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Last Updated: Mar 27, 2024

What is Big Data?

Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

It is not a hidden fact that organisations nowadays use customer data to enhance the customer experience and transform their business model. A large amount of data is produced every second that could be structured and unstructured. These sets of structured, semi-structured, unstructured data are used to extract the hidden meaning from which one pattern could be designed. This information will be helpful to decide to grab the new business opportunity, the betterment of our product/service, and ultimately the business growth. Before going ahead, let's look at this huge data / big data.

Big data

Every time you open your Facebook account and like a picture, you create massive data. These data are produced in such a number and are growing exponentially that till now, no tool could process and store these data efficiently. Day by day, data production is increasing a lot with the introduction of smartphones. According to an IDC report, the size of the global data sphere is expected to reach 175 zettabytes. Many of you would think, how big are these 175 zettabytes? Let me give you an example. If you start downloading the entire 175 zettabytes of the global data sphere at a downloading speed of 25 Mb/s, you will take 1.8 billion years to do it. That is how insanely big it is. These enormous amounts of data could be structured, unstructured and semi-structured. We will look at these three types of data below.

Big Data is defined as managing a huge volume of disparate data at the right speed and within the correct time frame to allow real-time analysis and reaction.

Source 

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Examples of Big Data

  1. Stock Exchanges
    Approximately 1 Tb of new trade data is generated per day on the New York stock exchange.
     
  2. Social Media Sites
    Approximately 500 TB of data is ingested in social media giants like Facebook. These data are primarily in images, stories, message exchanges, video or audio uploads etc.
     
  3. A Jet Engine
    An avg commercial flight, like a Boeing 737, produces more than 10 Tb of data every 30 mins. With so many flights running each day, you could think how much data all combined are generating per day.

Type of Data

As discussed above, each time you use the internet for social media or run a piece of music, you create tons of data. These sets of data could be structured,semi-structured and unstructured.

Let's look at what we meant by structured data, unstructured data and semi-structured data.

  • Structured Data
    Data is said to be structured if it's well structured, i.e., data that can be easily accessed, stored and processed. These data have well-defined columns. There is a particular order or consistency in which the data is stored. The below-defined student table could be defined as structured data. Datasets could be considered structured data sets.
Student_ID Student_Name Student_Age Student_Stream
019 Raghav 16 Commerce
018 Shailesh 15 Arts

  •  
  • Semi-Structured Data
    Semi-Structure Data is when data can be seen in some format but not in tables. Semi-structured data is a kind of data that falls between structured and unstructured data. Semi-structured data could apply a few structured data properties, but not all. E.g. of semi-structured data is the comma-separated file, XML.
     
  • Unstructured Data
    Data is unstructured if it doesn't follow any specific format. The form and structure of such data are not known. Most of the Data you encounter will be unstructured. E.g. to, unstructured Data is Data received from Satellite Images, Security Surveillance Video, Radar or Sonar Data

5V’s of Big Data

Earlier, Big Data was defined as 3V, but now it's 5V big data. These 5V are often termed as characteristic of Big Data. These 5V stands for Volume, Velocity, Variety, Veracity and Value.
 

  1. Volume
    The name Big-data in itself refers to its enormous size. Volume indicates a vast amount of data that is generated every second. To get information about data, the size of the data plays a significant role. Whether data needs to be referred to as big data depends on the volume of data. Data can only be said as Big data if the size of the data is enormous. Hadoop, a distributed system, is used to store and process such an amount of data.
     
  2. Velocity
    Velocity is a term we use for speed. Velocity in Big data refers to the speed at which the data is created or accumulated. In Big Data, data flows from social media, mobile phones, machines, networks etc. Since there is a vast and continuous flow of data, it helps us determine the potential of data and how fast the data generated could be processed to meet the demands.
     
  3. Variety
    Variety refers to the type of data, whether structured, semi-structured or unstructured. Variety is also referred to as the different heterogeneous sources of data. It's essential to store these different types of data efficiently. A detailed explanation of the data types is already done in the above section; please look at the section types of data.
     
  4. Veracity
    Veracity refers to the inconsistency and uncertainty in data. The data which is made available is sometimes irregular and messy. Controlling the Quality and Accuracy of such data is difficult. Data sets that we usually get are unstructured, so it becomes very important to filter out the unnecessary details and do the data processing from the remaining left. Big Data is variable because of the multitude of available data dimensions.
     
  5. Value
    The last V in Big data stands for Value. If we have an enormous bulk of data with no value, it's unsuitable for organisations. It needs to be turned into some useful form. Data in itself is of no helpful form. It was first converted into something valuable and cleaned to retrieve the essential information.

Importance of Big Data

Big Data is more of a revolution in the field of Information Technology. Its use is enhancing every
year. It has a high variety of volume value and velocity. With the help of Big data, we can perform
multiple operations in a single platform. Big Data helps organisations to work with the data efficiently and to use them to have new opportunities as well. It is helpful because it helps in.

