YouTube: big data analytics using Hadoop and map reduce

We live today in a digital world a tremendous amount of data is generated by each digital service we use. This vast amount of data generated is called Big Data. According to Wikipedia, Big Data is a word for large data sets or compositions that the traditional data monitoring application software is pitiful to compress [5]. Extensive data cannot be used to receive data, store data, analyse data, search, share, transfer, view, consult, and update and maintain the confidentiality of information. Google's streaming services, YouTube, are one of the best examples of services that produce a massive amount of data in a brief period. Data extraction of a significant amount of data is done using Hadoop and MapReduce to measure performance. Hadoop is a system that offers consistent memory. Storage is provided by HDFS (Hadoop Distributed File System) and MapReduce analysis. MapReduce is a programming model and a corresponding implementation for processing large data sets. This article presents the analysis of Big Data on YouTube using the Hadoop and MapReduce techniques.