Comparative Analysis of SpatialHadoop and GeoSpark for Geospatial Big Data Analytics

TL;DR

This paper compares two popular open-source tools, SpatialHadoop and GeoSpark, for geospatial big data analytics, focusing on their architecture, performance, and suitability for handling large geospatial datasets.

Contribution

It provides a detailed architectural comparison and performance analysis of SpatialHadoop and GeoSpark, highlighting their strengths and weaknesses for geospatial big data processing.

Findings

GeoSpark generally outperforms SpatialHadoop in execution time.

SpatialHadoop handles larger data volumes with different architectural advantages.

The paper offers insights into the suitability of each tool for specific geospatial big data applications.

Abstract

In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data analytics. But not all the big data analytics tools are capabilities to handle geospatial big data. In the present paper, it has been discussed about the recent two popular open source geospatial big data analytical tools i.e. Spatial- Hadoop and GeoSpark which can be used for analysis and process the geospatial big data in efficient manner. It has compared the architectural view of SpatialHadoop and GeoSpark. Through the architectural comparison, it has also summarised the merits and demerits of these tools according the execution times and volume of the data which has been used.