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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
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.
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.
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