Intelligent Archival and Tiered Storage: Strategies for Managing Big Data Growth in Modern Enterprises

Abstract

The exponential growth of enterprise data has created unprecedented challenges in storage management and optimization. Organizations face mounting pressure to efficiently manage expanding data volumes while maintaining performance and controlling costs. This comprehensive article addresses these challenges by presenting advanced strategies for intelligent archival and tiered storage management. Through automated workflows, columnar storage formats, and hybrid cloud architectures, enterprises can effectively balance performance requirements with cost considerations. The implementation of sophisticated partitioning strategies enables organizations to maintain optimal query performance while managing large datasets efficiently. The article demonstrates how modern compression techniques and automated lifecycle management systems significantly reduce storage footprints while maintaining data accessibility. By leveraging multi-tiered storage architectures and intelligent data classification frameworks, organizations can optimize resource allocation and reduce operational overhead. The integration of hybrid cloud models provides additional flexibility and cost benefits through strategic data distribution across storage tiers. These combined strategies enable businesses to maintain comprehensive access to historical data while prioritizing resources for frequently accessed information, resulting in improved operational efficiency and reduced management complexity.