A Hadoop cluster consists of a single master node and multiple worker nodes. The master node includes JobTracker, TaskTracker, NameNode, and DataNode. A slave or node acts as both a DataNode and a TaskTracker, although it can have data-only slave nodes and compute-only slave nodes. Hadoop requires JRE 1.6 (Java Runtime Environment) or later. Standard shutdown and startup scripts require Secure Shell to be configured between cluster nodes. In a larger cluster, an additional NameNode called a Secondary NameNode is configured to avoid single points of failure. HDFS is managed with dedicated NameNode to host the file system index and the secondary NameNode can generate snapshots of the namenode's memory structures. This way, it prevents file system errors or corruption and reduces data loss. Similarly, job scheduling can be managed via a standalone JobTracker server. In clusters, the Hadoop MapReduce engine deployed on an alternate file system, NameNode, DataNode, secondary NameNode.HDFS is a Master/Slave architecture, contains a Master node called NameNode and slave or worker nodes called Datanodes, usually one per node in the bunch. Which manage the storage attached to the nodes on which they run. The master server manages the namespace file system and controls access to files by clients. HDFS has a file system namespace and user can store data in files. Internally, a file is divided into a number of blocks stored in DataNodes. Namespace operations such as opening, closing, and renaming files and directories are performed by Namespace. This also determines the mapping of blocks to DataNode. Read and write requests from file system clients are the responsibility of DataNodes. DataNodes also perform creation, deletion, a...... middle of paper...... and scale an Apache Hadoop cluster within 10 minutes. Deploy clusters with MapReduce, HDFS, Hive, Hive servers and, Pig.Fully customizable configuration profile. Includes machines dedicated or shared with other workloads, DHCP or static IP networking, and local and shared storage. Accelerate analysis time to upload/download data, run MapReduce work, Pig and Hive scripts from Project Serengeti interface Through existing tools, user can consume data in HDFS via Hive server SQL connection. Elastic scale on demand as a separate compute node without losing data locality. Plus horizontal scalability and decommissioning of compute nodes on demand. Improved availability for the Apache Hadoop cluster. Which includes highly available NameNode and JobTracker to avoid single points of failure, fault tolerance (FT) of JobTracker and NameNode, and one-click HA for Pig, Hbase, and Hive.S