Application of Data Processing Pipelines for Large-Scale Industries using Deep Learning Techniques

Abstract

This article is devoted to the peculiarities of telemetry data processing pipelines optimization for the platforms of massively multiplayer gaming. Since the amount, velocity, and variety of gameplay data continue to increase, real-time data handling has to be optimised for the sake of system performance and player experience. Based on MPI, Apache Spark, machine learning models, the work identifies approaches for predictive analytics and real-time data processing. That it examines how cloud environments are addressing fault tolerance and proposed different ways of collecting, processing and deploying models. AI and edge computing’s future advancements are also expected to address problems with data privacy, delay, and expandability.