What is Big Data? Introduction, Types, Characteristics, Examples, Advantages and Disadvantages

Today technologies like Artificial Intelligence (AI) , Machine Learning (ML) and Data Science are being used in almost every field . And for this a huge amount of data is being used. But the question is, what is this Big Data after all? What is big data? And how does it work? Also how and from where is it obtained? And what is its use? Let us know in detail.

बिग डेटा क्या है?

बिग डेटा डेटा का एक संग्रह है जो मात्रा में बहुत बड़ा है, फिर भी समय के साथ तेजी से बढ़ रहा है। यह इतने बड़े आकार और जटिलता वाला डेटा है कि कोई भी पारंपरिक डेटा प्रबंधन उपकरण इसे संग्रहीत या कुशलतापूर्वक संसाधित नहीं कर सकता है। बड़ा डेटा भी एक डेटा है लेकिन विशाल आकार के साथ।

बड़ा डेटा यह सब बड़ी मात्रा में डेटा के संग्रह के बारे में है और यह समय के साथ तेजी से बढ़ रहा है। डेटा इतना विशाल है कि पारंपरिक डेटा प्रबंधन दृष्टिकोण के साथ आकार, जटिलता को प्रबंधित करना या इसे कुशलतापूर्वक संसाधित करना संभव नहीं है। हर दूसरा डेटा फोटो, वीडियो, ऑडियो आदि के रूप में तैयार किया जाता है। इसी तरह, ईमेल, स्मार्टफोन एप्लिकेशन, आंकड़े इत्यादि का डेटा है। 

Big Data

Data is the most valuable commodity today. And that is a fact. No two ways about it. Because nowadays, the help of data is taken in the operation of every small and big business . And it is also necessary. Because it is very difficult to run a business without data. That is why nowadays every small and big company keeps an eye on the data of its customers.

Today, almost every small and big company stores a large amount of data for its business operations. And use this data to know the likes and dislikes of customers, understand their buying patterns and behavior, improve their products and services, improve customer service and design new products according to the needs of customers. That is why data is very important.

कंपनियां जो बड़े डेटा का उपयोग कर रही हैं

बिग डेटा का उपयोग करने वाली कंपनियां निम्नलिखित हैं:

  1. गूगल
  2. सेब
  3. मेटा (पूर्व में फेसबुक)
  4. वीरांगना
  5. Netflix
  6. अमेरिकन एक्सप्रेस
  7. हेल्थकेयर कंपनियां

If you are an Online Job Seeker then you would know that nowadays many jobs related to Big Data are in great demand. And most of the money is also in these jobs. Because Big Data is a trending and future-oriented technology, which has immense career potential. That’s why it is advisable to read BigData nowadays . But the question is, what is this Big Data after all? And how is it used? Come on, let’s understand.

What is Big Data?

The word Big Data is made up of two words Big and Data. Here Big means big or huge. And Data means information, data or information. In this way Big Data means a huge collection of information or information. Or a large store of information, which includes Structured, Semi Structured and Unstructured Data.

बड़े डेटा के प्रकार:

इसके प्रकार निम्नलिखित हैं।

  1. स्ट्रक्चर्ड
  2. संयुक्त राष्ट्र संरचित
  3. अर्द्ध संरचित 

In fact, whatever information is being created or written digitally , they are all data . The article you are reading at the moment is also a data. That’s how you’re talking to someone; writing something on your phone or computer; clicking photos; Shooting a video or sending a message to someone – it’s all data. And by collecting this data, companies use it for their benefit.

BigData represents this informational data . That is, a lot of data together makes up Big Data. And it’s so big that you can’t even imagine. Unfortunately, Big Data is so vast that no data management tool can collect or store it efficiently.

Production of big data

We produce more than 2.5 Quintillion Bytes of data daily. The NewYork Stock Exchange alone produces more than a terabyte of data daily. Apart from this, social media platforms also contribute a lot in the production of Big Data. Similarly, all the airlines of the world together produce many Petabytes (1,000 Terabytes) of data every day. However, this data has no special importance for this common person. But this data is of great importance for big companies, news agencies and political parties.

That is, they collect this data and use it for their benefit. As you had known in the previous article ( Data Science ), how the information of work is extracted from it by processing Big Data ? And how is it used to his advantage?

5 V’s of Big Data

There are 5 biggest and main features of Big Data, which are known as 5 V’s of Data Science . These are the 5 V’s – Volume, Velocity, Variety, Veracity and Value. What do they mean? Come let’s know:-

1. Volume

Volume means quantity. That is, Amount of Data . It refers to the amount of Big Data. Volume is the fundamental element of Big Data. Because on the basis of this it is decided whether any data is Big Data or not? If the amount of data is large enough, then it can be considered as Big Data. But the small amount of data present cannot be considered as Big Data. That is, only the large amount of data is considered as Big Data.

