Optimizing Scalable Storage Architectures for Efficient Big Data Processing

Pardeep Mehta; Sudhakar Ranjan

Optimizing Scalable Storage Architectures for Efficient Big Data Processing

Author(s)	Mr. Pardeep Mehta, Sudhakar Ranjan
Country	India
Abstract	The digital era has witnessed an unprecedented surge in data generation, driven by diverse sources such as social media platforms, e-commerce, Internet of Things (IoT) devices, mobile applications, and enterprise systems. This explosive growth has led to the emergence of big data, characterized by the five Vs: volume, velocity, variety, veracity, and value. Managing such massive and complex datasets necessitates the use of scalable storage systems that can efficiently store, control, and retrieve data. Traditional storage infrastructures—such as centralized databases and file systems—struggle to meet the demands of big data applications due to limitations in scalability, fault tolerance, data redundancy, and performance bottlenecks. There are lot of problems with old system. In response, modern storage architectures have evolved to embrace distributed and cloud-based storage systems that offer horizontal scalability and high availability. The exponential increase in data generation across industries has intensified the demand for storage architectures that are both scalable and efficient. Conventional systems often face challenges in sustaining high ingestion rates, minimizing retrieval latency, and adapting to the dynamic requirements of large-scale data analytics. This research focuses on optimizing scalable storage architectures to enhance the performance of big data processing frameworks. It examines distributed file systems, object-based storage, and cloud-native approaches with particular attention to data distribution, replication mechanisms, metadata handling, and resource utilization. The study also analyzes the balance between scalability, fault tolerance, and consistency, while considering integration with platforms such as Hadoop and Spark. Through systematic benchmarking and performance evaluation, the proposed models aim to improve throughput, reliability, and cost-effectiveness. The outcomes of this research are intended to guide the development of next-generation storage solutions capable of supporting the continuous growth of big data ecosystems.
Keywords	Big Data, Volume, Velocity, Scalable storage system, Metadata, Sharding, Scalable Storage Architectures, Distributed File Systems, Cloud-Native Storage, Object-Based Storage, Data Partitioning, Replication Strategies, Metadata Management, Fault Tolerance, Hadoop, Apache Spark, Resource Optimization, High Throughput, Low Latency, Data-intensive Applications.
Field	Computer > Data / Information
Published In	Volume 6, Issue 6, November-December 2025
Published On	2025-12-20

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Optimizing Scalable Storage Architectures for Efficient Big Data Processing

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