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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM43472

Revefi for Snowflake Operations

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM43472

Volume:

9 |

3 |

Mar 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM43472

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SRK

Srinivasa Rao Karanam

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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

Cite This Article

Srinivasa Rao Karanam, "Revefi for Snowflake Operations," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 3, Mar 2025. https://doi.org/10.55041/IJSREM43472

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Publication Date

3/28/2025

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Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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