Scalable Analysis for Covid-19 and Vaccine Data

TL;DR

This paper demonstrates scalable methods using Big Data tools like Hadoop and Hive to analyze large Covid-19 and vaccine datasets, revealing correlations between vaccination rates and case reductions.

Contribution

It introduces scalable Big Data analysis techniques for Covid-19 data, enabling efficient processing and visualization of large-scale health data.

Findings

Higher vaccination rates correlate with fewer confirmed Covid-19 cases.

Big Data tools can effectively handle 3.2 GB Covid-19 datasets.

Visualizations aid in understanding the impact of vaccination.

Abstract

This paper explains the scalable methods used for extracting and analyzing the Covid-19 vaccine data. Using Big Data such as Hadoop and Hive, we collect and analyze the massive data set of the confirmed, the fatality, and the vaccination data set of Covid-19. The data size is about 3.2 Giga-Byte. We show that it is possible to store and process massive data with Big Data. The paper proceeds tempo-spatial analysis, and visual maps, charts, and pie charts visualize the result of the investigation. We illustrate that the more vaccinated, the fewer the confirmed cases.