A model for assessing the economic efficiency of hybrid stream data processing in high-load systems: a methodology for reducing operational latency

the purpose of this study is to present an author’s methodology for reducing operational latency and increasing economic efficiency through the application of hybrid stream data processing in high-load systems. Methods: the research employs analysis and synthesis of scientific literature in the fields of stream data processing and economic assessment of IT infrastructure, comparative analysis of architectural approaches (Batch Processing, streaming-only, and hybrid architectures), economic modeling of prevented losses and revenue optimization, as well as a case study based on modeling typical operational incidents in high-load digital products. Findings: the study proposes an original model for assessing the economic effect of hybrid stream data processing that accounts for prevented losses, optimization of managerial decision-making, and the total cost of ownership of infrastructure. It is demonstrated that the application of a hybrid architecture based on the Lambda Architecture concept ensures a linear growth of infrastructure costs as workload increases, eliminates the effect of business data blindness, and significantly reduces both direct and indirect economic losses compared to batch processing and proprietary SaaS analytics solutions. Conclusions: the findings confirm that the economic efficiency of stream data processing is determined not by maximum technical performance alone, but by the alignment of architectural solutions with business risk profiles and managerial objectives. The proposed model can be used as a tool for justifying investments in analytical systems and for evaluating the ROI of digital transformation initiatives in high-load environments.

hybrid stream data processing, economic efficiency, high-load systems, operational latency, business data blindness (Data Blindness), total cost of ownership, Lambda Architecture, prevented losses