Data integration plays a vital role in modern data pipelines by facilitating seamless data movement, transformation, and analysis. This study compares two prominent data integration frameworks—Apache Spark and Apache Kafka—based on key performance metrics such as throughput, latency, and resource utilization. Apache Spark, widely recognized for its batch and stream processing capabilities, is evaluated alongside Apache Kafka, a distributed event streaming platform, to assess their efficiency in real-time and large-scale data integration tasks. The research includes experimental benchmarking using structured and un-structured datasets, analyzing their performance under different workloads. The findings highlight the trade-offs between Kafka’s low-latency streaming and Spark’s high-throughput batch processing, providing insights into their suitability for various data integration scenarios. By exploring these differences, the study offers practical recommendations for selecting the right framework based on specific processing needs. This comparative analysis helps organizations optimize their data architectures, ensuring improved efficiency, scalability, and real-time data management in diverse environments.