An Exhaustive Survey of Big Data Storage Reduction Techniques

The exponential growth of data in the era of big data has introduced significant challenges in storage, processing, and analysis, necessitating the development of effective data reduction methods. This survey provides a concise overview of key big data reduction techniques, systematically reviewing methodologies such as data compression, data deduplication , dimensionality reduction, and feature selection. By analyzing data reduction techniques, algorithms, and frameworks, the study highlights the critical need for data reduction to manage storage demands, improve computational efficiency, and enable meaningful data analysis in large-scale systems. Key findings reveal that hybrid approaches combining multiple reduction strategies excel in maintaining data integrity while significantly reducing storage and computational overhead, with advanced techniques like deep learning-based compression and adaptive sampling proving highly effective. The study concludes that innovative data reduction methods are essential for efficient big data management and emphasizes the importance of aligning these strategies with specific application requirements, paving the way for future advancements in scalable and optimized storage frameworks.