2. Velocity

Velocity means velocity or speed. It actually refers to the speed at which the data is generated . That is, how fast is the data being generated? And at what speed is he going? It matters a lot. Because companies need to stream data fast. So that appropriate business decisions can be taken at the right time.

Organizations using Big Data need a continuous flow of data. That is, the data that is being generated needs to be analyzed and used at the right time. This data can flow from anywhere. Such as computer network, smartphone, social media etc.

3. Variety

Variety means diversity i.e. variation of data. It actually shows the diversity of Big Data. That is, refers to the types and diversity of data. Since data is collected by an organization from different sources (social media, forums, computer networks etc.) . That is why there is no uniformity in it. This data can be in different formats. Like Numbers, Texts, Documents, Images, Audio, Video, Email, Graphics etc.

4. Veracity

Variety means truthfulness. That is, the accuracy or authenticity of the data. It actually shows the inconsistencies and uncertainties of Big Data. That is, refers to the errors and errors present in the data. Because Big Data is variable due to flow from different sources. That is why it is very difficult to control  its quality (accuracy and quality).

5. Value

The fifth and last V is Value . ie value. It actually shows the usefulness of the data. That is, whether any data is useful or not? Or how useful? This is the biggest and most important feature of Big Data. Because no data without value is of no use.

If no useful information emerges from a huge collection of data , then it is of no use. That is, unless Big Data can be converted into something useful, it is useless. That’s why value is most important.

History of Big Data

Big data has a long history. If you go into history, you will come to know that Big Data was first used in 1663. This was the period when the Bubonic Plague was spreading in Europe. And John Graunt was doing research on it. In this connection, John Graunt had faced a huge amount of information.

Graunt was the first to use statistical data analysis. Later, in the early 1800s, there was a rapid development in the field of statistics data for the collection and analysis of data. But Big Data was first seen as a problem in 1880. At that time the US Census Bureau announced that it would take eight years to handle and process the data collected during the census program that year.

In 1881, a man from Hermann Hollerith Buero invented the Hollerith Tabulating Machine. This machine greatly simplified the task of calculation.

After that in the 20th century, the production of data increased rapidly. Because this was the time when machines, and computers, began to be built for storing information in a magnetic form and scanning patterns in messages. This made Big Data the main point of development. Then in 1965, the US government built the first data center for the purpose of storing millions of fingerprint sets and tax returns . After that, as the need arose, Big Data devices continued to be invented. such as –

1970

Edgar F. Codd invented a Relation Model in 1970 . Which provided access to information without knowing the structure and location of the data in large databases. It was a very useful tool for data management. Which made it much easier to manage big data.

1976

Later in 1976, the Material Requirements Planning (MRP) system was invented. Which was designed to organize and schedule information in business. It made business management very easy. Later it started being used extensively for  business management .

1989

Tim Berners Lee invented WWW (World Wide Web) in 1989 . This was an unprecedented discovery in the field of technology. Because after this a huge amount of data started being generated through the Internet.

2001

Doug Laney presented a paper describing the “3 Vs of Big Data”, describing the basic characteristics of Bigdata. This was the year when the term ” software-as-a-service ” was first shared with people.

2005

After the discovery of the World Wide Web and the Internet, data began to be generated so rapidly that collecting and processing data became a challenge. This resulted in the creation of an open-source software framework such as Hadoop . Which was designed to store Big Data.

2007

The term “Big Data” was introduced to the public in the Wired article “The End of Theory “: The Data Deluge Makes the Scientific Method Obsolete.

2008

A team of computer science researchers published the paper ” Big Data Computing : Creating Revolutionary Breakthroughs in Commerce, Science and Society  , which describes how BigData is changing the way companies and businesses operate.

2014

By this time more and more companies had started moving their Enterprise Resource Planning Systems ( ERP ) to the cloud . Because by this time the Internet of Things (IoT) was being used on a large scale. And a huge amount of data was being transmitted every day. At this time, IoT was being used with about 3.7 billion connected devices or things in use.

2016

The Obama Administration released the ” Federal Big Data Research and Strategic Development Plan “. In which it was said that Big Data Business has been designed to lead and run towards growth. This will directly benefit the society and the economy.

2017

A 2017 IBM study said that 2.5 quintals of bytes of data are currently being generated per day. And 90% of the world’s data is born in the last two years. After that the production of data is continuously increasing.

