Comparative Analysis of Big Data Processing Frameworks: Python with MPI vs. Apache Spark

In the era of exponential data growth, selecting the appropriate distributed computing framework is crucial for efficient big data processing. This paper presents a comprehensive comparative analysis of two prominent frameworks: Message Passing Interface (MPI) with Python and Apache Spark. Through extensive benchmarking and analysis, we evaluate these frame-works across multiple dimensions including performance characteristics, programming models, fault tolerance, and scalability. Our findings indicate that MPI consistently outperforms Spark by 2-10x in compute-intensive tasks, while Spark offers superior developer productivity and built-in fault tolerance. This research provides quantitative insights for framework selection based on specific big data processing requirements.