ARCHITECTING A CLOUD-BASED PREDICTIVE ANALYTICS SYSTEM FOR MEDIA SUBSCRIPTION MANAGEMENT: AN AWS, DATABRICKS, AND SNOWFLAKE INTEGRATION

Abstract

This article presents a comprehensive framework for implementing cloud-based predictive analytics in media subscription management by integrating AWS, Databricks, and Snowflake technologies. The framework addresses the evolving media consumption landscape, where traditional models are replaced by subscription-based services requiring sophisticated data analytics capabilities. It explores the architecture's key components, including data infrastructure foundation, processing pipeline development, machine learning model implementation, and production deployment strategies. The article demonstrates how this integrated approach enables media companies to enhance customer retention, optimize marketing strategies, and improve content delivery through real-time predictive analytics. The framework leverages advanced data processing, storage, and analysis technologies, incorporating both batch and streaming capabilities while maintaining high security and compliance standards. The findings highlight the significance of a well-architected cloud infrastructure in supporting modern media platforms' complex analytical requirements while providing scalability and cost efficiency through intelligent resource allocation and management.