REAL-TIME BIG DATA PROCESSING FRAMEWORKS

Abstract

Abstract - Real-Time Big Data Processing Frameworks have become a fundamental component of modern data-driven organizations by enabling the continuous collection, processing, analysis, and visualization of massive volumes of streaming data with minimal latency. Traditional batch processing systems are insufficient for applications that require immediate responses, such as fraud detection, financial trading, healthcare monitoring, smart cities, cybersecurity, and Internet of Things (IoT) systems. Real-time processing frameworks address this limitation by analyzing data as it is generated, allowing organizations to make timely and informed decisions. Modern frameworks such as Apache Spark Streaming, Apache Flink, Apache Kafka, Apache Storm, and cloud-based streaming platforms provide scalable, fault-tolerant, and distributed architectures for handling high-velocity data streams. This paper discusses the concept, objectives, architecture, components, popular frameworks, applications, benefits, challenges, and future trends of Real-Time Big Data Processing Frameworks.

Keywords: Big Data, Real-Time Processing, Stream Processing, Apache Spark, Apache Flink, Apache Kafka, Apache Storm, Data Streaming, Cloud Computing, Distributed Computing.