REVIEW ON ADAPTIVE MULTI-TIER OPTIMIZATION FRAMEWORK FOR MANAGING MASSIVE UNSTRUCTURED DATA IN CLOUD-NATIVE LAKE HOUSE ARCHITECTURES

Abstract

The exponential growth of unstructured data generated from IoT devices, social media platforms, enterprise applications, healthcare systems, surveillance networks, and industrial automation has introduced significant challenges in scalable storage, efficient querying, governance, and cost management. Traditional data warehouses are unable to process heterogeneous data types, while conventional data lakes often suffer from poor metadata control and performance bottlenecks. Cloud-native lake house architectures have emerged as a hybrid solution by integrating the flexibility of data lakes with transactional consistency and analytics capabilities of data warehouses. However, managing massive unstructured datasets within these architectures requires intelligent resource allocation, tiered storage optimization, workload balancing and adaptive query execution. This paper proposes an Adaptive Multi-Tier Optimization Framework (AMTOF) for managing large-scale unstructured data in cloud-native lake house environments. The framework combines intelligent data tiering, metadata-aware indexing, machine learning-based workload prediction, auto-scaling orchestration, and cost-performance optimization. Experimental analysis demonstrates improvements in query latency, storage utilization, scalability, and operational cost compared to conventional static architectures. The proposed model is highly suitable for next-generation enterprise analytics, smart cities, digital healthcare, and Industry 4.0 ecosystems.