MapReduce Technique : Hadoop Big Data

As a batch processing architecture, the major value of Hadoop is that it enables ad hoc queries to run against an entire data set and return results within a reasonable time frame. Distributed computing across a multi-node cluster is what allows this level of data processing to take place. MapReduce applications can process vast amounts (multiple terabytes) of data in parallel on large clusters in a reliable, fault-tolerant manner. MapReduce is a computational paradigm in which an application is divided into self-contained units of work. Each of these units of work can be issued on any node in the cluster. A MapReduce job splits the input data set into independent chunks that are processed by map tasks in parallel. The framework sorts the map outputs, which are then input to reduce tasks. Job inputs and outputs are stored in the file system. The MapReduce framework and the HDFS (Hadoop Distributed File System) are typically on the same set of nodes, which enabl...