Design and Implementation of a Big Data Architecture for Collecting, Processing, and Analyzing GitHub Events

Abstract

This paper presents a software solution for collecting, processing, and analyzing GitHub events that integrates batch processing and real-time stream processing. An approach for evaluating and ranking entities within the GitHub platform is introduced through the Gazer Score metric. The paper describes the system architecture and provides examples of its use. The results demonstrate that the proposed solution enables the comparison of entities across multiple dimensions, as well as the analysis of GitHub data tailored to different types of users.