What is Big Data Analytics? Things no one tells you

What is Big Data Analytics

Do you want to know What is Big Data Analytics? and its details. If yes, then give your few minutes to this blog to know What is Big Data Analytics?

Hello, & Welcome!

In this blog, I am gonna tell you-

  1. What is Big Data Analytics?
  2. Importance of Big Data Analytics.
  3. Big Data Analytics Tools (Hadoop)
  4. FAQ.

Firstly, I would like to start with-

What is Big Data Analytics?

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Before diving into Big Data analytics, firstly I would like to discuss, What is “Big Data”? As the name sounds big data is a huge amount of data which is generated daily by everyone. This data may be anything, one like to facebook post is also a kind of data.

Data is increasing very fast. According to one report, By 2025, it’s estimated that 463 exabytes of data will be created each day globally – that’s the equivalent of 212,765,957 DVDs per day!

Here Big data analytics come into place, to manage and process such a huge amount of data. This generated data is not in proper form, which means it is unstructured data. It may be image data, text data, audio data, and other kinds of data.

Big data analytics is basically the process of finding useful patterns from large amounts of unstructured data. It involves lots of steps starting from data cleaning to finding patterns. It is a concept to store and process huge amounts of data. Big data is defined by 3 V’s-

  1. Volume- It refers to the size of data, which means how much data is generated.
  2. Variety- It refers to the type of data, which means which type of data is generated like structured data or unstructured data.
  3. Velocity- It refers to the speed of data, which means at what speed data is generated.

Advantage of Big data-

  • It has unlimited data storage.
  • Big Data works on both types of data, structured & unstructured data.
  • It processes the data with high speed, unlike in SQL, when we write any SQL command it will take a lot of time for the background process.
  • Big Data has a schema-free architecture.

Importance of Big Data Analytics

Everyone has a question, why Big data is so popular nowadays? the answer is because of its robustness. It is full of specialized analytics systems and software and high powered computing. Big data is performing a superior job in every field but in business it is boon.

In business, Big data help to increase marketing, improve customer service, and much more. By finding a different pattern in the purchase history, a marketing strategy can be improved and a better strategy is used to improve the growth.

For example, if someone buys milk and bread together so put milk and bread together in a supermarket to increase the sale of both items. By putting together both items, a person who comes to buy only milk might purchase bread too. So using such tactics there is an increase in market growth.

What is Big Data Analytics?

Big Data Analytics Tools

There are various software or tools are present to deal with Big Data. Some of them are-

Here, I will discuss Hadoop and its companion data analytics tools.


It is an open-source framework software for distributed processing on large clusters of large computers. Father of Hadoop is Doug Cutting and Michael.

Hadoop works on a distributed system that’s why parallel processing is performed. Hadoop has two layers-

  • HDFS ( Hadoop Distributed File System ) Layer
  • MR (Map Reduce) Layer

HDFS Layer- It is a storage-based layer. In HDFS you can store any format of data. HDFS has a master-slave architecture.

It has 2 Hardware component-

  1. Master Node
  2. Slave Node

3 Software components-

  1. Name Node ( Master Node)
  2. Secondary Name Node (Master Node)
  3. Data Node ( Slave Node)

Map Reduce Layer- This layer performs the processing tasks. MR layer performs processing tasks like scheduling and resource allocation.

It also has 2 software components

  1. Job Tracker (Master Node)
  2. Task Tracker (Slave Node)

For processing such huge data, you need to perform some coding, but if you don’t know to code, then What?…don’t worry! You don’t need to write heavy code, there are some simple programming languages are present. They perform the same job as heavy code does by just writing one or two lines of code.

The most two used programming languages in Hadoop are-

  1. Apache PIG
  2. HIVE

I hope, now you have a clear idea about What is Big Data Analytics? why it is so popular, its applications, and tools.


1. What is the use of Big Data Analytics?

Big Data Analytics extracts useful patterns from a huge amount of raw data. This pattern plays an important role in various fields. Like in Business, Healthcare, Marketing, etc. In marketing, these patterns are used to increase the sale of a product.

2. Where is Big Data stored?

In Big Data, data is stored in the Hadoop Distributed File System( HDFS). In HDFS, information is stored in the form of clusters. These clusters are smaller blocks.

3. What are the 4 V’s of Big Data?

4 V’s in Big Data means- Volume, Variety, Velocity, and Veracity.

4. Is Big Data a good career?

Yes. Big data is one of the most demanding and bright careers. Because most of the companies are adopting Big Data for finding useful patterns. And use these patterns to find valuable insights.

5. Which language to use in Big Data?

Python is the most suitable language for Data Science related work. Data Scientists use Python language to explore Big Data.

6. What skills do you need for Big Data?

The most important skills required for Big Data-
1. Apache Hadoop.
2. Apache Spark.
3. NoSQL.
4. Machine Learning.
5. Data Mining.
6. Data Visualization.
7. Java.
8. Statistical Analysis.
9. Creativity and Problem-Solving.

7. Can I learn Big Data without Java?

Java is used for the implementation of Hadoop. But, to learn Big Data, you do not need the knowledge of Java.

Enjoy Machine Learning

All the Best!

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Thank YOU!

Though of the Day…

It’s what you learn after you know it all that counts.’

John Wooden
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Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.

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