The increasing demand for instantaneous data-driven decision-making has transformed the design and operation of modern software systems. Organizations across industries now rely on real-time data processing architectures to support applications such as financial trading, fraud detection, personalized recommendations, and industrial monitoring. These systems must process continuous streams of data with minimal latency while maintaining high levels of accuracy, scalability, and reliability. This paper examines the software engineering models and architectural principles underlying real-time data processing systems designed for low-latency decision-making. It explores how traditional batch-oriented approaches have evolved into event-driven and streaming architectures capable of handling high-velocity data flows. By analyzing the transition from static data processing to continuous, real-time computation, the study highlights the key design considerations required to build responsive and adaptive systems. The research focuses on core architectural components, including data ingestion pipelines, stream processing layers, and distributed messaging systems. It evaluates how these components interact to enable efficient data flow and support time-sensitive decision processes. Particular attention is given to event-driven design patterns, which decouple system components and facilitate asynchronous processing, thereby improving scalability and responsiveness. Low-latency engineering techniques are analyzed in detail, including in-memory processing, caching strategies, and parallel computation. These techniques are essential for minimizing processing delays and ensuring that decisions can be made within strict time constraints. The paper also examines the challenges of managing state in streaming systems, where maintaining consistency and accuracy across distributed components is critical. Scalability and fault tolerance are addressed as key requirements for real-time systems, with an emphasis on distributed processing and resilience mechanisms. The study further explores observability and monitoring practices, which provide visibility into system performance and enable continuous optimization. hrough the analysis of real-world use cases, the paper demonstrates how real-time data processing architectures support a wide range of applications requiring rapid decision-making. It also discusses emerging trends, including the integration of artificial intelligence into streaming systems and the development of ultra-low-latency architectures. By synthesizing concepts from software engineering and distributed systems, this paper provides a comprehensive framework for designing real-time data processing systems. The findings offer practical guidance for building scalable, reliable, and low-latency decision platforms capable of operating in dynamic and data-intensive environments.
AMIL USLU (Tue,) studied this question.