Analysing Large Sets of information by Using Cloud Object Storage Mechanisms
Keywords:
Cloud object storage, big data analytics, massive datasets, data analysis, cost-effectivenessAbstract
Emerging as a basic component for the administration & analysis of huge datasets, cloud object storage offers businesses a flexible & efficient way to store and evaluate both unstructured & the organized data. The exponential growth of data in many different fields usually makes normal data storage options unable to handle the great volume and variety of data. By providing a centralized platform for the businesses to easily store & access vast data, cloud object storage efficiently addresses scalability, durability & the cost-effectiveness, hence tackling these challenges. This article investigates how modern data analytics methods combined with the cloud object storage let companies get valuable insights from huge data volumes. Data lakes & distributed computing architectures are used in the cloud object storage to provide the availability of huge volumes of data for actual time analysis, therefore enabling faster & more informed decision-making. Performance optimization choices for cloud object storage—including data tiering and caching—which improve access speed and lower costs—are investigated in this work. It also highlights many uses for cloud object storage among companies in areas like banking, healthcare, and e-commerce, therefore highlighting how they acquire competitive advantages. Using the adaptability of cloud storages can help the businesses overcome traditional storage limitations & seize fresh ideas for development & the innovation. Ultimately, cloud object storage helps companies to leverage data-driven decision-making in an always changing digital world & simplifies the management of huge volumes of data.
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