Published Paper PDF: https://ijrmeet.org/wp-content/uploads/2025/06/IJRMEET0625370043_Comparative%20Analysis%20of%20Spark%20vs.%20Snowflake%20in%20Enterprise%20Data%20Processing.pdf
DOI: https://doi.org/10.63345/ijrmeet.org.v13.i6.4
Subodh Katyal
CCSU University
Jail Road, Meerut
Abstract
This manuscript presents a comprehensive comparative analysis of Apache Spark and Snowflake, two leading platforms for enterprise-scale data processing. As organizations seek to harness large volumes of structured and unstructured data for analytics and decision support, selecting the most suitable engine and architecture is critical. Apache Spark, an open-source distributed processing system, offers in-memory computing and a rich ecosystem for batch and streaming workloads. Snowflake, a cloud-native data warehouse, provides a fully managed service with automatic scaling, separation of storage and compute, and native support for SQL-based analytics. Through examination of system architectures, performance benchmarks, cost models, security features, and integration capabilities, this study elucidates the strengths and limitations of each platform. A statistical analysis comparing query latencies and resource utilization under varied workloads is presented in a tabular format. The methodology involves empirical testing on standardized workload profiles derived from the TPC-DS benchmark and real-world enterprise scenarios. Results indicate trade-offs between raw compute speed, operational simplicity, cost efficiency, and extensibility. The paper concludes by offering guidelines for platform selection based on organizational requirements such as workload diversity, skill set availability, budget constraints, and long-term scalability.
Keywords
Spark; Snowflake; Enterprise Data Processing; Performance Benchmarking; Cloud Data Warehouse; Distributed Computing
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