Data-Intelligent DevOps Architecture for Real-World Healthcare Systems: Integrating Databricks, SAP, and Oracle EBS for Continuous Software Quality Assurance

Lucas Johannes de Jong

doi:10.15662/IJRAI.2024.0705011

Authors

Lucas Johannes de Jong Data Engineer, Netherland Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0705011

Keywords:

Data-intelligent DevOps, continuous software quality assurance, Databricks, SAP S/4HANA, Oracle E-Business Suite, healthcare IT, machine learning, risk-based testing, cloud computing, compliance automation

Abstract

The growing complexity of healthcare information systems has created an urgent need for intelligent automation in software development and quality assurance. This paper proposes a Data-Intelligent DevOps Architecture that integrates Databricks, SAP, and Oracle E-Business Suite (EBS) to enable continuous software quality assurance (CSQA) for real-world healthcare applications. Traditional DevOps pipelines improve deployment frequency and operational agility but lack contextual intelligence to handle dynamic healthcare data, strict compliance requirements, and complex enterprise integrations. The proposed architecture embeds data-driven intelligence—powered by Databricks Lakehouse—across each stage of the DevOps lifecycle to optimize testing, risk prediction, and compliance verification.

By leveraging Databricks’ data unification and analytics capabilities, the framework enables real-time insights into software quality metrics derived from heterogeneous data sources, including SAP ERP logs, Oracle EBS transactions, and medical telemetry. AI and machine learning models perform continuous risk evaluation, defect prediction, and anomaly detection, ensuring proactive quality management. Integrating SAP and Oracle workloads allows for streamlined workflow synchronization between clinical and operational domains, improving efficiency and transparency.

A design-science methodology is used to develop, simulate, and evaluate the architecture on a cloud-native infrastructure. Experiments reveal improvements in defect detection accuracy, test optimization efficiency, and compliance traceability. The results indicate that data intelligence significantly enhances the adaptability and trustworthiness of DevOps processes in healthcare.

This research contributes to the field by demonstrating a unified data-intelligent DevOps model for end-to-end software quality assurance in healthcare ecosystems that depend on integrated enterprise and clinical systems.

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