Open AccessRasterhadoop: An Application Perspective of Raster Data Processing on Hadoop
Author(s) -
Ravi Bhushan,
Durvasula V. L. N. Somayajulu,
Sitalakshmi Venkatraman,
RBV Subramanayam
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.d4304.118419
Subject(s) - computer science , leaps , big data , data processing , raster graphics , file system , parallel processing , volume (thermodynamics) , perspective (graphical) , variety (cybernetics) , raster data , database , code (set theory) , operating system , parallel computing , computer graphics (images) , artificial intelligence , programming language , physics , set (abstract data type) , quantum mechanics , financial economics , economics
Hadoop is currently the most popular platform for parallel processing. With its two major components namely the Distributed File System (HDFS) and a parallel processing paradigm (MapReduce) in addition to its ease of installation and usage, Hadoop has become the chosen platform for efficiency whether in the commercial arena or the scientific arena such as Satellite Data Processing. The number of remote sensing satellites have also grown leaps and bounds and the data sent back by them for processing has all the three characteristics namely volume, velocity and variety that make it Big Spatial Data. In this paper, we present the extensions provided to Hadoop that enable Image Processing using legacy code and further elaborate on the various methods provided.