What Are Data Structures & Its Types?

May 23, 2024
20 min read

Data structures are the foundational elements in computer science that are critical for the efficient organization, storage, and management of data within software applications. They provide a systematic approach to data handling, facilitating various tasks like data retrieval and complex algorithmic operations. By understanding the different types of data structures, you can enhance the performance, scalability, and reliability of programs.

In this article, you will learn about data structures, types of data structures, their key features, and operations.

What Is a Data Structure?

A data structure is a specific way of storing, organizing, and managing data in a computer to enable efficient access and modification of data. It not only organizes data but also defines the relationships between data elements and the operations that can be performed on them.

Data structures are crucial for implementing efficient algorithms and helping manage and process data in different programming languages. They are comprised of basic and advanced types tailored to various applications.

Key Features of Data Structures

Here are some key features of data structures:

  • Organized Data: Data structure provides a systematic way to arrange data, helping define the relationships and interactions between different data elements. This feature also enables efficient data storage, retrieval, and manipulation.
  • Memory Management: Data structures efficiently handle memory allocation and deallocation, ensuring optimal use of memory resources. Effective memory management minimizes memory fragmentation and prevents memory leaks, enhancing overall system stability.
  • Scalability: Data structures are designed to scale effectively for growing data volumes. This scalability helps maintain the overall performance and functionality as the size of the data increases.
  • Complexity Analysis: Complexity analysis involves evaluating the time and space requirements of operations performed on data structures to understand how they scale with varying sizes of input data.

What Is the Importance of Data Structures?

Data structures aren’t just about storing data but are also essential for optimizing the speed and performance of software applications. Here are some of the benefits highlighting the significance of using data structures effectively:

  • Data structures can store diverse data types, from simple text and numbers to complex data such as images, database records, and software programs.
  • A data structure object can represent and access all data, and one single object can hold different types of data.
  • Data structures provide reusability of existing structures, reducing time and effort in designing and implementing software applications solutions.
  • Data structure abstraction allows developers to focus on the high-level functionality of their applications without having to stress the complexities of the underlying data operations.
  • Using data structures can help developers save time while performing data storage, retrieval, and processing operations. This is particularly essential when dealing with large data volumes.

What Are the Operations on Data Structures?

Data structures support various operations for efficiently storing, accessing, and manipulating data. Here are some common operations associated with data structures:

  • Search: It helps to find the location of a particular element within the data structure. You can perform search operations on data structures such as linked lists, arrays, trees, graphs, etc.
  • Insertion: The insertion operation allows you to add one or more elements to a data structure. You can insert the elements at the beginning, end, or any specific index.
  • Deletion: It involves removing or deleting an existing element from the data structure. You can delete an element based on a particular index or value.
  • Traversal: Traversal lets you visit and process all elements in a data structure in a specific order. You can also examine or modify each element sequentially.
  • Sorting: Sorting allows you to rearrange the elements in a data structure, making it easier for search and retrieval. By sorting the data structure, the time to search for a particular item is reduced.
  • Update: This includes modifying or updating the value of an existing element within the data structure. It could be changing the value of a data element stored at a specific location or more complex modifications.

What Are the Types of Data Structures?

Types of Data Structures?

Data structures are broadly divided into different types as follows:

Primitive Data Structures

Primitive data structures are pre-defined, basic types directly interacting with the system architecture. Some primitive data structures are:

  • Integer: An integer stores whole numbers that can be positive, negative, or zero. Depending on the programming language, they may have varying sizes. You can perform all the arithmetic operations using integer data types.
  • Float: Float represents decimal numbers and fractional values. It can store real numbers and may have single-precision or double-precision, depending on the programming language.
  • Character: A character data structure stores single characters like letters, digits, punctuation marks, and special characters.
  • Boolean: Boolean stores logical values that are either true or false.

Non-Primitive Data Structures

Non-primitive data structures are more complex forms derived from primitive types. They facilitate complex operations like searching, sorting, traversal, etc., and are further classified into two types:

Linear Data Structures

Linear data structures store elements sequentially; each element has a unique predecessor and successor (except for the first and last elements).


An array is a collection of data items of the same type stored together in adjacent memory locations. The data items are termed “element” and each element is accessed using the index value. The size of the array corresponds to the total number of elements in the array.

Indexing in an array starts at 0 for the first element and sequentially increments up to the array size minus one. For example, in an array of 5 elements, the indices range from 0 to 4, as shown in the figure below.


Arrays are used in algorithms that try to access elements randomly. They effectively store the lists of elements so that you can perform mathematical operations and image processing.


A stack works on the Last In, First Out (LIFO) principle, exclusively allowing data insertion and retrieval from one end. The stack's most recently added element is the first to be removed. The primary operations of a stack are push and pop. The push operation adds a new element to the top of the stack. Conversely, the pop operation deletes the topmost element of the stack.


Here are some other basic stack operations :

  • Peek: View the top element without removing it from the stack.
  • IsEmpty: Verifying whether the stack is empty.
  • IsFull: Verifying whether the stack has reached the maximum capacity.

Stacks are highly advantageous in data management scenarios where the sequence of operations is more significant.


A queue works on the first-in-first-out (FIFO) principle. Unlike in a stack, elements in a queue can be inserted from the rear end and removed from the front end.


The queue processes the data in the order they were received. An example of queues is job scheduling in operating systems. Queues are used to schedule and prioritize jobs waiting to be executed by the system. Then, jobs are processed in the order specified at the time of arrival.

