This paper presents a comparative analysis of Apache Spark 4.0 and Snowflake’s Snowpark 1.32.0, two leading frameworks for big data processing, as of May 2025. Using a multi-dimensional evaluation framework, we assess architecture, performance, scalability, ease of use, cost, security, adoption, and future roadmaps. Grounded in technical documentation, industry benchmarks, and case studies, the analysis highlights trade-offs between Spark’s flexibility and Snowpark’s simplicity. Visualizations, including radar charts and performance benchmarks, clarify findings. Results suggest Spark excels in real-time, heterogeneous workloads, while Snowpark suits SQL-heavy, managed environments. We propose a hybrid architecture to leverage both tools, offering insights for data engineers and researchers navigating cloud-native paradigms.