What is Big Data? Types, Characteristics & Examples

Before going to the introduction to Big Data, you first need to know what data itself is.

What is Data?

Data is the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.

Now, let us look at the Big Data definition.

What is Big Data?

Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is data of such large size and complexity that none of the traditional data management tools can store it or process it efficiently. Big Data is still data, but of a size that breaks the usual tools.

The diagram below sets that definition against the ordinary data an organisation already handles, so the jump in scale is easy to see.

What is Big Data?

What is an Example of Big Data?

Following are some of the Big Data examples-

Stock Exchange Data

The New York Stock Exchange is an example of Big Data that generates about one terabyte of new trade data per day.

Social Media

The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.

Jet Engines

A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, generation of data reaches up to many Petabytes.

These three figures are per-source snapshots from the period when the examples were first published, and they have only grown since. For a current sense of scale, the world as a whole now creates roughly 402 million terabytes of data every day, putting the 2026 annual total on track for more than 200 zettabytes.

Types of Big Data

Following are the types of Big Data:

1. Structured
2. Unstructured
3. Semi-structured

Structured

Any data that can be stored, accessed and processed in a fixed format is termed ‘structured’ data. Over time, computer science has achieved great success in developing techniques for working with this kind of data, where the format is well known in advance, and in deriving value out of it. However, we now face issues when the size of such data grows to a huge extent, with typical sizes running into multiple zettabytes.

Do you know? One zettabyte is 10²¹ bytes, which is one billion terabytes.

Looking at these figures one can easily understand why the name Big Data is given, and imagine the challenges involved in its storage and processing.

Do you know? Data stored in a relational database management system is one example of ‘structured’ data.

Examples of Structured Data

An ‘Employee’ table in a database is an example of structured data. Every row has the same five columns, and every column has a known type, which is exactly what makes the data easy to query.

Unstructured

Any data with an unknown form or structure is classified as unstructured data. In addition to the size being huge, unstructured data poses multiple challenges in terms of processing it to derive value. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Nowadays organisations have a wealth of data available to them, but unfortunately they do not know how to derive value out of it, since this data is in its raw or unstructured format.

Examples of Unstructured Data

The output returned by ‘Google Search’ is unstructured: the same query returns pages, images, snippets and links with no common schema behind them.

Example of unstructured data

Semi-structured

Semi-structured data can contain both of the forms above. It looks structured, but it is not defined by a formal schema such as a table definition in a relational DBMS. A common example of semi-structured data is data represented in an XML file. JSON documents and log lines with key-value pairs fall into the same category.

Examples of Semi-structured Data

Personal data stored in an XML file-

<rec><name>Prashant Rao</name><sex>Male</sex><age>35</age></rec>
<rec><name>Seema R.</name><sex>Female</sex><age>41</age></rec>
<rec><name>Satish Mane</name><sex>Male</sex><age>29</age></rec>
<rec><name>Subrato Roy</name><sex>Male</sex><age>26</age></rec>
<rec><name>Jeremiah J.</name><sex>Male</sex><age>35</age></rec>

Data Growth Over the Years

The chart below tracks how the mix of structured and unstructured data has shifted as total volume has climbed.

Data growth over the years

Please note that web application data, which is unstructured, consists of log files, transaction history files etc. OLTP systems are built to work with structured data, wherein data is stored in relations (tables).

Characteristics of Big Data

Big Data can be described by the following characteristics:

• Volume
• Variety
• Velocity
• Variability

(i) Volume – The name Big Data itself is related to a size which is enormous. The size of the data plays a very crucial role in determining the value that can be drawn from it. Also, whether a particular set of data can actually be considered Big Data or not depends upon its volume. Hence, ‘Volume’ is one characteristic which needs to be considered while dealing with Big Data solutions.

(ii) Variety – The next aspect of Big Data is its variety.

Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. In earlier days, spreadsheets and databases were the only sources of data considered by most applications. Nowadays, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. is also considered in analysis applications. This variety of unstructured data poses certain issues for storing, mining and analysing data.

(iii) Velocity – The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet demand determines the real potential in the data.

Big Data velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, social media sites, sensors, mobile devices, etc. The flow of data is massive and continuous.

(iv) Variability – This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.

Two more characteristics are commonly added to that list today, giving the widely quoted “five Vs”:

• Veracity – how trustworthy and accurate the data is. Duplicate records, sensor drift and missing fields all reduce veracity, and no amount of volume compensates for it.
• Value – what the data is actually worth once analysed. Data that is never turned into a decision only adds storage cost.

Advantages of Big Data Processing

The ability to process Big Data in a DBMS brings multiple benefits, such as-

Businesses Can Use Outside Intelligence When Making Decisions

Access to social data from search engines and sites like Facebook and X (formerly Twitter) enables organisations to fine-tune their business strategies.

Improved Customer Service

Traditional customer feedback systems are being replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are used to read and evaluate consumer responses.

Early Identification of Risk to Products and Services

Running analysis continuously over transaction records, sensor readings and support tickets surfaces defects, fraud patterns and service failures while they are still small, instead of waiting for a monthly report to reveal them.

Better Operational Efficiency

Big Data technologies can be used to create a staging area or landing zone for new data before identifying what should be moved to the data warehouse. In addition, such integration of Big Data technologies and the data warehouse helps an organisation offload infrequently accessed data.

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