Big Data Analytics and Data Mining Lecture 2: HDFS, MapReduce, Yarn, HIve

This session covers the essential components of the Hadoop ecosystem:
Topics Discussed:
HDFS (Hadoop Distributed File System): Explore how HDFS provides reliable, distributed storage for massive datasets.
MapReduce: Understand the programming model for distributed data processing with real-world examples.
YARN (Yet Another Resource Negotiator): Learn about resource management and job scheduling in Hadoop 2.0.
Hive: Discover how Hive simplifies data analysis with SQL-like queries and its applications in data warehousing and ETL processes.
Key Takeaways:
The architecture and functionality of HDFS, including its fault tolerance and high throughput capabilities.
The workflow of MapReduce, from mapping data to reducing it for insights.
How YARN optimizes resource management for large-scale Hadoop clusters.
Use cases of Hive in data warehousing, business intelligence, and log analysis.