BIG DATA STORAGE OBSERVATION SYSTEM

Abstract

The exponential growth of data generation across industries has created unprecedented challenges for storage infrastructure and management systems. This research examines the development and implementation of a Big Data Storage Observation System designed to monitor, analyze, and optimize storage performance in large-scale data environments. Traditional storage systems struggle with the velocity, volume, and variety of contemporary data streams, necessitating innovative observation mechanisms that provide real-time insights into storage health, utilization patterns, and performance bottlenecks. This study proposes a comprehensive observation framework integrating distributed monitoring agents, predictive analytics, and automated alerting mechanisms. Through experimental implementation across three enterprise environments handling 50-500 terabytes of daily data ingestion, the system demonstrated significant improvements in storage efficiency (23% average improvement), failure prediction accuracy (87% accuracy rate), and operational cost reduction (18% decrease). The research contributes both theoretical frameworks for storage observation architecture and practical guidelines for implementation in heterogeneous big data environments. Findings indicate that proactive storage observation systems enable organizations to transition from reactive maintenance to predictive optimization, enhancing overall data infrastructure reliability and performance.