Types of Big Data

Well, there are many types of data. But basically it is divided into three categories. These three categories are Structured, Un-Structured and Semi-Structured. What is the difference between the three? Come on, let’s understand.

Structured Data

The data which can be stored, processed and accessed in a certain format is called Structured Data . It happens in a uniform format, so businesses can make the most of it by analyzing it . Apart from this, Structured Data is also used in Machine Learning and Data Science . Today the creation of structured data is happening so fast that it has reached the mark of zettabytes.

Un-Structured Data

Unstructured data does not have any definite format or structure. That is why it is very difficult to process it. It is a large collection of files, which can contain all types of files. For example, Text Files, Image Files, Audio Files, Video Files, Social Media Posts etc. It can also be Human Generated . And also Machine Generated .

Although unstructured data can have internal structure . But it is not pre-defined by the data models . That is why processing it and extracting information is a challenging task. Because such data can be in any form.

Semi-Structured Data

Semi-Structured Data is a mixture of structured and unstructured data. It also contains Structured Data, and UnStructured as well. That is, there are both types of datasets . Although you can also understand Semi Structured Data as Structured Data, but you cannot show it inside the  database .

Data coming from web applications is a good example of Semi-Structured Data . This includes Unstructured Data such as Log Files, Receive-Transfer History Files etc. OLTP systems are built to work with structured data. In which data is stored according to a rule.

How is Big Data used?

Now the question is, how is Big Data used? How is bigdata used? So many advanced tools and machines are used for this. such as NoSQL Database. It is a special database, which is used to store big data. In this there is no need to follow strict rules of any particular model.

NoSQL database provides a flexible interface to obtain and analyze complete information about the data . With the help of which you can find out what is happening with the data? Usually BigData is divided into two parts to be collected, processed and analyzed. One Operational and the other Analytical Data.

Operational systems collect big data on multiple servers. These include inputs such as inventory, customer data, and purchases. At the same time, the data which is more important in Analytical Data is analyzed. And after that it is filtered and used for profit in the business.

Nowadays Big Data is used in almost every business. Companies use Big Data to understand the trends going on in the market , to know the likes and dislikes of the users, to advance the business and to reach the desired customers through advertising. Along with this, they also use Big Data to deal with the difficulties and challenges in the business.

Uses of Big Data

Now the question is what is the use of Big Data? What are the uses of bigdata? And where is it used? Let us see some examples. The uses of bigdata :-

Finance

Bigdata is used in the finance sector for fraud detection, risk assessment, loans, insurance, credit scores , brokerage services , blockchain technology and future benefits and risks with banks. In addition, financial institutions use BigData extensively to enhance their cyber security efforts and personalize financial decisions for customers.

Healthcare

Hospitals, researchers and pharmaceutical companies in the healthcare sector use BigData to improve health services and discover life-saving drugs. Along with this, the help of big data is taken in analyzing the data of a large number of patients and finding the treatment of serious diseases.

In fact, patient data is very important for medical research. Because it helps a lot in knowing the effects of diseases and finding their treatment. That is, by analyzing the data of patients, pharmaceutical companies can make correct and effective medicines. Usually, new drugs are developed similarly for diseases like cancer and Alzheimer’s.

Media & Entertainment

If you are fond of watching Movies, Web Series and Entertaining Programs on OTT Platforms (Netflix, Hotstar etc)! So you would know that you have to signup before using these platforms. That is, you have to create your account. And you have to state your choice. Along with this, your personal data also has to be shared.

Basically these apps keep an eye on your every activity. Like what are you watching? What are you searching? What kind of programs are you watching the most? In which formats are you taking more interest ? That is, watching more movies or web series? Watching more TV serials? Or reality shows? And what time of day are you watching? OTT Platforms collect all this information. And use it to their advantage.

That is, the collected data is not only used to recommend Personalized Content to the users. Rather, it is also done in the creation of such programs , which are most liked by the users. Because with the help of data, OTT platforms know what people want to see? Netflix also uses data from graphics, titles and colors to make decisions about customer preferences.

Agriculture

Nowadays BigData is used in many tasks from seed production to development of new varieties, soil health, crop rotation, pest management, water cycle , fertilizers, Automated Irrigation System and climate change. Along with this, Big Data is also used in the assessment of problems like hunger and malnutrition at the global level.

Today, a campaign is being run all over the world to fight hunger and malnutrition. And groups like Global Open Data for Agriculture & Nutrition (GODAN) are playing an important role in this. Sharing data on people living with hunger with groups like GODAN is helping to promote global nutrition and agriculture. It is also helping to end global hunger and malnutrition.

