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Types of Data Structures

Lindsey Patterson Dec 02, 2019
Having a set of data alone won't necessarily give you the answers you are looking for. In order for that data to make sense, it needs to be structured in a way that either you or a computer program can recognize and analyze. Data structure keeps information organized in a way that it is useful and can drive conclusions or decisions.

Types of Data Structures

Searching through organized data is much easier than trying to make sense of heaps of random information. Here's a brief rundown of types of data structure and how they might be applicable.


If you find yourself asking what is data structure, think of information plotted out on a graph. Easily the most familiar method of organizing data, graphs provide a visual example of real-world information. If you want to know when your busiest time of the day is as a business owner, that would be easy to plot out on a graph.
Likewise, the number of sales that are happening versus what season it is would also be a simple graphing data structure. Graphs are used in businesses everywhere to demonstrate ideas, project earnings, and view revenue streams.

Linear Data Structures

Linear data structures include ways of organizing information in a sequence in order to interpret data. There are many types of linear data structures, but a few of the most common are listed here.
An array is a contained amount of information that is all the same data type. For example, an array may store web pages that are found when you do a search on Google in a particular order based on their rankings. When the search results come up, the search engine is displaying one array at a time, in a particular order.
The most common example of a list used in a data structure is the linked list. Where arrays store information in sequential order, however, linked lists store information across the whole memory. This would be like Google returning search results to you that were random in their order instead of organized.
A queue is a sequence of information that functions in a first-in-first-out (FIFO) method. An easy example of this is a call queue that's answered in the order in which the calls were received.
A stack is the opposite of a queue and uses the last-in-first-out (LIFO) method. Interestingly, the name was given to this structure because when you are sorting through a physical stack of something, you must remove the last thing you put on top to get to the items down below.


A tree organizes information in a hierarchical order using nodes such that information resembles a tree. One common application of this kind of data is a chain of command tree used in businesses to show different supervision or authority levels.
Each node of data would represent a person or position within the company. Each node can have one or several nodes underneath it called child nodes.
There are many subdivisions of tree data structures which include binary trees, b-trees, heaps, trees, multiway trees, space-partitioning trees, and application-specific trees. Some of the differences between tree types include the following: binary trees contain nodes that have two child nodes at the most and grow downward and sideways.
B-trees are self-balancing, symmetrical, and grow only downward. In a heap, all of the nodes are in a certain order and have a maximum number of children each node can have.

Hashing Data Structures

Hashing data structures are a bit more technical and involved when it comes to data organization. Hashing essentially converts large information into smaller information that can be used as an index to access and retrieve stored data in a hash table. One of the advantages of hashing is it requires a smaller amount of memory or space to store the information.
Data structures help keep data organized and accessible. Most of us don't realize it, but these data structures enable us to do things like look up a local concert's start time, have TV shows recommended to us on streaming platforms, and much more. Data structures are essential to how the world we live in functions.