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As enterprises continue transitioning from traditional on-premise databases to cloud-native platforms, the contrast between performance tuning in relational database systems (RDBMS) and cloud-based solutions like Snowflake has emerged as a critical area of inquiry. This review explores the architectural, strategic, and operational differences in tuning techniques between these platforms. It presents comparative insights from case studies, performance benchmarks, and peer-reviewed literature. The findings reveal that while traditional RDBMS rely on manual indexing, execution plans, and hardware-bound strategies, Snowflake emphasizes elasticity, clustering, and virtual compute scaling. The paper also proposes a theoretical framework for adaptive tuning and discusses future research directions including AI-driven automation, hybrid platform strategies, and energy-efficient tuning.
Keywords:
Snowflake, RDBMS, performance tuning, data warehouse optimization, query optimization, cloud databases, concurrency scaling, virtual warehouses, data engineering, cloud-native computing
Cite Article:
"Snowflake vs RDBMS: Performance Tuning Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c825-c832, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505296.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator