🔹 Lesson 1: What is Data?

Data is a collection of raw facts and figures that can be processed to generate meaningful information.

Examples:

• Student marks

• Bank transactions

• Social media posts

• Online shopping history

Data can exist in different formats such as numbers, text, images, videos, and logs.

🔹 Lesson 2: Types of Data

1️⃣ Structured Data

• Stored in rows and columns

• Example: MySQL, Oracle databases

• Easy to query using SQL

2️⃣ Semi-Structured Data

• Not strictly tabular but organized

• Example: JSON, XML

3️⃣ Unstructured Data

• No predefined format

• Example: Videos, Images, Social media posts, Emails

🔹 Lesson 3: What is Big Data?

Big Data refers to extremely large datasets that cannot be processed using traditional database systems.

It includes:

• Massive volume

• High speed generation

• Different data formats

Traditional RDBMS fails when:

• Data size becomes too large

• Processing becomes slow

• Storage becomes expensive

🔹 Lesson 4: 3Vs / 5Vs of Big Data

🔸 Volume

Huge amount of data (TBs, PBs)

🔸 Velocity

Speed at which data is generated (real-time data)

🔸 Variety

Different types of data (structured, semi-structured, unstructured)

🔸 Veracity

Data accuracy and trustworthiness

🔸 Value

Business value extracted from data

🔹 Lesson 5: Challenges with Traditional Databases

• Scalability limitations

• High hardware cost

• Performance degradation

• Centralized architecture

Vertical Scaling vs Horizontal Scaling.

Traditional DB → Vertical Scaling
Big Data → Horizontal Scaling (Distributed Systems)

🔹 Lesson 6: Why Big Data Emerged?

Big Data emerged because:

• Social media growth

• E-commerce growth

• IoT devices

• Mobile applications

• Cloud computing

🔹 Lesson 7: Big Data Use Cases

You can explain:

• Banking → Fraud detection

• Healthcare → Disease prediction

• E-commerce → Recommendation systems

• Telecom → Customer churn analysis

• Social Media → Targeted Ads