Big Data Analytics (21CSH-471)

Understanding Big Data and the 5 V’s

1. Introduction to Big Data – Definition and Characteristics;
2. The 5 V’s of Big Data
   1. Volume: Data at scale,
   2. Velocity: Real-time data processing,
   3. Variety: Structured, semi-structured, unstructured data,
   4. Veracity: Uncertainty and trustworthiness in data,
   5. Value: Transforming data into insights; Challenges and Opportunities in Big Data; Big Data Use Cases in Real-World Applications

Big Data Architecture

1. Fundamentals of Big Data Architecture:
   1. Data ingestion
   2. Storage
   3. Processing and visualization layers
2. Hadoop Ecosystem in Big Data Architecture:
   1. Tools like HDFS, YARN, Hive and Sqoop
3. Streaming Data in Big Data:
   1. Tools such as Apache Kafka and Flink
4. Real-World Big Data Architecture:
   1. Lambda and Kappa Architectures,
   2. Hybrid Architecture for batch and real-time processing

The Hadoop Ecosystem

1. Introduction to the Hadoop Ecosystem
2. HDFS (Hadoop Distributed File System):
   1. Architecture and Functionality
3. MapReduce Programming Model:
   1. Workflow and Applications
4. YARN (Yet Another Resource Negotiator):
   1. Resource Management
5. Tools in the Ecosystem:
   1. Pig, HBase, Flume, and Oozie
6. Data Processing with Hadoop:
   1. ETL, Analytics and Reporting

Data Visualization (21CSH-461)

Chapter 1: Data Handling and Introduction to Visualization

1. Data extraction, cleaning, and annotation