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The ongoing outbreak of coronavirus disease (COVID-19) had burst out in WuhanChina, specifically in December 2019. COVID-19 has caused by a new virus thathad not been identified in human previously. This was followed by a widespreadand rapid spread of this epidemic throughout the world. Daily, the number ofthe confirmed cases are increasing rapidly, number of the suspect increases,based on the symptoms that accompany this disease, and unfortunately number ofthe deaths also increase. Therefore, with these increases in number of casesaround the world, it becomes hard to manage all these cases information withdifferent situations; if the patient either injured or suspect with whichsymptoms that appeared on the patient. Therefore, there is a critical need toconstruct a multi-dimensional system to store and analyze the generatedlarge-scale data. In this paper, a Comprehensive Storing System for COVID-19data using Apache Spark (CSS-COVID) is proposed, to handle and manage theproblem caused by increasing the number of COVID-19 daily. CSS-COVID helps indecreasing the processing time for querying and storing COVID-19 daily data.CSS-COVID consists of three stages, namely, inserting and indexing, storing,and querying stage. In the inserting stage, data is divided into subsets andthen index each subset separately. The storing stage uses set of storing-nodesto store data, while querying stage is responsible for handling the queryingprocesses. Using Apache Spark in CSS-COVID leverages the performance of dealingwith large-scale data of the coronavirus disease injured whom increase daily. Aset of experiments are applied, using real COVID-19 Datasets, to prove theefficiency of CSS-COVID in indexing large-scale data.

A Multi-Dimensional Big Data Storing System for Generated COVID-19 Large-Scale Data using Apache Spark