AI-Driven Autonomous Archiving: The Future of Sustainable Database Management

Abstract

Organizations today face escalating challenges from database bloat, where vast quantities of rarely accessed data accumulate in production systems, degrading performance, increasing operational costs, and expanding carbon footprints. Traditional database management approaches typically fail to address this issue effectively, resulting in overprovisioned infrastructure and suboptimal resource utilization. AI-driven autonomous archiving emerges as a transformative solution, leveraging machine learning algorithms to intelligently identify cold data and automatically migrate it to appropriate storage tiers while maintaining seamless accessibility. This intelligent lifecycle management system continuously analyzes access patterns, identifies data dependencies, and makes context-aware decisions about optimal data placement. By implementing sophisticated microservices architectures with transparent data access layers, these solutions enable organizations to maintain lean primary databases while preserving historical information in cost-effective storage environments. The resulting benefits extend beyond performance improvements to encompass substantial cost reductions, enhanced compliance capabilities, and significant environmental sustainability advantages through reduced energy consumption and optimized resource utilization across enterprise database ecosystems.