Authors: Priyanka Sharma
Abstract: Hybrid storage systems have increasingly become a pivotal architecture in the realm of large-scale data analytics, addressing the ever-growing demand for managing vast volumes of diverse data with speed and efficiency. By integrating multiple types of storage media, typically solid-state drives (SSDs) and hard disk drives (HDDs), hybrid storage optimizes data accessibility and throughput by leveraging the performance benefits of faster storage technologies alongside the cost-effectiveness and capacity of traditional drives. This synergy is particularly crucial in large-scale data analytics, where substantial datasets must be rapidly processed to derive actionable insights, impacting industries such as finance, healthcare, telecommunications, and scientific research. The influence of hybrid storage systems transcends mere data warehousing, affecting the efficiency of data retrieval, latency, system throughput, and computing cost. These systems support the flexible caching of hot data in faster tiers, while colder, less frequently accessed data remains in slower storage, thereby creating a dynamic environment that can adapt to workload variations. Furthermore, the architecture of hybrid systems is conducive to scalability and fault tolerance, essential features when dealing with petabyte-scale analytics clusters and distributed frameworks like Apache Hadoop and Spark. This article explores the architecture of hybrid storage systems, the performance implications they bear on large-scale data analytics, and the cost-performance balance they offer. Additionally, it examines case studies demonstrating improvements in real-world analytics applications, the challenges in managing hybrid storage environments, and future trends in storage technologies impacting analytics performance. By understanding these aspects, enterprises can better architect their storage infrastructure to meet the demanding requirements of data-intensive analytics workloads.
DOI: https://doi.org/10.5281/zenodo.17776151
