Open AccessMapreduce: Simplified Data Processing on Clusters with Privacy Preserving By using Anonymization Techniques
Author(s) -
Ashutosh Dixit,
Nidhi Tyagi
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f7773.038620
Subject(s) - computer science , software , enhanced data rates for gsm evolution , programming paradigm , data processing , distributed computing , big data , data mining , data science , database , operating system , artificial intelligence , programming language
Computerized Data from various sources, such as remote sensors, cutting-edge sequencing of bioinformatics and high-performance instruments, are increasing at extremely high speeds. To keep analyzing through results for programming, facilities and measurements, The Researches have to use new procedures and techniques. Google's team started MapReduce programming system which aims to manipulate huge data sets in disseminated frameworks; this design lets software engineers create applications that are extremely valuable to large data processing. The motive of this paper is to explore MapReduce research techniques and to increase current research efforts to improve the execution of MapReduce and its capabilities.