Big data is a valuable and powerful spreading technology that drives large IT industries and Business sectors since the 21st century. Now a day's companies use big data to accomplish business more effectively and take decisions related to business. Big Data enables data scientists, analytical modellers and other professionals to analyse large volumes of information on transactional data. Today big data projects are in huge demand in every industry for their commercial growth.


What is Big Data?

Big Data is a collection of a greater quantity of various information that arrives in larger volumes with ever-higher velocity from various data sources and has different formats. Big Data is data with very huge sizes such as Petabytes, Exabytes, Zettabytes and Yottabytes.

What is a Big Data Project?

A big data project is a data analysis project that uses machine learning algorithms and various data analytics techniques on a huge dataset for several purposes, including predictive modelling and other advanced analytics applications.

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Types of Big data:

Structured Data:

The data that can be processed, accessed and stored as a fixed format is named structured data. This data is profoundly coordinated with measurements and described by settings parameters. Structured data is generally tabulated with rows and columns that clearly define the attributes. Quantitative data such as an address, debit, expenses, contact, and billing come under structured data.

Unstructured Data:

The data which has an unfamiliar or unorganized structure is arranged as unstructured data. The size of unstructured data is large and it requires a lot of storage space to store the data. The operations such as storing, updating, deleting and searching are difficult due to unclear structure. Data such as audio files, video files, image files and log files are incorporated in unstructured data.

Semi-structured Data:

The data that does not conform to a data model but has some structure is referred to as semi-structured data. The inherent data such as time, location and device Id stamp are combined with the semi-structured data. The data such as Emails, Xml and Other markup languages, zipped files, webpages, TCP/IP packets, and Binary executables are examples of semi-structured data.

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Characteristics Of Big Data:

The characteristics of big data can be defined by 7v’s. They are


Volume is defined as the amount of data available. Data is growing exponentially with time such that it is measured in Zettabytes, Exabytes, Yottabytes and so on.


Velocity refers to the speed of data processing. The high velocity of performance is important for any big data processing. eg. Millions of YouTube videos, Social media articles and Photos posted every second are available soon.


Variety refers to different types of big data from various data sources. Organizing such data is quite an important task in big data since it affects performance.


Variability refers to the constant change or instability of data. eg. The data you gathered from a source yesterday will be changed today.


Veracity refers to the accuracy of data. It is very important to ensure the reliability of data from time to time.


Value refers to the benefits derived from the data. It is the most important among the characteristics of big data.


Visualization refers to Data that must be easily readable, understandable and available regardless of its format. Visual charts, graphs, etc., are a great choice to represent your data than excel sheets and numerical reports.

Why is big data important?

  • Big data is used by companies to improve their product marketing and to get better customer service which increases their revenue and profits.
  • Big data can be used to know the sales forecast of a product by analysing data on past sales, returns, online reviews and calls to customer service.
  • Healthcare organizations and government agencies get up-to-date information about infectious disease threats or outbreaks from social media, electronic health records etc.
  • By using big data, businesses can analyze both historical data and real-time data to analyse customer preferences to become more responsive to customer wants and needs.
  • Manufacturing companies rely on big data to maintain records of production, manage their supply chains and optimize delivery routines.
  • Big data systems are used for the real-time analysis of market data in stock market businesses and for risk management in financial firms.

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Applications Of Big Data:

The Uses of Bigdata are plenteous in various fields. Some of the domains where Big Data Applications have been revolutionized are as follows:


Big data is used by the government for uses such as emergency response, crime prevention, traffic control, smart city innovation, election result prediction, real-time controls etc.


The Big data is used in the insurance industry for customer acquisition and retention, risk assessment, fraud prevention and detection, analysing accidents and price it accordingly.


By using Big data technology medical researchers recognise disease symptoms, predict illness and risk factors that can prevent the spreading of disease in its early stage, real-time analysis etc.


Big data applications help in various ways, adding e-learning systems that include learning programs, re-framing study materials, scoring systems etc., which enhanced the learning of students.

Automobile Industry:

The automobile industry uses Big data analytics for manufacturing, to improve customer satisfaction, support autonomous driving, analyse fuel efficiency, provide safety alerts and record the vehicle’s condition in real-time.

Media and Entertainment:

Benefits of big data in this sector in terms of movie recommendations, on-demand media streaming, customer data insights, targeting the right audience, calculating the view rating of a program, understanding when the customers view mostly etc.


In the banking sector the data grows exponentially with time. Hence Big Data is essential to store documents, for proper investigation and analysis of such data, the detection of illegal acts such as credit/debit card fraud, money laundering, etc.

