Scalable Storage and Query Optimization Techniques for Big Data Management in Distributed NoSQL Systems

Abstract

The explosion of big data has made NoSQL databases indispensable in supporting distributed, scalable, and high-performance data management. This paper investigates modern storage and query optimization strategies in distributed NoSQL systems. It synthesizes existing literature to identify key developments, limitations, and challenges in the realm of big data systems. Through a focused analysis of storage efficiency, data distribution, indexing models, and real-time query processing techniques, this study proposes a comprehensive architectural view and evaluates emerging strategies including columnar formats, vectorized execution, adaptive indexing, and cost-based optimizations in systems like Cassandra, HBase, and MongoDB. Visual diagrams, performance comparisons, and a structured mind map support the conceptual framework. The paper concludes with future research directions toward autonomous NoSQL systems optimized via machine learning.