Efficient Storage and Retrieval Models for Large-Scale Unstructured Data Analytics

The exponential growth of digital information has led to an unprecedented surge in unstructured data originating from sources such as social media, multimedia platforms, sensor networks, and enterprise systems. Traditional relational databases and structured storage frameworks are increasingly inadequate for handling the scale, heterogeneity, and velocity of such data. This paper examines efficient storage and retrieval models for large-scale unstructured data analytics, focusing on distributed architectures, indexing strategies, and intelligent retrieval mechanisms. By synthesizing advances in cloud storage systems, NoSQL databases, vector search techniques, and machine learning–assisted data organization, the study evaluates how modern data infrastructures can optimize performance, scalability, and accessibility. The findings highlight the importance of hybrid storage paradigms and semantic retrieval frameworks in enabling rapid, accurate analysis of massive unstructured datasets, thereby supporting data-driven decision-making across industries.