problem of huge errors and time consuming.

Electrical data processing

It is the most expensive method in which data is processed using software programs. Upon receiving instructions from the software, the data is processed and gives output accordingly.

Mechanical data processing

Data is processed with the help of machines. Mechanical devices like printers and calculators are used for data processing. This is easy and errors are in small numbers, but it becomes hard due to an increase in data.


In a world where data plays a key role in lifestyle, the data needs to be arranged and processed properly so that its usage becomes easy. Many researchers and organisations are significantly dependent on the expanding data, so many data scientists and data engineers are required to process the data.