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Cloud-based Data Lakes in Healthcare: Challenges and Opportunities

Author(s) Goutham Bilakanti
Country India
Abstract The swift digital revolution of healthcare has produced enormous amounts of data that require effective storage, handling, and analysis. Cloud-based data lakes have become a key technology to manage large-scale healthcare data sets, supporting interoperability, real-time analytics, and AI-informed decision-making. Cloud-based data lakes like AWS S3, Azure Data Lake, and Google BigQuery offer elastic storage, high-security platforms, and sophisticated computing power that allow data integration and accessibility. This article illustrates how cloud-based data lakes simplify healthcare data management, enable machine learning uses, and improve clinical decision support systems. It also illustrates key challenges like data privacy, regulatory compliance, and cyber-attacks that occur in healthcare data environments. The study evaluates the impact of cloud-native designs in improving healthcare efficiency, reducing operational costs, and optimizing patient outcomes. Cloud-based data lakes are the foundation for predictive analytics, tailored medicine, and interoperable data transfer across healthcare networks based on the application of AI and big data technologies. The paper highlights the transformative potential of cloud technologies in revolutionizing modern paradigms for healthcare data management.
Keywords Cloud data lakes, healthcare data management, AWS S3, Azure Data Lake, Google BigQuery, interoperability, real-time healthcare data analytics, AI-based decision-making, regulatory compliance, cybersecurity, predictive analytics, personalized medicine, big data health, clinical decision support
Field Computer > Data / Information
Published In Volume 4, Issue 8, August 2023
Published On 2023-08-10
Cite This Cloud-based Data Lakes in Healthcare: Challenges and Opportunities - Goutham Bilakanti - IJLRP Volume 4, Issue 8, August 2023. DOI 10.5281/zenodo.15196922
DOI https://doi.org/10.5281/zenodo.15196922
Short DOI https://doi.org/g9d67w
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