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1. NoSQL Databases
Professional Lecture

2. Introduction
• • NoSQL = Not Only SQL
• • Non-relational databases for large,
distributed data
• • Handles structured, semi-structured, and
unstructured data
• • Provides flexibility, scalability, and high
performance

3. Why NoSQL?
• • Explosion of Big Data (IoT, Social Media, E-
commerce)
• • Need for horizontal scalability
• • Real-time and high-velocity data processing
• • Schema flexibility for evolving applications

4. Key Characteristics
• • Schema-less / Flexible Schema
• • Horizontal Scalability
• • High Performance
• • Polyglot Persistence
• • CAP Theorem: Consistency, Availability,
Partition Tolerance

5. CAP Theorem
• • Consistency: All nodes have the same data
• • Availability: Every request gets a response
• • Partition Tolerance: System works despite
failures
• ⚠️Cannot achieve all 3 simultaneously
• Examples:
• - Cassandra: AP
• - MongoDB: CP

6. Types of NoSQL Databases
• 1. Key-Value Stores (Redis, DynamoDB)
• 2. Document Stores (MongoDB, CouchDB)
• 3. Column-Family Stores (Cassandra, HBase)
• 4. Graph Databases (Neo4j, Amazon Neptune)

7. SQL vs NoSQL
• SQL (RDBMS):
• • Fixed Schema
• • Vertical Scaling
• • ACID Transactions
• • Examples: MySQL, Oracle
• NoSQL:
• • Flexible Schema
• • Horizontal Scaling

8. Advantages & Disadvantages
• ✅ Advantages:
• • Scalability, Performance, Flexibility
• • Real-time Big Data processing
• • Cost-effective with commodity hardware
• ❌ Disadvantages:
• • Lack of standardization
• • Eventual consistency
• • Limited ACID support

9. Real-World Use Cases
• • MongoDB → E-commerce catalogs, content
management
• • Redis → Caching, session storage,
leaderboard
• • Cassandra → IoT data, time-series analytics
• • Neo4j → Social networks, fraud detection,
recommendation engines

10. Future Trends
• • Multi-model databases (document + graph +
key-value)
• • Stronger ACID support
• • Cloud-native & serverless NoSQL systems
• • Integration with AI/ML pipelines for real-
time analytics