What is Big Data – A Complete Comprehensive Guide

In the era of the Digital universe, the word which we hear frequently is Big Data. The Big Data market is growing exponentially. There are no profitable organizations that are left behind the use of Big Data.

In this tutorial, we will study completely about Big Data. Let us now first start with the Big Data introduction.

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What is Big Data?

Big data is the data in huge size. In simple terms, it can be defined as the vast amount of data so complex and unorganized which can’t be handled with the traditional database management systems.

It is so complex and huge that we can not store and process it with the traditional database management tools or data processing applications.

You might think about how this data is being generated? Do we have any contribution to the creation of such huge Data?

Sources of Big Data

The quantity of data on earth is growing exponentially. Our day to day activities and different sources generate plenty of data. With the rise of the internet, mobile phones, and IoT devices, the whole world has gone online. With every single activity, we are leaving a digital trace.

Whenever one opens an application on his/her mobile phones or signs up online on any website or visits a web page or even types into a search engine, a piece of data is collected.

For example, Suppose we have opened up our browser and searched for ‘big data,’ and then we visited this link to read this article. This alone has contributed to the vast amount of data.

Now just imagine, the number of users spending time over the Internet, visiting different websites, uploading images, and many more. All of this sums up to the stockpile of data.

Thus the major Data Sources are mobile phones, social media platforms, websites, digital images, videos, sensor networks, web logs, purchase transaction records, medical records, eCommerce, military surveillance, medical records, scientific research, and many more.

All these amounts to around Quintillion bytes of data. At present, 40 Zettabytes of data are generated equivalent to adding every single grain of sand on the earth multiplied by seventy-five.

Types of Big Data

Big Data is generally found in three forms that are Structured, Semi-Structure, and Unstructured. Let us now explore these three forms in detail along with their examples.

1. Structured

Structured data are defined as the data which can be stored, processed and accessed in a fixed format. Structured data has a fixed schema and thus can be processed easily. We can use SQL to manage structured data.

Example of Structured Data: Data stored in RDBMS.

2. Semi-Structured

Semi-Structured data are the data that do not have any formal structure like table definition in RDBMS, but they have some organizational properties like markers and tags to separate semantic elements thus, making it easier for analysis.

Example of Semi-Structured Data: XML files or JSON documents.

3. Unstructured

Unstructured data have unknown form or structure and cannot be stored in RDBMS. We cannot analyze unstructured data until they are transformed into a structured format. 80 % of the data generated by the organizations are unstructured.

Example of Unstructured Data: Text files, multimedia contents like audio, video, images, etc.

Big Data Characteristics (5 V’s in Big Data)

There are 5 V’s that are Volume, Velocity, Variety, Veracity, and Value which define the big data and are known as Big Data Characteristics.

1. Volume

Volume refers to the amount of data generated day by day. The volume of data decides whether we consider particular data as big data or not. Hence, ‘Volume’ is one of the big data characteristics which we need to consider while dealing with Big Data.

2. Velocity

Velocity refers to the speed at which different sources are generating big data every day. This flow of data is continuous and massive.

At present, there are approx. 1.03 billion Daily Active Users on Facebook DAU on Mobile which increases 22% year-over-year. This depicts how rapidly the number of users on social media is increasing and how fast the data is getting generated every day.

If we can handle the velocity then we can easily generate insights and take decisions based on real-time data. 

3. Variety

Variety refers to the different forms of data generated by heterogeneous sources. It can be structured, unstructured, or semi-structured.

Earlier we get the data in the form of tables from excel and databases, but now the data is coming in the form of pictures, audios, videos, PDFs, etc. Hence, this variety of unstructured data creates problems in storing, capturing, mining and analyzing data.

4. Veracity

Veracity refers to the uncertainty of data because of data inconsistency and incompleteness. While dealing with Big Data, the organizations have to consider data uncertainty. 

5. Value

The data without information is meaningless. Big data is useless until we turn it into value. Just collecting big data and storing it is worthless until the data get analyzed and a useful output is generated.

Big Data Challenges

1. Data Growth

Data growing at such high speed is a challenge for finding insights from it. It is like finding a thin small needle in a haystack. Every second’s more and more data is being generated, thus picking out relevant data from such vast amounts of data is extremely difficult.

2. Data Storage

These increasing vast amounts of data are difficult to store and manage by the organizations. We need scalable and reliable storage systems to store this data.

3. Data Quality

The data generated by the organizations are incomplete, inconsistent, and messy. It is difficult to manage such uncertain data. The inconsistent data cost about $600 billion to companies in the US every year.

