Need and evolution

Hadoop is a framework for distributed processing of large data sets across clusters of computers using simple programming models. MapReduce is a programming model and implementation for processing large data sets with a parallel, distributed algorithm on a cluster.

Need for Hadoop & MapReduce:

1. The need for Hadoop and MapReduce emerged as a solution to the problem of processing and analyzing large-scale data sets. Traditional approaches to data processing, such as using a single server, were unable to handle the sheer volume of data being generated. Hadoop provides a distributed computing platform that can process large data sets across clusters of computers.

Evolution of Hadoop & MapReduce:

• Hadoop and MapReduce have evolved significantly since their inception. The original implementation of Hadoop included the Hadoop Distributed File System (HDFS) and MapReduce programming model. Over time, the ecosystem has grown to include additional components such as Apache Hive, Pig, and HBase. These components provide additional functionality, such as SQL-like queries, data processing pipelines, and real-time data processing.

Under Hadoop & MapReduce:

• Under Hadoop and MapReduce, data is stored in HDFS and processed using the MapReduce programming model. The MapReduce algorithm is designed to handle parallel processing of large data sets across clusters of computers. The algorithm is composed of two phases: the Map phase and the Reduce phase. The Map phase processes input data and generates key-value pairs, while the Reduce phase aggregates the output from the Map phase and generates final output. The output is then written back to HDFS for further processing or analysis.

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