Engineering and Technology
| Open Access | Resilient Data-Driven Infrastructure: Integrating Predictive Analytics, Secure Pipelines, and High-Performance Fault Diagnosis for Modern Computational Systems
Dr. Elena Morozova , University of LisbonAbstract
Background: Modern computational infrastructures—spanning cloud platforms, GPU-accelerated data centers, and enterprise data lakes—face interlocking challenges: increasing failure rates under intensive workloads, the necessity for secure continuous integration/continuous deployment (CI/CD) practices, and the demand to convert vast heterogeneous data into actionable intelligence (Zhang, 2022; Liu et al., 2023). A coherent, interdisciplinary framework that links predictive analytics, DevSecOps practices, and high-performance fault diagnosis is essential to raise reliability while maintaining scalability and security (Kumar, 2019; Konneru, 2021).
Methods: This article synthesizes theoretical foundations and applied methodologies from the provided literature to produce an integrative conceptual and operational framework. We perform a detailed cross-domain synthesis of techniques from predictive analytics, data engineering and lakehouse architectures, DevSecOps security integrations (SAST/DAST/SCA), high-performance geospatial and GPU computing approaches, and contemporary fault-prediction studies. The methodology includes comparative evaluation of algorithmic families, pipeline architectures, and failure-detection strategies, mapped onto practical system boundaries and operational constraints for cloud and on-premise GPU deployments (Kukreja & Zburivsky, 2021; Li, 2020; Liu et al., 2023).
Results: The synthesis highlights three convergent design principles: (1) unified telemetry and data curation through lakehouse principles to enable low-latency, high-fidelity feature generation (Kukreja & Zburivsky, 2021); (2) embedding predictive analytics into DevOps cycles to create anticipatory operations—thereby improving decision latency and reducing mean time to repair (Kumar, 2019); and (3) a layered fault-diagnosis approach combining supervised predictive models for imminent hardware faults with unsupervised anomaly detection to capture novel failure modes (Peterson et al., 2022; Xie et al., 2021; Liu et al., 2023). The integrated model demonstrates conceptual pathways to reduce unscheduled downtime, decrease non-revenue-impacting water in infrastructure analogues, and enhance security posture inside CI/CD (Kwikima et al., 2024; Konneru, 2021).
Conclusion: A resilient data-driven infrastructure must combine lakehouse data engineering, DevSecOps-integrated CI/CD, and robust predictive diagnostics tailored for high-performance workloads. The proposed framework provides a guide for engineering organizations to structure telemetry, model development, and deployment while accounting for security, scale, and the unique failure characteristics of GPUs and cloud components. Research and industrial practice will benefit from empirical validation campaigns, standardized telemetry schemas, and community-driven benchmarks for fault prediction and remediation orchestration (Liu et al., 2023; Lin & Gupta, 2021).
Keywords
Predictive analytics, DevSecOps, Lakehouse, GPU fault prediction
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