What Big Data is – Types, History, and Importance

What Big Data is

Big Data is a very popular term of the 21st century. It’s a revolutionary new technology that resolves consumer-centric and point-of-sale questions needed by the industries. It provides the answers to issues and duties that data owners had never considered before. This is high time to learn Big Data and to know what big data is.

The average Big Data engineer salary is $116,591 per year in the U.S. Due to its increasing popularity and high salary trends, many students want to build their careers in Big Data. They generally ask what big data is, its types, and its importance. In this blog, we will provide detailed answers to these questions. 

What is data?

In simple terms, we can say that data that contains Quantities, letters, or symbols used by a computer can be stored and sent as electrical impulses and recorded on magnetic, optical, or mechanical media.

Basics of big data

Data was largely created by people who worked in companies until recently. The data were generally organized in a certain way. It served as the foundation for keeping track of money paid, deliveries done, and staff employed, among other things. Businesses still rely on this information. Big data notions now imply that data processing must deal with: 

  • It Contains High volume (lots of data)
  • Big Data contains High velocity (data arriving at high speed)
  • Big Data gives High variety (many different data sources and formats)

Big data may be organized yet has a large volume. It might be semi-structured, like XML, or it can be user-defined. It’s also possible for it to be completely unstructured. Let us Discuss now what big data is.

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What Big Data is?

Big data refers to data that comes from various sources, arrives in higher amounts, and at high velocity. The three V’s is another name for it. In simple words, big data is more extensive, more complicated, and derived from new data sources. These data sets are enormous, and conventional data processing software is unable to handle them.

However, these vast amounts of data can be utilized to solve business challenges that cannot been solved previously. Here are the three V’s of big data which will help you to better understand what big data is-

Three V’s of Big Data


The amount of data being collected is rapidly increasing. Data floods into businesses at an extraordinary speed as the Internet of Things grows, and we must handle it quickly. We need to cope with data sets being driven by CCTV cameras, sensors, RFID tags, and smart meters. Every two years, the volume of data is expected to double.


Data is no longer kept in rows and columns. From organized, quantitative data in traditional databases to unstructured text documents, videos, emails, log files, audios, CCTV footage, and financial transactions, data comes in various formats.


The amount of data we deal with is massive, measuring in petabytes. Data is gathered from various sources that include Internet of Things (IoT) devices, commercial transactions,  industrial equipment, social media, movies, and more. Previously, it was a huge challenge to store this data, but the decreased cost of storage platforms like Hadoop and data lakes makes the process cheaper.

History of Big Data

People started to notice how much data users created through YouTube, Facebook, and other Internet services around 2005. Hadoop (an open-source platform for storing and analyzing extensive data sets) was founded that same year. During this time, NoSQL began to gain popularity.

The growth of big data was helped by the advent of open-source frameworks such as Hadoop (and, more recently, Spark), making massive data easier to deal with and store. The volume of big data has exploded in the years since then. Users are still generating massive volumes of data, but it isn’t just humans.

More products and devices are connected to the internet due to the Internet of Things (IoT), allowing companies to collect data on client usage patterns and the performance of the product. The rise of machine learning has resulted in even more data. While big data has gone a long way, its utility is barely beginning.

Cloud computing has opened up even more opportunities for big data. Developers may easily spin up ad hoc clusters to test a fraction of data in the cloud, which provides genuinely elastic scalability. With its ability to present vast volumes of data that makes analytics rapid and comprehensive, graph databases are becoming more important.

What are the Types of Big Data

The following are the three types of data that big data incorporates-

Structured Data

Structured data is any data that can be stored, accessed, and processed in a fixed format. Over time, computer science expertise has become more successful in inventing strategies for working with such data(when the format is fully understood in advance) and extracting value from it. However, we are now anticipating problems when the bulk of such data expands to enormous proportions, with typical sizes in the zettabyte. An employee table in a database is an example of structured data.

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Unstructured Data

Unstructured data is any data that has an unknown structure or form. In addition to its huge size, it has several processing challenges to extract value from it. A heterogeneous data source is an excellent example of unstructured data that includes a mix of simple text files, photos, videos, etc.

Companies have a vast amount of data available, but they don’t know how to extract value from it because the data is in an unstructured format.  An output returned by the google search engine is an example of unstructured data.


Structured and Unstructured both types of data can be found in semi-structured data. Semi-structured data appears to be structured in appearance, but it is not defined in the same way that a table is established in a relational database management system. Personal data stored in an XML file is an example of semi-structured data.

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Importance of Big Data

The importance of big data is determined by how a company uses the data it collects, not by how much data it has. Every business uses data in its unique way. The more effectively a business uses its data, the more growth potential it has. Data can be gathered from any source and analyzed by the company.

  • Find out the reason for problems, failures, and flaws.
  • Coupons are generated based on the customer’s purchasing patterns.
  • Recalculating risk portfolios within a short time.
  • Detecting fraudulent behavior and saving the company from its negative impact.
  • Increase the number of new customers you get 
  • Recognize market conditions.

The Working Process of Big Data Or How Big Data Works

Big data provides new perspectives that lead to new business models and possibilities. Three key activities are required to get started; let us discuss them one by one.


Big data combines information from a variety of sources and applications. New methodologies and technologies are required to evaluate large data sets on a terabyte or even petabyte scale. Extract, transform, and load (ETL) are common data integration procedures that aren’t adequate for the task.
During the integration process, you must bring in the data, process it, and ensure that it is prepared and available so that your business analysts can use it.


The basic need of big data is storage. Your storage solution might be cloud-based, on-premises, or a combination of both. You may store your data in any format you choose and then apply your processing needs and process engines to those data sets as needed. The cloud is quickly gaining appeal because it satisfies your present computation requirements and allows you to spin up resources as needed.


Your big data investment pays compensations when evaluating and acting on your data. With a visual study of your various data sets, you’ll get a fresh understanding. To create new findings, go deeper into the data. Share what you’ve learned with others. As we know, Machine learning, as well as artificial intelligence, may be used to create data models.

Big data analytics

Big data analytics applies qualitative research-based approaches to very large, heterogeneous (different types of data) big data sets, comprising structured, semi-structured, and unstructured data from various sources with sizes ranging from terabytes to zettabytes.

Big data analytics may help you make better and quicker decisions, model and forecast future events, and improve your business intelligence. Consider open-source software like Apache Hadoop, Apache Spark, and the full Hadoop ecosystem as cost-effective, flexible data processing and storage technologies intended to manage the volume of data created today as you construct your big data solution.


This blog has provided every piece of information about what big data is, its history, types, and importance. Social applications are widely used in the current scenario. As a result, the amount of data is increasing rapidly. Every day, billions of users connect on social media platforms, sharing information, photographs, videos, and much more.

However, the growing data is no longer a burden. It is being used by businesses to expand and beat their competition. Hopefully, now you understand what big data is and why it is the hottest topic these days. Moreover, if you still have any difficulty in Big Data project help or need Big Data Assignment Help, you can discuss it with our experts.

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Frequently Asked Questions

Who Uses big data?

Big data is now employed in various fields, including medical, agriculture, gaming, and environmental protection.

What are examples of big data?

Transaction processing systems
Medical records
Internet clickstream logs
Mobile apps
Social networks

Customer databases