Introduction to Big Data

1. What is Big Data?

Big Data includes massive volumes of structured, semi-structured, and unstructured data generated from various digital sources.

• Generated continuously
• Comes from multiple sources
• Requires advanced processing tools

2. Sources of Big Data

• Social media platforms
• IoT devices and sensors
• Online transactions
• Log files and web data
• Multimedia (images, videos)

3. Types of Big Data

• Structured: Tables, RDBMS data
• Semi-Structured: XML, JSON
• Unstructured: Images, videos, text

4. Characteristics of Big Data (5Vs)

• Volume: Huge amount of data
• Velocity: Speed of data generation
• Variety: Different data formats
• Veracity: Data accuracy and quality
• Value: Useful insights from data

5. Big Data Architecture

Data Sources
     |
     v
Data Ingestion (Kafka / Flume)
     |
     v
Distributed Storage (HDFS)
     |
     v
Data Processing (MapReduce / Spark)
     |
     v
Analytics & Visualization

6. Big Data Tools

• Hadoop
• Apache Spark
• HDFS
• NoSQL Databases (MongoDB, Cassandra)
• Apache Hive

7. Big Data vs Traditional Database

Traditional DB                Big Data
---------------------------  -----------------------------
Structured data only          Structured + Unstructured
Limited scalability           Highly scalable
Centralized                   Distributed
Small data size               Massive data size

8. Applications of Big Data

• Social media analytics
• Fraud detection
• Smart cities
• Healthcare analytics
• Recommendation systems

9. Advantages of Big Data

• Better decision making
• Real-time analysis
• Improved customer experience
• Cost optimization

10. Challenges of Big Data

• Data privacy and security
• Storage management
• Complex processing
• Skill requirements

Practice Questions

1. What is Big Data?
2. Explain 5Vs of Big Data.
3. List Big Data tools.
4. Differentiate Big Data and traditional DB.
5. List applications of Big Data.

Practice Task