Leveraging SonarQube and Snowflake for Advanced ETL Solutions

This article examines the integration of SonarQube for code quality and Snowflake's cloud platform to address critical challenges in ETL (Extract, Transform, Load) processes. Organizations processing large datasets frequently encounter pipeline failures due to code inefficiencies and resource c...

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Bibliographic Details
Published inEuropean Journal of Computer Science and Information Technology Vol. 13; no. 49; pp. 153 - 162
Main Author Godleti, Srihari Babu
Format Journal Article
LanguageEnglish
Published 26.06.2025
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ISSN2054-0957
2054-0965
DOI10.37745/ejcsit.2013/vol13n49153162

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Summary:This article examines the integration of SonarQube for code quality and Snowflake's cloud platform to address critical challenges in ETL (Extract, Transform, Load) processes. Organizations processing large datasets frequently encounter pipeline failures due to code inefficiencies and resource constraints. SonarQube's static analysis capabilities identify optimization opportunities and memory management issues before deployment, while Snowflake's decoupled architecture enables independent scaling of compute and storage resources. When combined, these technologies create a synergistic effect that dramatically reduces processing times, improves reliability, and enables handling of exponentially growing data volumes. Real-world implementations demonstrate substantial reductions in ETL processing times alongside improved stability, creating foundations for scalable data strategies that can evolve with changing business requirements.
ISSN:2054-0957
2054-0965
DOI:10.37745/ejcsit.2013/vol13n49153162