Published Paper Details:

The integration of Apache Spark and Apache Kafka has evolved into one of the most influential combinations in real-time data analysis, but fault tolerance and recovery still is a big problem. This research paper investigates fault tolerance mechanisms in such integration. Durability of Kafka messages is achieved by partitioning and replication. Apache Spark relies on resilient Distributed Datasets and checkpointing to handle failures. The research measures their effect on the system's reliability, consistency, and performance. Experiments show that using RDD lineage in Spark and log-based recovery in Kafka brings about considerable improvement in fault tolerance, reducing time and losses of data. Specifically, the findings are that optimizing these recovery procedures is what will make real-time data processing systems more resolute and resilient to failures in a distributed computing environment. This research details the analysis and gives practical recommendations on how to enhance fault tolerance in integrated Spark-Kafka systems.