Real-time data processing in distributed systems has become a critical capability for modern applications that require immediate insights and rapid decision-making. With the exponential growth of data generated from sources such as IoT devices, social media, financial transactions, and cloud applications, traditional batch processing approaches are no longer sufficient. This study explores the principles, architectures, and technologies that enable real-time data processing in distributed environments. It examines frameworks such as Apache Kafka, Apache Flink, Apache Spark Streaming, and Storm, highlighting their roles in handling high-velocity data streams with low latency and high scalability. The paper also discusses key concepts including stream processing, event-driven architectures, fault tolerance, and data consistency. Additionally, it addresses challenges such as latency management, scalability, data synchronization, and system reliability, along with strategies to overcome them. The study emphasizes the importance of real-time analytics in sectors like healthcare, finance, and e-commerce, where timely insights are crucial. The findings conclude that efficient real-time data processing is essential for building responsive, scalable, and intelligent distributed systems.
Meera Iyer (Tue,) studied this question.