Overview of Big Data Types and Classifications

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Mar 03, 2022

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Lately, the term big data is making rounds in the market but there are so many of us who are not aware of what it is exactly and what its applications are. So, if you are also a keen learner and want to know about types of big data then you have come to the right page on the internet. Big data is a very big term and there are so many things associated with it. So in this article, we are going to explain what big data, its characteristics, and its advantages are. Not only are we going to discuss the types of big data on this page, but we are also going to look at the differences between them so that you have a better understanding of all those types. So let us now begin and straight dive into the pool of knowledge. Also, if you want to have an in-depth knowledge of the terms related to big data, going for a data science online course could be very beneficial for you.

What is Big data, its Characteristics, and its Advantages?

There are so many large data sets that are available in all the organizations. These data sets are known as big data from which valuable information can be drawn once the data is processed. The information that can be achieved from these data sets is used for various strategies in the organization and the benefit of the business. These large data sets are storehouses of so much underlying information and when that information is processed, it becomes very useful for the organization. The data that is referred to as big data keeps on growing with time and the amount increases exponentially.

The data is so much in volume and the conventional data set rules do not apply to these data sets. The data is analyzed, stored and various types of processes like data mining and visualization are done to get the information from those data sets. There are certain characteristics associated with big data, like:

Variety: There are so many kinds of data that are gathered from various sources. Big data is divided into three types- Structure, unstructured and semi-structure. We are going to explain these types in the article below. There are so many sources like emails, SMS, videos, and audio that have the information for such data and a lot of variety of data is being collected from various sources.

Velocity: This characteristic of big data refers to the time needed for the data to get generated or even form the internal linking with the already existing data. This is one of the main characteristics of big data.

Volume: We are now aware of the fact that the data sets that we are using as big data have huge volumes. They are being generated from various places and they are being generated on a daily basis. These are so much in volume and the place where they are stored for processing is called a data warehouse.

Now, if we look at some of the features of big data, we can unearth some of the following advantages as well:

1. If we look at one of the biggest advantages of using big data in the organization, then it has to be the predictive analysis that can be obtained from all that data Using the various data sets available for processing as big data, businesses are able to get the predictive analysis done on their business strategies and can come up with great ideas to have better growth in their organization. Once you start with your Data Science Certification course, you will see how vast their area is and how many features it comes with and help the organization to grow at a very fast pace.

2. Another great advantage that the companies are going to have from big data is the enhancement in customer experience. We all know that people are using social media nowadays so with the big data tools for analysis of data from these social media platforms the companies can take steps towards increasing the user experience. They can introduce digital steaming and increase their products and services.

3. With the right tools in big data, the companies are able to increase their sales and hence have great growth in their revenue. Using these tools has enabled the companies to see what kinds of services and products are doing well in the market, which helps them to give their due time to that segment and hence increases the sales of that particular product in the market.

4. If you want to make it big in the market, you need to be one step ahead of all your competitors. For this, you need to know the ongoing trends and make sure that you are not lagging behind. With these tools, you will have an upper hand at those insights that you can use to take over any market.

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Types of Big Data

Now that we are aware of what big data is and what its features are, we will move towards big data classification.

1.  Structure

2.  Unstructured

3.  Semi-Structured

Structure Data

When the data that is needed to be processed, assessed, or stored in a fixed format, that kind of big data is known as structured data. When the format is well known in advance, it has taken some time over the period of years for the data scientist to hone and perfect the techniques that can be used to harness the information from that given structured data. These techniques are very useful for the team to find the valued information so that they can have better ideas from the huge pile of data. This is one of the most straightforward data sets to work with. The data which is known as structured as is already stored in the database in the fixed format. Almost around 20% of all the data available, is used in computer activities as well as programming activities.

There are generally two sources from which the structured data is being collected.

Machine-generated data: The machine-generated data includes all the data that is being collected from various devices like sensors, webcam applications, GPS data, and much more. They are being collected from these various machines and they are in a structured form. Also, the data collected from the trading platform is very huge and this can be used to find great insights into the going trends.

Human generated data: This kind of data which is structured is being collected when the humans put some input in the computer. This could include various personal details. Whenever the human is going online or on the computer, that data can be generated and collected to know the customer behaviors. Using these, the modifications and various enhancements are done in the application. These structured data provide great insight into human behaviors and allow the companies to increase the customer experience.

There are various advantages of using structured data. Using this, there is little to no time required to process this data and great results can be achieved. Moreover, the streamlined process which includes the merging of enterprise data can be attained using the structured data. However, there is very limited data in big data which can be considered as structured data. So now we will look at other types of data in big data.

Unstructured Data

When we are talking about the huge amount of data that is available for the origination to work on, most of it is not in the structured form or comes with the predefined way to use it. The data is not organized and this category of data in big data comes under the category of unstructured data. The unstructured data constitutes the majority of the data that is being collected in any organization and it takes so much time and effort to process that data as compared to the structured data. It is a known fact that if data is not interpretable, then it is of no use. So there are resources required to make sure that valued information can be drawn from those data sets.

One of the major challenges that come with unstructured data is that algorithms are needed and the machine needs to learn how to interpret that data so that the value can be yielded from the same. There are various processes that are being followed to achieve this goal. Unlike the structured data which is stored in a data warehouse, the unstructured data is stored in data lakes and in these lakes all the information is present and the data is in its raw form. One of the most common examples of unstructured data is email. There are various kinds of information that can be drawn from these data sets but they need processing as well.

This can be divided into two types. One is the captured data which is being captured as data based on the user’s behaviors and the example could be the GPS location of the user. Another form is the user-generated data where the human is going to enter the data themselves. This could be the likes, shares, comments on the platforms, or the tweets and other things that can be used by the organization to get the information from.

Semi-structured Data

Now that data is gathered with is slightly structured as well as untrusted comes under the category of semi-structured. There is no perfect line here and the data may appear as unstructured as first but with some processing or the close glace, the structured data can be obtained from that. The use of semi-structured data can be put to a significant value. The data can help AI and machine learning to learn and come up with the model to process the data in real-time. But it is also challenging as most of the data does not have structure and it might take some time to process all the data.

The Difference Between Structured, Unstructured, and Semi-structured data

Concurrency and transaction Management

In the structure data that is transaction management as well as various concurrency techniques but it is not present in unstructured data at all. If we talk about the semi structured data, the traction is adopted from the DBMS but it is not matured.


There is very little flexibility available in the structured data as there is a lot of dependency in the various data sets. But in the unstructured data sets, there is flexibility and also there is an absence of schema. If we look at the semi-structured data, the flexibility is present but it is very less than that of unstructured data.

Query running

If you are looking for data sets where you can run complex queries, then you should work with structured data. But in unstructured data, you will be able to use only textual queries whereas, in semi-structured data, you will have an option to use the anonymous nodes.


If we look at the technology which is being used by these types of big data, the structure uses the relational database; the semi-structured is based on RDF and XML. The unstructured data is based on the character and library data.

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Final Word

It is a fact that big data is bringing a revolution in the industry and now the companies are realizing their potential with the use of big data. Now more and more people are learning the importance as well as the ways to comprehend big data and make the best use of it. On this page, you have learned about the concepts of big data, its features, and the types of big data that are being used in the organization. Once you have a strong foundation, you can work with these kinds of big data. Data Science Training has become very popular and now whoever wants to enter this world is getting all the knowledge they require to make a name here.

If you are also looking to learn more about the concepts of data science like big data, machine learning AI and much more, then you should head to StarAgile and find the best course there for you to learn from. So, get into the best Data Science Course and have a bright future in the most demanding field of this era.

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