International Journal of Science, Engineering and TechnologyAuthors: Srinivasa Chakravarthy Seethala
Abstract: This study develops a unified predictive data engineering framework that addresses the escalating complexity of managing high throughput ETL pipelines deployed across Oracle Cloud, Google Cloud, and distributed SQL systems. Modern data ecosystems operate under intense velocity and scale, yet remain constrained by fragmented pipeline orchestration, reactive performance tuning, and inconsistent cross platform optimization strategies. The purpose of this research is to construct an integrated architecture that enables anticipatory workload management, dynamic resource allocation, and continuous quality validation by combining statistical profiling, feature driven workload prediction, and cloud native pipeline instrumentation. A mixed methodology is applied that blends quantitative analysis of historical ETL execution logs, latency distributions, anomaly trends, and throughput patterns with qualitative assessments of workflow bottlenecks, runtime behaviors, and control plane interactions across heterogeneous data platforms. Findings demonstrate that predictive modeling embedded within the orchestration layer significantly improves execution reliability, stabilizes throughput during peak load intervals, and reduces pipeline recovery overhead. The proposed framework introduces a harmonized predictive controller that learns from both cloud specific signals and distributed SQL characteristics, enabling proactive scheduling and error prevention across multiple execution environments. This contributes to strategic advancements in unified data engineering design and strengthens academic understanding of predictive pipeline governance across federated cloud systems. The study concludes that integrating predictive intelligence directly into ETL lifecycle management establishes a scalable foundation for next generation enterprise data operations and provides actionable insights for organizations seeking resilient, efficient, and cloud agnostic data processing capabilities.
