HADOOP OVERVIEW

Hadoop works in a master-worker / master-slave fashion.
Hadoop has two core components: HDFS and MapReduce.
HDFS (Hadoop Distributed File System) offers highly reliable and distributed storage, and ensures reliability, even on commodity hardware, by replicating the data across multiple nodes. Unlike a regular file system, when data is pushed to HDFS, it will automatically split into multiple blocks (configurable parameter) and store/replicates the data across various data nodes. This ensures high availability and fault tolerance.
MapReduce offers an analysis system that can perform complex computations on large datasets.
This component is responsible for performing all the computations and works by breaking down a large complex computation into multiple tasks and assigns those to individual worker/slave nodes and takes care of coordination and consolidation of results.
The master contains the Namenode and Job Tracker components.
Namenode holds the information about all the other nodes in the Hadoop Cluster, files present in the cluster, constituent blocks of files and their locations in the cluster, and other information useful for the operation of the Hadoop Cluster.
Job Tracker keeps track of the individual tasks/jobs assigned to each of the nodes and coordinates the exchange of information and results.
Each Worker / Slave contains the Task Tracker and Datanode components.
Task Tracker is responsible for running the task/computation assigned to it.
Datanode is responsible for holding the data.
The computers present in the cluster can be present in any location and there is no dependency on the location of the physical server.
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