Open AccessUber Data Analysis using Map Reduce
Author(s) -
P. Devika*,
Y. Prasanna,
P. Swetha,
G. Akhilesh Babu
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d7111.118419
Subject(s) - trips architecture , computer science , process (computing) , data processing , data mining , database , data science , parallel computing , operating system
Map Reduce has become of the foremost often used framework for processing of giant quantity of knowledge hold on in Hadoop cluster. It is used for multi processing of giant quantity of knowledge speedily. Firstly, it had been designed by google to produce the correspondence and cut back the fault tolerance of knowledge. We are using Uber Data for analyzing the vehicle with most popular trips. As mapreduce is used to process huge amounts of data, we are using mapreducing model to analyze uber data and give insights about the most used vehicle, number of trips it has covered. The main objective of this project is to investigate no of trips so as to produce data for the company to take care of the records and helps to company in creating huge information for long run endeavor.