BIG DATA & DATA ANALYTICS
In today’s digital world, huge volumes of data are created every second. Whenever we send a message, watch a video, purchase online, play games, use a smartwatch, or post on social media, we are generating data.
This massive and growing collection of data is called Big Data.
Traditional tools like Excel or simple databases cannot handle such huge and fast data.
To manage this, new technologies and techniques were developed. They are called Big Data Technologies and Data Analytics.
1.2 Learning Objectives
After studying this chapter, students will be able to:
- Understand the meaning of Big Data
- Identify characteristics of Big Data
- Recognize different types and sources of data
- Explain the concept of Data Analytics
- Understand how data is used to create insights
- Explore real-life applications of Big Data
- Know the advantages and challenges of Big Data
1.3 What is Big Data?
Big Data refers to:
- Very large amounts of data
- Generated at very high speed
- Coming in different formats (text, images, videos, sensor readings)
- And too complex for traditional tools to process
Examples of Big Data
- YouTube uploads 500+ hours of video every minute
- Banks process thousands of UPI payments every second
- Google handles more than 5 billion searches every day
Because this data is so large and fast, we cannot use normal software to store or analyze it.
1.4 Characteristics of Big Data (The 5 Vs)
Big Data is commonly explained through five key characteristics known as the 5 Vs.
1. Volume (How much data?)
- Refers to the large size of data.
Example:
Facebook stores photos, videos, messages of billions of users.
2. Velocity (How fast is data generated?)
- Data is created and processed at incredible speed.
Example:
Stock market price changes every second.
3. Variety (What type of data?)
Data comes in different formats:
- Text (messages, notes)
- Images (photos)
- Videos
- Audio recordings
- Sensor data
4. Veracity (How accurate is data?)
- Not all data is correct or reliable.
Example:
Fake news, incorrect profiles, spelling mistakes in forms.
5. Value (How useful is the data?)
- Value means the importance or benefit we can get from using data.
Example:
Netflix uses data to suggest movies you might like.
1.5 Types of Big Data
Big Data is classified into three types:
1. Structured Data
- Organized
- Stored in tables
- Easy to search
Example: Student database, library records.
2. Unstructured Data
- No fixed format
- Difficult to store
Examples: Photos, videos, PDFs, emails.
3. Semi-Structured Data
- Not fully organized but has tags
Examples: XML files, JSON files, email headers.
1.6 Sources of Big Data
Data is generated from many sources:
Source | Examples |
Social Media | Facebook posts, Instagram reels |
E-Commerce | Online shopping, customer reviews |
IoT Devices | Smartwatches, fitness trackers |
Healthcare | X-ray images, patient records |
Education | Online classes, LMS data |
Banking | UPI payments, ATM transactions |
1.7 What is Data Analytics?
Data Analytics means:
- Collecting data
- Cleaning data
- Organizing data
- Studying/Analyzing data
- Finding patterns
- Making decisions based on results
It helps organizations convert “raw data” into useful information.
Example:
A shopping website studies customers’ search and purchase history and shows relevant product recommendations.
1.8 Types of Data Analytics
There are four major types:
1. Descriptive Analytics
Explains what has happened.
Example: Monthly sales report.
2. Diagnostic Analytics
Explains why something happened.
Example: Why did sales decrease last month?
3. Predictive Analytics
Predicts future outcomes using patterns.
Example: Predicting weather or exam performance.
4. Prescriptive Analytics
Suggests best possible action.
Example: Choosing the best delivery route.
1.9 Big Data Technologies (Simple Explanation)
Although the backend is complex, a Class 9/10-level explanation is:
1. Hadoop
A popular Big Data framework used for storing and processing huge data.
- HDFS → Stores large files
- MapReduce → Processes data in parallel
- Hive → Helps run SQL-like queries
2. Apache Spark
- A faster tool used for big data processing and real-time data analysis.
3. NoSQL Databases
- Used to store unstructured or semi-structured data.
1.10 Applications of Big Data
Big Data is used in almost every field.
1. Healthcare
- Disease prediction
- Medical image analysis
2. Business
- Customer behavior study
- Sales forecasting
3. Banking and Finance
- Fraud detection
- Loan risk management
4. Social Media
- Trending topics
- User recommendations
5. Education
- Student performance analytics
- Personalized learning
1.11 Advantages of Big Data
- Helps in better decision-making
- Improves customer experience
- Reduces cost
- Helps detect fraud
- Supports real-time analysis
1.12 Challenges of Big Data
- Requires huge storage
- Needs skilled professionals
- Data privacy issues
- Data accuracy problems
- Requires advanced technology
Summary
Term | Meaning |
Big Data | Very large and complex datasets |
Analytics | Studying data to find insights |
Hadoop | A framework for storage & processing |
Structured Data | Organized data in tables |
Unstructured Data | Raw, unorganized data |
Value | Benefit we get from data |
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