4. Analytics

It often happens that most of the time organizations are unaware of the type of data they are dealing with, which makes data analysis more difficult.

5. Security

A huge amount of data in organizations becomes a target for advanced persistent threats. So data security is another challenge for organizations for keeping their data secure by authentication, authorization, data encryption, etc.

6. Miscellaneous Challenges

While dealing with Big Data, there are some other challenges as well like skill and talent availability, data integration, solution expenses, data accuracy, and processing of data in time.

Big Data Examples

  • The New York Stock Exchange (NYSE) produces one terabyte of new trade data every day.
  • Each day 500 million tweets are sent.
  • Amazon, in order to recommend products, on average, handles more than 15 million+ customer clickstreams per day.
  • Walmart an American Multinational Retail Corporation handle about 1 million+ customer transactions per hour.
  • 65 billion+ messages are sent on Whatsapp every day.
  • A single Jet engine generates more than 10 terabytes of data in-flight time of 30 minutes.
  • On average, everyday 294 billion+ emails are sent.
  • Modern cars have close to 100 sensors for monitoring tire pressure, fuel level, etc. , thus generating a lot of sensor data.
  • Facebook stores and analyzes more than 30 Petabytes of data generated by the users each day.
  • YouTube users upload about 48 hours of video every minute of the day.

Advantages of Big Data Analytics

This rising Big Data is of no use without analysis. There are many advantages of Data analysis. Some of them are:

1. With data analysis, Businesses can use outside intelligence while making decisions. They use data from sites like Facebook, twitter to fine-tune their business strategies.

2. Big Data Analysis helps organizations to improve their customer service. The traditional customer feedback systems are now getting replaced by new systems based on big data technologies. New systems use Big Data and natural language processing technologies to read and evaluate consumer responses.

Big data job opportunities

The big data market will grow to USD 229.4 billion by 2025, at a CAGR of 10.6%. The major reason for the growth of this market includes the increasing use of Internet of Things (IoT) devices, increasing data availability across the organization to gain insights and government investments in several regions for advancing digital technologies.

All these factors create tremendous job opportunities for those who are working in this domain. There are various roles which are offered in this domain like Data Analyst, Data scientists, Data architects, Database managers, Big data engineers, and many more.

Big Data Technologies Stack

There are many big data tools and technologies for dealing with these massive amounts of data. Some of the topmost technologies you should master to boost your career in the big data market are:

1. Apache Hadoop: It is an open-source distributed processing framework. It is best for batch processing.

2. Apache Spark: It is an open-source real-time processing framework. It has an in-memory computing capability.

3. Apache Kafka: This is a distributed streaming platform.

4. Apache Hive: It is an open-source data warehouse tool for querying a huge amount of data stored in Hadoop HDFS.

5. Apache Cassandra: It is an open-source distributed NoSQL database for storing and processing Big Data.

Big Data Applications

Big Data finds applications in many domains in various industries. The article enlisted some of the applications in brief.
1. Bank and Finance: In the banking and Finance sectors, it helps in detecting frauds, managing risks, and analyzing abnormal trading.

2. Agriculture: In agriculture sectors, it is used to increase crop efficiency. It can be done by planting test crops to store and record the data about crops’ reaction to different environmental changes and then using that stored data for planning crop plantation accordingly.

3. Advertising and Marketing: Advertising agencies use Big Data to understand the pattern of user behavior and collect information about customers’ interests.

4. Media and Entertainment: Media and Entertainment industries are using big data analysis to target the interested audience. They now understand the kind of advertisements that attract a customer as well as the most appropriate time for broadcasting the advertisements to seek maximum attention.

5. Education sector: The advent of Big Data analysis shapes the new world of education. There are many applications that use big data analytics to understand user learning capability and provide a common learning platform for all students.


In short, we can conclude that Big Data is the vast amount of data generated by heterogeneous sources like websites, mobile phones, weblogs, IoT devices, etc. We cannot handle Big data with the traditional database management system.

There are three forms of big data that are structured, semi-structured, and unstructured. The structured data have fix schema, the unstructured data are of unknown form, and semi-structured are the combination of structured and unstructured data.

The 5V’s that are Volume, Velocity, Variety, Veracity, and Value defines the Big Data characteristics. Companies like Facebook, Whatsapp, Twitter, Amazon, etc are generating and analyzing these vast amounts of data every day.

For building a career in the Big Data domain, one should learn different big data tools like Apache Hadoop, Spark, Kafka, etc.

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