Twitch Streaming Graph Analysis - Part 1

The blog post by Katarina Supe explores the process of analyzing Twitch streaming data using a backend architecture that employs various technologies, including Memgraph for graph analytics and Docker for containerization. It details the setup of a system that collects data from Twitch using a Python script, stores it in a Kafka cluster, and processes it with Memgraph, which applies algorithms like PageRank and betweenness centrality to determine the popularity and influence of streamers within the network. The backend, built using Flask and GQLAlchemy, queries Memgraph and serves data to a React frontend, which visualizes the Twitch network using D3.js. The post explains the project structure, data importation methods, including the use of CSV files and object graph mapping, and outlines the backend implementation with a focus on building an API to provide various statistics about streamers, games, and teams. The blog is the first part of a series, with subsequent parts focusing on frontend development and integrating Kafka for streaming data visualization.