International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Cloud-Native Transformation of Unstructured Data

Author(s) Anush kumar Thati
Country United States
Abstract Unstructured data's exponential expansion in cloud environments provides companies with difficult data
translation and analysis problems. Methodologies and tools for transforming unstructured data text,
photos, videos, and social media content into analyzable structured forms are thoroughly examined in this
article. Natural language processing, computer vision, and machine learning methods dominate a
methodical study of present cloud-based transformation technologies. Using empirical examination of
actual implementations across several sectors, it shows how scalable, effective processing of unstructured
data is enabled by cloud-native tools and serverless architectures. This article shows that effective
transformation plans call for a careful evaluation of processing architecture, data quality assurance, and
regulatory compliance in concert. Combining best practices for unstructured data transformation, offering
implementation recommendations for practitioners, and spotting developing trends in cloud-based data
processing, this article advances the discipline. Case studies from manufacturing, healthcare, and financial
industries confirm the conclusion that processing efficiency has improved significantly and
transformation costs have been drastically lowered as compared to conventional methods. This article
provides insightful analysis for companies trying to use cloud environments for unstructured data
transformation while keeping compliance with changing data governance criteria.
Keywords Keywords: Unstructured Data Transformation, Cloud Computing, Natural Language Processing, Data Governance, Machine Learning.
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-22
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33503
Short DOI https://doi.org/g8w22j
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