Big Data Technologies

Managing Big Data is not easy. Many technologies are used to manage it. That is, large amount of real time data analysis requires large data processing technologies . And for this the following technologies are used:-

1. Apache Hadoop

This is the most famous Bigdata Tool . Apache Hadoop is an open-source software framework . Which is developed by the Apache Software Foundation to store and process BigData. It is written in Java Language.

Hadoop Distributed File System (HDFS) is the most popular and most reliable data storage software in today’s time. It is an expensive, fault-tolerant and most commonly used framework. which can process data of any size and type. Hadoop stores and processes data in the computing environment of the Commodity Hardware .

Features of Apache Hadoop:

  • It is the most used software.
  • The chances of making a mistake in this are negligible.
  • The framework is designed in such a way that it can work even in adverse conditions like machine crash .
  • The framework stores data in commodity hardware, which makes Hadoop cost-effective.
  • It uses the Distributed File System . Due to which the data processing is very fast.

Companies using Hadoop are Facebook, LinkedIn, IBM , MapR, Intel, Microsoft, etc. Apart from these, there are many big companies who use Hadoop.

2. MongoDB

It is an open-source data analysis tool developed by MongoDB in 2009. It is a NoSQL Document-Oriented Database . And it is written in C, C++ and Javascript. It allows to store unstructured data in JSON format.

MongoDB is one of the most popular databases for BigData. It can also easily manage Unstructured, Semi-Structured and frequently changing data. MongoDB is easily executed on Software, MEAN Stack , NET Application and Java etc. Languages ​​as well as easily run in Cloud.

Features of MongoDB:

  • It is highly reliable and economical.
  • It uses MongoDB Query Language (MQL) , which is quite handy for developers.
  • It is a powerful database that is capable of fixing even the toughest of problems.
  • It has all the power of a relational database.
  • It solves problems like Ad hoc queries , Indexing, Sharding and Replication

Talking about users , companies like Facebook, eBay, MetLife and Google use MongoDB.

3. Apache Storm

It is a Distributed Real-Time Computational Framework , written in Clojure and Java language. It gives the facility of Unlimited Data Processing. And it can be used with any programming language. Apache Storm is used in tasks such as Real-Time Data Analysis , Continuous Computation, Online Machine Learning and ETL.

Features of Apache Storm:

  • Apache Storm is free and open-source technology.
  • It is Highly Scalable .
  • It is quite easy to use.
  • Apache Storm guarantees data processing.
  • It has the capacity to process millions of Tuples per second per node.

If we talk about users, then companies like Yahoo , Alibaba , Groupon, Twitter and Spotify use Apache Storm .

बड़े डेटा के लाभ:

  • यह व्यावसायिक प्रक्रियाओं को अनुकूलित करने में मदद करता है।
  • यह रोगी के रिकॉर्ड की उपलब्धता के साथ स्वास्थ्य सेवा में सुधार करने में मदद करता है।
  • इसने ग्राहक सेवा में सुधार किया।
  • असीमित जानकारी रखता है।
  • यह वित्तीय, शिक्षा में भी मदद करता है।
  • धोखाधड़ी का पता लगाने और रोकथाम। 

Advantages of Big Data

Now the question is what are the advantages of Big Data? What are the benefits of big data? Well, there are many benefits of Big Data. But here we will talk about some selected benefits only. So let us understand point by point what are the benefits of Big Data? The benefits of big data :-

  • Using Big Data, you can know the likes and dislikes of people. and understand the needs.
  • By using BigData, you can reduce the cost of your products.
  • Through this you can understand the  trends and innovations going on in the market .
  • With the help of Big Data, you can compete with big businesses.
  • This allows you to focus on local market preferences.
  • You can use BigData to increase your sales and trust.
  • Using BigData you can hire the right employees in the company.

Disadvantages of Big Data

You know the benefits of Big Data. But I would like to tell you that there are as many disadvantages as there are advantages of Big Data. Let us know about these disadvantages. The disadvantages of big data :-

  • Analyzing Big Data violates the principles of User Privacy .
  • Big data can be used for wrong purposes.
  • Storing Big Data in Traditional Storage is very expensive.
  • Big data can be used to manipulate customer records.
  • It can increase Social Stratification .
  • To take advantage of BigData, it has to be analyzed frequently and continuously.
  • Most of Big Data is unstructured. So it is a bit difficult to analyze it.
  • The results of BigData Analysis are sometimes doubtful.
  • Due to the fast updates in BigData, it does not match the figures of the real data.

