REAL TIME BIG DATA ANALYTICS WITH APACHE SPARK

Abstract: In the modern era of big data, organizations require rapid insights from continuously generated data streams. Real-time data analytics has become essential for decision-making in sectors such as finance, healthcare, IoT, and social media. Apache Spark, a powerful open-source distributed data processing framework, provides in-memory computation and supports both batch and stream processing. This paper explores the use of Apache Spark for real-time data analytics, focusing on its architecture, components, and advantages over traditional frameworks like Hadoop MapReduce. Through integration with tools such as Apache Kafka and HDFS, Spark enables scalable, fault-tolerant, and low-latency processing. Experimental analysis shows Spark’s capability to handle large-scale, high-velocity data with minimal delay, offering significant improvements in throughput and processing speed. The results confirm that Apache Spark is a highly efficient and scalable platform for real-time big data analytics.

Keywords: Real-Time Analytics, Apache Spark , Stream Processing, Kafka, Hadoop