Here are some basic operations performed on a queue:

  • Enqueue: Used to insert an element at the rear end of the queue.
  • Dequeue: Used to delete an element from the front end of the queue.
  • Peek: Returns the element, which is pointed by the front pointer in the queue.
  • Queue Overflow: The overflow condition is shown when the queue is full.
  • Queue Underflow: The underflow condition is shown when the queue is empty.
Linked List

A linked list is type of linear data structure in which elements are linked using pointers instead of being stored in adjacent memory locations, as shown below.

Linked List

Linked lists can grow dynamically, making them suitable in scenarios where the data size is unknown.

Non-Linear Data Structures

In non-linear data structures, the data elements are not stored sequentially but in a hierarchical manner. This makes it difficult to traverse all elements in a single run. Non-linear data structures are memory efficient, sharing nodes among multiple elements. A non-linear data structure is divided into two types:


A tree data structure represents a hierarchical model that does not store data sequentially. Instead, it organizes elements in a tree-like structure with multiple levels.


In the above tree structure, each node is labeled with a name.

  • Root Node: It is the topmost node in the tree data structure with no parent. In the above example, node A is the root.
  • Parent Node: The parent node is any node with child nodes directly under it.
  • Child Node: If the node descends from a parent node, it is termed a child node.
  • Siblings: The sibling nodes are those with the same parent.
  • Leaf or External Node: Leaf nodes have no child nodes. In the above example, F is a leaf node.
  • Internal Node: A node with at least one child node is termed an internal node.
  • Ancestor Nodes: A node's ancestors refer to any nodes preceding it on the path to the root. For example, nodes A and B are ancestors of node E.
  • Descendants: These are nodes accessible by traversing downwards from a given node in a tree. In the above data structure, nodes D and I are the descendants of node B.
  • Subtree: It refers to a portion of a tree that includes a node, also referred to as the root of the subtree, and all of its descendants. For example, if node B is the root of a subtree, then nodes D, H, and I comprise the descendants of the subtree.

A graph data structure is a collection of vertices (nodes) and edges that connect pairs of vertices. It is used to model relationships between objects and is fundamental in various computer science applications, including network analysis, social network modeling, and routing algorithms.


Graph components include:

  • Vertices: Vertices, also called nodes, are a graph's basic components. Each vertex or node may be assigned a label or remain unlabeled.
  • Edges: Edges serve as connections between two vertices in the graph. Edges connect any two vertices in a graph, and similar to vertices, edges may be labeled or remain unlabeled. In a directed graph, they can be represented as ordered pairs of vertices, whereas in undirected graphs, edges indicate a bidirectional relationship.

Applications of Data Structures

Data structures are a fundamental tool in computer science, designed to store, retrieve, and manipulate data in software applications. They can solve problems and be applied in various fields. Let us look at some of the practical applications of data structures:

  • Dynamic Programming: Data structures like arrays are extensively used in dynamic programming to store the results and optimize the algorithms. For example, the matrix chain multiplication algorithm relies on arrays to efficiently store and retrieve data.
  • Job Scheduling: Data structures like queues are used in task management systems for job scheduling. The first-in-first-out (FIFO) functionality of queues enables the orderly execution of jobs.
  • Bioinformatics: Data structures like graphs are used to model and analyze complex biological networks, such as gene regulatory networks and protein-protein interaction networks.

How to Decide Which Data Structure to Use?

Choosing the right data structure depends on various factors. Here are some key considerations to help you select the appropriate data structure:

Understand Your Data

The type of data you're dealing with can influence the data structure you choose. For example, a tree-based data structure may be more suitable if your data has a hierarchical structure. Conversely, an array data structure could be the better choice if you have simple data that needs to be accessed randomly.

Consider the Operations

Next, consider the operations you will perform on the data. Different data structures optimize different operations. For example, linked lists are efficient for frequent insertion and deletion, and binary trees are ideal for searching and sorting.

Analyze Time and Space Complexity

Consider the time and space complexity of the data structure. The time complexity refers to the efficiency of operations in terms of the input size, while the space complexity refers to the amount of memory required. Choose a data structure that meets your application performance requirements.

For example, an array is great for quick access to elements at random positions but not ideal for frequent insertions or deletions. Contrarily, a linked list is more efficient for insertions and deletions but less efficient for random access.

Available Libraries

Consider the accessibility of libraries for the specific data structure you choose. If your programming language provides built-in libraries for that particular data structure, it can make the execution and maintenance of your code more manageable.

libraries for that particular data structure, it can make the execution and maintenance of your code more manageable.

Unlock the Power of Data Structures with Airbyte


Now that you have an idea of data structures, operations, and types, it’s essential to consolidate data on a platform to perform any high-level transformations on the data. This is where Airbyte can help you.

Airbyte is a prominent data integration platform featuring connectors capable of ingesting any type of data structure from diverse sources. Irrespective of the format, Airbyte facilitates integration by empowering you to construct no-code data pipelines effortlessly. It offers more than 350 pre-built connectors to connect multiple sources and destinations. In addition to its pre-built connectors, Airbyte’s Connector Development Kit (CDK) empowers you to set up custom connectors. Moreover, Airbyte’s change data capture (CDC) capabilities facilitate efficient data migration by ensuring seamless synchronization of updated information with your designated target system.

Learn more about the types of data used in Airbyte here.


Data structures provide a framework for efficiently organizing, storing, and manipulating data. Different types of data structures exist, each with unique characteristics and applications. From arrays and linked lists to trees and graphs, each offers specific advantages. By selecting and implementing appropriate data structures, you can optimize the application's performance and maintenance. 

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