Big Data Analytics 1. Big Data :-

आज Internet और Digital Services के उपयोग से बहुत बड़ी मात्रा में Data generate और collect होता है। जैसे Online Shopping, Social Media Posts, GPS/Location Data, Online Transactions, Videos, Images आदि। इस बहुत बड़े और complex data को Big Data कहा जाता है।

सरल शब्दों में:
Big Data = बहुत बड़ी मात्रा में Generate और Collect किया गया Data

2. Big Data Analytics :-

Big Data Analytics एक process है जिसमें बहुत बड़े और complex data को Collect, Process और Analyze किया जाता है, ताकि उसमें से Patterns, Trends और Useful Information प्राप्त की जा सके।

Companies इसका उपयोग Better Decision Making, Customer Understanding, Business Improvement और Revenue Growth के लिए करती हैं।

Exam Definition:

Big Data Analytics is the process of analyzing large and complex datasets to discover useful information, patterns and trends.

3. Big Data के Examples

Internet पर users द्वारा generate किए जाने वाले data के examples:

• 🛒 Online Shopping Data
• 📱 Social Media Data
• 📍 GPS / Location Data
• 💳 Online Transaction Data
• 🎥 Videos
• 🖼️ Images
• 📧 Emails
• 🔎 Search Data

4. Big Data Analytics का उद्देश्य

Big Data Analytics का मुख्य उद्देश्य large datasets से useful information निकालना है।

इसके द्वारा:

1. Patterns identify किए जाते हैं।
2. Trends पता लगाए जाते हैं।
3. Better decisions लेने में सहायता मिलती है।
4. Customer behaviour को समझा जा सकता है।
5. Business performance improve की जा सकती है।
6. Future planning और prediction में सहायता मिल सकती है।

5. Applications of Big Data Analytics

Big Data Analytics का उपयोग कई अलग-अलग sectors में किया जाता है:

Banking

Fraud detection, customer analysis और transaction analysis में।

Healthcare

Patient data और medical information के analysis में।

Manufacturing

Production और machine performance को analyze करने में।

Education

Student performance और learning data के analysis में।

Online Advertising

Customer interests के आधार पर advertisements को understand और improve करने में।

Social Media

User behaviour, trends और engagement को analyze करने में।

E-Commerce

Customer preferences, shopping patterns और product recommendations के लिए।

6. Types of Big Data

Big Data को मुख्य रूप से तीन types में classify किया जाता है:

1. Structured Data

2. Unstructured Data

3. Semi-Structured Data

1. Structured Data :-

ऐसा data जिसका fixed format और well-defined structure होता है और जिसे आसानी से rows और columns में organize किया जा सकता है।

Example: Database table, Student records, Employee records.

Exam Point:
Structured Data = Fixed Structure

2. Unstructured Data

ऐसा data जिसका कोई predefined/fixed structure नहीं होता।

Examples:

• Images
• Videos
• Audio
• Social Media Posts
• Text Documents

Exam Point:
Unstructured Data = No Fixed Structure

3. Semi-Structured Data

ऐसा data जिसमें कुछ structure या organizational information होती है, लेकिन यह पूरी तरह structured नहीं होता।

Examples:

• XML
• JSON
• Emails

Exam Point:
Semi-Structured Data = Partially Structured

English  What is Big Data?

A very large amount of data is generated and collected through the use of the Internet and Digital Services.

Examples include:

• Online Shopping Data
• Social Media Data
• GPS / Location Data
• Online Transaction Data
• Videos
• Images
• Emails
• Search Data

Such large and complex data is called Big Data.

In Simple Words:

Big Data = A very large amount of generated and collected data

 What is Big Data Analytics?

Big Data Analytics is a process of collecting, processing and analyzing large and complex datasets to discover:

• Useful Information
• Patterns
• Trends

Companies use Big Data Analytics for better decision making, customer understanding, business improvement and revenue growth.

Exam Definition:

Big Data Analytics is the process of analyzing large and complex datasets to discover useful information, patterns and trends.

 Examples of Big Data

Major examples of Big Data include:

1. Online Shopping Data
2. Social Media Data
3. GPS / Location Data
4. Online Transaction Data
5. Videos
6. Images
7. Emails
8. Search Data

 Objectives of Big Data Analytics

The main objective of Big Data Analytics is to obtain useful information from large datasets.

It helps to:

1. Identify Patterns.
2. Find Trends.
3. Make better decisions.
4. Understand customer behaviour.
5. Improve business performance.
6. Support future planning and prediction.

 Applications of Big Data Analytics 1. Banking

Used for fraud detection, transaction analysis and customer analysis.

2. Healthcare

Used for patient data and medical information analysis.

3. Manufacturing

Used for production and machine performance analysis.

4. Education

Used for student performance and learning data analysis.

5. Online Advertising

Used to understand customer interests and improve advertisements.

6. Social Media

Used to analyze user behaviour, trends and engagement.

7. E-Commerce

Used for customer preferences, shopping patterns and product recommendations.

 Types of Big Data :-

Big Data is mainly classified into three types:

1 Structured Data

Structured Data is data that has a fixed format and well-defined structure and can be easily organized into rows and columns.

Examples:

• Database Tables
• Student Records
• Employee Records

Exam Point:

Structured Data = Fixed Structure

2 Unstructured Data

Unstructured Data is data that has no predefined or fixed structure.

Examples:

• Images
• Videos
• Audio
• Social Media Posts
• Text Documents

Exam Point:

Unstructured Data = No Fixed Structure

3 Semi-Structured Data

Semi-Structured Data is data that contains some structure or organizational information, but it does not have a completely fixed structure like a traditional database table.

Examples:

• XML
• JSON
• Emails