Big Data Analytics (21CSH-471)

Big Data Frameworks

1. Hadoop & Apache Spark and their Comparison;
2. NoSQL databases:
   1. MongoDB
   2. Cassandra**
   3. HBase
3. Big Data Visualization Tools:
   1. Tableau
   2. Power BI
   3. Zeppelin
4. Real-Time Big Data Processing:
   1. Apache Storm and Flink
   2. Emerging trends in Big Data Technologies.

Big SQL and NO SQL Databases**

1. Overview of SQL vs. NoSQL:
   1. Differences and Use Cases;
2. Introduction to Big SQL:
   1. Big SQL Features –
      1. Scalability, support for structured and unstructured data
      2. Query optimization Techniques in Big SQL
3. NoSQL Database Types:
   1. Key-Value stores (Redis, DynamoDB),
   2. Document stores (CouchDB),
   3. Column-family stores (Cassandra**, HBase),
4. Graph Databases (Neo4j);
5. Advantages and limitations of Big SQL and NoSQL.

AI in Big Data

1. Introduction to IBM Watson:**
   1. Overview and capabilities of Watson AI
   2. Watson’s role in Big data and decision-making
2. Key Watson Services:
   1. Watson Discovery
   2. Watson Studio**
   3. Watson Assistant
   4. Integration of Watson with Big Data tools
3. AI and Machine Learning Applications in Big Data:
   1. Tools such as Apache Kafka and Flink
4. Real-World Big Data Architecture:
   1. Natural Language Processing (NLP),
   2. Sentiment Analysis
   3. Predictive Analytics.

Data Visualization (21CSH-461)

Chapter 1: Visualization Techniques