Metocean Big Data Processing Using Hadoop

This report will discuss about MapReduce and how it handles big data. In this report, Metocean (Meteorology and Oceanography) Data will be used as it consist of large data. As the number and type of data acquisition devices grows annually, the sheer size and rate of data being collected is rapidly expanding. These big data sets can contain gigabytes or terabytes of data, and can grow on the order of megabytes or gigabytes per day. While the collection of this information presents opportunities for insight, it also presents many challenges. Most algorithms are not designed to process big data sets in a reasonable amount of time or with a reasonable amount of memory. MapReduce allows us to meet many of these challenges to gain important insights from large data sets. The objective of this project is to use MapReduce to handle big data. MapReduce is a programming technique for analysing data sets that do not fit in memory. The problem statement chapter in this project will discuss on how MapReduce comes as an advantage to deal with large data. The literature review part will explain the definition of NoSQL and RDBMS, Hadoop Mapreduce and big data, things to do when selecting database, NoSQL database deployments, scenarios for using Hadoop and Hadoop real world example. The methodology part will explain the waterfall method used in this project development. The result and discussion will explain in details the result and discussion from my project. The last chapter in this project report is conclusion and recommendation