data

1. What is Big Data?
 Refers to large datasets that traditional computing systems cannot handle.
 Requires special tools and frameworks for processing.
 Sources of Big Data:
o Human Activity: Emails, social media, logs.
o Machine Activity: Sensors, meters, IoT devices.
o Institutional Data: Open APIs from Twitter, Google, etc.
2. Importance of Big Data
 Business: Real-time decision-making, fraud detection, recommendation
systems.
 Healthcare: Predictive analysis, remote monitoring.
 Security: Cybersecurity intelligence, fraud prevention.
 Education: Student performance tracking, learning analytics.
3. Big Data Characteristics (The 4 Vs)
 Volume: Massive amounts of data.
 Velocity: High-speed data generation.
 Variety: Multiple formats (text, video, databases, images).
 Veracity: Ensuring data accuracy and trustworthiness.
4. Big Data Technologies
 Google MapReduce: Divides tasks into smaller jobs and processes them in
parallel.
 NoSQL Databases: Handle large, unstructured datasets eƯiciently.
5. Big Data Architecture
 Data Collection: Sensors, logs, APIs, transactions.
 Storage: Distributed systems like Hadoop HDFS.
 Processing: Batch processing (MapReduce) and real-time processing.

motivation