  • Cost Reduction: When it comes to storing large amounts of data, technologies such as Hadoop helps to store it and thus reduce the cost.
  • Better and Faster Decision Making: With the advancement of technologies like Hadoop, ability to analyse new sources of data has increased and thus decision making has improved a lot.
  • New Products and Services: With the ability to get to know about the customer’s needs and their satisfaction through analytics, customers can get what they want.

Real-life Benefits of Big data

With the growth of Big data, there has been enormous growth in other industries as well.
For Example:
1.Banking
2.Manufacture
3.Technology
4.Consumers

In the banking sector, it can be seen especially that tools like Apache hive are used to query to
get the result in a very short span of time. Big data is a revolution in the education field as well, as there is a new option of research and development. Big Data is so useful in knowing the customer’s needs in advance. Job opportunities have been increased with Big data with titles like Big Data Analyst, Big Data Engineer, Business Intelligence Consultant, Solution Architect, etc.

Roles in this industry

There are many different roles that an individual can take up in this industry. However, broadly, it can be categorized into two groups: Engineering and Analytics.

Big data engineering:  This deals with planning and maintaining a system to handle large amounts of data. These systems are put in place to make relevant data available for various internal applications.

Big data analytics: This is about using large amounts of data from the systems and analysing trends and patterns from the data. It also deals with developing various prediction and classification algorithms from the data.

So, which field is most suitable for you? It will depend on your interest and your background. However, both these roles are equally essential in the big data industry. The world of big data is quite dynamic and it keeps changing. So, you can expect exciting innovations happening in this field in the coming years.

Background

Your background knowledge will be given a lot of weight when you are entering this field. The industries require similar skill sets as machine learning industries and data science industries. Two extremely important skills are:

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  1. Mathematics and Statistics: You should be well-versed in several topics like calculus, linear algebra, probability and statistics. It will help you learn different machine learning techniques like linear and logistic regression, decision trees, random forests, KNN’s and vector machines.

pasted image 0 (16)

  1. Programming: You have to get yourself acquainted with a few programming languages if you want to deal with it. The most popular programming languages in this field are R and Python. Learn more about visualisation, data analysis and machine languages. For Python, you need to learn about NumPy, Pandas, SciPy, scikit-learn, etc. If you are going for R, then learn diplyr, readr, tidyr, etc. To be a data scientist, you have to be well-versed with SQL too.

Technologies in demand

Now, you know the basics and what your background should be if you want to enter this field. However, not every technology is equally respected in this Industry. While this industry is always evolving, these technologies have made a positive mark in this industry:

pasted image 0 (17)

Apache Hadoop: This is an open-source software framework which allows large scale processing of data sets on clusters of commodity hardware. A few components of Hadoop which are in high demand are Pig, Hive, HDFS, HBase, etc.

 

  • Amazon S3: This is a cloud tool which is quite popular in the big data field. It is best if you are familiar with it.

pasted image 0 (18)

  • Apache Spark: Like Hadoop, this is another big data computation framework which is gaining a lot of popularity in the field.
  • NoSQL: Many traditional SQL databases like Oracle and DB2 are getting replaced by NoSQL databases which include MongoDB, Couchbase, etc.

If you have the knowledge and if you constantly work to improve your skills, then getting hired in this industry is not difficult. Just keep yourself updated with the latest technology, interact with the community of coders, and work on yourself. If you wish to take a course on data science, Coding Ninjas has just the best one to offer. Be patient and persistent and one day, you will receive your desired job offer!

Frequently Asked Questions

What is the 5V of Big Data?


5V's of Big Data are often termed as characteristic of Big Data. These 5V stands for Volume, Velocity, Variety, Veracity and Value.
 

What is the difference between structured and unstructured data?


Unstructured Data doesn't follow any specific format. The form and structure of such data are not known. On the other hand, Structured  Data is data that can be easily accessed, stored and processed. These data have well-defined columns. Data is stored in a particular order or consistency.
 

What is Big Data?


Big Data is defined as managing a huge volume of disparate data at the right speed and within the correct time frame to allow real-time analysis and reaction.

Conclusion

In this article, we have discussed Big Data in detail. We have briefly explained different types of data,5V of Big data, and Big data applications.

I hope this article must have helped you improve your learning about Big Data. To get more knowledge about Big Data, practice some quality SQL questions. Also, please visit our Guided Path in  Coding Ninjas Studio to get more knowledge about such content. If you are preparing for an interview, visit our Interview Experience Section. To become more confident in DSA, try out Interview Problems in our Code studio. Till then, all the best for all your future adventures and Happy Coding.

Topics covered
1.
Introduction
2.
Big data
3.
Examples of Big Data
4.
Type of Data
5.
5V’s of Big Data
6.
Importance of Big Data
7.
Real-life Benefits of Big data
8.
Roles in this industry
9.
Background
10.
Technologies in demand
11.
Frequently Asked Questions
11.1.
What is the 5V of Big Data?
11.2.
What is the difference between structured and unstructured data?
11.3.
What is Big Data?
12.
Conclusion