International Journal of Science, Engineering and TechnologyAuthors: Sriram Ghanta
Abstract: This study examines how enterprises can design scalable and reliable data processing environments by integrating Spring Boot based microservices with centralized feature store ecosystems. The research addresses the problem of fragmented data pipelines, inconsistent feature computation, and limited operational scalability that often emerge when organizations rely on traditional ETL oriented workflows. The purpose of the study is to investigate whether a unified architectural blueprint that combines Spring Boot orchestration, distributed data flow patterns, and feature store integration can provide measurable improvements in performance, consistency, and model readiness. A mixed methodological design supports this investigation, combining quantitative evaluation of system throughput, latency behavior, and feature materialization efficiency with qualitative examination of architectural alignment, maintainability, and developer experience. The findings suggest that Spring Boot provides a stable foundation for modular and event driven processing, while feature stores introduce structured versioning, lineage visibility, and repeatable transformations that enhance the reliability of downstream machine learning pipelines. The proposed architecture contributes strategically by offering a reusable blueprint for modern data platform design and academically by positioning feature store-based integration as a significant advancement in data engineering research. Observed outcomes indicate that this integrated approach has substantial implications for organizations seeking predictable performance, consistent feature delivery, and long-term scalability across analytical and operational workloads.
DOI: http://doi.org/10.5281/zenodo.17760715
