Author: Dr. Priyanka Malhotra, Mr. Ankit Verma.
Abstract: The exponential growth of digital data, coupled with the need for immediate insights, has propelled real-time big data stream processing to the forefront of modern computing. Unlike traditional batch processing, real-time stream processing enables the analysis, transformation, and storage of continuous data flows within milliseconds. This paper explores the foundational concepts, architectures, and technologies underpinning real-time big data stream processing. It examines industry-leading frameworks such as Apache Kafka, Apache Flink, and Spark streaming, along with use cases in finance, healthcare, and IoT. Additionally, it addresses the challenges of scalability, fault tolerance, latency, and exactly-once processing, and proposes best practices for building reliable, high-performance systems.
Keywords: Real-Time Processing, Big Data, Stream Processing, Apache Kafka, Low Latency, Data Pipelines
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