Year: 2025
Type: article
Abstract: Snowflake has emerged as one of the leading cloud data warehouse platforms, offering near-infinite scalability, on-demand compute, and separation of storage and compute resources. While these characteristics facilitate a wide range of data-driven use cases, enterprises often face challenges around costs, performance, and data quality. Revefi, an emerging Data Operations Cloud platform, addresses these core challenges through AI-driven insights, rapid setup, and a metadata-focused architecture designed to bolster security. This technical exploration delves into the key aspects of Revefi for Snowflake operations, covering its integration model, architecture, AI-driven optimizations, real-world case studies, and known limitations. The discussion is framed in a research-oriented format to guide technical readers through the rationale, methods, and outcomes associated with adopting Revefi for Snowflake. Keywords: Snowflake, Data Warehouse, Performance, Observability, Quality, AI, Machine Learning, Data Security, Raden AI, Integration
DOI: 10.55041/ijsrem43472
Author: Srinivasa Rao Karanam
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4409603546