Big Data Projects for All Levels:

Big Data Projects For Beginners:

  • Text Mining Projects
  • Traffic Control using Big Data
  • Medical Insurance Fraud Detection
  • Dynamic neural controller for permanent magnet DC Motors
  • Energy efficient transmission for dynamic resource allocation
  • Visualising Wikipedia Trends
  • Credit Scoring
  • Project On Sentimental Analysis

Big Data Projects For IntermediateLevel:

  • Big Data Cyber Security
  • Disease Prediction Based On Symptom
  • Generating Recommendation System
  • Analysis Of Airline Dataset
  • Detection Of Fake News On Social Media
  • Data Warehouse Design For E-commerce Environments
  • Anonimous Detection In Cloud Servers
  • Image Caption Generator
  • Tourist Behaviour Analysis

Big Data Projects For Advanced Level:

  • Big Data For Cyber Security
  • Driver Drowsiness Detection Using Python
  • Malicious User Detection In Big Data collection
  • Electricity Price Forecasting
  • Health Status Prediction
  • Web Server Log Analysis
  • Smart Cities Using Big Data
  • Real-Time Traffic Analysis
  • Prediction Of Calamities In A Given Area
  • Real-Time Tracking Of Vehicles

Advantages of Big Data Projects:

There are various advantages to Big data projects. A few of them are given below:

Better Decision Making:

Big data facilitates the decision-making process by providing business intelligence and advanced analytical insights. Data-driven insights enable the business to create more customised products and services, well-informed campaigns and strategies for the betterment of the organisation.

Reduce Costs Of Business Processes:

The businesses have integrated automated processes to enhance their performance. As companies collect more data on their customers and the complexities of their supply chain, reach an unprecedented level beyond the traditional Enterprise Resource Plan(ERP) systems. As a result, a new market for big data connecting ERP systems has emerged. This allows a large volume of data to be analyzed and automated in their systems. Thus reducing operational costs greatly.

Fraud Detection:

Big data analysts use machine learning algorithms and artificial intelligence to detect anomalies and transactions. Big data projects are important for credit card companies to identify account information, material or product access. And also can serve its customers by early identification of frauds before something goes wrong.

Increased Productivity:

Utilising big data to expedite and improve the daily procedures of the business, measure performance and give more accurate readings on productivity. Big data also help productivity on a more individual level and can give different insights into how productive an employee will big data not only benefit businesses but also benefits the employee's productivity individually by tracking their progress digitally.

Improved Customer Service:

Big data analytics provide more information to businesses, that can be utilized to create more target marketing schemes. It also helps companies to know about their customer's experience with their products and services. Thus helpful in offering a better service that can improve customer satisfaction, enhance relationships and build loyalty.

Increased Agility:

Big data helps companies to improve their business tactics and strategies which are very helpful in aligning their business. Big data projects analyse huge data sets related to customers and enable companies to gain insights ahead of their competitors by addressing the pain points of customers more efficiently and effectively.

Disadvantages Of Big Data Projects:

There are a few limitations to Big Data Projects. They are

Questionable Data Quality:

The Quality of the data sets and resulting analysis must be good for Data-driven decisions and Operational strategies. There is a risk that the insights found from the analytics of such data might be worthless or may be harmful.


Since the data analysed lies behind a private cloud or a firewall, it takes a technical risk to efficiently get this data. Further, it may be difficult to consistently transfer data to specialists for repeat analysis.

Cost and Infrastructure Issues:

Being big data in constant evolution and with the objective of processing larger and larger volumes of information, the cost of its implementation is so high that only large companies can afford the investment in the advancement of their big data techniques.


The information collected from consumers and other organizations must navigate which increases the complexity. Therefore strict data privacy rules and regulatory compliance demands. Since big data volume increases, the storage, transmission, and data governance tasks become difficult to manage.

Security Risk:

Data Privacy is a great challenge in this digital world. It aims to safeguard personal and sensitive information from cyberattacks, security breaches and intentional or unintentional data losses. Big data analysis violates privacy principles.

Sources of Big Data:

There are 6 sources of big data from where information is gathered for present and future use.

  1. Black Box Data
  2. This is the data generated by aeroplanes, including jets and helicopters. Black box data contains information on aircraft performance, flight crew voices and microphone recordings.

  3. Social Media Data
  4. This is the data developed by social media sites such as Twitter, Facebook, Instagram, Pinterest, Google+ and so on.

  5. Stock Exchange Data
  6. This is data from the stock share market about the selling and buying of shares. i.e, the decisions made by customers.

  7. Power Grid Data
  8. This is data from power grids. It has useful information such as data on particular nodes, transformers, capacitors etc.

  9. Transport Data
  10. This includes information about the possible capacity of a vehicle, its model, availability, and distance covered by a vehicle at a particular time.

  11. Search Engine Data
  12. It is the most significant source of big data where data is generated automatically and information is developed by multiple sources.

How does Big Data work?

Big Data involves handling digital information and implementing tools over it to identify hidden patterns in the data. The following three main steps describe how big data works.

Step 1:


Sourcing data from different sources is fundamental in big data, and in most cases, combined data from multiple sources must be integrated to build pipelines that can retrieve data.

Step 2:


The multiple sources discussed above are appropriately managed. Since relying on & handling physical systems becomes difficult, now most organizations rely on cloud computing services to handle their big data.

Step 3:


This is the most crucial part of implementing a big data project. In revealing hidden patterns, businesses utilize Implementing data analytics algorithms over datasets to assist in making better decisions.

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