बिग डेटा के नुकसान:

  • डेटा जनरेशन की प्रक्रिया महंगी है।
  • उच्च गुणवत्ता वाले सॉफ़्टवेयर को स्टोर करने और चलाने के लिए आवश्यक सर्वर और हार्डवेयर महंगे हैं।
  • इसका उपयोग ग्राहक रिकॉर्ड के हेरफेर के लिए किया जा सकता है।
  • यह सामाजिक पदानुक्रम को बढ़ा सकता है।
  • बहुत सारे बिग डेटा असंरचित हैं।

Career In Big Data

If we look at the career wise, then Big Data is one such field. In which immense career opportunities are visible Because it is a Futuristic Technology . And its use is increasing rapidly. That is why it is a great opportunity in terms of career. But for this you must have some necessary skills . If you want to become a Big Data Engineer then you must have the following skills :-

Programming Language

It is very important for a Big Data Engineer to have knowledge of Programming Languages . Because programming languages ​​are used a lot in the field of Big Data. That is why if you want to become a Big Data Engineer, then you should have a good knowledge of languages ​​like C +, C ++ , Java and Python .

Database and SQL

A Big Data Engineer should have good knowledge of DBMS and SQL . Because it helps to understand how to manage and maintain the data in the database? Some of the database management systems commonly used for Big Data Engineers are MySQL, Oracle Database and Microsoft SQL Server. And to become a Big Data Engineer it is necessary to learn all these.

ETL And Data Warehousing

A Big Data Engineer should know how to build and use a Data Warehouse . Because as a Big Data Engineer, you have to collect data from different sources. That is why one should be well aware of the tools used in it such as Talend, IBM Datastage, Pentaho and Informatica .

Operating Systems

Multiple Operating Systems are used in the field of Big Data. That is, all popular operating systems like Unix, Linux, Windows and Solaris are used. That is why as a Big Data Engineer you should know how which Operating Systems work?

Hadoop Tools & Frameworks

It is very important for a Big Data Engineer to have experience in Hadoop Based Analytics. Because Hadoop is one of the most used Big Data Tools . And it is used everywhere. Therefore, as a Big Data Engineer, it is very important for you to have experience in Apache Hadoop based technologies such as HDFS, MapReduce, Apache Pig, Hive and Apache HBase .

Apache Spark

A Big Data Engineer has to work with a large amount of data. That’s why an Analytics Engine like Spark is needed. Apache Spark is used for both batch and real-time data processing . Spark can process live streaming data from multiple sources such as Twitter, Instagram and Facebook.

Data Mining And Modeling

To become a Big Data Engineer , it is very important for you to have experience in techniques like Data Wrangling , Data Mining and Data Modeling . To learn all these skills, you can take the course of M.Sc Data Science or B.Tech Big Data Analytics. Nowadays many Big Data Courses have come, with the help of which you can become a Big Data Engineer.

Big Data : Summary

By now you must have understood very well how important data is. What we communicate through online messaging applications is also a data. And it can be profitable for any company in any way. Many companies also misuse this type of data.

Although all this is an integral part of the Internet world. Because whatever is on the internet is not completely secure. But nowadays it is very important to analyze the data to know the progress of business and the trends going on in the market. Because without this you will not be able to grow your business.

Hope you got BigData through this article ? How is it used? And what is the importance of BigData for a business? Also, how can you make a career in the field of Big Data ? A lot of useful information would have been found in this subject. If you liked this article then like and share it.

Big Data : FAQs

Question 1. What is Big Data?

Answer: Big data means big data. That is, a large collection of information or a large store of information, which includes Structured, Semi Structured and Unstructured Data.

Question 2. How many types of Big Data are there? And which ones?

Answer: There are three types of Big Data. First, Structured, Second Un-Structured and Third, Semi Structured

Question 3. What are the 5 V of Big Data?

Answer: 5 V of Big Data i.e. 5 characteristics of Big Data are :- 1. Volume, 2. Velocity, 3. Variety, 4. Veracity and 5. Value

Question-4. What are the tools available for Big Data?

Answer: There are many tools available for Big Data like Apache Hadoop, Apache Storm, Apache Spark, Apache Hive, Apache Cassandra , MongoDB, Tableau, RapidMiner, MapReduce, Qubole , IBM and Microsoft Azure .

Question-5. What qualifications should a Big Data Engineer have?

Answer: Big Data Engineer must have knowledge of Programming Languages, Database , Operating Systems, ETL , Data Warehousing, Data Mining , Data Modeling, Data Science, Machine Learning and Hadoop Tools & Frameworks.