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Integrating Scalable Analytical Tools and Data Warehouses on Private Cloud.

Kazumasa Kishimoto1,2, Osamu Sugiyama3, Tomohide Iwao1,2

  • 1Kyoto University, Kyoto, Japan.

Studies in Health Technology and Informatics
|August 8, 2025
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Summary

This study introduces a scalable data warehouse solution integrating on-premises and private cloud environments. It utilizes Kubernetes for secure virtual machines, achieving fast data extraction and secure cloud API access for diverse analytical needs.

Keywords:
Clinical Data WarehouseData IntegrationReal-World Data

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Area of Science:

  • Computer Science
  • Data Engineering
  • Cloud Computing

Background:

  • The increasing demand for efficient and scalable data warehousing solutions.
  • Challenges in integrating on-premises data with private cloud infrastructures securely.
  • Need for dynamic and secure environments for data analysis.

Purpose of the Study:

  • To develop and evaluate an efficient and scalable data warehouse solution.
  • To integrate on-premises environments with private cloud infrastructure.
  • To provide secure, independent virtual environments for data analysis.

Main Methods:

  • Utilized Kubernetes for dynamic generation of secure virtual machines.
  • Implemented VPN connection between hospital networks and Google Cloud for secure API access.
  • Conducted performance testing on data extraction and query speeds.

Main Results:

  • Achieved fast query speeds, extracting 240,000 records from a 301GB dataset in 12.4 seconds.
  • Demonstrated secure integration of on-premises data with Google Cloud.
  • Validated the scalability of the infrastructure for diverse analytical requirements.

Conclusions:

  • The developed solution offers an efficient and scalable approach to data warehousing.
  • Kubernetes and secure cloud connections enable robust data analysis environments.
  • The infrastructure supports diverse analytical needs while maintaining data security.