Hadoop and Map Reduce

Hadoop and MapReduce, These are two popular technologies used in the field of Big Data processing.

1. Hadoop: Apache Hadoop is an open-source software framework used for the distributed storage and processing of large datasets. It uses the Hadoop Distributed File System (HDFS) to store data across multiple machines and provides high-throughput access to the stored data.
2. MapReduce: MapReduce is a programming model and processing technique used with Hadoop to perform parallel processing on large datasets. It consists of two main phases: the Map and Reduce phases.

• • Map Phase: The input data is divided into chunks, and a map function is applied to each fragment. The map function takes key-value pairs as input and produces a set of intermediate key-value pairs.
  • Reduce Phase: The intermediate key-value pairs produced by the map function are grouped by key, and a reduce function is applied to each group. The reduce function combines these grouped key-value pairs to create a smaller set of key-value pairs as the final output.

Hadoop and MapReduce provide a robust and scalable solution for processing large amounts of data across a distributed computing environment. These technologies are widely used in various domains, including data analysis, machine learning, scientific computing, and more.